Tag: technological change

Energy Ponds – l’état des lieux

I drift away from investor-relations sites, and I go back to my ‘Energy Ponds’ concept. I feel like going through it once again. First thing first, I want to lay out the idea such as I have figured it out so far. The second of the first things is that I am describing a yet-unimplemented, tentative technological solution, which combines water management with the generation of renewable energies. Ram-pumps are installed in the stream of a river. Kinetic energy of the river creates a by-flow through the ram-pumps, endowed with its own kinetic energy and flow rate, derived from those of the river. That by-flow is utilized in two ways. Some of it, within the limits of environmental sustainability of the riverine ecosystem, is pumped into wetland-type structures, which serve the purpose of water retention. On the way to wetlands, that sub-stream passes through elevated tanks, which create a secondary hydraulic head and allow placing hydroelectric turbines on pipes leading from elevated tanks to wetlands, i.e. back to ground level. The remaining part of the ram-pumped by-flow, before going back into the river, is recirculated through hydroelectric turbines located close to the ram-pump. The basic idea is shown graphically in Figure 1.

The remaining part of the article develops a complex proof of concept for ‘Energy Ponds’. Component solutions are discussed against the background of relevant literature, and a quantitative simulation of its basic parameters is presented, for simulated location in Poland, author’s home country.

Figure 1

‘Energy Ponds’ are supposed to create a system of water retention with as little invasive change in the landscape as possible. Typical reservoirs, such as artificial ponds and lakes, need space, and that space ought to be taken away from other employments thereof. This is a dilemma in land management: do we use a given portion of terrain for a retention reservoir or do we use it for other purposes? In densely populated regions, that dilemma becomes acute, when it comes to mutual arrangement of human architectural structures, agricultural land, and something which, fault of a better word, can be called ‘natural landscape’ (quotation marks refer to the fact that many landscapes which we intuitively gauge as ‘natural’ are actually man-made and artificially maintained).  

Water management, as a distinct field of technology, with an environmental edge, could benefit from innovation-boosting tools typical for other technological fields (Wehn & Montalvo 2018[1]; Mvulirwenande & Wehn 2020[2]). Technological change as regards water management is needed both in rural areas and in cities. Interestingly enough, urban environments seem to be more conservative than agriculture in terms of water management (Wong, Rogers & Brown 2020[3]). There is a quest for scientifically based constants in water management, such as the required quantity of water per person per day; Hogeboom (2020[4]) argues it is roughly 3800 liters after covering all the conventional uses of water. On the other hand, Mohamed et al. (2020[5]) claim that the concept of ‘arid region’, so far reserved for desertic environments, is de facto a type of environmental context when we systematically lack accurate environmental data as regards quickly changing availability of water. Kumar et al. (2020[6]) go even further and postulate something called ‘socio-hydrology’: human societies seem to develop characteristically differentiated patterns of collective behaviour in different hydrological conditions. Other research suggests that societies adapt to increased use of water by visibly increasing the productivity of that use, whilst increased income per capita seems being correlated with increased productivity in the use of water (Bagstad et al. 2020[7]).

In the ‘Energy Ponds’ concept, retention of water is supposed to be a slow, looped recirculation through local ecosystems, rather than accumulation in fixed reservoirs. Once water has been ram-pumped from a river into wetlands, the latter allow slow runoff, via ground waters, back into the hydrological system of the river basin. It is as if rain was falling once again in that river basin, with rainwater progressively collected by the drainage of the river. In hydrology, such a system is frequently referred to as quasi-reservoirs (Harvey et al. 2009[8]; Phiri et al. 2021[9]). Groundwater seems being the main factor of the observable water storage anomalies (Neves, Nunes, & Monteiro 2020[10]). Purposeful control of the size and density in the patches of green vegetation seems to be a good practical regulator of water availability, whence the interest in using wetlands as reservoirs (Chisola, Van der Laan, & Bristow 2020[11]).

Ram-pumps placed in the stream of a river become distributed energy resources based on renewable energy: they collect and divert part of the kinetic energy conveyed by the flow of water. Of course, one can ask: isn’t it simpler to put just hydroelectric turbines in that stream, without ram-pumps as intermediary? A ram-pump, properly instrumented with piping, becomes a versatile source of kinetic energy, which can be used for many purposes. In ‘Energy Ponds’, the idea is to use that energy both for creating a system of water retention, and for generating electricity. The former has its constraints. The amount of water to adsorb from the river is limited by the acceptable impact on ecosystems downstream. That impact can be twofold. Excessive adsorption upstream can expose the downstream ecosystems to dangerously low water levels, yet the opposite can happen as well: when we consider wetlands as pseudo-reservoirs, and thus as a reserve of water, its presence can stabilize the flow downstream (Hunt et al. 2022[12]), and the biological traits of ecosystems downstream are not necessarily at risk (Zhao et al. 2020[13]). Strong local idiosyncrasies can appear in that respect (Xu et al. 2020[14]).

Major rivers, even those in plains, have a hydraulic head counted in dozens of meters and a flow rate per second in the hundreds of cubic meters per second. With the typical efficiency of ram-pumps ranging from 35% to 66%, the basic physical model of ram-pumping (Fatahi-Alkouhi et al. 2019[15];  Zeidan & Ostfeld 2021[16]) allows pumping from the river more water than it is possible to divert into the local wetlands.  

Thus, two sub-streams are supposed to be ram-pumped in the ‘Energy Ponds’ system: one sub-stream ultimately directed to and retained in the local wetlands, and another one being the non-retainable excess, to be redirected immediately back into the river. The latter can immediately flow through hydroelectric turbines placed as close as possible to ram-pumps, in order not to lose kinetic energy. The other goes further, through the system of elevated tanks. Why introducing elevated tanks in the system? Isn’t it better to direct the retainable flow of water as directly as possible to wetlands, thus along a trajectory as flat as is feasible in the given terrain? The intuition behind elevated tanks is the general concept of Roman siphon (e.g. Angelakis et al. 2020[17]). When we place an artificially elevated tank along the flow of water from the river to the wetlands, it allows transporting water over a longer distance without losing kinetic energy in that flow. Therefore, the wetlands themselves, as well as the points of discharge from the piping of ‘Energy Ponds’ can be spread over a greater radius from the point of intake from the river. That gives flexibility as regards adapting the spatial distribution of the whole installation to the landscape at hand. Elevated water tanks can increase the efficiency of water management (Abunada et al. 2014[18]; Njepu, Zhang & Xia 2019[19]).

A water tank placed at the top of the Roman siphon is a reserve of energy in pumped storage, and thus allows creating a flow with the same kinetic energy as was generated by ram-pumps, yet further away from the point of intake in the river. Depending on the volumetric capacity and the relative height of the elevated tank, various amounts of energy can be stored. Two points are to consider in that respect. ‘Energy Ponds’ is supposed to be a solution as friendly as possible to the landscape. Big water towers are probably to exclude, and the right solution for elevated tanks seems closer to those encountered in farms, with relatively light structure. If there is need to store more energy in a local installation of ‘Energy Ponds’, there can be more such elevated tanks, scattered across the landscape. With respect to the relative height, documented research indicates a maximum cost-effective elevation between 30 and 50 meters, with 30 looking like a pragmatically conservative estimate (Inthachot et al. 2015[20]; Guo et al. 2018[21]; Li et al. 2021[22]). Elevated tanks such as conceived in ‘Energy Ponds’ can be various combinations of small equalizing tanks (serving just to level up intermittence in the flow of water), and structures akin raingardens (see e.g. Bortolini & Zanin 2019[23]), thus elevated platforms with embedded vegetal structures, capable of retaining substantial amounts of water.

As an alternative to artificial elevated tanks, and to the extent of possibilities offered by natural landscape, a mutation of the STORES technology (short-term off-river energy storage) can be used (Lu et al. 2021[24]; Stocks et al. 2021[25]). The landscape needed for that specific solution is a low hill, located next to the river and to the wetland. The top of such hill can host a water reservoir.

The whole structure of ‘Energy Ponds’, such as conceptually set for now, looks like a wetland, adjacent to the channel of a river, combined with tubular structures for water conduction, ram pumps and hydroelectric turbines. As for the latter, we keep in mind the high likelihood of dual stream: one close to ram-pumps, the other one after the elevated tanks. Proper distribution of power between the generating units can substantially reduce the amount of water used to generate the same amount of energy (Cordova et al. 2014[26]).

Hydroelectricity is collected in energy storage installations, which sell electricity to its end users. The whole concept follows the general stream of research on creating distributed energy resources coupled with landscape restoration (e.g.  Vilanova & Balestieri 2014[27]; Vieira et al. 2015[28]; Arthur et al. 2020[29] ; Cai, Ye & Gholinia 2020[30] ; Syahputra & Soesanti 2021[31]). In that path of research, combinations of energy sources (small hydro, wind and photovoltaic) plus battery backup, seem to be privileged as viable solutions, and seem allowing investment in RES installations with an expected payback time of approximately 10 – 11 years (Ali et al. 2021[32]). For the sake of clarity in the here-presented conceptualization of ‘Energy Ponds’, only the use of hydropower is considered, whilst, of course, the whole solution is open to adding other power sources to the mix, with limitations imposed by the landscape. As wetlands are an integral component of the whole concept, big windfarms seem excluded from the scope of energy sources, as they need solid support in the ground. Still, other combinations are possible. Once there is one type of RES exploited in the given location, it creates, with time, a surplus of energy which can be conducive to installing other types of RES power stations. The claim seems counterintuitive (if one source of energy is sufficient, then it is simply sufficient and crowds out other sources), yet there is evidence that local communities can consider RES according to the principle that ‘appetite grows as we eat’ (Sterl et al. 2020[33]). Among different solutions, floating solar farms, located on the surface of the wetland, could be an interesting extension of the ‘Energy Ponds’ concept (Farfan & Breyer 2018[34]; Sanchez et al. 2021[35]).

Material and methods

The general method of studying the feasibility of ‘Energy Ponds’ is always specific to a location, and it unfolds at two levels: a) calibrating the basic physical properties of the installation and b) assessing its economic viability. Hydrological determinants are strongly idiosyncratic, especially the amount of water possible do adsorb from the local river and to retain in wetlands, and the impact of retention in wetlands upon the ecosystems downstream. With wetlands as a vital component, the conceptual scheme of the ‘Energy Ponds’ naturally belongs to plains, as well as to wide river valleys surrounded by higher grounds. That kept in mind, there are conceptual developments as regards artificially created wetlands in the mountains (Shih & Hsu 2021[36]).

The local feasibility of ‘Energy Ponds’ starts with the possible location and size of wetlands. Places where wetlands either already exist or used to exist in the past, before being drained, seem to be the most natural, as local ecosystems are likely to be more receptive to newly created or expanded wetlands. Conflicts in land management between wetlands and, respectively, farmland and urban settlements, should be studied. It seems that the former type is sharper than the latter. There is documented technological development as regards the so-called Sponge Cities, where urban and peri-urban areas can be transformed into water-retaining structures, including the wetland-type ones (Sun et al. 2020[37]; Köster 2021[38]; Hamidi, Ramavandi & Sorial 2021[39]). On the other hand, farmland is a precious resource and conflicts between retention of water and agriculture are (and probably should be) settled in favour of agriculture.   

Quantitatively, data regarding rivers boils down to the flow rate in cubic meters per second, and to the hydraulic head. Flow and head are the elementary empirical observables of the here-presented method, and they enter into the basic equation of ram-pumping, as introduced by Zeidan & Ostfeld (2021 op.cit.), namely:

HR*QR*η = HT*QT                     (1)

…where HR is the hydraulic head of the river, QR is its flow rate in m3/s, HT is the relative elevation where water is being ram-pumped, QT is the quantity of pumped water, and η is a coefficient of general efficiency in the ram-pumps installed. That efficiency depends mostly on the length of pipes and their diameter, and ranges between 35% and 66% in the solutions currently available in the market. Knowing that QT is a pre-set percentage p of QR and, given the known research, p = QT/QR ≤ 20%, it can be written that QT = QRp. Therefore, equation (1) can be transformed:  

η = [HT*QR*p] / [HR*QR] = HT*p / HR              (2)

The coefficient η is predictable within the range that comes with the producer’s technology. It is exogenous to the ‘Energy Ponds’ system as such unless we assume producing special ram-pumps in the project. With a given flow per second in the river, efficiency η natural dictates the amount of water being ram-pumped, thus the coefficient of adsorption p.  Dual utilization of the ram-pumped flow (i.e. retention in wetlands and simple recirculation through proximate turbines) allows transforming equation (1) into equivalence (3):

{[HRQRHTp / HR] = [HRQC + HTQW] } {QRHTp = [HRQC + HTQW]}         (3)

…where Q­C stands for the sub-flow that is just being recirculated through turbines, without retention in wetlands, and QW is the amount retained. The balance of proportions between QC and QW is an environmental cornerstone in the ‘Energy Ponds’ concept, with QW being limited by two factors: the imperative of supplying enough water to ecosystems downstream, and the retentive capacity of the local wetlands. The latter is always a puzzle, and its thorough understanding requires many years of empirical observation. Still, a more practical method is proposed here: observable change in the surface of wetlands informs about changes in the amount of water stored. Of course, this is a crude observable, yet it can serve for regulating the amount of water conducted into the wetland.      

The hydraulic head of the river (HR) is given by the physical properties thereof, and thus naturally exogenous. Therefore, the fundamental technological choice in ‘Energy Ponds’ articulates into four ‘big’ variables: a) the producer-specific technology of ram-pumping b) the relative height HT of elevated tanks c) the workable fork of magnitudes in the amount of water QW to store in wetlands, and d) the exact technology of energy storage for hydroelectricity. These 4 decisions together form the first level of feasibility as regards the ‘Energy Ponds’ concept. They are essentially adaptive: they manifest the right choice for the given location, with its natural and social ecosystem.

A local installation of ‘Energy Ponds’ impacts the local environment at two levels, namely the retention of water, and the supply of energy. Water retained in wetlands has a beneficial impact on the ecosystem, yet it is not directly consumable: it needs to pass through the local system of supply in potable water first. The direct consumable generated by ‘Energy Ponds’ is hydroelectricity. Besides, there is some empirical evidence for a positive impact of wetlands upon the value of the adjacent, residential real estate (Mahan et al. 2000[40]; Tapsuwan et al. 2009[41]; Du & Huang 2018[42]). Thus comes the second level of feasibility for ‘Energy Ponds’, namely the socio-economic one. As ‘Energy Ponds’ is an early-stage concept, bearing significant uncertainty, the Net Present Value (NPV) of discounted cash flows seems suitable in that respect. Can the thing pay its own bills, and bring a surplus?

Answering that question connects once again to the basic hydrological metrics, namely head and flow. Hydroelectric power is calculated, in watts, as: water density (1000 kg/m3) * gravity acceleration constant (9,8 m/s2) * Net Head (meters) * Q (water flow rate m3/s). The output of electricity is derived from the power generated. It is safe to assume 50 weeks per year of continuous hydrogeneration, with the remaining time reserved for maintenance, which gives 50*7*24 = 8400 hours. Based on the previous formulations, power W generated in an installation of ‘Energy Ponds’ can be expressed with equation (4), and the resulting output E of electricity is given by equation (5):

W[kW] = ρ * g * (HRQC + HTQW) = 9,81 * (HRQC + HTQW)      (4)

E[kWh] = 8400 * W           (5)

The Net Present Value (NPV) of cash flow in an ‘Energy Ponds’ project is the residual part of revenue from the sales of electricity, as in equation (6).

The revenue is calculated as RE = PE*E , with PE standing for the price of electricity per 1 kWh. Investment outlays and the current costs of maintenance can be derived from the head and the flow specific to the given location. In that respect, the here-presented method, including parameters in equation (6), follows that by Hatata, El-Saadawi, & Saad (2019[43]). A realistic, technological lifecycle of an installation can be estimated at 12 years. Crossflow turbines seem optimal for flow rates inferior or equal to 20 m3 per second, whilst above 20 m3 Kaplan turbines look like the best choice. Investment and maintenance costs relative to ram pumps, elevated tanks, and the necessary piping remain largely uncertain, and seemingly idiosyncratic as regards the exact location and its physical characteristics. That methodological difficulty, seemingly inherent to the early conceptual phase of development in the ‘Energy Ponds’ concept, can be provisionally bypassed with the assumption that those hydraulic installations will consume the cost which would normally correspond to the diversion weir and intake, as well as to the cost of the powerhouse building. The variable IH corresponds to investment outlays in the strictly hydrological part of ‘Energy Ponds’ (i.e. ram-pumps, piping, and elevated tanks), whilst the ITU component, on the other hand, represents investment in the strictly spoken turbines and the adjacent power equipment (generator, electrical and mechanical auxiliary, transformer, and switchyard). The LCOS variable in equation (6) is the Levelized Cost of Storage, estimated for a discharge capacity of 6 hours in Li-Ion batteries, at €0,28 per 1 kWh (Salvini & Giovannelli 2022[44]; Chadly et al. 2022[45]). The ‘0,000714’ factor in equation (6) corresponds to the 6 hours of discharge as a fraction of the total 8400 working hours of the system over 1 year.

Case study with calculations

The here presented case study simulates the environmental and economic impact which could possibly come from the hypothetical development of the ‘Energy Ponds’ concept in author’s own country, namely Poland. The concept is simulated in the mouths of 32 Polish rivers, namely: Wisła, Odra, Warta, Narew, Bug, Noteć, San, Wieprz, Pilica, Bzura, Biebrza, Bóbr, Łyna, Drwęca, Barycz, Wkra, Gwda, Prosna, Dunajec, Brda, Pisa, Wisłoka, Nida, Nysa Kłodzka, Wisłok, Drawa, Krzna, Parsęta, Rega, Liwiec, Wełna, Nysa Łużycka (the spelling is original Polish). Flow rates, in cubic meters per second, as observed in those mouths, are taken as the quantitative basis for further calculations, and they are provided in Table 1, below. Figure 1, further below, presents the same graphically, on the map of Poland. The corresponding locations are marked with dark ovals. There are just 28 ovals on the map, as those located near Warsaw encompass more than one river mouth. That specific place in Poland is exceptionally dense in terms of fluvial network. Further in this section, one particular location is studied, namely the mouth of the Narew River, and it is marked on the map as a red oval.  

Table 1

River nameMouth opening on…Average flow rate [m3/s]River nameMouth opening on…Average flow rate [m3/s]
WisłaBaltic Sea1080ProsnaWarta17,4
OdraBaltic Sea567DunajecWisła85,5
WartaOdra216BrdaWisła28
NarewWisła313PisaNarew26,8
BugNarew155WisłokaWisła35,5
NotećWarta76,6NidaWisła21,1
SanWisła129Nysa KłodzkaOdra37,7
WieprzWisła36,4WisłokSan24,5
PilicaWisła47,4DrawaNoteć21,3
BzuraWisła28,6KrznaBug11,4
BiebrzaNarew35,3ParsętaBaltic Sea29,1
BóbrOdra44,8RegaBaltic Sea21,1
ŁynaPregoła[46]34,7LiwiecBug12,1
DrwęcaWisła30WełnaWarta9,2
BaryczOdra18,8Nysa ŁużyckaOdra31
WkraNarew22,3

Figure 2

The hypothetical location of Energy Ponds installations at the mouths of rivers is based on the availability of hydrological data, and more specifically the flow rate in cubic meters per second. That variable is being consistently observed at the mouths of rivers, for the most part, unless some specific research is conducted. Thus, locating the simulated Energy Ponds at the mouths of rivers is not a substantive choice. Real locations should be selected on the grounds of observable properties in the local ecosystem, mostly as regards the possibility of storing water in wetlands. 

The map of location chosen for this simulation goes pretty much across all the available fluvial valleys in Poland, the physical geography of which naturally makes rivers grow as they head North, and therefore the northern part of the country gives the most water to derive from rivers. Once again, when choosing real locations for the Energy Ponds installations, more elevated ground is a plausible location as well. Most of the brown ovals on the map are in the broad vicinity of cities. This is another feature of the Polish geography: high density of population, the latter being clearly concentrated along rivers. This is also an insight into the function of Energy Ponds in real life. Such as it is simulated in Poland, i.e. in a densely populated environment, it is a true challenge to balance environmental services provided by wetlands, on the one hand, and the need for agricultural land, on the other hand. The simulation allows guessing that Energy Ponds can give more to city dwellers than to those living in the countryside.  

Another important trait of this specific simulation for Energy Ponds is the fact that virtually all the locations on the map correspond to places where wetlands used to exist in the past, before being drained and dried for the needs of human settlements. Geological and hydrological conditions are naturally conducive to swamp and pond formation in these ecosystems. It is important to prevent any ideological take on these facts. The present article is far from pushing simplistic claims such as “nature is better than civilisation”. Still, the draining and drying of wetlands in the past happened in the context of technologies which did not really allow reliable construction amidst a wetland-type environment. Today, we dispose of a much better technological base, such as comfortable barge-based houses, for example. The question of cohabitation between wetlands and human habitat can be reconsidered productively.   

