Tag: smart cities

Locally smart. Case study in finance.

 

My editorial on You Tube

 

Here I go, at the frontier between research and education. This is how I earn my living, basically, combining research and education. I am presenting and idea I am currently working on, in a team, regarding a financial scheme for local governments. I am going to develop it here as a piece of educational material for my course « Fundamentals of Finance ». I am combining educational explanation with specific techniques of scientific research.

 

Here is the deal: creating a financial scheme, combining pooled funds, crowdfunding, securities, and cryptocurriences, for facilitating smart urban development through the creation of local start-up businesses. A lot of ideas in one concept, but this is science, for one, and thus anything is possible, and this is education, for two, hence we need to go through as many basic concepts as possible. It goes more or less as follows: a local government creates two financial instruments, a local investment fund, and a local crowdfunding platform. Both serve to facilitate the creation and growth of local start-ups, which, in turn, facilitate smart urban development.

 

We need a universe in order to do anything sensible. Good. Let’s make a universe, out of local governments, local start-up businesses, and local projects in smart urban development. Projects are groups of people with a purpose and a commitment to achieve it together. Yes, wars are projects, just as musical concerts and public fundraising campaigns for saving the grey wolf. Projects in smart urban development are groups of people with a purpose and a commitment to do something interesting about implementing new technologies into the urban infrastructures and this improving the quality, and the sustainability of urban life.

 

A project is like a demon. It needs a physical body, a vessel to carry out the mission at hand. Projects need a physical doorstep to put a clear sign over it. It is called ‘headquarters’, it has an official address, and we usually need it if we want to do something collective and social. This is where letters from the bank should be addressed to. I have the idea to embody local projects of smart urban development in physical bodies of local start-up businesses. This, in turn, implies turning those projects into profitable ventures. What is the point? A business has assets and it has equity. Assets can back equity, and liabilities. Both equity and liabilities can be represented with financial instruments, namely tradable securities. With that, we can do finance.

 

Why securities? The capital I need, and which I don’t have, is the capital somebody is supposed to entrust with me. Thus, by acquiring capital to finance my project, I give other people claims on the assets I am operating with. Those people will be much more willing to entrust me with their capital if those claims are tradable, i.e. when they can back off out of the business really quickly. That’s the idea of financial instruments: making those claims flow and float around, a bit like water.

 

Question: couldn’t we just make securities for projects, without embodying them in businesses? Problematic. Any financial instrument needs some assets to back it up, on the active side of the balance sheet. Projects, as long as they have no such back up in assets, are not really in a position to issue any securities. Another question: can we embody those projects in institutional forms other than businesses, e.g. foundations, trusts, cooperatives, associations? Yes, we can. Each institutional form has its pluses and its minuses. Business structures have one peculiar trait, however: they have at their disposal probably the broadest range of clearly defined financial instruments, as compared to other institutional forms.

 

Still, we can think out of the box. We can take some financial instruments peculiar to business, and try to transplant them onto another institutional body, like that of an association. Let’s just try and see what happens. I am a project in smart urban development. I go to a notary, and I write the following note: “Whoever hands this note on December 31st of any calendar year from now until 2030, will be entitled to receive 20% of net profits after tax from the business identified as LHKLSHFKSDHF”. Signature, date of signature, stamp by the notary. Looks like a security? Mmmwweeelll, maybe. Let’s try and put it in circulation. Who wants my note? What? What do I want in exchange? Let’s zeeee… The modest sum of $2 000 000? You good with that offer?

 

Some of you will say: you, project, you stop right there and you explain a few things. First of all, what if you really have those profits, and 20% of them really make it worth to hand you $2 000 000 now? How exactly can anyone claim those 20%? How will they know the exact sum they are entitled to? Right, say I (project), we need to write some kind of contract with those rules inside. It can be called corporate bylaw, and we need to write it all down. For example, what if somebody has this note on December 31st, 2025, and then they sell it to someone else on January 2nd, 2026, and the profits for 2025 will be really accounted for like in February 2026 at best, and then, who is entitled to have those 20% of profits: the person who had the note on December 31st, 2025, or the one presenting it in 2026, when all is said and done about profits? Sort of tricky, isn’t it? The note says: ‘Whoever hands this note on December 31st… etc.’, only the act of handing is now separated from the actual disclosure of profits. We keep that in mind: the whole point of making a claim into a security is to make it apt for circulation. If the circulation in itself becomes too troublesome, the security loses a lot of its appeal.

 

See? This note contains a conditional claim. Someone needs to hand the note at the right moment and in the right place, there need to be any profit to share etc. That’s the thing about conditional claims: you need to know exactly how to apprehend those conditions, which the claim is enforceable upon.

 

As I think about the exact contents of that contract, it looks like me and anyone holds that note are partners in business. We are supposed to share profits. Profits come from the exploitation of some assets, and they become real only after all the current liabilities have been paid. Hence, we actually share equity in those assets. The note is an equity-based security, a bit primitive, yes, certainly, still an equity-based security.

 

Another question from the audience: “Project, with all the due respect, I don’t really want to be partners in business with you. Do you have an alternative solution to propose?”. Maybe I have… What do you say about a slightly different note, like “Whoever hands this note on December 31st of any calendar year from now until 2030, will be entitled to receive $500 000 from the bank POIUYTR not later than until January 15th of the next calendar year”. Looks good? You remember what is that type of note? This is a draft, or routed note, a debt-based security. It embodies an unconditional claim, routed on that bank with an interesting name, a bit hard to spell aloud. No conditions attached, thus less paperwork with a contract. Worth how much? Maybe $2 000 000, again?

 

No conditions, yet a suggestion. If, on the one hand, I grant you a claim on 20% of my net profit after tax, and, on the other hand, I am ready to give an unconditional claim on $500 000, you could search some mathematical connection between the 20% and the $500 000. Oh, yes, and there are those $2 000 000. You are connecting the dots. Same window in time, i.e. from 2019 through 2030, which makes 11 occasions to hand the note and claim the money. I multiply occasions by unconditional claims, and I go 11*$500 000 = $5 500 000. An unconditional claim on $5 000 000 spread over 11 annual periods is being sold for $2 000 000. Looks like a ton of good business to do, still let’s do the maths properly. You could invest your $2 000 000 in some comfy sovereign bonds, for example the federal German ones. Rock solid, those ones, and they can yield like 2% a year. I simulate: $2 000 000*(1+0,02)11 =  $2 486 748,62. You pay me $2 000 000, you forego the opportunity to earn $486 748,62, and, in exchange, you receive an unconditional claim on $5 500 000. Looks good, at least at the first sight. Gives you a positive discount rate of ($5 500 000 – $2 486 748,62)/ $2 486 748,62 = 121,2% on the whole 11 years of the deal, thus 121,2%/11 = 11% a year. Not bad.