Three levels of impact upon the riverine ecosystem are simulated as three hypothetical percentages of adsorption from the river through ram pumping: 5% of the flow, 10% and 20%, where 20% corresponds to the maximum possible pump-out as regards environmental impact. With these assumptions, the complete hypothetical set of 32 installations would yield 5 163 231 600 m3 a year at 5% of adsorption, and, respectively 10 326 463 200 m3 and 20 652 926 400 m3 with the adsorption rates at 10% and 20%.  In 2020, the annual precipitations were around 201,8 billion of m3, which means the 32 hypothetical installations of Energy Ponds could recirculate from 2,5% to 10% of that total volume, and that, in turn, translates into a significant environmental impact. 

Let’s focus on one particular location in order to further understand the environmental impact: the place where the Narew River mouths into Vistula River, north of Warsaw. The town of Nowy Dwór Mazowiecki, population 28 615, is located right at this junction of rivers. With the average consumption of water at the level of households being around 33,7 m3 a year, that local population consumes annually some 964 326 m3 of water. The flow rate in the Narew River close to its mouth into Vistula is 313 m3 per second, which amounts to a total of 9 870 768 000,00 m3 a year. Adsorbing 5%, 10% or 20% from that total flow amounts to, respectively, 493 538 400 m3, 987 076 800 m3, and 1 974 153 600 m3. From another angle, the same annual consumption of water in households, in Nowy Dwór Mazowiecki, corresponds to 0,0098% of the annual waterflow in the river mouth. The ‘Energy Ponds’ concept would allow to recirculate easily into the surrounding ecosystem the entire annual household consumption of water in this one single town.           

Let’s stay in this specific location and translate water into energy, and further into investment. The first issue to treat is a workable approach to using the capacity of ram-pumps in that specific location, or, in other words, a realistic estimation of the total pumped volume QC + QW . Metric flow per second at the mouth of the Narew River is 313 m3 on average. It is out of the question to install ram-pumps across the entire width of the stream, i.e. some 300 metres on average, as Narew is a navigable river. Still, what if we replaced width with length, i.e. what if a row of ram-pumps was placed along one bank of the river, over a total length of 1 km? Then, with the average efficiency of ram-pumps pegged at 50,5%, it can be assumed that 50,5% of the total flow per second, thus 50,5%*313 m3/s = 158,065 m3/s would flow through ram-pumps. With the baseline head of the Narew River being 92 meters, Table 2 below offers an estimation of the electric power thus possible to generate at the mouth, in an installation of ‘Energy Ponds’, according to equation (4).

Table 2 – Electric power [kW] possible to generate in an installation of ‘Energy Ponds’ at the mouth of the Narew River, Poland.

 Percentage of the total flow to be stored in wetlands (QW)
The relative height of elevated tanks5%10%20%
10 meters130 067,65117 478,4892 300,13
20 meters131 602,92120 549,0198 441,19
30 meters133 138,18123 619,54104 582,25

Source: author’s

When the highest possible elevated tanks are chosen (30 m), combined with the lowest percentage of the flow retained in wetlands (5%), electric power generated is the greatest, i.e. 133,138 MW. The optimal point derives logically from natural conditions. Comparatively to the artificially elevated tanks and their 30 meters maximum, the head of the river itself (92 meters) is an overwhelming factor. An interesting aspect of the ‘Energy Ponds’ concept comes out: the most power can be derived from the natural denivelation of terrain, with elevated tanks and their Roman siphon being just an additional source of potential energy. Further calculations, as regards the necessary investment outlays and the cost of storage, demonstrate that the lowest investment per unit of useful power – 987,16 Polish Zloty (PLN) per 1 kW – is reached precisely at the same point. Comparatively, the lowest power – generated at 20% of the flow adsorbed into wetlands and the lowest height of 10 meters of elevated tanks – is connected to the highest investment per unit, namely 1 111,13 PLN per 1 kW.       

The local urban population in Nowy Dwór Mazowiecki represents an annual consumption of electricity amounting to 828 752 048 kWh, and electricity hypothetically generated at that greatest power amounts to 1 118 360 718,72 kWh a year, thus covering, with an overhead, the local needs. This output of energy would be currently worth PLN 845,48 million a year at the retail prices of electricity[47] (i.e. when sold in a local peer-to-peer market). Should it be sold at wholesale prices[48], to the national power grid, it would represent PLN 766,08 million annually. Corrected with an annual Levelized Cost of Storage estimated at 107 161,99 PLN for Li-Ion batteries, that stream of revenue gives a 12-year discounted present value of PLN 5 134 million at retail prices, and PLN 4 647 million at wholesale prices.  With investment outlays estimated, according to the method presented earlier in this article, at some PLN 131,43 million, the project seems largely profitable. As a matter of fact, it could reach a positive Net Present Value already on the first year.

Comparatively, at the point of lowest power and highest investment per unit thereof, thus at 20% of adsorption into wetlands and 10 meters of height in elevated tanks, the 12-year discounted stream of revenue corrected for LCOS would be PLN 3 555,3 million (retail) and PLN 3 217,8 million (wholesale), against an investment of PLN 102,56 million.                   

Conclusion

The above-presented case study in the hypothetical implementation of the ‘Energy Ponds’ concept sums up optimistically. Still, the ‘Energy Ponds’ is still just a concept, and the study of its possible feasibility is hypothetical. That suggests caution, and the need to take a devil’s advocate’s stance. The case study can be utilized for both outlining the positive potential in ‘Energy Ponds’ and showing the possible angles of stress-testing the concept. The real financial value and the real engineering difficulty of investment in the basic hydraulic infrastructure of ‘Energy Ponds’ has been just touched upon, and essentially bypassed with a few assumptions. Those assumptions seem to be holding when written down, but confrontation with real life can bring about unpredicted challenges. This superficiality stems from the author’s own limitations, as an economist attempting to form an essentially engineering solution. Still, even from the economic point of view, one factor of uncertainty emerges: the pace of technological change. The method used for this feasibility study is a textbook one, similar to calculating the Levelized Cost of Energy: there is an initial investment, which we spread over the expected lifecycle of the technology in question. However, technologies can change at an unexpected pace, and the actual lifecycle of an installation – especially a highly experimental one – might be much shorter than expected. In the author’s (very intuitive) perspective, technological uncertainty is a big pinch of salt to add to the results of the case study.

Another factor of uncertainty is the real possibility of restoring wetlands in densely populated areas. Whilst new technologies in construction and agriculture do allow better overlapping between wetlands, cities, and farming, this is still just overlapping. At the bottom line, wetlands objectively take land away from many other possible uses. Literature is far from decisive about solutions in that respect. The great majority of known projects in the restoration of wetlands aim at and end up in restoring wildlife, not in assuring smooth coexistence between nature and civilisation. Some serious socio-economic experimentation might be involved in projects such as ‘Energy Ponds’.

Hydrogeneration in ‘Energy Ponds’ belongs to the category of Distributed Energy Resources (DER). DER systems become more and more popular, across the world, and they prove to be workable solutions in very different conditions of physical geography (McIlwaine et al. 2021[49]). Connection to local markets of energy, and into local smart grids, seems critical for the optimization of DER systems (Zakeri et al. 2021[50]; Touzani et al. 2021[51]; Haider et al. 2021[52]; Zhang et al. 2022[53]). How necessary is the co-existence – or pre-existence – of such a local network for the economically successful deployment of ‘Energy Ponds’?  What happens when the local installation of ‘Energy Ponds’ is the only Distributed Energy Resource around?

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[46] The Pregoła river is located in the Russian Kaliningrad district, whilst much of the Łyna river is located in Poland. The hypothetical location of Energy Ponds is assumed to be in Poland, thus upstream from the mouth into Pregoła. 

[47] https://www.globalpetrolprices.com/Poland/electricity_prices/ last access May 17th, 2022

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Different paths

I keep digging in the business models of hydrogen-oriented companies, more specifically five of them: 

>> Fuel Cell Energy https://investor.fce.com/Investors/default.aspx

>> Plug Power https://www.ir.plugpower.com/overview/default.aspx

>> Green Hydrogen Systems https://investor.greenhydrogen.dk/

>> Nel Hydrogen https://nelhydrogen.com/investor-relations/

>> Next Hydrogen (previously BioHEP Technologies Ltd.) https://nexthydrogen.com/investor-relations/why-invest/

I am studying their current reports. This is the type of report which listed companies publish when something special happens, which goes beyond the normal course of everyday business, and can affect shareholders. I have already started with Fuel Cell Energy and their current report from July 12th, 2022 (https://d18rn0p25nwr6d.cloudfront.net/CIK-0000886128/b866ae77-6f4a-421e-bedd-906cb92850d7.pdf ), where they disclose a deal with a group of financial institutions: Jefferies LLC, B. Riley Securities, Inc., Barclays Capital Inc., BMO Capital Markets Corp., BofA Securities, Inc., Canaccord Genuity LLC, Citigroup Global

Markets Inc., J.P. Morgan Securities LLC and Loop Capital Markets LLC. Strange kind of deal, I should add. Those 10 financial firms are supposed to either buy or intermediate in selling to third parties parcels of 95 000 000 shares in the equity of Fuel Cell Energy. The tricky part is that the face value of those shares is supposed to be $0,0001 per share, just as it is the case with the ordinary 837 000 000 shares outstanding, whilst the market value of Fuel Cell Energy’s shares is currently above $4,00 per share, thus carrying an addition of thousands of percentage points of capital to pay.

It looks as if the part of equity in Fuel Cell Energy which is free floating in the stock market – quite a tiny part of their share capital – was becoming subject to quick financial gambling. I don’t like it. Whatever. Let’s go further, i.e. to the next current report of Fuel Cell Energy, that from July 7th, 2022 (https://d18rn0p25nwr6d.cloudfront.net/CIK-0000886128/77053fbf-f22a-4288-b702-6b82a039f588.pdf ). It brings updates on two projects:

>> The Toyota Project: a 2,3 megawatt trigeneration platform for Toyota at the Port of Long Beach, California.

>> The Groton Project: a 7.4 MW platform at the U.S. Navy Submarine Base in Groton, Connecticut.

Going further back in time, I browse through the current report from June 9th, 2022 (https://d18rn0p25nwr6d.cloudfront.net/CIK-0000886128/9f4b19f0-0a11-4d27-acd2-f0881fdefbc3.pdf ). It is the official version of a press release regarding financial and operational results of Fuel Cell Energy by the end of the 1st quarter 2022. As I am reading through it, I find data about other projects:

>> Joint Development Agreement with ExxonMobil, related to carbon capture and generation, which includes the 7,4 MW LIPA Yaphank fuel cell project

>>  a carbon capture project with Canadian National Resources Limited

>> a program with U.S. Department of Energy regarding solid oxide. I suppose that ‘solid oxide’ stands for solid oxide fuel cells, which use a solid, ceramic core of fuel, which is being oxidized and produces energy in the process.     

I pass to the current reports of Plug Power (https://www.ir.plugpower.com/financials/sec-filings/default.aspx ). Interesting things start when I go back to the current report from June 23rd, 2022 (https://d18rn0p25nwr6d.cloudfront.net/CIK-0001093691/36efa8c2-a675-451b-a41f-308221f5e612.pdf ). This is a summary presentation of something which looks like the company’s strategy. Apparently, Plug Power plans to have 13 plants with Green Hydrogen running in the United States by 2025, with a total expected yield of 500 tons per day. In a more immediate perspective, the company plans to locate 5 new plants in the U.S. over 2022 (total capacity of 70 tons per day) and 2023 (200 tons per day). Further, I read that what I thought was a hydrogen-focused company, has, in fact, a broader spectrum of operations: eFuel and methanol, ammonia, vehicle refueling, blending and heating, refining of natural oil, and the storage of renewable energy.  

As part of its strategy, Plug Power announces the acquisitions of companies supposed to bring additional technological competences: Frames Group (https://www.frames-group.com/ ) with power transmission systems and technology for building electrolyzers, ACT (Applied Cryo Technologies: https://www.appliedcryotech.com/ ) for cryogenics, and Joule (https://www.jouleprocess.com/about ) for the liquefaction of hydrogen. My immediate remark as regards those acquisitions, sort of intellectually straight-from-the-oven-still-warm-sorry-but-I-told-you-still-warm, is that Plug Power is acquiring a broad technological base rather than a specialized one. Officially, those acquisitions serve to enhance the Plug Power’s capacity as regards the deployment of hydrogen-focused technologies. Yet, as I am rummaging through the websites of those acquired companies, their technological competences go far beyond hydrogen.

Sort of contingent (adjacent?) to that current report is the piece of news, still on the Plug Power’s investors-relations site, from June 8th, 2022. It regards the deployment of a project in Europe, more specifically in the Port of Antwerp-Bruges (https://www.ir.plugpower.com/press-releases/news-details/2022/Plug-to-Build-Large-Scale-Green-Hydrogen-Generation-Plant-in-Europe-at-Port-of-Antwerp-Bruges/default.aspx ). This is supposed to be something labelled as a ‘Gigafactory’.

A little bit earlier this year, on my birthday, May 9th, Plug Power published a current report (https://d18rn0p25nwr6d.cloudfront.net/CIK-0001093691/203fd9c3-5302-4fa1-9edd-32fe4905689c.pdf ) coupled with a quarterly financial report (https://d18rn0p25nwr6d.cloudfront.net/CIK-0001093691/c7ad880f-71ff-4b58-8265-bd9791d98740.pdf ). Apparently, in the 1st quarter 2022, they had revenues 96% higher than 1Q 2021. Nice. There are interesting operational goals signaled in that current report. Plug Power plans to reduce services costs on a per unit basis by 30% in the 12 months following the report, thus until the end of the 1st quarter 2023. The exact quote is: ‘Plug remains focused on delivering on our previously announced target to reduce services costs on a per unit basis by 30% in the next 12 months, and 45% by the end of 2023. We are pleased to report that we have begun to see meaningful improvement in service margins on fuel cell systems and related infrastructure with a positive 30% increase in first quarter of 2022 versus the fourth quarter of 2021. The service margin improvement is a direct result of the enhanced technology GenDrive units that were delivered in 2021 which reduce service costs by 50%. The performance of these enhanced units demonstrates that the products are robust, and we expect these products will help support our long-term business needs. We believe service margins are tracking in the right direction with potential to break even by year end’.

When a business purposefully and effectively works on optimizing margins of profit, and the corresponding costs, it is a step forward in the lifecycle of the technologies used. This is a passage from the phase of early development towards late development, or, in other words, it is the phase when the company starts getting in control of small economic details in its technology.

I switch to the next company on my list, namely to Green Hydrogen Systems (Denmark, https://investor.greenhydrogen.dk/ ). They do not follow the SEC classification of reports, and, in order to get an update on their current developments, I go to their ‘Announcements & News’ section (https://investor.greenhydrogen.dk/announcements-and-news/default.aspx ).  On July 18th, 2022, Green Hydrogen Systems held an extraordinary General Meeting of shareholders. They amended their Articles of Association, as regards the Board of Directors, and the new version is: ‘The board of directors consists of no less than four and no more than nine members, all of whom must be elected by the general meeting. Members of the board of directors must resign at the next annual general meeting, but members of the board of directors may be eligible for re-election’. At the same extraordinary General Meeting, three new directors have been elected to the Board, on the top of the six already there.

To the extent that I know the Scandinavian ways of corporate governance, appointment of new directors to the Board usually comes with new business ties of the company. Those people are supposed to be something like intermediaries between the company and some external entities (research units? other companies? NGOs?). That change in the Board of Directors at Green Hydrogen Systems suggests something like the broadening of their network. That intuition is somehow confirmed by an earlier announcement, from June 13th (https://investor.greenhydrogen.dk/announcements-and-news/news-details/2022/072022-Green-Hydrogen-Systems-announces-changes-to-the-Board-of-Directors-and-provides-product-status-update/default.aspx ). The three new members of the Board come, respectively, from: Vestas Wind Systems, Siemens Energy, and Sonnedix (https://www.sonnedix.com/ ).

Still earlier this year, on April 12th, Green Hydrogen Systems announced ‘design complications in its HyProvide® A-Series platform’, and said complications are supposed to affect adversely the financial performance in 2022 (https://investor.greenhydrogen.dk/announcements-and-news/news-details/2022/Green-Hydrogen-Systems-announces-technical-design-complications-in-its-HyProvide-A-Series-platform/default.aspx ). When I think about it, design normally comes before its implementation, and therefore before any financial performance based thereon. When ‘design complications’ are serious enough for the company to disclose them and announce a possible negative impact on the financial side of the house, it means some serious mistakes years earlier, when that design was being conceptualized. I say ‘years’ because I notice the trademark symbol ‘®’ by the name of the technology. That means there had been time to: a) figure out the design b) register it as a trademark. That suggests at least 2 years, maybe more.

I quickly sum up my provisional conclusions from browsing current reports at Fuel Cell Energy, Plug Power, and Green Hydrogen Systems. I can see three different courses of events as regards the business models of those companies. At Fuel Cell Energy, broadly spoken marketing, including financial marketing, seems to be the name of the game. Both the technology and the equity of Fuel Cell Energy seems to be merchandise for trading. My educated guess is that the management of Fuel Cell Energy is trying to attract more financial investors to the game, and to close more technological deals, of the joint-venture type, at the operational level. It further suggests an attempt at broadening the business network of the company, whilst keeping the strategic ownership in the hands of the initial founders. As for Plug Power, the development I see is largely quantitative. They are broadening their technological base, including the acquisitions of strategically important assets, expanding their revenues, and ramping up their operational margins. This a textbook type of industrial development. Finally, at Green Hydrogen Systems, this still seems to be the phase of early development, with serious adjustments needed to both the technology owned and the team that runs it.

Those hydrogen-oriented companies seem to be following different paths and to be at different stages in the lifecycle of their technological base.

The real deal

I am blogging again, after months of break. My health required some attention, and my life priorities went a bit wobbly for some time, possibly because of the opioid pain killers which I took in hospital, after my surgery. Anyway, I am back in the game, writing freestyle.

Restarting after such a long break is a bit hard, and yet rewarding. I am removing rust from my thoughts, as if I were giving a new life to an old contrivance. I need to work up to cruise speed in my blogging. Currently, I am working on two subjects. One is my concept of Energy Ponds: a solution which combines ram pumps, hydropower, and the retention of water in wetlands. The other one pertains to business models in the broadly spoken industry of new sources of energy: electric vehicles (I am and remain a faithful investor in Tesla), technologies of energy storage, hydrogen and fuel cells based thereon, photovoltaic, wind and nuclear.

As I am thinking about it, the concept of Energy Ponds is already quite structured, and I am working on structuring it further by attracting the attention of people with knowledge and skills complementary to mine. On the other hand, the whole business models thing is foggy theoretically, and, at the same time, it is important to me at many levels, practical strategies of investment included. I know by experience that such topics – both vague and important – are the best for writing about on my blog.

Here comes the list of companies which I observe more or less regularly with respect to their business models:

>> Tesla https://ir.tesla.com/#quarterly-disclosure

>> Rivian https://rivian.com/investors

>> Lucid Group https://ir.lucidmotors.com/

>> Nuscale Power https://ir.nuscalepower.com/overview/default.aspx 

>> First Solar https://investor.firstsolar.com/home/default.aspx

>> SolarEdge https://investors.solaredge.com/

>> Fuel Cell Energy https://investor.fce.com/Investors/default.aspx

>> Plug Power https://www.ir.plugpower.com/overview/default.aspx

>> Green Hydrogen Systems https://investor.greenhydrogen.dk/

>> Nel Hydrogen https://nelhydrogen.com/investor-relations/

>> Next Hydrogen (précédemment BioHEP Technologies Ltd.) https://nexthydrogen.com/investor-relations/why-invest/

>> Energa https://ir.energa.pl/en

>> PGE https://www.gkpge.pl/en

>> Tauron https://raport.tauron.pl/en/tauron-in-2020/stock-exchange/investor-relations/

>> ZPUE  https://zpue.com/   

Two classifications come to my mind as I go through that list. Firstly, there are companies which I currently hold an investment position in: Tesla, Nuscale Power, Energa, PGE, Tauron et ZPUE. Then come those which I used to flirt with, namely Lucid Group, First Solar and SolarEdge. Finally, there are businesses which I just keep watching from a distance: Rivian, Fuel Cell Energy, Plug Power, Green Hydrogen Systems, Nel Hydrogen, and Next Hydrogen.