 

When you have done the maths from the preceding paragraph, you can assume that I expect, in that project of smart urban development, a future stream of net profit after tax, over the 11 fiscal periods to come, somewhere around those $5 500 000. Somewhere around could be somewhere above or somewhere below.  Now, we enter the world of behavioural finance. I have laid my cards on the table, with those two notes. Now, you try to figure out my future behaviour, as well as the behaviour to expect in third parties. When you hold a claim, on whatever and whomever you want, this claim has two financial characteristics: enforceability and risk on the one hand, and liquidity on the other hand. You ask yourself, what exactly can the current holder of the note enforce in terms of payback from my part, and what kind of business you can do by selling those notes to someone else.

 

In a sense, we are playing a game. You face a choice between different moves. Move #1: buy the equity-based paper and hold. Move #2: buy the equity-based one and sell it to third parties. Move #3: buy the debt-based routed note and hold. Move #4: buy the routed note and sell it shortly after. You can go just for one of those moves, or make a basket thereof, if you have enough money to invest more than one lump injection of $2 000 000 into my project of smart urban development.

 

You make your move, and you might wonder what kind of move will I make, and what will other people do. Down that avenue of thinking, madness lies. Finance means, very largely, domesticated madness, and thus, when you are a financial player, instead of wondering what other people will do, you look for reliable benchmarks in the existing markets. This is an important principle of finance: quantities and prices are informative about the human behaviour to expect. When you face the choice between moves #1 ÷ #4, you will look, in the first place, for and upon the existing markets. If I grant you 20% of my profits in exchange of $2 000 000, which, in fact, seem corresponding to at least $500 000 of future annual cash flow. If 20% of something is $500 000, the whole something makes $500 000/ 20% = $2 500 000. How much equity does it correspond to? Here it comes to benchmarking. Aswath Damodaran, from NYU Stern Undergraduate College, publishes average ROE (return on equity) in different industries. Let’s suppose that my project of smart urban development is focused on Environmental & Waste Services. It is urban, it claims being smart, hence it could be about waste management. That makes 17,95% of average ROE, i.e. net profit/equity = 17,95%. Logically, equity = net profit/17,95%, thus I go $2 500 000/17,95% = $13 927 576,60 and this is the equity you can reasonably expect I expect to accumulate in that project of smart urban development.

 

Among the numerous datasets published by Aswath Damodaran, there is one containing the so-called ROIC, or return on invested capital, thus on the total equity and debt invested in the business. In the same industry, i.e. Environmental & Waste Services, it is 13,58%. It spells analogously to ROE, thus it is net profit divided by the total capital invested, and, logically, total capital invested = net profit / ROIC = $2 500 000 / 13,58% = $18 409 425,63. Equity alone makes $13 927 576,60, equity plus debt makes $18 409 425,63, therefore debt = $18 409 425,63 – $13 927 576,60 =  $4 481 849,02.

 

With those rates of return on, respectively, equity and capital invested, those 11% of annual discount, benchmarked against German sovereign bonds, look acceptable. If I take a look at the financial instruments listed in the AIM market of London Stock Exchange, and I dig a bit, I can find corporate bonds, i.e. debt-based securities issued by incorporated business structures. Here come, for example, the bonds issued by 3i Group, an investment fund. They are identified with ISIN (International Securities Identification Number) XS0104440986, they were issued in 1999, and their maturity date is December 3rd, 2032. They are endowed with an interest rate of 5,75% a year, payable in two semi-annual instalments every year. Once again, the 11% discount offered on those imaginary routed notes of my project look interesting in comparison.

 

Before I go further, I am once again going to play at anticipating your questions. What is the connection between the interest rate and the discount rate, in this case? I am explaining numerically. Imagine you buy corporate bonds, like those 3i Group bonds, with an interest rate 5,75% a year. You spend $2 000 000 on them. You hold them for 5 years, and then you sell them to third persons. Just for the sake of simplifying, I suppose you sell them for the same face value you bought them, i.e. for $2 000 000. What happened arithmetically, from your point of view, can be represented as follows: – $2 000 000 + 5*5,75%*$2 000 000 + $2 000 000 = $575 000. Now, imagine that instead of those bonds, you bought, for an amount of $2 000 000,  debt-based routed notes of my project, phrased as follows: “Whoever hands this note on December 31st of any calendar year from now until Year +5, will be entitled to receive $515 000 from the bank POIUYTR not later than until January 15th of the next calendar year”. With such a draft (remember: another name for a routed note), you will total – $2 000 000 + 5*$515 000 = $575 000.

 

Same result at the end of the day, just phrased differently. With those routed notes of mine, I earn a a discount of $575 000, and with the 3i bonds, you earn an interest of $575 000. You understand? Whatever you do with financial instruments, it sums up to a cash flow. You spend your capital on buying those instruments in the first place, and you write that initial expenditure with a ‘-’ sign in your cash flow. Then you receive some ‘+’ cash flows, under various forms, and variously described. At the end of the day, you sum up the initial outflow (minus) of cash with the subsequent inflows (pluses).

 

Now, I look back, I mean back to the beginning of this update on my blog, and I realize how far have I ventured myself from the initial strand of ideas. I was about to discuss a financial scheme, combining pooled funds, crowdfunding, securities, and cryptocurriences, for facilitating smart urban development through the creation of local start-up businesses. Good. I go back to it. My fundamental concept is that of public-private partnership, just peppered with a bit of finance. Local governments do services connected to waste and environmental care. The basic way they finance it is through budgetary spending, and sometimes they create or take interest in local companies specialized in doing it. My idea is to go one step further, and make local governments create and run investment funds specialized in taking interest in such businesses.

 

One of the basic ideas when running an investment fund is to make a portfolio of participations in various businesses, with various temporal horizons attached. We combine the long term with the short one. In some companies we invest for like 10 years, and in some others just for 2 years, and then we sell those shares, bonds, or whatever. When I was working on the business plan for the BeFund project, I had a look at the shape those investment portfolios take. You can sort of follow back that research of mine in « Sort of a classical move » from March 15th, 2018. I had quite a bit of an exploration into the concept of smart cities. See « My individual square of land, 9 meters on 9 », from January 11, 2018, or « Smart cities, or rummaging in the waste heap of culture » from January 31, 2018, as for this topic. What comes out of my research is that the combination of digital technologies with the objectively growing importance of urban structures in our civilisation brings new investment opportunities. Thus, I have this idea of local governments, like city councils, becoming active investors in local businesses, and that local investment would combine the big, steady ventures – like local waste management companies – with a lot of small startup companies.

 

This basic structure in the portfolio of a local investment fund reflects my intuitive take on the way a city works. There is the fundamental, big, heavy stuff that just needs to work – waste management, again, but also water supply, energy supply etc. – and there is the highly experimental part, where the city attempts to implement radically new solutions on the grounds of radically new technologies. The usual policy that I can observe in local governments, now, is to create big local companies for the former category, and to let private businesses take over entirely the second one. Now, imagine that when you pay taxes to the local government, part of your tax money goes into an investment fund, which takes participations in local startups, active in the domain on those experimental solutions and new technologies. Your tax money goes into a portfolio of investments.