The other classification is based on the concept of owners’ earnings such as defined by Warren Buffett: net income plus amortization minus capital expenses. Tesla, PGE, Energa, ZPUE, Tauron, First Solar, SolarEdge – these guys generate a substantial stream of owners’ earnings. The others are cash-negative. As for the concept of owners’ earnings itself, you can consult both the investor-relations site of Berkshire Hathaway (https://www.berkshirehathaway.com/  ) or read a really good book by Robert G.Hagstrom « The Warren Buffett Way » (John Wiley & Sons, 2013, ISBN 1118793994, 9781118793992). I guess the intuition behind hinging my distinctions upon the cash-flow side of the house assumes that in the times of uncertainty, cash is king. Rapid technological change is full of uncertainty, especially when that change affects whole infrastructures, as it is the case with energy and propulsion. Besides, I definitely buy into Warren Buffett’s claim that cash-flow is symptomatic of the lifecycle in the given business.

The development of a business, especially on the base of innovative technologies, is cash-consuming. Cash, in business, is something we harvest rather than simply earn. Businesses which are truly able to harvest cash from their operations, have internal financing for moving to the next cycle of technological change. Those in need of cash from outside will need even more cash from outside in order to finance further innovation.

What’s so special about, cash in a business model? The most intuitive answer that comes to my mind is a motto heard from a banker, years ago: “In the times of crisis, cash is king”. Being a king means sovereignty in a territory, like “This place is mine, and, with all the due respect, pay respect or f**k off”. Having cash means having sovereignty of decision in business. Yet, nuance is welcome. Cash is cash. Once you have it, it does not matter that much where it came from, i.e. from operations or from external sources. When I have another look at businesses without positive owners’ earnings – Nuscale Power, Rivian, Fuel Cell Energy, Plug Power, Green Hydrogen Systems, Nel Hydrogen, and Next Hydrogen – I shift my focus from their cash-flow statements to their balance sheets and I can see insane amounts of cash on the assets’ side of the house. These companies, in their assets, have more cash than they have anything else. They look almost like banks, or investment funds.

Thus, my distinction between business models with positive owners’ earnings, on the one hand, and those without it, on the other hand, is a distinction along the axis of strategic specificity. When the sum total of net income and amortization, reduced by capital expenses, is positive and somehow in line with the market capitalization of the whole company, that company is launched on some clear tracks. The business is like a river: it is predictable and clearly traceable in its strategic decisions. On the other hand, a business with lots of cash in the balance sheet but little cash generated from operations is like lord Byron (George Gordon): those guys assume that the only two things worth doing are poetry and cavalry, only they haven’t decided yet the exact mix thereof.      

That path of thinking implies that a business model is more than a way of conducting operations; it is a vehicle for change through investment, thus for channeling capital with strategic decisions. Change which is about to come is somehow more interesting than change which is already there. Seen under this angle, businesses on my list convey different degrees of vagueness, and, therefore, different doses of intellectual provocation. I focus on the hydrogen ones, probably because in my country, Poland, we have that investment program implemented by the government: the hydrogen valleys.

As I have another look at the hydrogen-oriented companies on my list – Fuel Cell Energy, Plug Power, Green Hydrogen Systems, Nel Hydrogen, and Next Hydrogen – an interesting discrepancy emerges as regards the degree of technological advancement. Green Hydrogen Systems, Nel Hydrogen, and Next Hydrogen are essentially focused on making and supplying hydrogen. This is good old electrolysis, a technology with something like a century of industrial tradition, combined with the storage and transport of highly volatile gases. Only two, namely Fuel Cell Energy and Plug Power, are engaged into fuel cells based on hydrogen, and those fuel cells are, in my subjective view, the real deal as it comes to hydrogen-related innovation.

Tesla first in line

Once again, a big gap in my blogging. What do you want – it happens when the academic year kicks in. As it kicks in, I need to divide my attention between scientific research and writing, on the one hand, and my teaching on the other hand.

I feel like taking a few steps back, namely back to the roots of my observation. I observe two essential types of phenomena, as a scientist: technological change, and, contiguously to that, the emergence of previously unexpected states of reality. Well, I guess we all observe the latter, we just sometimes don’t pay attention. I narrow it down a bit. When it comes to technological change, I am just bewildered with the amounts of cash that businesses have started holding, across the board, amidst an accelerating technological race. Twenty years ago, any teacher of economics would tell their students: ‘Guys, cash is the least productive asset of all. Keep just the sufficient cash to face the most immediate expenses. All the rest, invest it in something that makes sense’. Today, when I talk to my students, I tell them: ‘Guys, with the crazy speed of technological change we are observing, cash is king, like really. The greater reserves of cash you hold, the more flexible you stay in your strategy’.

Those abnormally big amounts of cash that businesses tend to hold, those last years, it has two dimensions in terms of research. On the one hand, it is economics and finance, and yet, on the other hand, it is management. For quite some time, digital transformation has been about the only thing worth writing about in management science, but that, namely the crazy accumulation of cash balances in corporate balance sheets, is definitely something worth writing about. Still, there is amazingly little published research on the general topic of cash flow and cash management in business, just as there is very little on financial liquidity in business. The latter topic is developed almost exclusively in the context of banks, mostly the central ones. Maybe it is all that craze about the abominable capitalism and the general claim that money is evil. I don’t know.

Anyway, it is interesting. Money, when handled at the microeconomic level, tells the hell of a story about our behaviour, our values, our mutual trust, and our emotions. Money held in corporate balance sheets tells the hell of a story about decision making. I explain. Please, consider the amount of money you carry around with you, like the contents of your wallet (credit cards included) plus whatever you have available instantly on your phone. Done? Visualised? Good. Now, ask yourself what percentage of all those immediately available monetary balances you use during your one average day. Done? Analysed? Good. In my case, it would be like 0,5%. Yes, 0,5%. I did that intellectual exercise with my students, many time. They usually hit no more than 10%, and they are gobsmacked. Their first reaction is WOKEish: ‘So I don’t really need all that money, right. Money is pointless, right?’. Not quite, my dear students. You need all that money; you just need it in a way which you don’t immediately notice.

There is a model in the theory of complex systems, called the ants’ colony (see for example: (Chaouch, Driss & Ghedira 2017[1]; Asghari & Azadi 2017[2]; Emdadi et al. 2019[3]; Gupta & Srivastava 2020[4]; Di Caprio et al. 2021[5]). Yes, Di Caprio. Not the Di Caprio you intuitively think about, though. Ants communicate with pheromones. They drop pheromones somewhere they sort of know (how?) it is going to be a signal for other ants. Each ant drops sort of a standard parcel of pheromones. Nothing to write home about, really, and yet enough to attract the attention of another ant which could drop its individual pheromonal parcel in the same location. With any luck, other ants will discover those chemical traces and validate them with their individual dumps of pheromones, and this is how the colony of ants maps its territories, mostly to find and exploit sources of food. This is interesting to find out that in order for all that chemical dance to work, there needs to be a minimum number of ants on the job. In there are not enough ants per square meter of territory, they just don’t find each other’s chemical imprints and have no chance to grab hold of the resources available. Yes, they all die prematurely. Money in human societies could be the equivalent of a pheromone. We need to spread it in order to carry out complex systemic changes. Interestingly, each of us, humans, is essentially blind to those complex changes: we just cannot wrap our mind around quickly around the technical details of something apparently as simple as the manufacturing chain of a gardening rake (do you know where exactly and in what specific amounts all the ingredients of steel come from? I don’t).  

All that talk about money made me think about my investments in the stock market. I feel like doing things the Warren Buffet’s way: going to the periodical financial reports of each company in my portfolio, and just passing in review what they do and what they are up to. By the way, talking about Warren Buffet’s way, I recommend my readers to go to the source: go to https://www.berkshirehathaway.com/ first, and then to  https://www.berkshirehathaway.com/2020ar/2020ar.pdf as well as to https://www.berkshirehathaway.com/qtrly/3rdqtr21.pdf . For now, I focus on studying my own portfolio according to the so called “12 immutable tenets by Warren Buffet”, such as I allow myself to quote them:

>> Business Tenets: Is the business simple and understandable? Does the business have a consistent operating history? Does the business have favourable long-term prospects?

>> Management Tenets: Is management rational? Is management candid with its shareholders? Does management resist the institutional imperative?

>> Financial Tenets Focus on return on equity, not earnings per share. Calculate “owner earnings.” Look for companies with high profit margins. For every dollar retained, make sure the company has created at least one dollar of market value.

>> Market Tenets: What is the value of the business? Can the business be purchased at a significant discount to its value?

(Hagstrom, Robert G.. The Warren Buffett Way (p. 98). Wiley. Kindle Edition.)

Anyway, here is my current portfolio:

>> Tesla (https://ir.tesla.com/#tab-quarterly-disclosure),

>> Allegro.eu SA (https://about.allegro.eu/ir-home ),

>> Alten (https://www.alten.com/investors/ ),

>> Altimmune Inc (https://ir.altimmune.com/ ),

>> Apple Inc (https://investor.apple.com/investor-relations/default.aspx ),

>> CureVac NV (https://www.curevac.com/en/investor-relations/overview/ ),

>> Deepmatter Group PLC (https://www.deepmatter.io/investors/ ), 

>> FedEx Corp (https://investors.fedex.com/home/default.aspx ),

>> First Solar Inc (https://investor.firstsolar.com/home/default.aspx )

>> Inpost SA (https://www.inpost.eu/investors )

>> Intellia Therapeutics Inc (https://ir.intelliatx.com/ )

>> Lucid Group Inc (https://ir.lucidmotors.com/ )

>> Mercator Medical SA (https://en.mercatormedical.eu/investors/ )

>> Nucor Corp (https://www.nucor.com/investors/ )

>> Oncolytics Biotech Inc (https://ir.oncolyticsbiotech.com/ )

>> Solaredge Technologies Inc (https://investors.solaredge.com/ )

>> Soligenix Inc (https://ir.soligenix.com/ )

>> Vitalhub Corp (https://www.vitalhub.com/investors )

>> Whirlpool Corp (https://investors.whirlpoolcorp.com/home/default.aspx )

>> Biogened (https://biogened.com/ )

>> Biomaxima (https://www.biomaxima.com/325-investor-relations.html )

>> CyfrPolsat (https://grupapolsatplus.pl/en/investor-relations )

>> Emtasia (https://elemental-asia.biz/en/ )

>> Forposta (http://www.forposta.eu/relacje_inwestorskie/dzialalnosc_i_historia.html )

>> Gameops (http://www.gameops.pl/en/about-us/ )

>> HMInvest (https://grupainwest.pl/relacje )

>> Ifirma (https://www.ifirma.pl/dla-inwestorow )

>> Moderncom (http://moderncommercesa.com/wpmccom/en/dla-inwestorow/ )

>> PolimexMS (https://www.polimex-mostostal.pl/en/reports/raporty-okresowe )

>> Selvita (https://selvita.com/investors-media/ )

>> Swissmed (https://swissmed.com.pl/?menu_id=8 )   

Studying that whole portfolio of mine through the lens of Warren Buffet’s tenets looks like a piece of work, really. Good. I like working. Besides, as I have been reading Warren Buffett’s annual reports at https://www.berkshirehathaway.com/ , I realized that I need a real strategy for investment. So far, I have developed a few efficient hacks, such as, for example, the habit of keeping my s**t together when other people panic or when they get euphoric. Still, hacks are not the same as strategy.

I feel like adding my own general principles to Warren Buffet’s tenets. Principle #1: whatever I think I do my essential strategy consists in running away from what I perceive as danger. Thus, what am I afraid of, in my investment? What subjective fears and objective risks factors shape my actions as investor? Once I understand that, I will know more about my own actions and decisions. Principle #2: the best strategy I can think of is a game with nature, where each move serves to learn something new about the rules of the game, and each move should be both decisive and leaving me with a margin of safety. What am I learning as I make my moves? What my typical moves actually are?

Let’s rock. Tesla (https://ir.tesla.com/#tab-quarterly-disclosure), comes first in line, as it is the biggest single asset in my portfolio. I start my digging with their quarterly financial report for Q3 2021 (https://www.sec.gov/Archives/edgar/data/1318605/000095017021002253/tsla-20210930.htm ), and I fish out their Consolidated Balance Sheets (in millions, except per share data, unaudited: https://www.sec.gov/Archives/edgar/data/1318605/000095017021002253/tsla-20210930.htm#consolidated_balance_sheets ).

Now, I assume that if I can understand why and how numbers change in the financial statements of a business, I can understand the business itself. The first change I can spot in that balance sheet is property, plant and equipment, net passing from $12 747 million to $17 298 million in 12 months. What exactly has happened? Here comes Note 7 – Property, Plant and Equipment, Net, in that quarterly report, and it starts with a specification of fixed assets comprised in that category. Good. What really increased in this category of assets is construction in progress, and here comes the descriptive explanation pertinent thereto: “Construction in progress is primarily comprised of construction of Gigafactory Berlin and Gigafactory Texas, expansion of Gigafactory Shanghai and equipment and tooling related to the manufacturing of our products. We are currently constructing Gigafactory Berlin under conditional permits in anticipation of being granted final permits. Completed assets are transferred to their respective asset classes, and depreciation begins when an asset is ready for its intended use. Interest on outstanding debt is capitalized during periods of significant capital asset construction and amortized over the useful lives of the related assets. During the three and nine months ended September 30, 2021, we capitalized $14 million and $52 million, respectively, of interest. During the three and nine months ended September 30, 2020, we capitalized $13 million and $33 million, respectively, of interest.

Depreciation expense during the three and nine months ended September 30, 2021 was $495 million and $1.38 billion, respectively. Depreciation expense during the three and nine months ended September 30, 2020 was $403 million and $1.13 billion, respectively. Gross property, plant and equipment under finance leases as of September 30, 2021 and December 31, 2020 was $2.60 billion and $2.28 billion, respectively, with accumulated depreciation of $1.11 billion and $816 million, respectively.

Panasonic has partnered with us on Gigafactory Nevada with investments in the production equipment that it uses to manufacture and supply us with battery cells. Under our arrangement with Panasonic, we plan to purchase the full output from their production equipment at negotiated prices. As the terms of the arrangement convey a finance lease under ASC 842, Leases, we account for their production equipment as leased assets when production commences. We account for each lease and any non-lease components associated with that lease as a single lease component for all asset classes, except production equipment classes embedded in supply agreements. This results in us recording the cost of their production equipment within Property, plant and equipment, net, on the consolidated balance sheets with a corresponding liability recorded to debt and finance leases. Depreciation on Panasonic production equipment is computed using the units-of-production method whereby capitalized costs are amortized over the total estimated productive life of the respective assets. As of September 30, 2021 and December 31, 2020, we had cumulatively capitalized costs of $1.89 billion and $1.77 billion, respectively, on the consolidated balance sheets in relation to the production equipment under our Panasonic arrangement.”

Good. I can try to wrap my mind around the contents of Note 7. Tesla is expanding its manufacturing base, including a Gigafactory in my beloved Europe. Expansion of the manufacturing capacity means significant, quantitative growth of the business. According to Warren Buffett’s philosophy: “The question of where to allocate earnings is linked to where that company is in its life cycle. As a company moves through its economic life cycle, its growth rates, sales, earnings, and cash flows change dramatically. In the development stage, a company loses money as it develops products and establishes markets. During the next stage, rapid growth, the company is profitable but growing so fast that it cannot support the growth; often it must not only retain all of its earnings but also borrow money or issue equity to finance growth” (Hagstrom, Robert G.. The Warren Buffett Way (p. 104). Wiley. Kindle Edition).  Tesla looks like they are in the phase of rapid growth. They have finally nailed down how to generate profits (yes, they have!), and they are expanding capacity-wise. They are likely to retain earnings and to be in need of cash, and that attracts my attention to another passage in Note 7: “Interest on outstanding debt is capitalized during periods of significant capital asset construction and amortized over the useful lives of the related assets”. If I understand correctly, the financial strategy consists in not servicing (i.e. not paying the interest due on) outstanding debt when that borrowed money is really being used to finance the construction of productive assets, and starting to service that debt only after the corresponding asset starts working and paying its bills. That means, in turn, that lenders are being patient and confident with Tesla. They assume their unconditional claims on Tesla’s future cash flows (this is one of the possible ways to define outstanding debt) are secure.   

Good. Now, I am having a look at Tesla’s Consolidated Statements of Operations (in millions, except per share data, unaudited: https://www.sec.gov/Archives/edgar/data/1318605/000095017021002253/tsla-20210930.htm#consolidated_statements_of_operations ). It is time to have a look at Warren Buffett’s Business Tenets as regards Tesla. Is the business simple and understandable? Yes, I think I can understand it. Does the business have a consistent operating history? No, operational results changed in 2020 and they keep changing. Tesla is passing from the stage of development (which took them a decade) to the stage of rapid growth. Does the business have favourable long-term prospects? Yes, they seem to have good prospects. The market of electric vehicles is booming (EV-Volumes[6]; IEA[7]).

Is Tesla’s management rational? Well, that’s another ball game. To develop in my next update.

[1] Chaouch, I., Driss, O. B., & Ghedira, K. (2017). A modified ant colony optimization algorithm for the distributed job shop scheduling problem. Procedia computer science, 112, 296-305. https://doi.org/10.1016/j.procs.2017.08.267

[2] Asghari, S., & Azadi, K. (2017). A reliable path between target users and clients in social networks using an inverted ant colony optimization algorithm. Karbala International Journal of Modern Science, 3(3), 143-152. http://dx.doi.org/10.1016/j.kijoms.2017.05.004

[3] Emdadi, A., Moughari, F. A., Meybodi, F. Y., & Eslahchi, C. (2019). A novel algorithm for parameter estimation of Hidden Markov Model inspired by Ant Colony Optimization. Heliyon, 5(3), e01299. https://doi.org/10.1016/j.heliyon.2019.e01299

[4] Gupta, A., & Srivastava, S. (2020). Comparative analysis of ant colony and particle swarm optimization algorithms for distance optimization. Procedia Computer Science, 173, 245-253. https://doi.org/10.1016/j.procs.2020.06.029

[5] Di Caprio, D., Ebrahimnejad, A., Alrezaamiri, H., & Santos-Arteaga, F. J. (2021). A novel ant colony algorithm for solving shortest path problems with fuzzy arc weights. Alexandria Engineering Journal. https://doi.org/10.1016/j.aej.2021.08.058

[6] https://www.ev-volumes.com/

[7] https://www.iea.org/reports/global-ev-outlook-2021/trends-and-developments-in-electric-vehicle-markets

The batteries we don’t need anymore

I continue on the thread I started to develop in my last update in French, titled ‘De quoi parler à la prochaine réunion de faculté’, i.e. I am using that blog, and the fact of writing, to put some order in the almost ritual mess that happens at the beginning of the academic year. New calls for tenders start in the ministerial grant programs, new syllabuses need to be prepared, new classes start. Ordinary stuff, mind you, this is just something about September, as if I were in Vivaldi’s ‘Four seasons’: the hot, tumultuous Summer slowly folds into the rich, textured, and yet implacably realistic Autumn.

My central idea is to use some of the science which I dove into during the summer holidays as an intellectual tool for putting order in that chaos. That almost new science of mine is mostly based on the theory of complex systems, and my basic claim is that technological change is an emergent phenomenon in complex social systems. We don’t know why exactly our technologies change the way they change. We can trace the current technologies back to their most immediate ancestors and sometimes we can predict their most immediate successors, but that’s about it. Futuristic visions of technologies that could be there in 50 years from now are already some kind of traditional entertainment. The concept of technological progress, when we try to find a developmental logic in the historically known technological change, is usually standing on wobbly legs, on the other hand. Yes, electricity allowed the emergence of medical technologies used in hospitals, and that saved a lot of human lives, but there is no way Thomas Edison could have known that. The most spectacular technological achievements of mankind, such as the Egyptian pyramids, the medieval cathedrals, the Dutch windmills from the 16th century, or the automobile, seen from the historical distance, look ambiguous. Yes, it all solved some problems, but it facilitated the emergence of new problems. The truly unequivocal benefit of those technological leaps, which could have been actually experienced by the people who made them, was to learn how to develop technologies.

The studies I did during the Summer holidays 2021 focused on four essential, mathematical models of emergent technological change: cellular automata, flock of birds AKA particle swarm, ants’ nest, and imperfect Markov chains. I start with passing in review the model of cellular automata. At any given moment, the social complexity can be divided into a finite number of social entities (agents). They can be individual humans, businesses, NGOs, governments, local markets etc. Each such entity has an immediate freedom of movement, i.e. a finite number of one-step moves. The concept is related to the theory of games and corresponds to what happens in real life. When we do something social, we seldom just rush forwards. Most frequently, we make one step, observe the outcomes, adjust, then we make the next step etc. When all social agents do it, the whole social complexity can be seen as a collection of cells, or pixels. Each such cell (pixel) is a local state of being in society. A social entity can move into that available state, or not, at their pleasure and leisure. All the one-step moves a social entity can make translate into a trajectory it can follow across the social space. Collective outcomes we strive for and achieve can be studied as temporary complex states of those entities following their respective trajectories. The epistemological trick here is that individual moves and their combinations can be known for sure only ex post. All we can do ex ante is to define the possible states, and just wait where does the reality go.