 

Imagine even more. There is local crowdfunding platform, similar to Kickstarter or StartEngine, where you can put your money directly into those local ventures, without passing by the local investment fund as a middleman. On that crowdfunding platform, the same local investment fund can compete for funding with other ventures. A cryptocurrency, internal to that crowdfunding platform, could be used to make clearer financial rules in the investment game.

 

When I filed that idea for review, in the form of an article, with a Polish scientific journal, I received back an interestingly critical review. There were two main lines of criticism. Firstly, where is the advantage of my proposed solution over the presently applied institutional schemes? How could my solution improve smart urban development, as compared to what local governments currently do? Secondly, doesn’t it go too far from the mission of local governments? Doesn’t my scheme push public goods too far into private hands and doesn’t it make local governments too capitalistic?

 

I need to address those questions, both for revising my article, and for giving a nice closure to this particular, educational story in the fundamentals of finance. Functionality first, thus: what is the point? What can be possibly improved with that financial scheme I propose? Finance has two essential functions: it meets the need for liquidity, and, through the mechanism of financial markets. Liquidity is the capacity to enter in transactions. For any given situation there is a total set T of transactions that an entity, finding themselves in this situation, could be willing to enter into. Usually, we can’t enter it all, I mean we, entities. Individuals, businesses, governments: we are limited in our capacity to enter transactions. For the given total set T of transactions, there is just a subset Ti that i-th entity can participate in. The fraction « Ti/T » is a measure of liquidity this entity has.

 

Question: if, instead of doing something administratively, or granting a simple subsidy to a private agent, local governments act as investment funds in local projects, how does it change their liquidity, and the liquidity of local communities they are the governments of? I went to the website of the Polish Central Statistical Office, there I took slightly North-East and landed in their Local Data Bank. I asked around for data regarding the financial stance of big cities in Poland, and I found out some about: Wroclaw, Lodz, Krakow, Gdansk, Kielce, and Poznan. I focused on the investment outlays of local governments, the number of new business entities registered every year, per 10 000 residents, and on population. Here below, you can find three summary tables regarding these metrics. You will see by yourself, but in a bird’s eye view, we have more or less stationary populations, and local governments spending a shrinking part of their total budgets on fixed local assets. Local governments back off from financing those assets. In the same time, there is growing stir in business. There are more and more new business entities registered every year, in relation to population. Those local governments look as if they were out of ideas as for how to work with that local business. Can my idea change the situation? I develop on this one further below those two tables.

 

 

The share of investment outlays in the total expenditures of the city council, in major Polish cities
  City
Year Wroclaw Lodz Krakow Gdansk Kielce Poznan Warsaw
2008 31,8% 21,0% 19,7% 22,6% 15,3% 27,9% 19,8%
2009 34,6% 23,5% 20,4% 20,6% 18,6% 28,4% 17,8%
2010 24,2% 15,2% 16,7% 24,5% 21,2% 29,6% 21,4%
2011 20,3% 12,5% 14,5% 33,9% 26,9% 30,1% 17,1%
2012 21,5% 15,3% 12,6% 38,2% 21,9% 20,8% 16,8%
2013 15,0% 19,3% 11,0% 28,4% 18,5% 18,1% 15,0%
2014 15,6% 24,4% 16,4% 27,0% 18,6% 11,8% 17,5%
2015 18,4% 26,8% 13,7% 21,3% 23,8% 24,1% 10,2%
2016 13,3% 14,3% 11,5% 15,2% 10,7% 17,5% 9,0%
2017 11,7% 10,2% 11,5% 12,2% 14,1% 12,3% 12,0%
               
Delta 2017 – 2008 -20,1% -10,8% -8,2% -10,4% -1,2% -15,6% -7,8%

 

 

Population of major cities
  City
Year Wroclaw Lodz Krakow Gdansk Kielce Poznan Warsaw
2008 632 162 747 152 754 624 455 581 205 094 557 264 1 709 781
2009 632 146 742 387 755 000 456 591 204 835 554 221 1 714 446
2010 630 691 730 633 757 740 460 509 202 450 555 614 1 700 112
2011 631 235 725 055 759 137 460 517 201 815 553 564 1 708 491
2012 631 188 718 960 758 334 460 427 200 938 550 742 1 715 517
2013 632 067 711 332 758 992 461 531 199 870 548 028 1 724 404
2014 634 487 706 004 761 873 461 489 198 857 545 680 1 735 442
2015 635 759 700 982 761 069 462 249 198 046 542 348 1 744 351
2016 637 683 696 503 765 320 463 754 197 704 540 372 1 753 977
2017 638 586 690 422 767 348 464 254 196 804 538 633 1 764 615
               
Delta 2017 – 2008 6 424 (56 730) 12 724 8 673 (8 290) (18 631) 54 834

 

Number of newly registered business entities per 10 000 residents, in major Polish cities
  City
Year Wroclaw Lodz Krakow Gdansk Kielce Poznan Warsaw
2008 190 160 200 190 140 210 200
2009 195 167 205 196 149 216 207
2010 219 193 241 213 182 238 274
2011 221 169 204 195 168 244 249
2012 228 187 230 201 168 255 274
2013 237 187 224 211 175 262 307
2014 236 189 216 217 157 267 303
2015 252 183 248 236 185 283 348
2016 265 186 251 238 176 270 364
2017 272 189 257 255 175 267 345
               
Delta 2017 – 2008 82,00 29,00 57,00 65,00 35,00 57,00 145,00

 

Let’s take two cases from the table: my hometown Krakow, and my capital Warsaw. In the former case, the negative gap in the investment outlays of the local government is – 44 mlns of zlotys – some €10 mln – and in the latter case it is minus 248,46 millions of zlotys, thus about €56,5 mln. If we want to really get after new technologies in cities, we need to top up those gaps, possibly with a surplus. How can my idea help to save the day?

 

When I try to spend €10 mln euro more on the urban fixed assets, I need to have all those €10 mln. I need to own them directly, in my balance sheet, before spending them. On the other hand, when I want to create an investment fund, which would take part in local startups, and by their intermediary would make those €10 mln worth of assets to happen in real life, I need much less. I start with the balance sheet directly attached to those assets: €10 mln in fixed assets = equity of the startup(s) + liabilities of the startup(s). Now, equity of the startup(s) = shares of our investment fund + shares of other partners. At the end of the day, the local government could finance assets of €10 mln with 1 or 2 millions of euro of own equity, maybe even less.

 

From there on, it went sort of out of hand. I have that mental fixation on things connected to artificial intelligence and neural networks. You can find the latest account in English in the update entitled « What are the practical outcomes of those hypotheses being true or false? ». If you speak French, there is a bit more, and more recent, in « Surpopulation sauvage ou compétition aux États-Unis ». Anyway, I did it. I made a neural network in order to simulate the behaviour of my financial concept. Below, I am presenting a graphical idea of that network. It combines a strictly spoken multilayer perceptron with components of deep learning: observation of the fitness function, and the feeding back of it, as well as selection and preference regarding different neural outputs of the network. I am using that neural network as a simulator of collective intelligence.

 

So, as I am assuming that we are collectively intelligent in our local communities, I make the following logical structure. Step 1: I take four input variables, as listed below. They are taken from real statistics about those 7 big Polish cities, named above – Wroclaw, Lodz, Krakow, Gdansk, Kielce, Poznan, Warsaw – over the period from 2008 through 2017.

 

Input variable 1: Investment outlays of the local government [mln]

Input variable 2: Overall expenses of the local government [mln]

Input variable 3: Population [headcount]

Input variable 4: Number of new business entities registered annually [coefficient]

 

In step 2, I attach to those real input variables an Output variable – Hypothetical variable: capital engaged in the local governments investment fund, initially calculated as if 5% of new business entities were financed with €100 000 each. I calculate the average value of that variable across the whole sample of 7 cities, and it makes €87 mln as expected value. This is the amount of money the average city among those seven could put in that local investment fund to support local startups and their projects of smart urban development.

 

In step 3, I run my neural network through the empirical data, and then I make it do additional 5000 experimental rounds, just to make it look for a match between the input variables – which can change as they want – and the output variable, which I have almost pegged at €87 mln. I say ‘almost’, as in practice the network will generate a bit of wobbling around those €87 mln. I want to see what possible configurations of the input variables can arise, through different patterns of collective learning, around that virtually pegged value of the output variable.

 

I hypothesise 5 different ways of learning, or 5 different selections in that Neuron 4 you can see in the picture above. Learning pattern #1 consists in systematically preferring the neural output of the sigmoid neural function. It is a type of function, which systematically calms down any shocks and sudden swings in input phenomena. It is like a collective pretention that whatever kind of s**t is really going on, everything is just fine. Learning pattern #2 prefers the output of the hyperbolic tangent function. This one tends to be honest, and when there is a shock, it yields a shock, without any f**kery about it. It is like a market with clear rules of competition. Learning pattern #3 takes the least error of the two functions. It is a most classical approach in neural networks. The closer I get to the expected value, the better I am learning, that sort of things. Learning pattern #4 makes an average of those two functions. The greatest value among those being averaged has the greatest impact on the resulting average. Thus, the average of two functions is like hierarchy of importance, expressed in one number. Finally, learning pattern #5 takes that average, just as #3, but it adds the component of growing resistance to new information. At each experimental round, it divides the value of the error fed back into the network by the consecutive number of the round. Error generated in round 2 gets divided by 2, and that generated in round 4000 is being divided by 4000 etc. This is like a person who, as they process new information, develops a growing sentiment of being fully schooled on the topic, and is more and more resistant to new input.

 

In the table below, I present the results of those simulations. Learning patterns #2 and #4 develop structures somehow more modest than the actual reality, expressed as empirical averages in the first numerical line of the table. These are urban communities, where that investment fund I am thinking about slightly grows in importance, in relation to the whole municipal budget. Learning patterns #1 and #3 develop crazy magnitudes in those input variables. Populations grow 9 or 10 times bigger than the present ones, the probability of having new businesses in each 10 000 people grows 6 or 7 times, and municipal budgets swell by 14 ÷ 15 times. The urban investment fund becomes close to insignificant. Learning pattern #5 goes sort of in the middle between those extremes.

 

 

  Input variable 1 Input variable 2 Input variable 3 Input variable 4 Output variable
Initial averages of empirical values  €177 mln  €996 mln                     721 083                               223  €87 mln
Type of selection in neural output Sample results of simulation with the neural network
Sigmoid preferred €2 440 mln €14 377 mln 7 093 526,21 1 328,83 €87 mln
Hyperbolic Tangent preferred €145 mln €908 mln 501 150,03 237,78 €87 mln
Least error preferred €2 213 mln €13 128 mln 6 573 058,50 1 490,28 €87 mln
Average of the two errors €122 mln €770 mln 432 702,57 223,66 €87 mln
Average of the two errors, with growing resistance to learning €845 mln €5 043 mln 2 555 800,36 661,61 €87 mln

 

What is the moral of the fairy tale? As I see it now, it means that for any given initial situation as for that financial scheme I have in mind for cities and their local governments, future development can go two opposite ways. The city can get sort of slightly smaller and smarter, with more or less the same occurrence of new businesses emerging every year. It happens when the local community learns, as a collective intelligence, with little shielding from external shocks. This is like a market-oriented city. In terms of quantitative dynamics, it makes me think about cities like Vienna (Austria), Lyon (France), or my home city, Krakow (Poland). On the other hand, the city can shield itself somehow against socio-economic shocks, for example with heavy subsidies, and then it gets out of control. It grows big like hell, and business starts just to pop around.

 

At the first sight, it seems counterintuitive. We associate market-based, open-to-shocks solutions with uncontrolled growth, and interventionist, counter-cyclical policies with sort of a tame status quo. Still, cities are strange beasts. They are like crocodiles. When you make them compete for food and territory, they grow just to a certain size, ‘cause when they grow bigger than that, they die. Yet, when you allow a crocodile to live in a place without much competition, and plenty of food around, it grows to enormous proportions.

 

My temporary conclusion is that my idea of a local investment fund to boost smart change in cities is workable, i.e. has the chances to thrive as a financial mechanism, when the whole city is open to market-based solutions and receives little shielding from economic shocks.

 

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?

The business plan for the BeFund project – ready for reading

My editorial

It’s been like 10 days since I last updated my research blog and someone could think I had some time out. Well, yes and no. Yes, I stayed away from blogging. No, I did not remain idle. I have been writing that business plan for my BeFund project. I have finished by now, and I am back to blogging. You can find, in the library of my blog, both the English version of the business plan, and the French one. I am currently rewriting the whole thing in Polish, still the concept is mature enough to be disseminated.

I am sharing with you some observations as for the very process of business planning that I have been, and still am going through. This is the educational side of my blog. I teach my students how to prepare a business plan, and it can be useful to describe my actual experience in this respect.

First, the timeline, which has followed some kind of accelerating pace of work. I started with some six weeks of more or less informal a sniffing around the topic, kind of making myself familiar with my own idea. It was just about putting together any business concept proper for the environment of smart cities. Then, by the end of February, I finally knew what I want: a behavioural, experimental lab coupled with an investment fund for startups. The next two weeks or so had been devoted to coining up like pieces of the whole concept. Here some benchmarking for the lab, there some benchmarking for the investment fund, here some research about the market of startups, there some methodological planning for the fund etc. Finally, last Monday, I felt so full of separate ideas that it was either going full power and finally writing that business plan, or writing poems and joining the cavalry, as Lord Byron recommended, in the same time.