As we are talking about the possible states of social complexity, I found an interesting mathematical mindf**k at quite an unexpected source, namely in the book titled ‘Aware. The Science and Practice of Presence. The Groundbreaking Meditation Practice’ by Daniel J. Siegel [Penguin Random House LLC, 2018, Identifiers: LCCN 2018016987 (print), LCCN 2018027672 (ebook), ISBN 9780143111788, ISBN 9781101993040 (hardback)]. This is a mathematical way of thinking, apparently taken from quantum physics. Here is the essence of it. Everything that happens does so as 100% probability of the given thing happening. Each phenomenon which takes place is the actualization of the same phenomenon being just likely to happen.

Actualization of probability can be seen as collision of two vehicles in traffic. When the two vehicles are at a substantial distance from each other, the likelihood of them colliding is zero, for all practical purposes. As they converge towards each other, there comes a point when they become sort of provisionally entangled, e.g. they find themselves heading towards the same crossroads. The probability of collision increases slightly, and yet it is not even the probability of collision, it is just the probability that these two might find themselves in a vicinity conducive to a possible collision. Nothing to write home about, yet, like really. It can be seen as a plateau of probability slowly emerging out of the initial soup of all the things which can possibly happen.

As the two cars drive closer and closer to the crossroads in question, the panoply of possible states narrows down. There is a very clear chunk of reality which gains in likelihood, as if it was a mountain range pushing up from the provisional plateau. There comes a point where the two cars (and their drivers) just come on collision course, and there is no way around it, and this is a peak of 100% probability. Boom! Probability is being consumed.

What do those cars have in common with meditation and with the emergence of technological change? As regards meditation, thought can be viewed as a progressively emerging actualization of something that was just a weak probability, sort of a month ago it was just weakly probable that today I would think what I think, it became much more likely yesterday, as the thoughts from yesterday have an impact on the thoughts of today, and today it all comes to fruition, i.e. to the 100% probability. As regards emergent technological change, the way technology changes today can be viewed as actualization of something that was highly probable last year, just somehow probable 10 years ago, and had been just part of the amorphous soup of probability 30 years ago. Those trajectories followed by individual agents inside social complexity, as defined in the theory of cellular automata, are entangled together precisely according to that pattern of emergent probabilities. Two businesses coming up with two mutually independent, and yet similar technologies, are like two peak actualizations of 100% probability in a plateau of probable technological change, which, in turn, has been slowly emerging for some time.

Those other theories I use explain and allow to model mathematically that entanglement. The theory of particle swarm, pertinent to flocks of birds, assumes that autonomous social agents strive for a certain level of behavioural coupling. We expect some level of predictability from others, and we can cooperate with others when we are satisfactorily predictable in our actions. The strive for social coherence is, therefore, one mechanism of entanglement between individual trajectories of cellular automata. The theory of ants’ nest focuses on a specific category of communication systems in societies, working like pheromones. Ants organize by marking, reinforcing and following paths across their environment, and their pheromones serve as markers and reinforcement agents for those paths. In human societies, there are social pheromones. Money and financial markets make probably the most obvious example, but scientific publications are another one. The more scientific articles are being published on a given topic, the more likely are other articles being written on the same topic, until the whole thing reaches a point of saturation, when some ants (pardon me, scientists) start thinking about another path to mark with intellectual pheromones.

Cool. I have (OK, we have) complex social states, made of entangled probabilities that something specific happens, and they encompass technology. Those complex states change, i.e. one complex state morphs into another. Now, how the hell can I know, as a researcher, what is happening exactly? Such as the theory of complex systems suggests it, I can never know exactly, for one, and I need to observe, for two. As I don’t know exactly what is it exactly, that thing which I label ‘technological change’, it is problematic to set too many normative assumptions as for which specific path that technological change should take. I think this is the biggest point of contention as I apply my theory, such as I have just outlined it, to my main field of empirical research, namely energy economics, and technological change in the sector of energy. The more I do that research, the more convinced I am that the so-called ‘energy policies’, ‘climate policies’ etc. are politically driven bullshit based on wishful thinking, with not much of a chance to bring the positive change we expect. I have that deep feeling that setting a strategy for future innovations in our business/country/world is very much like that Polish expression ‘sharing the skin of a bear which is still running in the woods’. First, you need to kill the bear, only then you can bicker about who takes what part of the skin. In the case of innovation, long-term strategies in that domain consist in predicting what we will do when we have something we don’t even know yet what is it exactly.

I am trying to apply this general theory in the grant applications which I am in charge of preparing now, and in my teaching. We have that idea, at the faculty, to apply for funding to study the market of electric vehicles in Europe and in Poland. This is an interesting situation as regards business models. In the US, the market of electric cars is clearly divided among three categories of players. There is Tesla, which is a category and an industry in itself, with its peculiar strategy of extreme vertical integration. Then there are the big, classical car makers, such as Toyota, General Motors etc., with their business models based on rather a short vertical chain of value added inside the business, and a massive supply chain upstream of the house. Finally, there is a rising tide of small start-ups in the making of electric vehicles. I wonder what I could be in Europe. As our European market of electric vehicles is taking off, it is dominated by the incumbent big manufacturers, the old school ones, with Tesla building a factory in Germany, and progressively building a beachhead in the market. There is some timid movement towards small start-up businesses in the field, but it is really timid. In my home country, Poland, the most significant attempt at starting up an electric vehicle made in Poland is a big consortium of state-controlled companies, running under the name of ‘Electromobility Poland’.  

I have that intuition, which I provisionally express as a working hypothesis, namely that business models are an emergent property of technologies which they use. As regards the market of electric vehicles, it means that Tesla’s business model is not an accidental explosion of Elon Musk’s genius mind: it is an emergent characteristic of the technologies involved.

Good. I have some theory taking shape, nice and easy. I let it ripen a bit, and I start sniffing around for facts. What is a business model, in my mind? It is the way of operating the chain of value added, and getting paid for it, in the first place. Then, it is the way of using capital. I noticed that highly innovative environments force businesses to build up and keep large amounts of cash money, arguably to manage the diverse uncertainties emerging as technologies around morph like hell. In some cases, e.g. in biotech, the right business model for rapid innovation is a money-sucker, with apparently endless pay-ins of additional equity by the shareholders, and yet with a big value in terms of technological novelty created. I can associate that phenomenon of vacuum cleaning equity with the case of Tesla, who just recently started being profitable, and had gone through something like a decade in permanent operational loss. That is all pertinent to fixed costs, thus to the cash we need to build up and keep in place the organizational structure required for managing the value chain the way we want to manage it.

I am translating those loose remarks of mine into observable phenomena. Everything I have just mentioned is to be found in the annual financial reports. This is my first source of information. When I want to study business models in the market of electric vehicles, I need to look into financial and corporate reports of businesses active in the market. I need to look into the financial reports of Mercedes Benz, BMW, Renault, PSA, Volkswagen, Fiat, Volvo, and Opel – thus the European automotive makers – and see how it is going, and whether whatever is going on can be correlated with changes in the European market of electric vehicles. Then, it is useful to look into the financial reports of global players present in the European market, e.g. Tesla, Toyota, Honda and whatnot, just to see what changes in them as the European market of electric vehicles is changing.

If my intuition is correct, i.e. if business models are truly an emergent property of technologies used, the fact of engaging into the business of electric vehicles should be correlated with some sort of recurrent pattern in those companies.         

Good. This is about the big boys in the playground. Now, I turn toward the small ones, the start-up businesses. As I already said, it is not like we have a crowd of them in the European industry of electric vehicles. The intuitive axis of research which comes to my mind is to look at start-ups active in the U.S., study their business models, and see if there is any chance of something similar emerging in Europe. Somehow tangentially to that, I think it would be interesting to check whether the plan of Polish government regarding ‘Electromobility Poland’, that is the plan to develop it with public and semi-public money, and then sell it to private investors, has any grounds and under what conditions it can be a workable plan.

Good. I have rummaged a bit in my own mind, time to do the same to other people. I mean, I am passing to reviewing the literature. I type ‘electric vehicles Europe business model’ at the https://www.sciencedirect.com/ platform, and I look at what’s popping up. Here comes the paper by Pardo-Bosch, F., Pujadas, P., Morton, C., & Cervera, C. (2021). Sustainable deployment of an electric vehicle public charging infrastructure network from a city business model perspective. Sustainable Cities and Society, 71, 102957., https://doi.org/10.1016/j.scs.2021.102957 . The abstract says: ‘The unprecedented growth of global cities together with increased population mobility and a heightened concern regarding climate change and energy independence have increased interest in electric vehicles (EVs) as one means to address these challenges. The development of a public charging infrastructure network is a key element for promoting EVs, and with them reducing greenhouse gas emissions attributable to the operation of conventional cars and improving the local environment through reductions in air pollution. This paper discusses the effectiveness, efficiency, and feasibility of city strategic plans for establishing a public charging infrastructure network to encourage the uptake and use of EVs. A holistic analysis based on the Value Creation Ecosystem (VCE) and the City Model Canvas (CMC) is used to visualise how such plans may offer public value with a long-term and sustainable approach. The charging infrastructure network implementation strategy of two major European cities, Nantes (France) and Hamburg (Germany), are analysed and the results indicate the need to involve a wide range of public and private stakeholders in the metropolitan areas. Additionally, relevant, and fundamental patterns and recommendations are provided, which may help other public managers effectively implement this service and scale-up its use and business model.’

Well, I see there is a lot of work to do, as I read that abstract. I rarely find a paper where I have so much to argue with, just after having read the abstract. First of all, ‘the unprecedented growth of global cities’ thing. Actually, if you care to have a look at the World Bank data on urban land (https://data.worldbank.org/indicator/AG.LND.TOTL.UR.K2 ), as well as that on urban population (https://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS ), you will see that urbanization is an ambiguous phenomenon, strongly region-specific. The central thing is that cities become increasingly distinct from the countryside, as types of human settlements. The connection between electric vehicles and cities is partly clear, but just partly. Cities are the most obvious place to start with EVs, because of the relatively short distance to travel between charging points. Still, moving EVs outside the cities, and making them functional in rural areas, is the next big challenge.

Then comes the ‘The development of a public charging infrastructure network is a key element for promoting EVs’ part. As I studied the thing in Europe, the network of charging stations, as compared to the fleet of EVs in the streets is so dense that we have like 12 vehicles per charging station on average, across the European Union. There is no way a private investor can have it for their money, when financing a private charging station, with that average density. We face a paradox: there are so many publicly funded charging stations, in relation to the car fleet out there, that private investment gets discouraged. I agree that it could be an acceptable transitory state in the market, although it begs the question whether private charging stations are a viable business in Europe. Tesla has based a large part of its business model in the US precisely on the development of their own charging stations. Is it a viable solution in Europe?

Here comes another general remark, contingent to my hypothesis of business models being emergent on the basis of technologies. Automotive technologies in general, thus the technology of a vehicle moving by itself, regardless the method of propulsion (i.e. internal combustion vs electric) is a combination of two component technologies. Said method of propulsion is one of them, and the other one is the technology of distributing the power source across space. Electric vehicles can be viewed as cousins to tramways and electric trains, with just more pronounced a taste for independence: instead of drinking electricity from a permanent wiring, EVs carry their electricity around with them, in batteries.

As we talk about batteries, here comes another paper in my cursory rummaging across other people’s science: Albertsen, L., Richter, J. L., Peck, P., Dalhammar, C., & Plepys, A. (2021). Circular business models for electric vehicle lithium-ion batteries: An analysis of current practices of vehicle manufacturers and policies in the EU. Resources, Conservation and Recycling, 172, 105658., https://doi.org/10.1016/j.resconrec.2021.105658 . Yes, indeed, the advent of electric vehicles creates a problem to solve, namely what to do with all those batteries. I mean two categories of batteries. Those which we need, and hope to acquire easily when the time comes for changing them in our vehicles, in the first place, and those we don’t need anymore and expect someone to take care of them swiftly and elegantly.       

It is important to re-assume the meaning

It is Christmas 2020, late in the morning. I am thinking, sort of deeply. It is a dysfunctional tradition to make, by the end of the year, resolutions for the coming year. Resolutions which we obviously don’t hold to long enough to see them bring anything substantial. Yet, it is a good thing to pass in review the whole passing year, distinguish my own f**k-ups from my valuable actions, and use it as learning material for the incoming year.

What I have been doing consistently for the past year is learning new stuff: investment in the stock market, distance teaching amidst epidemic restrictions, doing research on collective intelligence in human societies, managing research projects, programming, and training consistently while fasting. Finally, and sort of overarchingly, I have learnt the power of learning by solving specific problems and writing about myself mixing successes and failures as I am learning.

Yes, it is precisely the kind you can expect in what we tend to label as girls’ readings, sort of ‘My dear journal, here is what happened today…’. I keep my dear journal focused mostly on my broadly speaking professional development. Professional development combines with personal development, for me, though. I discovered that when I want to achieve some kind of professional success, would it be academic, or business, I need to add a few new arrows to my personal quiver.    

Investing in the stock market and training while fasting are, I think, what I have had the most complete cycle of learning with. Strange combination? Indeed, a strange one, with a surprising common denominator: the capacity to control my emotions, to recognize my cognitive limitations, and to acknowledge the payoff from both. Financial decisions should be cold and calculated. Yes, they should, and sometimes they are, but here comes a big discovery of mine: when I start putting my own money into investment positions in the stock market, emotions flare in me so strongly that I experience something like tunnel vision. What looked like perfectly rational inference from numbers, just minutes ago, now suddenly looks like a jungle, with both game and tigers in it. The strongest emotion of all, at least in my case, is the fear of loss, and not the greed for gain. Yes, it goes against a common stereotype, and yet it is true. Moreover, I discovered that properly acknowledged and controlled, the fear of loss is a great emotional driver for good investment decisions, and, as a matter of fact, it is much better an emotional driver than avidity for gain. I know that I am well off when I keep the latter sort of weak and shy, expecting gains rather than longing for them, if you catch my drift.

Here comes the concept of good investment decisions. As this year 2020 comes to an end, my return on cash invested over the course of the year is 30% with a little change. Not bad at all, compared to a bank deposit (+1,5%) or to sovereign bonds (+4,5% max). I am wrapping my mind around the second most fundamental question about my investment decisions this year – after, of course, of the question about return on investment – and that second question is ontological: what my investment decisions actually have been? What has been their substance? The most general answer is tolerable complexity with intuitive hedging and a pinch of greed. Complexity means that I have progressively passed from the otherwise naïve expectation of one perfect hit to a portfolio of investment positions. Thinking intuitively in terms of portfolio has taught me just as intuitive approach to hedging my risks. Now, when I open one investment position, I already think about another possible one, either to reinforce my glide on the wave crest I intend to ride, or to compensate the risks contingent to seeing my ass gliding off and down from said wave crest.

That portfolio thinking of mine happens in layers, sort of. I have a portfolio of industries, and that seems to be the basic structuring layer of my decisions. I think I can call myself a mid-term investor. I have learnt to spot and utilise mid-term trends of interest that investors in the stock market attach to particular industries. I noticed there are cyclical fashion seasons in the stock market, in that respect. There is a cyclically recurrent biotech season, due to the pandemic. There is just as cyclical a fashion for digital tech, and another one for renewable energies (photovoltaic, in particular). Inside the digital tech, there are smaller waves of popularity as regards the gaming business, others connected to FinTech etc.

Cyclicality means that prices of stock in those industries grow for some time, ranging, by my experience, from 2 to 13 weeks. Riding those waves means jumping on and off at the right moment. The right moment for jumping on is as early as possible after the trend starts to ascend, and jump just as early as possible after it shows signs of durable descent.

The ‘durable’ part is tricky, mind you. I saw many episodes, and during some of them I shamefully yielded to short-termist panic, when the trend curbs down just for a few days before rocketing up again. Those episodes show well what it means in practical terms to face ‘technical factors’. The stock market is like an ocean. There are spots of particular fertility, and big predators tend to flock just there. In the stock market, just as in the ocean, you have bloody big sharks swimming around, and you’d better hold on when they start feeding, ‘cause they feed just as real sharks do: they hit quickly, cause abundant bleeding, and then just wait until their pray bleeds out enough to be defenceless.

When I see, for example, a company like the German Biontech (https://investors.biontech.de/investors-media) suddenly losing value in the stock market, whilst the very vaccine they ganged up with Pfizer to make is being distributed across the world, I am like: ‘Wait a minute! Why the stock price of a super-successful, highly innovative business would fall just at the moment when they are starting to consume the economic fruit of their innovation?’. The only explanation is that sharks are hunting. Your typical stock market shark hunts in a disgusting way, by eating, vomiting and then eating their vomit back with a surplus. It bites a big chunk of a given stock, chews it for a moment, spits it out quickly – which pushes the price down a bit – then eats back its own vomit of stock, with a tiny surplus acquired at the previously down-driven price, and then it repeats. Why wouldn’t it repeat, as long as the thing works?

My personal philosophy, which, unfortunately, sometimes I deviate from when my emotions prevail, is just to sit and wait until those big sharks end their feeding cycle. This is another useful thing to know about big predators in the stock market: they hunt similarly to big predators in nature. They have a feeding cycle. When they have killed and consumed a big prey, they rest, as they are both replete with eating and down on energy. They need to rebuild their capital base.      

My reading of the stock market is that those waves of financial interest in particular industries are based on expectations as for real business cycles going on out there. Of course, in the stock market, there is always the phenomenon of subsidiary interest: I invest in companies which I expect other investors to invest to, as well, and, consequently, whose stock price I expect to grow. Still, investors in the stock market are much more oriented on fundamental business cycles than non-financial people think. When I invest in the stock of a company, and I know for a fact that many other investors think the same, I expect that company to do something constructive with my trust. I want to see those CEOs take bold decisions as for real investment in technological assets. When they really do so, I stay with them, i.e. I hold that stock. This is why I keep holding the stock of Tesla even amidst episodes of while swings in its price. I simply know Elon Musk will always come up with something which, for him, are business concepts, and for the common of mortals are science-fiction. If, on the other hand, I see those CEOs just sitting and gleaming benefits from trading their preferential shares, I leave.

Here I connect to another thing I started to learn during 2020: managing research projects. At my university, I have been assigned this specific job, and I discovered something which I did not expect: there is more money than ideas, out there. There is, actually, plenty of capital available from different sources, to finance innovative science. The tricky part is to translate innovative ideas into an intelligible, communicable form, and then into projects able to convince people with money. The ‘translating’ part is surprisingly complex. I can see many sparse, sort of semi-autonomous ideas in different people, and I still struggle with putting those people together, into some sort of team, or, fault of a team, into a network, and make them mix their respective ideas into one, big, articulate concept. I have been reading for years about managing R&D in corporate structures, about how complex and artful it is to manage R&D efficiently, and now, I am experiencing it in real life. An interesting aspect of that is the writing of preliminary contracts, the so-called ‘Non-Disclosure Agreements’ AKA NDAs, the signature of which is sort of a trigger for starting serious networking between different agents of an R&D project.

As I am wrapping my mind around those questions, I meditate over the words written by Joseph Schumpeter, in his Business Cycles: “Whenever a new production function has been set up successfully and the trade beholds the new thing done and its major problems solved, it becomes much easier for other people to do the same thing and even to improve upon it. In fact, they are driven to copying it if they can, and some people will do so forthwith. It should be observed that it becomes easier not only to do the same thing, but also to do similar things in similar lines—either subsidiary or competitive ones—while certain innovations, such as the steam engine, directly affect a wide variety of industries. This seems to offer perfectly simple and realistic interpretations of two outstanding facts of observation : First, that innovations do not remain isolated events, and are not evenly distributed in time, but that on the contrary they tend to cluster, to come about in bunches, simply because first some, and then most, firms follow in the wake of successful innovation ; second, that innovations are not at any time distributed over the whole economic system at random, but tend to concentrate in certain sectors and their surroundings”. (Business Cycles, Chapter III HOW THE ECONOMIC SYSTEM GENERATES EVOLUTION, The Theory of Innovation). In the Spring, when the pandemic was deploying its wings for the first time, I had a strong feeling that medicine and biotechnology will be the name of the game in technological change for at least a few years to come. Now, as strange as it seems, I have a vivid confirmation of that in my work at the university. Conceptual balls which I receive and which I do my best to play out further in the field come almost exclusively from the faculty of medical sciences. Coincidence? Go figure…

I am developing along two other avenues: my research on cities and my learning of programming in Python. I have been doing research on cities as manifestations of collective intelligence, and I have been doing it for a while. See, for example, ‘Demographic anomalies – the puzzle of urban density’ or ‘The knowingly healthy people’. As I have been digging down this rabbit hole, I have created a database, which, for working purposes, I call ‘DU_DG’. DU_DG is a coefficient of relative density in population, which I came by with some day and which keeps puzzling me.  Just to announce the colour, as we say in Poland when playing cards, ‘DU’ stands for the density of urban population, and ‘DG’ is the general density of population. The ‘DU_DG’ coefficient is a ratio of these two, namely it is DU/DG, or, in other words, this is the density of urban population denominated in the units of general density in population. In still other words, if we take the density of population as a fundamental metric of human social structures, the DU_DG coefficient tells how much denser urban population is, as compared to the mean density, rural settlements included.