I went for the business plan. I can’t ride on horseback (I mean, never have learnt the thing, maybe I could come to like it), and my rhyme tends to be sort of awkward. One week, and it is basically done. Now, I am progressively disseminating the business plan, and expect to engage into some kind of productive negotiations about it. A really wild idea comes to my mind: “Could I come up with a business plan for any concept in a similar time frame, i.e. around 3 months?”. Interesting. Maybe I will try. Any suggestions of any business ideas?

In the process of writing the business plan itself, so over the last 10 days, the part which made me sweat the most was probably the financial plan. There is that strange little something in finance: when I need to translate my ideas into a sequence of events measurable with financial aggregates, my thinking changes. I have to form equations, either fully consciously or somewhere in the backstage of what I call thinking. Structures form, step by step, and this is really a living proof that algebra reflects some deep patterns in our brain. Structures form, and it kicks my ass to hatch them.

The interesting introspection about this step is that it is made of two steps, as a matter of fact. At first, I have like one possible, financial path in my mind. “It has to go the way I think it has to go”, sort of. Then, whatifs start popping up. “What if I take like this fixed cost and I upgrade it by €100 000? What if some actions fail, just sort of statistically?”. They are tenacious those whatifs. They keep drilling my mind until I satisfy them, i.e. until I use financial values to express a coherent logic, not a uniform vision. My whatifs are precious, too. They force me to review each major point of my business concept. Stands to reason: if there is to be any logic in my numbers, it must the same logic all across the business plan.

Anyway, the business plan is ready, still warm. I invite you to read it, possibly to comment on it.

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. 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?

Any given piece of my behaviour (yours too, by the way)

My editorial

Those last weeks I am very much involved with designing experimental environments. I want to develop a business plan for investing in smart cities, and a good business plan could do with the understanding of how can human behaviour change in various experimental conditions. Thus, how are people likely to behave when living in a smart city? How is their behaviour going to be different from those living in a classical, non-smart (dumb?) city? Behaviour is what we do in response to stimuli from our environment. OK, so now I begin by defining what I do. When I take on defining something apparently so bloody complex that my head is turning at the very thought of defining it, the first thing I do is to structure what I do, i.e. I distinguish pieces in the whole.

What kind of categories can I distinguish in what I do? One of the first distinctions I can come up with is by the degree of recurrence. There are things I do so regularly that I even don’t always notice I do them. When repeating those actions, I practically fly on automatic pilot. Walking is one of them. I breathe quite systematically as well, as I think about it. I drive my car almost every day, along mostly repetitive itineraries. As I sail onto the waters of the incidental, and the shore of mindless recurrence progressively vanishes behind me, I cross that ring of islands, like the rings of the Saturn, where big things happen just sometimes, yet they happen in many, periodically spaced sometimes. These are summer holidays, Christmases, or wedding anniversaries. As I sail past the reef of those reassuringly festive occasions, I enter the hardly charted waters of whatever happens next: these are things that I subjectively perceive as absolutely uncertain, and still which happen with a logic I cannot grasp on the happening.

So, here we are with one distinction inside our behaviour: the steady stream of routine, decorated with the regular patches of periodical, ritualized, big events, and all that occasionally visited by the hurricanes of the uncertain. When people live in an urban environment (in any environment, as a matter of fact), their living is composed of three behavioural types: routines, cyclical actions, and reactions to what they perceive as unpredictable. If living in smart cities is about to change our urban lives, it should have some kind of impact upon our behaviour, thus on routines, cyclical events and emergencies. How can it happen and how can we experiment with it?

Another intuitive distinction about our behaviour is that between freedom and constraint. I perceive some of my actions as taken and done out of my sheer free will, whilst I see some others as done under significant constraint. I know that the very concept of free will is arguable, yet I decided to rely on my intuition, and this not a good moment to back off. Thus, I rely and I distinguish. There are actions, which I perceive as undertaken and carried out freely. Trying to be logical, now, I interpret that feeling of freedom as being my own experience of choice. In some situations, I am experiencing quite a broad repertoire of alternative paths to take in my action. A lot of alternatives means that I don’t have enough information to figure out one best way of doing things, and I am entertaining myself with my own feeling of uncertainty. Freedom is connected to uncertainty, but not just to uncertainty. If I can do things in many alternative ways, it means nobody tells me to do those things in one, precise way. There are no ready-made recipes for the situation, or relevant social norms, in my local culture. On the other hand, my highly constrained behaviour corresponds to situations tightly regulated by social norms.

When I have two different distinctions, I can make a third one, two-dimensional, this time. In the most obvious and the least elaborate form it is a table, as shown below:

  Free behaviour (no detailed social norms) Constrained behaviour (normatively regulated)
Routine behaviour Modality #1 Modality #2
Cyclical behaviour Modality #3 Modality #4
Emergency behaviour Modality #5 Modality #6

A normal, fully sane person would leave that table as it is, but I am a scientist, and I have inside me that curious ape, that happy bulldog, and the austere monk. I just need some maths to have something for rummaging in. I just have convert my table into a manifold, with those two nice axes. Maybe I could even trace an indifference curve in it, who knows? Anyway, I need converting modalities into numbers. The kind of numbers I see here are probabilities. The head of the table, namely the distinction between freedom and constraint can be translated into the probability that any given piece of my behaviour (yours too, by the way) is regulated by an unequivocal social norm. It is more fun than you think, as a matter of fact, as we have lots of situations when there are many social norms involved and they are kind of conflicting. I am driving, in order to pick my kid from school, and suddenly I drive over a dog. I should stop and give emergency care to the dog, but then I will not pick up my child from school at time. Of course, at the end of the day, we can convert all such dilemmas into the Hamletic “to be or not to be”, which really narrows down the scope of available options. Still, real life is complicated.

Anyway, I am passing now to scaling numerically the side of my table, as a probability, and I am bumping against a problem: if I translate the recurrence of anything as a probability, it would be the probability of happening in a definite period of time. Thus, it would be a binomial distribution of probability. I take my period of time, like one month, for example, and I just stuff each occurrence in my behaviour into one of the two bags: “yes, it happens at least once in one month” or “no, it doesn’t”. The binomial distribution is fascinating for studying the issue of structural stability (see Fringe phenomena, which happen just sometimes), but in a numerical manifold it gives just two discrete classes, which is not much of a numerical approach, really. I have to figure out something else and that something else is simply recurrence, understood as the cycle of happening, like every day, every three days, every millennium etc.