I want to rework through my DU_DG database in order both to practice my programming skills, and to reassess the main axes of research on the collective intelligence of cities. I open JupyterLab from my Anaconda panel, and I create a new Notebook with Python 3 as its kernel. I prepare my dataset. Just in case, I make two versions: one in Excel, another one in CSV. I replace decimal comas with decimal points; I know by experience that Python has issues with comas. In human lingo, a coma is a short pause for taking like half a breath before we continue uttering the rest of the sentence. From there, we take the coma into maths, as decimal separator. In Python, as in finance, we talk about decimal point as such, i.e. as a point. The coma is a separator.

Anyway, I have that notebook in JupyterLab, and I start by piling up what I think I will need in terms of libraries:

>> import numpy as np

>> import pandas as pd

>> import os

>> import math

I place my database in the root directory of my user profile, which is, by default, the working directory of Anaconda, and I check if my database is visible for Python:

>> os.listdir()

It is there, in both versions, Excel and CSV. I start with reading from Excel:

>> DU_DG_Excel=pd.DataFrame(pd.read_excel(‘Dataset For Perceptron.xlsx’, header=0))

I check with ‘DU_DG_Excel.info()’. I get:

<class ‘pandas.core.frame.DataFrame’>

RangeIndex: 1155 entries, 0 to 1154

Data columns (total 10 columns):

 #   Column                                                                Non-Null Count  Dtype 

—  ——                                                                      ————–  —– 

 0   Country                                                                1155 non-null   object

 1   Year                                                                      1155 non-null   int64 

 2   DU_DG                                                                1155 non-null   float64

 3   Population                                                           1155 non-null   int64 

 4   GDP (constant 2010 US$)                                  1042 non-null   float64

 5   Broad money (% of GDP)                                  1006 non-null   float64

 6   urban population absolute                                 1155 non-null   float64

 7   Energy use (kg of oil equivalent per capita)    985 non-null    float64

 8   agricultural land km2                                        1124 non-null   float64

 9   Cereal yield (kg per hectare)                                         1124 non-null   float64

dtypes: float64(7), int64(2), object(1)

memory usage: 90.4+ KB  

Cool. Exactly what I wanted. Now, if I want to use this database as a simulator of collective intelligence in human societies, I need to assume that each separate ‘country <> year’ observation is a distinct local instance of an overarching intelligent structure. My so-far experience with programming opens up on a range of actions that structure is supposed to perform. It is supposed to differentiate itself into the desired outcomes, on the one hand, and the instrumental epistatic traits manipulated and adjusted in order to achieve those outcomes.

As I pass in review my past research on the topic, a few big manifestations of collective intelligence in cities come to my mind. Creation and development of cities as purposeful demographic anomalies is the first manifestation. This is an otherwise old problem in economics. Basically, people and resources they use should be disposed evenly over the territory those people occupy, and yet they aren’t. Even with a correction taken for physical conditions, such as mountains or deserts, we tend to like forming demographic anomalies on the landmass of Earth. Those anomalies have one obvious outcome, i.e. the delicate balance between urban land and agricultural land, which is a balance between dense agglomerations generating new social roles due to abundant social interactions, on the one hand, and the local food base for people endorsing those roles. The actual difference between cities and the surrounding countryside, in terms of social density, is very idiosyncratic across the globe and seems to be another aspect of intelligent collective adaptation.

Mankind is becoming more and more urbanized, i.e. a consistently growing percentage of people live in cities (World Bank 1[1]). In 2007 – 2008, the coefficient of urbanization topped 50% and keeps progressing since then. As there is more and more of us, humans, on the planet, we concentrate more and more in urban areas. That process defies preconceived ideas about land use. A commonly used narrative is that cities keep growing out into their once-non-urban surroundings, which is frequently confirmed by anecdotal, local evidence of particular cities effectively sprawling into the neighbouring rural land. Still, as data based on satellite imagery is brought up, and as total urban land area on Earth is measured as the total surface of peculiar agglomerations of man-made structures and night-time lights, that total area seems to be stationary, or, at least, to have been stationary for the last 30 years (World Bank 2[2]). The geographical distribution of urban land over the entire land mass of Earth does change, yet the total seems to be pretty constant. In parallel, the total surface of agricultural land on Earth has been growing, although at a pace far from steady and predictable (World Bank 3[3]).

There is a theory implied in the above-cited methodology of measuring urban land based on satellite imagery. Cities can be seen as demographic anomalies with a social purpose, just as Fernand Braudel used to state it (Braudel 1985[4]) : ‘Towns are like electric transformers. They increase tension, accelerate the rhythm of exchange and constantly recharge human life. […]. Towns, cities, are turning-points, watersheds of human history. […]. The town […] is a demographic anomaly’. The basic theoretical thread of this article consists in viewing cities as complex technologies, for one, and in studying their transformations as a case of technological change. Logically, this is a case of technological change occurring by agglomeration and recombination. Cities can be studied as demographic anomalies with the specific purpose to accommodate a growing population with just as expanding a catalogue of new social roles, possible to structure into non-violent hierarchies. That path of thinking is present, for example, in the now classical work by Arnold Toynbee (Toynbee 1946[5]), and in the even more classical take by Adam Smith (Smith 1763[6]). Cities can literally work as factories of new social roles due to intense social interactions. The greater the density of population, the greater the likelihood of both new agglomerations of technologies being built, and new, adjacent social roles emerging. A good example of that special urban function is the interaction inside age groups. Historically, cities have allowed much more abundant interactions among young people (under the age of 25), that rural environments have. That, in turn, favours the emergence of social roles based on the typically adolescent, high appetite for risk and immediate rewards (see for example: Steinberg 2008[7]). Recent developments in neuroscience, on the other hand, allow assuming that abundant social interactions in the urban environment have a deep impact on the neuroplastic change in our brains, and even on the phenotypical expression of human DNA (Ehninger et al. 2008[8]; Bavelier et al. 2010[9]; Day & Sweatt 2011[10]; Sweatt 2013[11])

At the bottom line of all those theoretical perspectives, cities are quantitatively different from the countryside by their abnormal density of population. Throughout this article, the acronymic symbol [DU/DG] is used to designate the density of urban population denominated in the units of (divided by) general density of population, and is computed on the grounds of data published by combining the above cited coefficient of urbanization (World Bank 1) with the headcount of population (World Bank 4[12]), as well as with the surface of urban land (World Bank 2). The general density of population is taken straight from official statistics (World Bank 5[13]). 

The [DU/DG] coefficient stays in the theoretical perspective of cities as demographic anomalies with a purpose, and it can be considered as a measure of social difference between cities and the countryside. It displays intriguing quantitative properties. Whilst growing steadily over time at the globally aggregate level, from 11,9 in 1961 to 19,3 in 2018, it displays significant disparity across space. Such countries as Mauritania or Somalia display a [DU/DG] > 600, whilst United Kingdom or Switzerland are barely above [DU/DG] = 3. In the 13 smallest national entities in the world, such as Tonga, Puerto Rico or Grenada, [DU/DG] falls below 1. In other words, in those ultra-small national structures, the method of assessing urban space by satellite-imagery-based agglomeration of night-time lights fails utterly. These communities display peculiar, categorially idiosyncratic a spatial pattern of settlement. The cross-sectional variability of [DU/DG] (i.e. its standard deviation across space divided by its cross-sectional mean value) reaches 8.62, and yet some 70% of mankind lives in countries ranging across the 12,84 ≤ [DU/DG] ≤ 23,5 interval.

Correlations which the [DU/DG] coefficient displays at the globally aggregate level (i.e. at the scale of the whole planet) are even more puzzling. When benchmarked against the global real output in constant units of value (World Bank 6[14]), the time series of aggregate, global  [DU/DG] displays a Pearson correlation of r = 0,9967. On the other hand, the same type of Pearson correlation with the relative supply of money to the global economy (World Bank 7[15]) yields r = 0,9761. As the [DU/DG] coefficient is supposed to represent the relative social difference between cities and the countryside, a look at the latter is beneficial. The [DU/DG] Pearson-correlates with the global area of agricultural land (World Bank 8[16]) at r = 0,9271, and with the average, global yield of cereals, in kgs per hectare (World Bank 9[17]), at r = 0,9858. That strong correlations of the [DU/DG] coefficient with metrics pertinent to the global food base match its correlation with the energy base. When Pearson-correlated with the global average consumption of energy per capita (World Bank 10[18]), [DU/DG] proves significantly covariant, at r = 0,9585. All that kept in mind, it is probably not that much of a surprise to see the global aggregate [DU/DG] Pearson correlated with the global headcount of population (World Bank 11[19]) at r = 0,9954.    

It is important to re-assume the meaning of the [DU/DG] coefficient. This is essentially a metric of density in population, and density has abundant ramifications, so to say. The more people live per 1 km2, the more social interactions occur on the same square kilometre. Social interactions mean a lot. They mean learning by civilized rivalry. They mean transactions and markets as well. The greater the density of population, the greater the probability of new skills emerging, which possibly translates into new social roles, new types of business and new technologies. When two types of human settlements coexist, displaying very different densities of population, i.e. type A being many times denser than type B, type A is like a factory of patterns (new social roles and new markets), whilst type B is the supplier of raw resources. The progressively growing global average [DU/DG] means that, at the scale of the human civilization, that polarity of social functions accentuates.

The [DU/DG] coefficient bears strong marks of a statistical stunt. It is based on truly risky the assumption, advanced implicitly by through the World Bank’s data, that total surface of urban land on Earth has remained constant, at least over the last 3 decades. Moreover, denominating the density of urban population in units of general density of population was purely intuitive from the author’s part, and, as a matter of fact, other meaningful denominators can easily come to one’s mind. Still, with all that wobbly theoretical foundation, the [DU/DG] coefficient seems to inform about a significant, structural aspect of human societies. The Pearson correlations, which the global aggregate of that coefficient yields with the fundamental metrics of the global economy, are of an almost uncanny strength in social sciences, especially with respect to the strong cross-sectional disparity in the [DU/DG].

The relative social difference between cities and the countryside, measurable with the gauge of the [DU/DG] coefficient, seems to be a strongly idiosyncratic adaptative mechanism in human societies, and this mechanism seems to be correlated with quantitative growth in population, real output, production of food, and the consumption of energy. That could be a manifestation of tacit coordination, where a growing human population triggers an increasing pace of emergence in new social roles by stimulating urban density. As regards energy, the global correlation between the increasing [DU/DG] coefficient and the average consumption of energy per capita interestingly connects with a stream of research which postulates intelligent collective adaptation of human societies to the existing energy base, including intelligent spatial re-allocation of energy production and consumption (Leonard, Robertson 1997[20]; Robson, Wood 2008[21]; Russon 2010[22]; Wasniewski 2017[23], 2020[24]; Andreoni 2017[25]; Heun et al. 2018[26]; Velasco-Fernández et al 2018[27]).

It is interesting to investigate how smart are human societies in shaping their idiosyncratic social difference between cities and the countryside. This specific path of research is being pursued, further in this article, through the verification and exploration of the following working hypothesis: ‘The collective intelligence of human societies optimizes social interactions in the view of maximizing the absorption of energy from the environment’.  

[1] World Bank 1: https://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS

[2] World Bank 2: https://data.worldbank.org/indicator/AG.LND.TOTL.UR.K2

[3] World Bank 3:  https://data.worldbank.org/indicator/AG.LND.AGRI.K2

[4] Braudel, F. (1985). Civilisation and Capitalism 15th and 18th Century–Vol. I: The Structures of Everyday Life, Translated by S. Reynolds, Collins, London, pp. 479 – 482

[5] Royal Institute of International Affairs, Somervell, D. C., & Toynbee, A. (1946). A Study of History. By Arnold J. Toynbee… Abridgement of Volumes I-VI (VII-X.) by DC Somervell. Oxford University Press., Section 3: The Growths of Civilizations, Chapter X.

[6] Smith, A. (1763-1896). Lectures on justice, police, revenue and arms. Delivered in the University of Glasgow in 1763, published by Clarendon Press in 1896, pp. 9 – 20

[7] Steinberg, L. (2008). A social neuroscience perspective on adolescent risk-taking. Developmental review, 28(1), 78-106. https://dx.doi.org/10.1016%2Fj.dr.2007.08.002

[8] Ehninger, D., Li, W., Fox, K., Stryker, M. P., & Silva, A. J. (2008). Reversing neurodevelopmental disorders in adults. Neuron, 60(6), 950-960. https://doi.org/10.1016/j.neuron.2008.12.007

[9] Bavelier, D., Levi, D. M., Li, R. W., Dan, Y., & Hensch, T. K. (2010). Removing brakes on adult brain plasticity: from molecular to behavioral interventions. Journal of Neuroscience, 30(45), 14964-14971. https://www.jneurosci.org/content/jneuro/30/45/14964.full.pdf

[10] Day, J. J., & Sweatt, J. D. (2011). Epigenetic mechanisms in cognition. Neuron, 70(5), 813-829. https://doi.org/10.1016/j.neuron.2011.05.019

[11] Sweatt, J. D. (2013). The emerging field of neuroepigenetics. Neuron, 80(3), 624-632. https://doi.org/10.1016/j.neuron.2013.10.023

[12] World Bank 4: https://data.worldbank.org/indicator/SP.POP.TOTL

[13] World Bank 5: https://data.worldbank.org/indicator/EN.POP.DNST

[14] World Bank 6: https://data.worldbank.org/indicator/NY.GDP.MKTP.KD

[15] World Bank 7: https://data.worldbank.org/indicator/FM.LBL.BMNY.GD.ZS

[16] World Bank 8: https://data.worldbank.org/indicator/AG.LND.AGRI.K2

[17] World Bank 9: https://data.worldbank.org/indicator/AG.YLD.CREL.KG

[18] World Bank 10: https://data.worldbank.org/indicator/EG.USE.PCAP.KG.OE

[19] World Bank 11: https://data.worldbank.org/indicator/SP.POP.TOTL

[20] Leonard, W.R., and Robertson, M.L. (1997). Comparative primate energetics and hominoid evolution. Am. J. Phys. Anthropol. 102, 265–281.

[21] Robson, S.L., and Wood, B. (2008). Hominin life history: reconstruction and evolution. J. Anat. 212, 394–425

[22] Russon, A. E. (2010). Life history: the energy-efficient orangutan. Current Biology, 20(22), pp. 981- 983.

[23] Waśniewski, K. (2017). Technological change as intelligent, energy-maximizing adaptation. Energy-Maximizing Adaptation (August 30, 2017).

[24] Wasniewski, K. (2020). Energy efficiency as manifestation of collective intelligence in human societies. Energy, 191, 116500.

[25] Andreoni, V. (2017). Energy Metabolism of 28 World Countries: A Multi-scale Integrated Analysis. Ecological Economics, 142, 56-69

[26] Heun, M. K., Owen, A., & Brockway, P. E. (2018). A physical supply-use table framework for energy analysis on the energy conversion chain. Applied Energy, 226, 1134-1162

[27] Velasco-Fernández, R., Giampietro, M., & Bukkens, S. G. (2018). Analyzing the energy performance of manufacturing across levels using the end-use matrix. Energy, 161, 559-572

#howcouldtheyhavedoneittome

I  am considering the idea of making my students – at least some of them – into an innovative task force in order to develop new technologies and/or new businesses. My essential logic is that I teach social sciences, under various possible angles, and the best way of learning is by trial and error. We learn the most when we experiment with many alternative versions of ourselves and select the version which seems the fittest, regarding the values and goals we pursue. Logically, when I want my students to learn social sciences, like really learn, the first step is to make them experiment with the social roles they currently have and make many alternative versions thereof. You are 100% student at the starting point, and now you try to figure out what is it like to be 80% student and 20% innovator, or 50% student and 50% innovator etc. What are your values? Well, as it comes to learning, I advise assuming that the best learning occurs when we get out of our comfort zone but keep the door open for returning there. I believe it can be qualified as a flow state. You should look for situations when you feel a bit awkward, and the whole thing sucks a bit because you feel you do not have all the skills you need for the situation, and still you see like a clear path of passage between your normal comfort zone and that specific state of constructive suck.   

Thus, when I experiment with many alternative versions of myself, without being afraid of losing my identity, thus when I behave like an intelligent structure, the most valuable versions of myself as learning comes are those which push me slightly out of my comfort zone. When you want to learn social sciences, you look for those alternative versions of yourself which are a bit uncomfortably involved in that whole social thing around you. That controlled uncomfortable involvement makes you learn faster and deeper.

The second important thing I know about learning is that I learn faster and deeper when I write and talk about what I am learning and how I am learning. I have just experienced that process of accelerated figuring my s**t out as regards investment in the stock market. I started by the end of January 2020 (see Back in the game or Fathom the outcomes ) and, with a bit of obsessive self-narration, I went from not really knowing what I am doing and barely controlling my emotions to a portfolio of some 20 investment positions, capable to bring me at least 10% a month in terms of return on capital (see Fire and ice. A real-life business case).

Thus, consistently getting out of your comfort zone just enough to feel a bit of suck, and then writing about your own experience in that place, that whole thing has the hell of a propulsive power. You can really burn the (existential) rubber, under just one condition: the ‘consistently’ part. Being relentless in making small everyday steps is the third ingredient of that concoction. We learn by forming habits. Daily repetition of experimenting in the zone of gentle suck makes you be used to that experimentation, and once you are used to that, well, man, you have the turbo boost on, in your existence.

This is precisely what I fathom to talk my students into: experimenting outside of their comfort zone, with a bit of uncomfortably stimulating social involvement into the development of an innovative business concept. The type of innovation I am thinking about is some kind of digital technology or digital product, and I want to start exploration with rummaging a little bit in the investor-relations sites of publicly listed companies, just to see what they are up to and to find some good benchmarks for business modelling. I start with one of the T-Rexes of the industry, namely with Microsoft (https://www.microsoft.com/en-us/investor ). As I like going straight for the kill, I dive into the section of SEC filings (https://www.microsoft.com/en-us/Investor/sec-filings.aspx ), and there, a pleasant surprise awaits: they end their fiscal year by the end of June, them people at Microsoft, and thus I have their annual report for the fiscal year 2020 ready and available even before the calendar year 2020 is over. You can download the report from their site or from my archives: https://discoversocialsciences.com/wp-content/uploads/2020/10/Microsoft-_FY20Q4_10K.docx .

As I grab my machete and my camera and I cut myself a path through that document, I develop a general impression that digital business goes more and more towards big data and big server power more than programming strictly speaking. I allow myself to source directly from that annual report the table from page 39, with segment results. You can see it here below:

Intelligent Cloud, i.e. Microsoft Azure (https://azure.microsoft.com/en-us/ ), seems to be the most dynamic segment in their business. In other words, a lot of data combined with a lot of server power, and with artificial neural networks to extract patterns and optimize. If I consider the case of Microsoft as representative for the technological race taking place in the IT industry, cloud computing seems to be the main track in that race.

Before I forget: IBM has just confirmed that intuition of mine. If you go and call by https://www.ibm.com/investor , you can pick up their half-year results (https://www.ibm.com/investor/att/pdf/IBM-2Q20-Earnings-Press-Release.pdf ) and their latest strategic update (https://www.ibm.com/investor/att/pdf/IBM-Strategic-Update-2020-Press-Release.pdf ). One fact comes out of it: cloud computing at IBM brings the most gross margin and the most growth in business. It goes to the point of IBM splitting their business in two, with cloud computing spinning out of all the rest, as a separate business.

I would suggest my students to think about digital innovations in the domain of cloud computing. Microsoft Azure (https://azure.microsoft.com/en-us/ ) and cloud computing provided by Okta (https://investor.okta.com/ ), seen a bit more in focus in their latest annual report (https://discoversocialsciences.com/wp-content/uploads/2020/10/Okta-10K-2019.pdf ), serve me as quick benchmarks. Well, as I think about benchmarks, there are others, more obvious or less obvious, depending on the point of view. You Tube, when you think about it, does cloud computing. It stores data – yes, videos are data – and it adapts the list of videos presented to each user according to the preferences of said used, guessed by algorithms of artificial intelligence. Netflix – same thing: a lot of data, in the form of movies, shows and documentaries, and a lot of server power to support the whole thing.     

My internal curious ape has grabbed this interesting object – innovations in the domain of cloud computing – and now my internal happy bulldog starts playing with it, sniffing around and digging holes, haphazardly, in the search for more stuff like that. My internal austere monk watches the ape and the bulldog, holding his razor ready, I mean the Ockham’s razor to cut bullshit out, should such need arise.