And so I come up with that nice behavioural graph in PDF, available from the library of my blog . See? Didn’t I tell you I would make an indifference curve? This is the red one in the graph. It is convex, with its tails nicely, assymptotically gliding the long of the axes of reference, so it is bound to be an indifference curve, or an isoquant. The only problem is that I haven’t figured out, yet, what kind of constant quantity it measures. It will come to me, no worries. Still, for the moment, what comes is the idea that on the two tails of this curve I have somehow opposite patterns of behaviour, mostly as for their modifiability. On the bottom right tail, where those ritualized routines dwell, I can modify human behaviour simply by modifying one simple rule, or just a few of them. From now on, I tell those people (or myself) to do things in way B, instead of way A, and Bob’s my uncle: with any luck, and with a little help from Mr Selten and Mr Hammerstein (1994[1]) those people (or me) will soon forget that the rule has ever been changed. On the opposite, upper left tail of that curve, I have things happening really just sometimes, and virtually no rules to regulate human behaviour. How the hell can I modify behavioural patterns in these whereabouts? Honestly, nothing sensible comes to my mind.

Smart cities mean lots of digital technologies. I have just watched a short video, featuring a robot (well, a pair of automated arms fixed to the structure of a bar), which can prepare hundreds of meals, like a professional cook, imitating the movements of a human. Looks a bit scary, I can tell you, but this is what a smart city can look like: some repetitive jobs done by robots. Besides robots, what can we have in a smart city, in terms of smart technologies? GPS tracking, real-time functional optimization (sounds complicated, but this is what you have, for example, in those escalators, which suddenly speed up when you step onto them), personal identification, quick interpersonal communication, and the Internet of things (an escalator can send emails to a cooling pump, which, in turn, can get friends, via social media, among the local smart energy grids). These technologies can take the functional form of: robots (something moving), mobile apps in a phone, in a pair of glasses etc. (something that makes people and things move), and infrastructure (something that definitely shouldn’t move). In their smart form, these things can optimize energy, and learn. We use to call the latter capacity Artificial Intelligence. I think that it is precisely the learning part that can affect our lives the most, in a smart city. We, humans, are kind of historically used to be learning faster than our environment. We are proudly accustomed to figure out things about things before those things change. In a smart city, we have things figuring out things about us, and at an accelerating pace.

In one of my previous updates (see Smart cities, or rummaging in the waste heap of culture ) I made those four hypotheses about smart cities. Good, now I can reappraise those four hypotheses in terms of human behaviour. We behave the way we behave because we have learnt to do so. In a smart city, we will be behaving in the presence of technologies, which can possible learn faster and better than us. Now, keeping in mind that table and that graph, above, how can the coexistence with something possibly smarter than us modify our patterns of behaviour? Following the logic, which I have just unfolded, modification of behaviour can start in the bottom right area of my graph, or with Modality #2 in the tabular form, and then it could possibly move kind of along the red curve in the graph. Thus, what I previously wrote about new patterns observable in handling money, in consuming energy, in rearranging the geography of our habitat, and finally in shaping our social hierarchies, means that smart cities, and their inherently intelligent technologies can impact our behaviour first and most of all by creating and enforcing new rules for highly recurrent, ritualized actions in our life.

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. You can 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] Hammerstein, P., & Selten, R. (1994). Game theory and evolutionary biology. Handbook of game theory with economic applications, 2, 929-993.

Smart cities, or rummaging in the waste heap of culture

My editorial

I am trying to put together my four big ideas. I mean, I think they are big. I feel small when I consider them. Anyway, they are: smart cities, Fintech, renewable energies, and collective intelligence. I am putting them together in the framework of a business plan. The business concept I am entertaining, and which, let’s face it, makes a piece of entertaining for my internal curious ape, is the following: investing in the development of a smart city, with a strong component of renewable energies supplanting fossil fuels, and financing this development partly or totally, with FinTech tools, i.e. mostly with something like a cryptocurrency as well as with a local platform for financial transactions. The whole thing is supposed to have collective intelligence, i.e. with time, the efficiency in using resources should increase in time, on the condition that some institutions of collective life emerge in that smart city. Sounds incredible, doesn’t it? It doesn’t? Right, maybe I should explain it a little bit.

A smart city is defined by the extensive use of digital technologies, in order to optimize the local use of resources. Digital technologies age relatively quickly, as compared to technologies that make the ‘hard’ urban infrastructure. If, in a piece of urban infrastructure, we have an amount KH of capital invested in the hard infrastructure, and an amount KS invested in the smart technologies with a strong digital component, the rate of depreciation D(KH) of the capital invested in KH will be much lower than D(KS) invested in KS.

Mathematically,

[D(KS)/ KS] > [D(KH)/ KH]

and the ‘>’ in this case really means business.

The rate of depreciation in any technology depends on the pace that new technologies come into the game, thus on the pace of research and development. The ‘depends’, here, works in a self-reinforcing loop: the faster my technologies age, the more research I do to replace them with new ones, and so my next technologies age even faster, and so I put metaphorical ginger in the metaphorical ass of my research lab and I come with even more advanced technologies at even faster a pace, and so the loop spirals up. One day, in the future, as I will be coming back home from work, the technology embodied in my apartment will be one generation more advanced than the one I left there in the morning. I will have a subscription with a technology change company, which, for a monthly lump fee, will assure smooth technological change in my place. Analytically, it means that the residual difference in the rates of depreciation, or [D(KS)/ KS] – [D(KH)/ KH] , will widen.

On the grounds of the research I did in 2017, I can stake three hypotheses as for the development of smart cities. Hypothesis #1 says that the relative infusion of urban infrastructure with advanced and quickly ageing technologies will generate increasing amounts of highly liquid assets, monetary balances included, in the aggregate balance sheets of smart cities  (see Financial Equilibrium in the Presence of Technological Change Journal of Economics Library, Volume 4 (2), June 20, s. 160 – 171 and Technological Change as a Monetary Phenomenon Economics World, May-June 2018, Vol. 6, No. 3, 203-216 ). This, in turn, means that the smarter the city, the more financial assets it will need, kind of around and at hand, in order to function smoothly as a social structure.

On the other hand, in my hypothesis #2, I claim that the relatively fast pace of technological change associated with smart cities will pump up the use of energy per capita, but the reciprocal push, namely from energy-intensity to innovation-intensity will be much weaker, and this particular loop is likely to stabilize itself relatively quickly in some sort of energy-innovation standstill (see Technological change as intelligent, energy-maximizing adaptation Journal of Economic and Social Thought, Volume 4 September 3  ). Mind you, I am a bit less definitive on this one than on hypothesis #1. This is something I found out to exist, in human civilisation, as a statistically significant correlation. Yet, in the precise case of smart cities, I still have to put my finger on the exact phenomena, likely corresponding to the hypothesis. Intuitively, I can see some kind of social change. The very transformation of an ordinary (i.e. dumb) urban infrastructure into a smart one means, initially, lots of construction and engineering work being done, just to put the new infrastructure in place. That means additional consumption of energy. Those advanced technologies embodied in the tissues of the smart cities will tend to be advanced for a consistently shortening amount of time, and as they will be replaced, more and more frequently, with consecutive generations of technological youth. All that process will result in the consumption of energy spiralling up in the particular field of technological change itself. Still, my research suggests some kind of standstill, in that particular respect, coming into place quite quickly. I am thinking about our basic triad in energy consumption. If we imagined our total consumption of energy, I mean as civilisation, as a round cake, one third of that cake would correspond to household consumption, one third to transportation, and the remaining third to the overall industrial activity. With that pattern of technological change, which I have just sketched regarding smart cities, the cake would go somehow more to industrial activity, especially as said activity should, technically, contribute to energy efficiency in households and in transports. I can roughly assume that the spiral of more energy being consumed in the process of changing for more energy-efficient technologies can find some kind of standstill in the proportions between that particular consumption of energy, on the one hand, and the household & transport use. I mean, scrapping the bottom of the energy barrel just in order to install consecutive generations of smart technologies is the kind of strategy, which can quickly turn dumb.