What’s cloud computing from the point of view of a team made of an ape and a bulldog? This is essentially a f**king big amount of data, permeated with artificial neural networks, run on and through f**king big servers, consuming a lot of computational power and a lot of energy. As cloud computing is becoming a separate IT business on its own right, I try to decompose it into key factors of value added. The technology of servers as such is one such factor. Energy efficiency, resilience to factors of operational risk, probably fiberoptics as regards connectivity, sheer computational power per 1 cubic meter of space, negotiably low price of electricity – all those things are sort of related to servers.

Access to big, useful datasets is another component of that business. I see two openings here. Acquiring now intellectual property rights to datasets which are cheap today, but likely to be expensive tomorrow is certainly important. People tend to say that data has become a commodity, and it is partly true. Still, I see that data is becoming an asset, too. As I look at the financials of Netflix (see, for example, The hopefully crazy semester), thus at cloud computing for entertainment, I realize that cloud-stored (clouded?) data can be both a fixed asset and a circulating one. It all depends on its lifecycle. There is data with relatively short shelf life, which works as a circulating asset, akin to inventories. It earns money when it flows: some parcels of data flow into my server, some flow out, and I need that flow to stay in the flow of business. There is other data, which holds value for a longer time, similarly to a fixed asset, and yet is subject to depreciation and amortization.

Here is that emerging skillset: data trader. Being a data trader means that you: a) know where to look for interesting datasets b) have business contacts with people who own it c) can intuitively gauge its market value and its shelf life d) can effectively negotiate its acquisition and e) can do the same on the selling side. I think one more specific skill is to add: intuitive ability to associate the data I am trading with proper algorithms of artificial intelligence, just to blow some life into the otherwise soulless databases. One more comes to my mind: the skill to write and enforce contracts which effectively protect the acquired data from infringement and theft.

Cool. There are the servers, and there is the data. Now, we need to market it somehow. The capacity to invent and market digital products based on cloud computing, i.e. on lots of server power combined with lots of data and with agile artificial neural networks, are another aspect of the business model. As I think of it, it comes to my mind that the whole fashion for Blockchain technology and its emergent products – cryptocurrencies and smart contracts – arose when the technology of servers passed a critical threshold, allowing to play with computational power as a fixed asset.

I am very much Schumpeterian, i.e. I am quite convinced that Joseph Schumpeter’s theory of business cycles was and still is a bloody deep vision, which states, among other things, that with the advent of new technologies and new assets, some incumbent technologies and assets will inevitably disappear. Before inevitability consumes itself, a transitory period happens, when old assets coexist with the new ones and choosing the right cocktail thereof is an art and a craft, requiring piles of cash on the bank account, just to keep the business agile and navigable.     

Another thing strikes me: the type of emergent programming languages. The Python, the R, the Pragma Solidity: all that stuff is primarily about managing data. Twenty years ago, programming was mostly about… well, about programming, i.e. about creating algorithms to make those electronics do what we want. Today, programming is more and more about data management. When we invent new languages for a new type of business, we really mean business, as a collective intelligence.

It had to come. I mean, in me. That mild obsession of mine about collective intelligence just had to poke its head from around the corner. Whatever. Let’s go down that rabbit hole. Collective intelligence consists in an intelligent structure experimenting with many alternative versions of itself whilst staying coherent. The whole business of cloud computing, as it is on the rise and before maturity, consists very largely in experimenting with many alternative versions of claims on data, claims on server power, as well as with many alternative digital products sourced therefrom. Some combinations are fitter than others. What are the criteria of fitness? At the business scale, it would be return on investment, I guess. Still, at the collective level of whole societies, it would be about the capacity to assure high employment and low average workload per person. Yes, Sir Keynes, it still holds.

As I indulge in obsessions, I go to another one of mine: the role of cities in our civilization. In my research, I have noticed strange regularities as for the density of urban population. When I compute a compound indicator which goes as density of urban population divided by the general density of population, or [DU/DG], that coefficient enters into strange correlations with other socio-economic variables. One of the most important observations I made about it is that the overall DU/DG for the whole planet is consistently growing. There is a growing difference in social density between cities and the countryside. See Demographic anomalies – the puzzle of urban density, from May 14th, 2020, in order to make yourself an idea. I think that we, humans, invented cities as complex technologies which consist in stacking a large number of homo sapiens (for some humans, it is just allegedly sapiens, let’s face it) on a relatively small surface, with a twofold purpose: that of preserving and developing agricultural land as a food base, and that of fabricating new social roles for new humans, through intense social interaction in cities. My question regarding the rise of technologies in cloud computing is whether it is concurrent with growing urban density, or, conversely, is it a countering force to that growth. In other words, are those big clouds of data on big servers a by-product of citification or is it rather something completely new, possibly able to supplant cities in their role of factories making new social roles?

When I think about cloud computing in terms of collective intelligence, I perceive it as a civilization-wide mechanism which helps making sense of growing information generated by growing mankind. It is a bit like an internal control system inside a growing company. Cloud computing is essentially a pattern of maintaining internal cohesion inside the civilization. Funny how it plays on words. Clouds form in the atmosphere when the density of water vapour passes a critical threshold. As the density of vaporized water per 1 cubic meter of air grows, other thresholds get passed. The joyful, creamy clouds morph into rain clouds, i.e. clouds able to re-condensate water from vapour back to liquid. I think that technologies of cloud computing do precisely that. They collect sparse, vaporized data and condensate it into effective action in and upon the social environment.

Now comes the funny part. Rain clouds turn into storm clouds when they get really thick, i.e. when wet and warm air – thus air with a lot of water vaporized in it and a lot of kinetic energy in its particles – collides with much colder and drier air. Rain clouds pile up and start polarizing their electric charges. The next thing we know, lightning starts hitting, winds become scary etc. Can a cloud of data pile up to the point of becoming a storm cloud of data, when it enters in contact with a piece of civilisation poor in data and low on energy? Well, this is something I observe with social media and their impact. Any social medium, I mean Twitter, Facebook, Instagram, whatever pleases, essentially, is a computed cloud of data. When it collides with population poor in data (i.e. poor in connection with real life and real world), and low on energy (not much of a job, not much of adversity confronted, not really a pile of business being done), data polarizes in the cloud. Some of it flows to the upper layers of the cloud, whilst another part, the heavier one, flows down to the bottom layer and starts attracting haphazard discharges of lighter data, more sophisticated data from the land underneath. The land underneath is the non-digital realm of social life. The so-polarized cloud of data becomes sort of aggressive and scary. It teaches humans to seek shelter and protection from it.           

Metaphors have various power. This one, namely equating a cloud of data to an atmospheric cloud, seems pretty kickass. It leads me to concluding that cloud computing arises as a new, big digital business because there are good reasons for it to do so. There is more and more of us, humans, on the planet. More and more of us live in cities, in a growing social density, i.e. with more and more social interactions. Those interactions inevitably produce data (e.g. #howcouldtheyhavedoneittome), whence growing information wealth of our civilisation, whence the computed clouds of data.

Metaphors have practical power, too, namely that of making me shoot educational videos. I made two of them, sort of in the stride of writing. Here they are, to your pleasure and leisure (in brackets, you have links to You Tube): International Economics #3 The rise of cloud computing [ https://youtu.be/FerCBcsGyq0], for one, and Managerial Economics and Economic Policy #4 The growth of cloud computing and what can governments do about it [ https://youtu.be/J-T4QQDEdlU], for two.

4 units of quantity in technological assets to make one unit of quantity in final goods

My editorial on You Tube

I am writing a book, right now, and I am sort of taken, and I blog much less frequently than I planned. Just to keep up with the commitment, which any blogger has sort of imprinted in their mind, to deliver some meaningful content, I am publishing, in this update, the outline of my first chapter. It has become almost a truism that we live in a world of increasingly rapid technological change. When a statement becomes almost a cliché, it is useful to pass it in review, just to be sure that we understand what the statement is about. In a very pragmatic perspective of an entrepreneur, or, as a matter of fact, that of an infrastructural engineer, technological change means that something old needs to be coupled with or replaced by something new. When a new technology comes around, it is like a demon: it is essentially an idea, frequently prone to protection through intellectual property rights, and that idea looks for a body to sneak into. Humans are supposed to supply the body, and they can do it in two different ways. They can tell the new idea to coexist with some older ones, i.e. we embody new technologies in equipment and solutions which we couple functionally with older ones. Take any operational system for computers or mobile phones. On the moment, the people who are disseminating it claim it is brand new but scratch the surface just a little bit and you find 10-year-old algorithms underneath. Yes, they are old, and yes, they still work.

Another way to embody a new technological concept is to make it supplant older ones completely. We do it reluctantly, yet sometimes it really looks like a better idea. Electric cars are a good example of this approach. Initially, the basic idea seems to have consisted in putting electric engines into an otherwise unchanged structure of vehicles propelled by combustion engines. Still, electric propulsion is heavier, as we need to drive those batteries around. Significantly greater weight means the necessity to rethink steering, suspension, structural stability etc., whence the need to design a new structure.   

Whichever way of embodying new technological concepts we choose, our equipment ages. It ages physically and morally, in various proportions. Aging in technologies is called depreciation. Physical depreciation means physical wearing and destruction in a piece of equipment. As it happens – and it happens to anything used frequently, e.g. shoes – we choose between repairing and replacing the destroyed parts. Whatever we do, it requires resources. From the economic point of view, it requires capital. As strange as it could sound, physical depreciation occurs in the world of digital technologies, too. When a large digital system, e.g. that of an airport, is being run, something apparently uncanny happens: some component algorithms of that system just stop working properly, under the burden of too much data, and they need to be replaced sort of on the go, without putting the whole system on hold. Of course, the essential cause of that phenomenon is the disproportion between the computational scale of pre-implementation tests, and that of real exploitation. Still, the interesting thing about those on-the-go patches of the system is that they are not fundamentally new, i.e. they do not express any new concept. They are otherwise known, field-tested solutions, and they have to be this way in order to work. Programmers who implement those patches do not invent new digital technologies; they just keep the incumbent ones running. They repair something broken with something working smoothly. Functionally, it is very much like repairing a fleet of vehicles in an express delivery business.   

As we take care of the physical depreciation occurring in our incumbent equipment and software, new solutions come to the market, and let’s be honest: they are usually better than what we have at the moment. The technologies we hold become comparatively less and less modern, as new ones appear. That phenomenon of aging by obsolescence is called moral depreciation. Proportions of the actual physical depreciation & moral depreciation-cocktail depend on the pace of technological race in the given industry. When a lot of alternative, mutually competing solutions emerge, moral obsolescence accelerates and tends to become the dominant factor of aging in our technological assets. Moral depreciation creates a tension: as we look the state-of-the-art in our industry progressively moving away from our current technological position, determined by the assets we have, we find ourselves under a growing pressure to do something about it. Finally, we come to the point of deciding to invest in something definitely more up to date than what we currently have.      

Both layers of depreciation – physical and moral – absorb capital. It seems pertinent to explain how exactly they do so. We need money to pay for goods and services necessary for repairing and replacing the physically used parts of our technological basket. We obviously need money to pay for the completely new equipment, too. Where does that money come from? Are there any patterns as for its sourcing? The first and the most obvious source of money to finance depreciation in our assets is the financial scheme of amortization. In many legal regimes, i.e. in all the developed countries and in a large number of emerging and developing economies, an entity being in possession of assets subject to depreciation is allowed to subtract from its income tax base, a legally determined financial amount, in order to provide for depreciation.

The legally possible amount of amortization is calculated as a percentage of book value ascribed to the corresponding assets, and this percentage is based on their assumed. If a machine is supposed to have a useful life of five years, after all is said and done as for its physical and moral depreciation, I can subtract from my tax base 1/5th = 20% of its book value. Question: which exact book value, the initial one or the current one? It depends on the kind of deal an entrepreneur makes with tax authorities. Three alternative ways are possible: linear, decreasing, and increasing. When I do linear amortization, I take the initial value of the machine, e.g. $200 000, I divide it into 5 equal parts right after the purchase, thus in 5 instalments of $40 000 each, and I subtract those instalments annually from my tax base, starting from the current year. After linear amortization is over, the book value of the machine is exactly zero.  

Should I choose decreasing amortization, I take the current value of my machine as the basis for the 20% reduction of my tax base. The first year, the machine is brand new, worth $200 000, and so I amortize 20% * $200 000 = $40 000. The next year, i.e. in the second year of exploitation, I start with my machine being worth $200 000 – $40 000 = (1 – 20%) * $200 000 =  $160 000. I repeat the same operation of amortizing 20% of the current book value, and I do: $160 000 – 20% * $160 000 = $160 000 – $32 000 = $128 000. I subtracted $32 000 from my tax base in this second year of exploitation (of the machine), and, and the end of the fiscal year, I landed with my machine being worth $128 000 net of amortization. A careful reader will notice that decreasing amortization is, by definition, a non-linear function tending asymptotically towards zero. It is a never-ending story, and a paradox. I assume a useful life of 5 years in my machine; hence I subtract 1/5th = 20% of its current value from my tax base, and yet the process of amortization takes de facto longer than 5 years and has no clear end. After 5 years of amortization, my machine is worth $65 536 net of amortization, and I can keep going. The machine is technically dead as useful technology, but I still have it in my assets.      

Increasing amortization is based on more elaborate assumptions than the two preceding methods. I assume that my machine will be depreciating over time at an accelerating pace, e.g. 10% of the current value in the first year, 20% annually over the years 2 – 4, and 30% in the 5th year. The underlying logic is that of progressively diving into the stream of technological race: the longer I have my technology, the greater is the likelihood that someone comes up with something definitely more modern. With the same assumption of $200 000 as initial investment, that makes me write off my tax base the following amounts: 1st year – $20 000, 2nd ÷ 4th year – $40 000, 5th year – $60 000. After 5 years, the net value of my equipment is zero. 

The exact way I can amortize my assets depends largely on the legal regime in force – national governments have their little ways in that respect, using the rates of amortization as incentives for certain types of investment whilst discouraging other types – and yet there is quite a lot of financial strategy in amortization, especially in large business structures with ownership separate from management. We can notice that linear amortization gives comparatively greater savings in terms of tax due. Still, as amortization consists in writing an amount off the tax base, we need any tax base at all beforehand. When I run a well-established, profitable business way past its break-even point, tax savings are a sensible idea, and so is linear amortization in my fixed assets. However, when I run a start-up, still deep in the red zone below the break-even point, there is not really any tax base to subtract amortization from. Recording a comparatively greater amortization from operations already running at a loss just deepens the loss, which, at the end of the day, has to be subtracted from the equity of my business, and it doesn’t look good in the eyes of my prospective investors and lenders. Relatively quick, linear amortization is a good strategy for highly profitable operations with access to lots of cash. Increasing amortization could be good for that start-up business, when relatively the greatest margin of operational income turns up some time after the day zero of operations.

Interestingly, the least obvious logic comes with decreasing amortization. What is the practical point of amortizing my assets asymptotically down to zero, without ever reaching zero? Good question, especially in the light of a practical fact of life, which the author challenges any reader to test by themselves: most managers and accountants, especially in small and medium sized enterprises, will intuitively amortize the company’s assets precisely this way, i.e. along the decreasing path. Question: why people do something apparently illogical? Answer: because there is a logic to that, it is just hard to phrase out. What about the logic of accumulating capital? Both the linear amortization and the increasing one lead to having, at some point in time, the book value of the corresponding assets drops down to zero. A lot of value off my assets means that either I subtract the corresponding amount from the passive side of my balance sheet (i.e. I repay some loans or I give away some equity), or I compensate the write-off with new investment. Either I lose cash, or I am in need of more cash. When I am in tight technological race, and my assets are subject to quick moral depreciation, those sudden drops down to zero can put a lot of financial streets on my balance sheet. When I do something apparently detached from my technological strategy, i.e. when I amortize decreasingly, sudden capital quakes are replaced by a gentle descent, much more predictable. Predictable means e.g. negotiable with banks who lend me money, or with investors buying shares in my equity.

This is an important pattern to notice in commonly encountered behaviour regarding capital goods: most people will intuitively tend to protect the capital base of their organisation, would it be a regular business or a public agency. When choosing between amortizing their assets faster, so as to reflect the real pace of their ageing, or amortizing them slower, thus a bit against the real occurrence of depreciation, most people will choose the latter, as it smoothens the resulting changes in the capital base. We can notice it even in ways that most of us manage our strictly private assets. Let’s take the example of an ageing car. When a car reaches the age when an average household could consider to change it, like 3 – 4 years, only a relatively tiny fraction of the population, probably not more than 16%, will really change for a new car. The majority (the author of this book included, by the way) will rather patch and repair, and claim that ‘new cars are not as solid as those older ones’. There is a logic to that. A new car is bound to lose around 25% of its market value annually over the first 2 – 3 years of its useful life. An old car, aged 7 years or more, loses around 10% or less per year. In other words, when choosing between shining new things that age quickly and the less shining old things that age slowly, only a minority of people will choose the former. The most common behavioural pattern consists in choosing the latter.

When recurrent behavioural patterns deal with important economic phenomena, such as technological change, an economic equilibrium could be poking its head from around the corner. Here comes an alternative way of denominating depreciation and amortization, i.e. instead of denominating it as a fraction of value attributed to assets, we can denominate over the revenue of our business. Amortization can be seen as the cost of staying in the game. Technological race takes a toll on our current business. The faster our technologies depreciate, the costlier it is to stay in the race. At the end of the day, I have to pay someone or something that helps me keeping up with the technological change happening around, i.e. I have to share, with that someone or something, a fraction of what my customers pay me for the goods and services I offer. When I hold a differentiated basket of technological assets, each ageing at a different pace and starting from a different moment in time, the aggregate capital write-off that corresponds to their amortization is the aggregate cost of keeping up with science.

When denoting K as the book value of assets, with a standing for the rate of amortization corresponding to one of the strategies sketched above, P representing the average price of goods we sell, and Q their quantity, we can sketch the considerations developed above in a more analytical way, as a coefficient labelled A, as in equation (1) below.

A = (K*a)/(P*Q)         (1)

The coefficient A represents the relative burden of aggregate amortization of all the fixed assets in hand, upon the revenues recorded in a set of economic agents. Equation (1) can be further transformed so as to extract quantities at both levels of the fraction. Factors in the denominator of equation (1), i.e. prices and quantities of goods sold in order to generate revenues will be further represented as, respectively, PG and QG, whilst the book value of assets subject to amortization will be symbolized as the arithmetical product QK*PK of market prices PK of assets, and the quantity QK thereof. Additionally, we drive the rate of amortization ‘a’ down to what it really is, i.e. inverted representation of an expected lifecycle F, measured in years, and ascribed to our assets. Equation (2) below shows an analytical development in this spirit.

A = (1/F)*[(PK*QK)/(PG*QG)]        (2)

Before the meaning of equation (2) is explored more in depth, it is worth explaining the little mathematical trick that economists use all the time, and which usually raises doubts in the minds of bystanders. How can anyone talk about an aggregate quantity QG of goods sold, or that of fixed assets, the QK? How can we distil those aggregate quantities out of the facts of life? If anyone in their right mind thinks about the enormous diversity of the goods we trade, and the assets we use, how can we even set a common scale of measurement? Can we add up kilograms of BMW cars with kilograms of food consumed, and use it as denominator for kilograms of robots summed up with kilograms of their operating software?

This is a mathematical trick, yet a useful one. When we think about any set of transactions we make, whether we buy milk or machines for a factory, we can calculate some kind of weighted average price in those transactions. When I spend $1 000 000 on a team of robots, bought at unitary price P(robot), and $500 000 on their software bought at price P(software), the arithmetical operation P(robot)*[$1 000 000 / ($1 000 000 + $500 000)] + P(software)*[$500 000 / ($1 000 000 + $500 000)] will yield a weighted average price P(robot; software) made in one third of the price of software, and in two thirds of the price of robots. Mathematically, this operation is called factorisation, and we use it when we suppose the existence of a common, countable factor in a set of otherwise distinct phenomena. Once we suppose the existence of recurrent transactional prices in anything humans do, we can factorise that anything as Price Multiplied By Quantity, or P*Q. Thus, although we cannot really add up kilograms of factories with kilograms of patents, we can factorise their respective prices out of the phenomenon observed and write PK*QK. In this approach, quantity Q is a semi-metaphysical category, something like a metaphor for the overall, real amount of the things we have, make and do.    

Keeping those explanations in mind, let’s have a look at the empirical representation of coefficient A, as computed according to equation (2), on the grounds of data available in Penn Tables 9.1 (Feenstra et al. 2015[1]), and represented graphically in Figure I_1 below. The database known as Penn Tables provides direct information about three big components of equation (2): the basic rate of amortization, the nominal value of fixed assets, and the nominal value of Gross Domestic Product GDP) for each of the 182 national economies covered. One of the possible ways of thinking about the wealth of a nation is to compute the value of all the final goods and services made by said nation. According to the logic presented in the preceding paragraph, whilst the whole basket of final goods is really diversified, it is possible to nail down a weighted, average transactional price P for all that lot, and, consequently, to factorise the real quantity Q out of it. Hence, the GDP of a country can be seen as a very rough approximation of value added created by all the businesses in that territory, and changes over time in the GDP as such can be seen as representative for changes in the aggregate revenue of all those businesses.

Figure I_1 introduces two metrics, pertinent to the empirical unfolding of equation (2) over time and across countries. The continuous line shows the arithmetical average of local, national coefficients A across the whole sample of countries. The line with square markers represents the standard deviation of those national coefficients from the average represented by the continuous line. Both metrics are based on the nominal computation of the coefficient A for each year in each given national economy, thus in current prices for each year from 1950 through 2017. Equation (2) gives many possibilities of change in the coefficient A – including changes in the proportion between the price PG of final goods, and the market price PK of fixed assets – and the nominal computation used in Figure I_1 captures that factor as well.            

[Figure I_1_Coefficient of amortization in GDP, nominal, world, trend]

In 1950, the average national coefficient A, calculated as specified above, was equal to 6,7%. In 2017, in climbed to A = 20,8%. In other words, the average entrepreneur in 1950 would pay less than one tenth of their revenues to amortize the depreciation of technological assets, whilst in 2017 it was more than one fifth. This change in proportion can encompass many phenomena. It can the pace of scientific change as such, or just a change in entrepreneurial behaviour as regards the strategies of amortization, explained above. Show business is a good example. Content is an asset for television stations, movie makers or streaming services. Content assets age, and some of them age very quickly. Take the tonight news show on any TV channel. The news of today are much less of a news tomorrow, and definitely not news at all the next month. If you have a look at annual financial reports of TV broadcasters, such as the American classic of the industry, CBS Corporation[1], you will see insane nominal amounts of amortization in their cash flow statements. Thus, the ascending trend of average coefficient A, in Figure I_1, could be, at least partly, the result of growth in the amount of content assets held by various entities in show business. It is a good thing to deconstruct that compound phenomenon into its component factors, which is being undertaken further below. Still, before the deconstruction takes place, it is good to have an inquisitive look at the second curve in Figure I_1, the square-marked one, representing standard deviation of coefficient A across countries.

In common interpretation of empirical numbers, we almost intuitively lean towards average values, as the expected ones in a large set, and yet the standard deviation has a peculiar charm of its own. If we compare the paths followed by the two curves in Figure I_1, we can see them diverge: the average A goes resolutely up whilst the standard deviation in A stays almost stationary in its trend. In the 1950ies or 1960ies, the relative burden of amortization upon the GDP of individual countries was almost twice as disparate than it is today. In other words, back in the day it mattered much more where exactly our technological assets are located. Today, it matters less. National economies seem to be converging in their ways of sourcing current, operational cash flow to provide for the depreciation of incumbent technologies.

Getting back to science, and thus back to empirical facts, let’s have a look at two component phenomena of trends sketched in Figure I_1: the pace of scientific invention, and the average lifecycle of assets. As for the former, the coefficient of patent applications per 1 mln people, sourced from the World Bank[2], is used as representative metric. When we invent an original solution to an existing technological problem, and we think we could make some money on, we have the option of applying for legal protection of our invention, in the form of a patent. Acquiring a patent is essentially a three-step process. Firstly, we file the so-called patent application to the patent office adequate for the given geographical jurisdiction. Then, the patent office publishes our application, calling out for anyone who has grounds for objecting to the issuance of patent, e.g. someone we used to do research with, hand in hand, but hands parted as some point in time. As a matter of fact, many such disputes arise, which makes patent applications much more numerous than actually granted patents. If you check patent data, granted patents define a currently appropriated territories of intellectual property, whilst patent applications are pretty much informative about the current state of applied science, i.e. about the path this science takes, and about the pressure it puts on business people towards refreshing their technological assets.       

Figure I_2 below shows the coefficient of patent applications per 1 mln people in the global economy. The shape of the curve is interestingly similar to that of average coefficient A, shown in Figure I_1, although it covers a shorter span of time, from 1985 through 2017. At the first sight, it seems making sense: more and more patentable inventions per 1 million humans, on average, puts more pressure on replacing old assets with new ones. Yet, the first sight may be misleading. Figure I_3, further below, shows the average lifecycle of fixed assets in the global economy. This particular metric is once again calculated on the grounds of data available in Penn Tables 9_1 (Feenstra et al. 2015 op. cit.). The database strictly spoken contains a variable called ‘delta’, which is the basic rate of amortization in fixed assets, i.e. the percentage of their book value commonly written off the income tax base as provision for depreciation. This is factor ‘a’ in equation (1), presented earlier, and reflects the expected lifecycle of assets. The inverted value ‘1/a’ gives the exact value of that lifecycle in years, i.e. the variable ‘F’ in equation (2). Here comes the big surprise: although the lifecycle ‘F’, computed as an average for all the 182 countries in the database, does display a descending trend, the descent is much gentler, and much more cyclical that what we could expect after having seen the trend in nominal burden A of amortization, and in the occurrence of patent applications. Clearly, there is a push from science upon businesses towards shortening the lifecycle of their assets, but businesses do not necessarily yield to that pressure.  

[Figure I_2_Patent Applications per 1 mln people]

Here comes a riddle. The intuitive assumption that growing scientific input provokes shorter a lifespan in technological assets proves too general. It obviously does not encompass the whole phenomenon of increasingly cash-consuming depreciation in fixed assets. There is something else. After having casted a look at the ‘1/F’  component factor of equation (2), let’s move to the  (PK*QK)/(PG*QG) one. Penn Tables 9.1 provide two variables that allow calculating it: the aggregate value of fixed assets in national economies, at current prices, and the GDP of those economies, in current prices as well. Interestingly, those two variables are provided in two versions each: one at constant prices of 2011, the other at current prices. Before the consequences of that dual observation are discussed, let’s remind some basic arithmetic: we can rewrite (PK*QK)/(PG*QG) as (PK/PG)*(QK/QG). The (PK/PG) component fraction corresponds to the proportion between weighted average prices in, respectively, fixed assets (PK), and final goods (PG). The other part, i.e. (QK/QG) stands for the proportion between aggregate quantities of assets and goods. Whilst we refer here to that abstract concept of aggregate quantities, observable only as something mathematically factorized out of something really empirical, there is method to that madness. How big a factory do we need to make 20 000 cars a month? How big a server do we need in order to stream 20 000 hours of films and shows a month? Presented under this angle, the proportion (QK/QG)  is much more real. When both the aggregate stock of fixed assets in national economies, and the GDP of those economies are expressed in current prices, both the (PK/PG) factor, and the (QK/QG) really change over time. What is observed (analytically) is the full (PK*QK)/(PG*QG) coefficient. Yet, when prices are constant, the (PK/PG) component factor does not actually change over time; what really changes, is just the proportion between aggregate quantities of assets and goods.

The factorisation presented above allows another trick at the frontier of arithmetic and economics. The trick consists in using creatively two types of economic aggregates, commonly published in publicly available databases: nominal values as opposed to real values. The former category represents something like P*Q, or price multiplied by quantity. The latter is supposed to have kicked prices out of the equation, i.e. to represent just quantities. With those two types of data we can do something opposite to the procedure presented earlier, which serves to distil real quantities out of nominal values. This time, we have externally provided products ‘price times quantity’, and just quantities. Logically, we can extract prices out of the nominal values.    When we have two coefficients given in the Penn Tables 9.1 database – the full (PK*QK)/(PG*QG) (current prices) and the partial (QK/QG) (constant prices) – we can develop the following equation: [(PK*QK)/(PG*QG)]/ (QK/QG) =  PK/PG.  We can use the really observable proportion between the nominal value of fixed assets and that of Gross Domestic Product, divide it by the proportion between real quantities of, respectively assets and final goods, in order to calculate the proportion between weighted average prices of assets and goods.

Figure I_4, below, attempts to represent all those three phenomena – the change in nominal values, the change in real quantities, and the change in prices – in one graph. As different magnitudes of empirical values are involved, Figure I_4 introduces another analytical method, namely indexation over constant denominator. When we want to study temporal trends in values, which are either measured with different units or display very different magnitudes, we can choose one point in time as the peg value for each of the variables involved. In the case of Figure I_4, the peg year is 2011, as Penn Tables 9.1 use 2011 as reference year for constant prices. Aggregate values of capital stock and national GDP, when measured in constant prices, are measured in the prices of the year 2011. For each of the three variables involved – the nominal proportion of capital stock to GDP (PK*QK)/(PG*QG), the real proportion thereof  QK/QG  and the proportion between the prices of assets and the prices of goods PK*QK – we take their values in 2011 as denominators for the whole time series. Thus, for example, the nominal proportion of capital stock to GDP in 1990 is the quotient of the actual value in 1990 divided by the value in 2011 etc. As a result, we can study each of the three variables as if the value in 2011 was equal to 1,00.     

[Figure I_4 Comparative indexed trends in the proportion between the national capital stock and the GDP]

The indexed trends thus computed are global averages of across the database, i.e. averages of national values computed for individual countries. The continuous blue line marked with red triangles represents the nominal proportion between the national stocks of fixed assets, and the respective GDP of each country, or the full (PK*QK)/(PG*QG) coefficient. It has been consistently climbing since 1950, and since the mid-1980ies the slope of that climb seems to have increased. Just to give a glimpse of actual non-indexed values, in 1950 the average (PK*QK)/(PG*QG) coefficient was 1.905, in 1985 it reached 2.197, in the reference year 2011it went up to 3.868, to end up at 4.617 in 2017. The overall shape of the curve strongly resembles that observed earlier in the coefficient of patent applications per 1 mln people in the global economy, and in another indexed trend to find in Figure I_4, that of price coefficient PK*PG.  Starting from 1985, that latter proportion seems to be following almost perfectly the trend in patentable invention, and its actual, non-indexed values seem to be informative about a deep change in business in connection with technological change. In 1950, the proportion between average weighted prices of fixed assets, and those of final goods was PK*PG = 0,465, and even in the middle of the 1980ies it kept roughly the same level, PK*PG = 0,45. To put it simply, fixed assets were half as expensive as final goods, per unit of quantity. Yet, since 1990, something had changed: that proportion started to grow: productive assets started to be more and more valuable in comparison to the market prices of the goods they served to make. In 2017, PK*PG reached 1,146. From a world, where technological assets were just tools to make final goods we moved into a world, where technologies are goods in themselves. If we look carefully at digital technologies, nanotechnologies or at biotech, this general observation strongly holds. A new molecule is both a tool to make something, and a good in itself. It can make a new drug, and it can be a new drug. An algorithm can create value added as such, or it can serve to make another value-creating algorithm.

Against that background of unequivocal change in the prices of technological assets, and in their proportion to the Gross Domestic Product of national economies, we can observe a different trend in the proportion of quantities: QK/QG. Hence, we return to questions such as ‘How big a factory we need in order to make the amount of final goods we want?’. The answer to that type of question takes the form of something like a long business cycle, with a peak in 1994, at QK/QG = 5,436. The presently observed QK/QG  (2017) = 4,027 looks relatively modest and is very similar to the value observed in 1950ies. Seventy years ago, we used to be a civilization, which needed around 4 units of quantity in technological assets to make one unit of quantity in final goods. Then, starting from the mid-1970ies, we started turning into a more and more technology intensive culture, with more and more units of quantity in assets required to make one unit of quantity in final goods. In the mid-1990ies, that asset-intensity reached its peak, and now it is back at the old level.

[1] https://investors.cbscorporation.com last access November 4th, 2019

[2] https://data.worldbank.org/indicator/IP.PAT.RESD last access November 4th, 2019

[1] Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), “The Next Generation of the Penn World Table” American Economic Review, 105(10), 3150-3182, available for download at www.ggdc.net/pwt

The point of doing manually what the loop is supposed to do

My editorial on You Tube

OK, here is the big picture. The highest demographic growth, in absolute numbers, takes place in Asia and Africa. The biggest migratory flows start from there, as well, and aim at and into regions with much less of human mass in accrual: North America and Europe. Less human accrual, indeed, and yet much better conditions for each new homo sapiens. In some places on the planet, a huge amount of humans is born every year. That huge amount means a huge number of genetic variations around the same genetic tune, namely that of the homo sapiens. Those genetic variations leave their homeland, for a new and better homeland, where they bring their genes into a new social environment, which assures them much more safety, and higher odds of prolonging their genetic line.

What is the point of there being more specimens of any species? I mean, is there a logic to increasing the headcount of any population? When I say ‘any’, is ranges from bacteria to us, humans. After having meddled with the most basic algorithm of a neural network (see « Pardon my French, but the thing is really intelligent » and « Ce petit train-train des petits signaux locaux d’inquiétude »), I have some thoughts about what intelligence is. I think that intelligence is a class, i.e. it is a framework structure able to produce many local, alternative instances of itself.

Being intelligent consists, to start with, in creating alternative versions of itself, and creating them purposefully imperfect so as to generate small local errors, whilst using those errors to create still different versions of itself. The process is tricky. There is some sort of fundamental coherence required between the way of creating those alternative instances of oneself, and the way that resulting errors are being processed. Fault of such coherence, the allegedly intelligent structure can fall into purposeful ignorance, or into panic.

Purposeful ignorance manifests as the incapacity to signal and process the local imperfections in alternative instances of the intelligent structure, although those imperfections actually stand out and wave at you. This is the ‘everything is just fine and there is no way it could be another way’ behavioural pattern. It happens, for example, when the function of processing local errors is too gross – or not sharp enough, if you want – to actually extract meaning from tiny, still observable local errors. The panic mode of an intelligent structure, on the other hand, is that situation when the error-processing function is too sharp for the actually observable errors. Them errors just knock it out of balance, like completely, and the function signals general ‘Error’, or ‘I can’t stand this cognitive dissonance’.

So, what is the point of there being more specimens of any species? The point might be to generate as many specific instances of an intelligent structure – the specific DNA – as possible, so as to generate purposeful (and still largely unpredictable) errors, just to feed those errors into the future instantiations of that structure. In the process of breeding, some path of evolutionary coherence leads to errors that can be handled, and that path unfolds between a state of evolutionary ‘everything is OK, no need to change anything’ (case mosquito, unchanged for millions of years), and a state of evolutionary ‘what the f**k!?’ (case common fruit fly, which produces insane amount of mutations in response to the slightest environmental stressor).

Essentially, all life could be a framework structure, which, back in the day, made a piece of software in artificial intelligence – the genetic code – and ever since that piece of software has been working on minimizing the MSE (mean square error) in predicting the next best version of life, and it has been working by breeding, in a tree-like method of generating variations,  indefinitely many instances of the framework structure of life. Question: what happens when, one day, a perfect form of life emerges? Something like TRex – Megalodon – Angelina Jolie – Albert Einstein – Jeff Bezos – [put whatever or whoever you like in the rest of that string]? On the grounds of what I have already learnt about artificial intelligence, such a state of perfection would mean the end of experimentation, thus the end of multiplying instances of the intelligent structure, thus the end of births and deaths, thus the end of life.

Question: if the above is even remotely true, does that overarching structure of life understand how the software it made – the genetic code – works? Not necessarily. That very basic algorithm of neural network, which I have experimented with a bit, produces local instances of the sigmoid function Ω = 1/(1 + e-x) such that Ω < 1, and that 1 + e-x > 1, which is always true. Still, the thing does it just sometimes. Why? How? Go figure. That thing accomplishes an apparently absurd task, and it does so just by being sufficiently flexible with its random coefficients. If Life In General is God, that God might not have a clue about how the actual life works. God just needs to know how to write an algorithm for making actual life work. I would even say more: if God is any good at being one, he would write an algorithm smarter than himself, just to make things advance.

The hypothesis of life being one, big, intelligent structure gives an interesting insight into what the cost of experimentation is. Each instance of life, i.e. each specimen of each species needs energy to sustain it. That energy takes many forms: light, warmth, food, Lexus (a form of matter), parties, Armani (another peculiar form of matter) etc. The more instances of life are there, the more energy they need to be there. Even if we take the Armani particle out of the equation, life is still bloody energy-consuming. The available amount of energy puts a limit to the number of experimental instances of the framework, structural life that the platform (Earth) can handle.

Here comes another one about climate change. Climate change means warmer, let’s be honest. Warmer means more energy on the planet. Yes, temperature is our human measurement scale for the aggregate kinetic energy of vibrating particles. More energy is what we need to have more instances of framework life, in the same time. Logically, incremental change in total energy on the planet translates into incremental change in the capacity of framework life to experiment with itself. Still, as framework life could be just the God who made that software for artificial intelligence (yes, I am still in the same metaphor), said framework life could not be quite aware of how bumpy could the road be, towards the desired minimum in the Mean Square Error. If God is an IT engineer, it could very well be the case.

I had that conversation with my son, who is graduating his IT engineering studies. I told him ‘See, I took that algorithm of neural network, and I just wrote its iterations out into separate tables of values in Excel, just to see what it does, like iteration after iteration. Interesting, isn’t it? I bet you have done such thing many times, eh?’. I still remember that heavy look in my son’s eyes: ‘Why the hell should I ever do that?’ he went. ‘There is a logical loop in that algorithm, you see? This loop is supposed to do the job, I mean to iterate until it comes up with something really useful. What is the point of doing manually what the loop is supposed to do for you? It is like hiring a gardener and then doing everything in the garden by yourself, just to see how it goes. It doesn’t make sense!’. ‘But it’s interesting to observe, isn’t it?’ I went, and then I realized I am talking to an alien form of intelligence, there.

Anyway, if God is a framework life who created some software to learn in itself, it could not be quite aware of the tiny little difficulties in the unfolding of the Big Plan. I mean acidification of oceans, hurricanes and stuff. The framework life could say: ‘Who cares? I want more learning in my algorithm, and it needs more energy to loop on itself, and so it makes those instances of me, pumping more carbon into the atmosphere, so as to have more energy to sustain more instances of me. Stands to reason, man. It is all working smoothly. I don’t understand what you are moaning about’.

Whatever that godly framework life says, I am still interested in studying particular instances of what happens. One of them is my business concept of EneFin. See « Which salesman am I? » as what I think is the last case of me being like fully verbal about it. Long story short, the idea consists in crowdfunding capital for small, local operators of power systems based on renewable energies, by selling shares in equity, or units of corporate debt, in bundles with tradable claims on either the present output of energy, or the future one. In simple terms, you buy from that supplier of energy tradable claims on, for example, 2 000 kWh, and you pay the regular market price, still, in that price, you buy energy properly spoken with a juicy discount. The rest of the actual amount of money you have paid buys you shares in your supplier’s equity.

The idea in that simplest form is largely based on two simple observations about energy bills we pay. In most countries (at least in Europe), our energy bills are made of two components: the (slightly) variable value of the energy actually supplied, and a fixed part labelled sometimes as ‘maintenance of the grid’ or similar. Besides, small users (e.g. households) usually pay a much higher unitary price per kWh than large, institutional scale buyers (factories, office buildings etc.). In my EneFin concept, a local supplier of renewable energy makes a deal with its local customers to sell them electricity at a fair, market price, with participations in equity on the top of electricity.

That would be a classical crowdfunding scheme, such as you can find with, StartEngine, for example. I want to give it some additional, financial spin. Classical crowdfunding has a weakness: low liquidity. The participatory shares you buy via crowdfunding are usually non-tradable, and they create a quasi-cooperative bond between investors and investees. Where I come from, i.e. in Central Europe, we are quite familiar with cooperatives. At the first sight, they look like a form of institutional heaven, compared to those big, ugly, capitalistic corporations. Still, after you have waved out that first mist, cooperatives turn out to be very exposed to embezzlement, and to abuse of managerial power. Besides, they are quite weak when competing for capital against corporate structures. I want to create highly liquid a transactional platform, with those investments being as tradable as possible, and use financial liquidity as a both a shield against managerial excesses, and a competitive edge for those small ventures.

My idea is to assure liquidity via a FinTech solution similar to that used by Katipult Technology Corp., i.e. to create some kind of virtual currency (note: virtual currency is not absolutely the same as cryptocurrency; cousins, but not twins, so to say). Units of currency would correspond to those complex contracts « energy plus equity ». First, you create an account with EneFin, i.e. you buy a certain amount of the virtual currency used inside the EneFin platform. I call them ‘tokens’ to simplify. Next, you pick your complex contracts, in the basket of those offered by local providers of energy. You buy those contracts with the tokens you have already acquired. Now, you change your mind. You want to withdraw your capital from the supplier A, and move it to supplier H, you haven’t considered so far. You move your tokens from A to H, even with a mobile app. It means that the transactional platform – the EneFin one – buys from you the corresponding amount of equity of A and tries to find for you some available equity in H. You can also move your tokens completely out of investment in those suppliers of energy. You can free your money, so to say. Just as simple: you just move them out, even with a movement of your thumb on the screen. The EneFin platform buys from you the shares you have moved out of.