Anyway, the development of smart cities, as I see it, is likely to disrupt the geography of energy consumption in the overall spatial structure of human settlement. Smart cities, although energy-smart, are likely to need, on the long run, more energy to run. Yet, I am focusing on another phenomenon, now. Following in the footsteps of Paul Krugman (see Krugman 1991[1];  Krugman 1998[2]), and on the grounds of my own research ( see Settlement by energy – Can Renewable Energies Sustain Our Civilisation? International Journal of Energy and Environmental Research, Vol.5, No.3, pp.1-18  ) I am formulating hypothesis #3: if the financial loop named in hypothesis #1, and the engineering loop from hypothesis #2 come together, the development of smart cities will create a different geography of human settlement. Places, which will turn into smart (and continuously smarter) cities will attract people at faster a pace than places with relatively weaker a drive towards getting smarter. Still, that change in the geography of our civilisation will be quite idiosyncratic. My own research (the link above) suggests that countries differ strongly in the relative importance of, respectively, access to food and access to energy, in the shaping of social geography. Some of those local idiosyncrasies can come as quite a bit of a surprise. Bulgaria or Estonia, for example, are likely to rebuild their urban tissue on the grounds of local access to energy. People will flock around watermills, solar panels, maybe around cold fusion. On the other hand, in Germany, Iran or Mexico, where my research indicates more importance attached to food, the new geography of smart human settlement is likely to gravitate towards highly efficient farming places.

Now, there is another thing, which I am just putting my finger on, not even enough to call it a hypothesis. Here is the thing: money gets hoarded faster and more easily than fixed assets. We can observe that the growing monetization of the global economy (more money being supplied per unit of real output) is correlated with increasing social inequalities . If, in a smart and ever smarter city, more financial assets are being around, it is likely to create a steeper social hierarchy. In those smart cities, the distance from the bottom to the top of the local social hierarchy is likely to be greater than in other places. I know, I know, it does not exactly sound politically correct. Smart cities are supposed to be egalitarian, and make us live happily ever after. Still, my internal curious ape is what it is, i.e. a nearly pathologically frantic piece of mental activity in me, and it just can’t help rummaging in the waste heap of culture. And you probably know that thing about waste heaps: people tend to throw things, there, which they wouldn’t show to friends who drop by.

I am working on making science fun and fruitful, and I intend to make it a business of mine. I am doing by best to stay consistent in documenting my research in a hopefully interesting form. Right now, I am at the stage of crowdfunding. You can 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] Krugman, P., 1991, Increasing Returns and Economic Geography, The Journal of Political Economy, Volume 99, Issue 3 (Jun. 1991), pp. 483 – 499

[2] Krugman, P., 1998, What’s New About The New Economic Geography?, Oxford Review of Economic Policy, vol. 14, no. 2, pp. 7 – 17

The other cheek of business

My editorial

I am turning towards my educational project. I want to create a step-by-step teaching method, where I guide a student in their learning of social sciences, and this learning is by doing research in social sciences. I have a choice between imposing some predefined topics for research, or invite each student to propose their own. The latter seems certainly more exciting. As a teacher, I know what a brain storm is, and believe: a dozen dedicated and bright individuals, giving their ideas about what they think it is important to do research about, can completely uproot your (my own?) ideas as what it is important to do research about. Still, I can hardly imagine me, individually, handling efficiently all that bloody blissful diversity of ideas. Thus, the first option, namely imposing some predefined topics for research, seems just workable, whilst still being interesting. People can get creative about methods of research, after all, not just about topics for it. Most of the great scientific inventions was actually methodology, and what was really breakthrough about it consisted in the universal applicability of those newly invented methods.

Thus, what I want to put together is a step-by-step path of research, communicable and teachable, regarding my own topics for research. Whilst I still admit the possibility of student-generated topics coming my way, I will consider them as a luxurious delicacy I can indulge in under the condition I can cope with those main topics. Anyway, my research topics for 2018 are:

  1. Smart cities, their emergence, development, and the practical ways of actually doing business there
  2. Fintech, and mostly cryptocurrencies, and even more mostly those hybrid structures, where cryptocurrencies are combined with the “traditional” financial assets
  • Renewable energies
  1. Social and technological change as a manifestation of collective intelligence

Intuitively, I can wrap (I), (II), and (III) into a fancy parcel, decorated with (IV). The first three items actually coincide in time and space. The fourth one is that kind of decorative cherry you can put on a cake to make it look really scientific.

As I start doing research about anything, hypotheses come handy. If you investigate a criminal case, assuming that anyone could have done anything anyhow gives you certainly the biggest possible picture, but the picture is blurred. Contours fade and dance in front on your eyes, idiocies pop up, and it is really hard to stay reasonable. On the other hand, if you make some hypotheses as for who did what and how, your investigation gathers both speed and sense. This is what I strongly advocate for: make some hypotheses at the starting point of your research. Before I go further with hypothesising on my topics for research, a few preliminary remarks can be useful. First of all, we always hypothesise about anything we experience and think. Yes, I am claiming this very strongly: anything we think is a hypothesis or contains a hypothesis. How come? Well, we always generalise, i.e. we simplify and hope the simplification will hold. We very nearly always have less data than we actually need to make the judgments we make with absolute certainty. Actually, everything we pretend to claim with certainty is an approximation.

Thus, we hypothesise intuitively, all the time. Now, I summon the spirit of Milton Friedman from the abyss of pre-Facebook history, and he reminds us the four basic levels of hypothesising. Level one: regarding any given state of nature, we can formulate an indefinitely great number of hypotheses. In practice, there is infinitely many of them. Level two: just some of those infinitely many hypotheses are checkable at all, with the actual access to data I have. Level three: among all the checkable hypotheses, with the data at hand, there are just some, regarding which I can say with reasonable certainty whether they are true or false. Level four: it is much easier to falsify a hypothesis, i.e. to say under what conditions it does not hold at all, than to verify it, i.e. claiming under what conditions it is true. This comes from level one: each hypothesis has cousins, who sound almost exactly the same, but just almost, so under given conditions many mutually non-exclusive hypotheses can be true.