You have an even different idea. Instead of investing your tokens into the equity of a provider of energy, you want to lend them. You move your tokens to the field ‘lending’, you study the interest rates offered on the transactional platform, and you close the deal. Now, the corresponding number of tokens represents securitized (thus tradable) corporate debt.

Question: why the hell bothering about a virtual currency, possibly a cryptocurrency, instead of just using good old fiat money? At this point, I am reaching to the very roots of the Bitcoin, the grandpa of all cryptocurrencies (or so they say). Question: what amount of money you need to finance 20 transactions of equal unitary value P? Answer: it depends on how frequently you monetize them. Imagine that the EneFin app offers you an option like ‘Monetize vs. Don’t Monetize’. As long as – with each transaction you do on the platform – you stick to the ‘Don’t Monetize’ option, your transactions remain recorded inside the transactional platform, and so there is recorded movement in tokens, but there is no monetary outcome, i.e. your strictly spoken monetary balance, for example that in €, does not change. It is only when you hit the ‘Monetize’ button in the app that the current bottom line of your transactions inside the platform is being converted into « official » money.

The virtual currency in the EneFin scheme would serve to allow a high level of liquidity (more transactions in a unit of time), without provoking the exactly corresponding demand for money. What connection with artificial intelligence? I want to study the possible absorption of such a scheme in the market of energy, and in the related financial markets, as a manifestation of collective intelligence. I imagine two intelligent framework structures: one incumbent (the existing markets) and one emerging (the EneFin platform). Both are intelligent structures to the extent that they technically can produce many alternative instances of themselves, and thus intelligently adapt to their environment by testing those instances and utilising the recorded local errors.

In terms of an algorithm of neural network, that intelligent adaptation can be manifest, for example, as an optimization in two coefficients: the share of energy traded via EneFin in the total energy supplied in the given market, and the capitalization of EneFin as a share in the total capitalization of the corresponding financial markets. Those two coefficients can be equated to weights in a classical MLP (Multilayer Perceptron) network, and the perceptron network could work around them. Of course, the issue can be approached from a classical methodological angle, as a general equilibrium to assess via « normal » econometric modelling. Still, what I want is precisely what I hinted in « Pardon my French, but the thing is really intelligent » and « Ce petit train-train des petits signaux locaux d’inquiétude »: I want to study the very process of adaptation and modification in those intelligent framework structures. I want to know, for example, how much experimentation those structures need to form something really workable, i.e. an EneFin platform with serious business going on, and, in the same time, that business contributing to the development of renewable energies in the given region of the world. Do those framework structures have enough local resources – mostly capital – for sustaining the number of alternative instances needed for effective learning? What kind of factors can block learning, i.e. drive the framework structure either into deliberate an ignorance of local errors or into panic?

Here is an example of more exact a theoretical issue. In a typical economic model, things are connected. When I pull on the string ‘capital invested in fixed assets’, I can see a valve open, with ‘Lifecycle in incumbent technologies’, and some steam rushes out. When I push the ‘investment in new production capacity’ button, I can see something happening in the ‘Jobs and employment’ department. In other words, variables present in economic systems mutually constrain each other. Just some combinations work, others just don’t. Now, the thing I have already discovered about them Multilayer Perceptrons is that as soon as I add some constraint on the weights assigned to input data, for example when I swap ‘random’ for ‘erandom’, the scope of possible structural functions leading to effective learning dramatically shrinks, and the likelihood of my neural network falling into deliberate ignorance or into panic just swells like hell. What degree of constraint on those economic variables is tolerable in the economic system conceived as a neural network, thus as a framework intelligent structure?

There are some general guidelines I can see for building a neural network that simulates those things. Creating local power systems, based on microgrids connected to one or more local sources of renewable energies, can be greatly enhanced with efficient financing schemes. The publicly disclosed financial results of companies operating in those segments – such as Tesla[1], Vivint Solar[2], FirstSolar[3], or 8Point3 Energy Partners[4] – suggest that business models in that domain are only emerging, and are far from being battle-tested. There is still a way to pave towards well-rounded business practices as regards such local power systems, both profitable economically and sustainable socially.

The basic assumptions of a neural network in that field are essentially behavioural. Firstly, consumption of energy is greatly predictable at the level of individual users. The size of a market in energy changes, as the number of users change. The output of energy needed to satisfy those users’ needs, and the corresponding capacity to install, are largely predictable on the long run. Consumers of energy use a basket of energy-consuming technologies. The structure of this basket determines their overall consumption, and is determined, in turn, by long-run social behaviour. Changes over time in that behaviour can be represented as a social game, where consecutive moves consist in purchasing, or disposing of a given technology. Thus, a game-like process of relatively slow social change generates a relatively predictable output of energy, and a demand thereof. Secondly, the behaviour of investors in any financial market, crowdfunding or other, is comparatively more volatile. Investment decisions are being taken, and modified at a much faster pace than decisions about the basket of technologies used in everyday life.

The financing of relatively small, local power systems, based on renewable energies and connected by local microgrids, implies an interplay of the two above-mentioned patterns, namely the relatively slower transformation in the technological base, and the quicker, more volatile modification of investors’ behaviour in financial markets.

I am consistently delivering good, almost new science to my readers, and love doing it, and I am working on crowdfunding this activity of mine. As we talk business plans, I remind you that you can download, from the library of my blog, the business plan I prepared for my semi-scientific project Befund  (and you can access the French version as well). You can also get a free e-copy of my book ‘Capitalism and Political Power’ You can support my research by donating directly, any amount you consider appropriate, to my PayPal account. You can also consider going to my Patreon page and become my patron. If you decide so, I will be grateful for suggesting me two things that Patreon suggests me to suggest you. Firstly, what kind of reward would you expect in exchange of supporting me? Secondly, what kind of phases would you like to see in the development of my research, and of the corresponding educational tools?

[1] http://ir.tesla.com/ last access December, 18th, 2018

[2] https://investors.vivintsolar.com/company/investors/investors-overview/default.aspx last access December, 18th, 2018

[3] http://investor.firstsolar.com/ last access December, 18th, 2018

[4] http://ir.8point3energypartners.com/ last access December, 18th, 2018

Pardon my French, but the thing is really intelligent

My editorial on You Tube

And so I am meddling with neural networks. It had to come. It just had to. I started with me having many ideas to develop at once. Routine stuff with me. Then, the Editor-in-Chief of the ‘Energy Economics’ journal returned my manuscript of article on the energy-efficiency of national economies, which I had submitted with them, with a general remark that I should work both on the clarity of my hypotheses, and on the scientific spin of my empirical research. In short, Mr Wasniewski, linear models tested with Ordinary Least Squares is a bit oldie, if you catch my drift. Bloody right, Mr Editor-In-Chief. Basically, I agree with your remarks. I need to move out of my cavern, towards the light of progress, and get acquainted with the latest fashion. The latest fashion we are wearing this season is artificial intelligence, machine learning, and neural networks.

It comes handy, to the extent that I obsessively meddle with the issue of collective intelligence, and am dreaming about creating a model of human social structure acting as collective intelligence, sort of a beehive. Whilst the casting for a queen in that hive remains open, and is likely to stay this way for a while, I am digging into the very basics of neural networks. I am looking in the Python department, as I have already got a bit familiar with that environment. I found an article by James Loy, entitled “How to build your own Neural Network from scratch in Python”. The article looks a bit like sourcing from another one, available at the website of ProBytes Software, thus I use both to develop my understanding. I pasted the whole algorithm by James Loy into my Python Shell, made in run with an ‘enter’, and I am waiting for what it is going to produce. In the meantime, I am being verbal about my understanding.

The author declares he wants to do more or less the same thing that I, namely to understand neural networks. He constructs a simple algorithm for a neural network. It starts with defining the neural network as a class, i.e. as a callable object that acts as a factory for new instances of itself. In the neural network defined as a class, that algorithm starts with calling the constructor function ‘_init_’, which constructs an instance ‘self’ of that class. It goes like ‘def __init__(self, x, y):’. In other words, the class ‘Neural network’ generates instances ‘self’ of itself, and each instance is essentially made of two variables: input x, and output y. The ‘x’ is declared as input variable through the ‘self.input = x’ expression. Then, the output of the network is defined in two steps. Yes, the ‘y’ is generally the output, only in a neural network, we want the network to predict a value of ‘y’, thus some kind of y^. What we have to do is to define ‘self.y = y’, feed the real x-s and the real y-s into the network, and expect the latter to turn out some y^-s.

Logically, we need to prepare a vessel for holding the y^-s. The vessel is defined as ‘self.output = np.zeros(y.shape)’. The ‘shape’ function defines a tuple – a table, for those mildly fond of maths – with given dimensions. What are the dimensions of ‘y’ in that ‘y.shape’? They have been given earlier, as the weights of the network were being defined. It goes as follows. It starts, thus, right after the ‘self.input = x’ has been said, ‘self.weights1 = np.random.rand(self.input.shape[1],4)’ fires off, closely followed by ‘self.weights2 =  np.random.rand(4,1)’. All in all, the entire class of ‘Neural network’ is defined in the following form:

class NeuralNetwork:

    def __init__(self, x, y):

        self.input      = x

        self.weights1   = np.random.rand(self.input.shape[1],4)

        self.weights2   = np.random.rand(4,1)                

        self.y          = y

        self.output     = np.zeros(self.y.shape)                

The output of each instance in that neural network is a two-dimensional tuple (table) made of one row (I hope I got it correctly), and four columns. Initially, it is filled with zeros, so as to make room for something more meaningful. The predicted y^-s are supposed to jump into those empty sockets, held ready by the zeros. The ‘random.rand’ expression, associated with ‘weights’ means that the network is supposed to assign randomly different levels of importance to different x-s fed into it.

Anyway, the next step is to instruct my snake (i.e. Python) what to do next, with that class ‘Neural Network’. It is supposed to do two things: feed data forward, i.e. makes those neurons work on predicting the y^-s, and then check itself by an operation called backpropagation of errors. The latter consists in comparing the predicted y^-s with the real y-s, measuring the discrepancy as a loss of information, updating the initial random weights with conclusions from that measurement, and do it all again, and again, and again, until the error runs down to very low values. The weights applied by the network in order to generate that lowest possible error are the best the network can do in terms of learning.

The feeding forward of predicted y^-s goes on in two steps, or in two layers of neurons, one hidden, and one final. They are defined as:

def feedforward(self):

        self.layer1 = sigmoid(np.dot(self.input, self.weights1))

        self.output = sigmoid(np.dot(self.layer1, self.weights2))

The ‘sigmoid’ part means sigmoid function, AKA logistic function, expressed as y=1/(1+e-x), where, at the end of the day, the y always falls somewhere between 0 and 1, and the ‘x’ is not really the empirical, real ‘x’, but the ‘x’ multiplied by a weight, ranging between 0 and 1 as well. The sigmoid function is good for testing the weights we apply to various types of input x-es. Whatever kind of data you take: populations measured in millions, or consumption of energy per capita, measured in kilograms of oil equivalent, the basic sigmoid function y=1/(1+e-x), will always yield a value between 0 and 1. This function essentially normalizes any data.

Now, I want to take differentiated data, like population as headcount, energy consumption in them kilograms of whatever oil equals to, and the supply of money in standardized US dollars. Quite a mix of units and scales of measurement. I label those three as, respectively, xa, xb, and xc. I assign them weights ranging between 0 and 1, so as the sum of weights never exceeds 1. In plain language it means that for every vector of observations made of xa, xb, and xc I take a pinchful of  xa, then a zest of xb, and a spoon of xc. I make them into x = wa*xa + wb*xb + wc*xc, I give it a minus sign and put it as an exponent for the Euler’s constant.

That yields y=1/(1+e-( wa*xa + wb*xb + wc*xc)). Long, but meaningful to the extent that now, my y is always to find somewhere between 0 and 1, and I can experiment with various weights for my various shades of x, and look what it gives in terms of y.

In the algorithm above, the ‘np.dot’ function conveys the idea of weighing our x-s. With two dimensions, like the input signal ‘x’ and its weight ‘w’, the ‘np.dot’ function yields a multiplication of those two one-dimensional matrices, exactly in the x = wa*xa + wb*xb + wc*xc drift.

Thus, the first really smart layer of the network, the hidden one, takes the empirical x-s, weighs them with random weights, and makes a sigmoid of that. The next layer, the output one, takes the sigmoid-calculated values from the hidden layer, and applies the same operation to them.

One more remark about the sigmoid. You can put something else instead of 1, in the nominator. Then, the sigmoid will yield your data normalized over that something. If you have a process that tends towards a level of saturation, e.g. number of psilocybin parties per month, you can put that level in the nominator. On the top of that, you can add parameters to the denominator. In other words, you can replace the 1+e-x with ‘b + e-k*x’, where b and k can be whatever seems to make sense for you. With that specific spin, the sigmoid is good for simulating anything that tends towards saturation over time. Depending on the parameters in denominator, the shape of the corresponding curve will change. Usually, ‘b’ works well when taken as a fraction of the nominator (the saturation level), and the ‘k’ seems to be behaving meaningfully when comprised between 0 and 1.

I return to the algorithm. Now, as the network has generated a set of predicted y^-s, it is time to compare them to the actual y-s, and to evaluate how much is there to learn yet. We can use any measure of error, still, most frequently, them algorithms go after the simplest one, namely the Mean Square Error MSE = [(y1 – y^1)2 + (y2 – y^2)2 + … + (yn – y^n)2]0,5. Yes, it is Euclidean distance between the set of actual y-s and that of predicted y^-s. Yes, it is also the standard deviation of predicted y^-s from the actual distribution of empirical y-s.

In this precise algorithm, the author goes down another avenue: he takes the actual differences between observed y-s and predicted y^-s, and then multiplies it by the sigmoid derivative of predicted y^-s. Then he takes the transpose of a uni-dimensional matrix of those (y – y^)*(y^)’ with (y^)’ standing for derivative. It goes like:

    def backprop(self):

        # application of the chain rule to find derivative of the loss function with respect to weights2 and weights1

        d_weights2 = np.dot(self.layer1.T, (2*(self.y – self.output) * sigmoid_derivative(self.output)))

        d_weights1 = np.dot(self.input.T,  (np.dot(2*(self.y – self.output) * sigmoid_derivative(self.output), self.weights2.T) * sigmoid_derivative(self.layer1)))

        # update the weights with the derivative (slope) of the loss function

        self.weights1 += d_weights1

        self.weights2 += d_weights2

    def sigmoid(x):

    return 1.0/(1+ np.exp(-x))

    def sigmoid_derivative(x):

     return x * (1.0 – x)

I am still trying to wrap my mind around the reasons for taking this specific approach to the backpropagation of errors. The derivative of a sigmoid y=1/(1+e-x) is y’ =  [1/(1+e-x)]*{1 – [1/(1+e-x)]} and, as any derivative, it measures the slope of change in y. When I do (y1 – y^1)*(y^1)’ + (y2 – y^2)*(y^2)’ + … + (yn – y^n)*(y^n)’ it is as if I were taking some kind of weighted average. That weighted average can be understood in two alternative ways. Either it is standard deviation of y^ from y, weighted with the local slopes, or it is a general slope weighted with local deviations. Now I take the transpose of a matrix like {(y1 – y^1)*(y^1)’ ; (y2 – y^2)*(y^2)’ ; … (yn – y^n)*(y^n)’}, it is a bit as if I made a matrix of inverted terms, i.e. 1/[(yn – y^n)*(y^n)’]. Now, I make a ‘.dot’ product of those inverted terms, so I multiply them by each other. Then, I feed the ‘.dot’ product into the neural network with the ‘+=’ operator. The latter means that in the next round of calculations, the network can do whatever it wants with those terms. Hmmweeellyyeess, makes some sense. I don’t know what exact sense is that, but it has some mathematical charm.

Now, I try to apply the same logic to the data I am working with in my research. Just to give you an idea, I show some data for just one country: Australia. Why Australia? Honestly, I don’t see why it shouldn’t be. Quite a respectable place. Anyway, here is that table. GDP per unit of energy consumed can be considered as the target output variable y, and the rest are those x-s.

Table 1 – Selected data regarding Australia

Year GDP per unit of energy use (constant 2011 PPP $ per kg of oil equivalent) Share of aggregate amortization in the GDP Supply of broad money, % of GDP Energy use (tons of oil equivalent per capita) Urban population as % of total population GDP per capita, ‘000 USD
  y X1 X2 X3 X4 X5
1990 5,662020744 14,46 54,146 5,062 85,4 26,768
1991 5,719765048 14,806 53,369 4,928 85,4 26,496
1992 5,639817305 14,865 56,208 4,959 85,566 27,234
1993 5,597913126 15,277 56,61 5,148 85,748 28,082
1994 5,824685357 15,62 59,227 5,09 85,928 29,295
1995 5,929177604 15,895 60,519 5,129 86,106 30,489
1996 5,780817973 15,431 62,734 5,394 86,283 31,566
1997 5,860645225 15,259 63,981 5,47 86,504 32,709
1998 5,973528571 15,352 65,591 5,554 86,727 33,789
1999 6,139349354 15,086 69,539 5,61 86,947 35,139
2000 6,268129418 14,5 67,72 5,644 87,165 35,35
2001 6,531818805 14,041 70,382 5,447 87,378 36,297
2002 6,563073754 13,609 70,518 5,57 87,541 37,047
2003 6,677186947 13,398 74,818 5,569 87,695 38,302
2004 6,82834791 13,582 77,495 5,598 87,849 39,134
2005 6,99630318 13,737 78,556 5,564 88 39,914
2006 6,908872246 14,116 83,538 5,709 88,15 41,032
2007 6,932137612 14,025 90,679 5,868 88,298 42,022
2008 6,929395465 13,449 97,866 5,965 88,445 42,222
2009 7,039061961 13,698 94,542 5,863 88,59 41,616
2010 7,157467568 12,647 101,042 5,649 88,733 43,155
2011 7,291989544 12,489 100,349 5,638 88,875 43,716
2012 7,671605162 13,071 101,852 5,559 89,015 43,151
2013 7,891026044 13,455 106,347 5,586 89,153 43,238
2014 8,172929207 13,793 109,502 5,485 89,289 43,071

In his article, James Loy reports the cumulative error over 1500 iterations of training, with just four series of x-s, made of four observations. I do something else. I am interested in how the network works, step by step. I do step-by-step calculations with data from that table, following that algorithm I have just discussed. I do it in Excel, and I observe the way that the network behaves. I can see that the hidden layer is really hidden, to the extent that it does not produce much in terms of meaningful information. What really spins is the output layer, thus, in fact, the connection between the hidden layer and the output. In the hidden layer, all the predicted sigmoid y^ are equal to 1, and their derivatives are automatically 0. Still, in the output layer, when the second random distribution of weights overlaps with the first one from the hidden layer. Then, for some years, those output sigmoids demonstrate tiny differences from 1, and their derivatives become very small positive numbers. As a result, tiny, local (yi – y^i)*(y^i)’ expressions are being generated in the output layer, and they modify the initial weights in the next round of training.

I observe the cumulative error (loss) in the first four iterations. In the first one it is 0,003138796, the second round brings 0,000100228, the third round displays 0,0000143, and the fourth one 0,005997739. Looks like an initial reduction of cumulative error, by one order of magnitude at each iteration, and then, in the fourth round, it jumps up to the highest cumulative error of the four. I extend the number to those hand-driven iterations from four to six, and I keep feeding the network with random weights, again and again. A pattern emerges. The cumulative error oscillates. Sometimes the network drives it down, sometimes it swings it up.

F**k! Pardon my French, but just six iterations of that algorithm show me that the thing is really intelligent. It generates an error, it drives it down to a lower value, and then, as if it was somehow dysfunctional to jump to conclusions that quickly, it generates a greater error in consecutive steps, as if it was considering more alternative options. I know that data scientists, should they read this, can slap their thighs at that elderly uncle (i.e. me), fascinated with how a neural network behaves. Still, for me, it is science. I take my data, I feed it into a machine that I see for the first time in my life, and I observe intelligent behaviour in something written on less than one page. It experiments with weights attributed to the stimuli I feed into it, and it evaluates its own error.

Now, I understand why that scientist from MIT, Lex Fridman, says that building artificial intelligence brings insights into how the human brain works.

I am consistently delivering good, almost new science to my readers, and love doing it, and I am working on crowdfunding this activity of mine. As we talk business plans, I remind you that you can download, from the library of my blog, the business plan I prepared for my semi-scientific project Befund  (and you can access the French version as well). You can also get a free e-copy of my book ‘Capitalism and Political Power’ You can support my research by donating directly, any amount you consider appropriate, to my PayPal account. You can also consider going to my Patreon page and become my patron. If you decide so, I will be grateful for suggesting me two things that Patreon suggests me to suggest you. Firstly, what kind of reward would you expect in exchange of supporting me? Secondly, what kind of phases would you like to see in the development of my research, and of the corresponding educational tools?