Now, some of you could legitimately ask ‘Good, so I need to start with formulating infinitely many hypotheses, then check which of them are checkable, then identify those allowing more or less rigorous scientific proof? Great. It means that at the very start I get entangled for eternity into checking how checkable is each of the infinitely many hypotheses I can think of. Not very promising as for results’. This is legit to say that, and this is the reason why, in science, we use that tool known as the Ockham’s razor. It serves to give a cognitive shave to badly kept realities. In its traditional form it consists in assuming that the most obvious answer is usually the correct one. Still, as you have a closer look at this precise phrasing, you can see a lot of hidden assumptions. It assumes you can distinguish the obvious from the dubious, which, in turn, means that you have already applied the razor beforehand. Bit of a loop. The practical way of wielding that razor is to assume that the most obvious thing is observable reality. I start with finding my bearings in reality. Recently, I gave an example of that: check ‘My individual square of land, 9 meters on 9’  . I look around and I assess what kind of phenomena, which, at this stage of research, I can intuitively connect to the general topic of my research, and which I can observe, measure, and communicate intelligibly about. These are my anchors in reality.

I look at those things, I measure them, and I do my best to communicate by observations to other people. This is when the Ockham’s razor is put to an ex post test: if the shave has been really neat, other people can easily understand what I am communicating. If I and a bunch of other looneys (oops! sorry, I wanted to say ‘scientists’) can agree on the current reading of the density of population, and not really on the reading of unemployment (‘those people could very well get a job! they are just lazy!), then the density of population is our Ockham’s razor, and unemployment not really (I love the ‘not really’ expression: it can cover any amount of error and bullshit). This is the right moment for distinguishing the obvious from the dubious, and to formulate my first hypotheses, and then I move backwards the long of the Milton Friedman’s four levels of hypothesising. The empirical application of the Ockham’s razor has allowed me to define what I can actually check in real life, and this, in turn, allows distinguishing between two big bags, each with hypotheses inside. One bag contains the verifiable hypotheses, the other one is for the speculative ones, i.e. those non-verifiable.

Anyway, I want my students to follow a path of research together with me. My first step is to organize the first step on this path, namely to find the fundamental, empirical bearings as for those four topics: smart cities, Fintech, renewable energies and collective intelligence. The topic of smart cities certainly can find its empirical anchors in the prices of real estate, and in the density of population, as well as in the local rate of demographic growth. When these three dance together – once again, you can check ‘My individual square of land, 9 meters on 9’ – the business of building smart cities suddenly gets some nice, healthy, reddish glow on its cheeks. Businesses have cheeks, didn’t you know? Well, to be quite precise, businesses have other cheeks. The other cheek, in a business, is what you don’t want to expose when you already get hit somewhere else. Yes, you could call it crown jewels as well, but other cheek sounds just more elegantly.

As for Fintech, the first and most obvious observation, from my point of view, is diversity. The development of Fintech calls into existence many different frameworks for financial transactions in times and places when and where, just recently, we had just one such framework. Observing Fintech means, in the first place, observing diversity in alternative financial frameworks – such as official currencies, cryptocurrencies, securities, corporations, payment platforms – in the given country or industry. In terms of formal analytical tools, diversity refers to a cross-sectional distribution and its general shape. I start with I taking a convenient denominator. The Gross Domestic Product seems a good one, yet you can choose something else, like the aggregate value of intellectual property embodied in selfies posted on Instagram. Once you have chosen your denominator, you measure the outstanding balances, and the current flows, in each of those alternative, financial frameworks, in the units of your denominator. You get things like market capitalization of Ethereum as % of GDP vs. the supply of US dollar as % of its national GDP etc.

I pass to renewable energies, now. When I think about what is the most obviously observable in renewable energies, it is a dual pattern of development. We can have renewable sources of energy supplanting fossil fuels: this is the case in the developed countries. On the other hand, there are places on Earth where electricity from renewable sources is the first source of electricity ever: those people simply didn’t have juice to power their freezer before that wind farm started up in the whereabouts. This is the pattern observable in the developing countries. In the zone of overlapping, between those two patterns, we have emerging markets: there is a bit of shifting from fossils to green, and there is another bit of renewables popping up where nothing had dared to pop up in the past. Those patterns are observable as, essentially, two metrics, which can possibly be combined: the final consumption of energy per capita, and the share of renewable sources in the final consumption of energy. Crude as they are, they allow observing a lot, especially when combined with other variables.

And so I come to collective intelligence. This is seemingly the hardest part. How can I say that any social entity is kind of smart? It is even hard to say in humans. I mean, virtually everybody claims they are smart, and I claim I’m smart, but when it comes to actual choices in real life, I sometimes feel so bloody stupid… Good, I am getting a grip. Anyway, intelligence for me is the capacity to figure out new, useful things on the grounds of memory about old things. There is one aspect of that figuring out, which is really intriguing my internal curious ape: the phenomenon called ultra-socialisation, or supersocialisation. I am inspired, as for this one, by the work of a group of historians: see ‘War, space, and the evolution of Old World complex societies’ (Turchin et al. 2013[1]). As a matter of fact, Jean Jacques Rousseau, in his “Social Contract”, was chasing very much the same rabbit. The general point is that any group of dumb assholes can get social on the level of immediate gains. This is how small, local societies emerge: I am better at running after woolly mammoths, you are better at making spears, which come handy when the mammoth stops running and starts arguing, and he is better at healing wounds. Together, we can gang up and each of us can experience immediate benefits of such socialisation. Still, what makes societies, according to Jean Jacques Rousseau, as well as according to Turchin et al., is the capacity to form institutions of large geographical scope, which require getting over the obsession of immediate gains and provide long-term, developmental a kick. What is observable, then, are precisely those institutions: law, state, money, universally enforceable contracts etc.

Institutions – and this is the really nourishing takeaway from that research by Turchin et al. (2013[2]) – are observable as a genetic code. I can decompose institutions into a finite number of observable characteristics, and each of them can be observable as switched on, or switched off. Complex institutional frameworks can be denoted as sequences of 1’s and 0’s, depending on whether the given characteristics is, respectively, present or absent. Somewhere between the Fintech, and collective intelligence, I have that metric, which I found really meaningful in my research: the share of aggregate depreciation in the GDP. This is the relative burden, imposed on the current economic activity, due to the phenomenon of technologies getting old and replaced by younger ones. When technologies get old, accountants accounts for that fact by depreciating them, i.e. by writing off the book a fraction of their initial value. All that writing off, done by accountants active in a given place and time, makes aggregate depreciation. When denominated in the units of current output (GDP), it tends to get into interesting correlations (the way variables can socialize) with other phenomena.

And so I come with my observables: density of population, demographic growth, prices of real estate, balances and flows of alternative financial platforms expressed as percentages of the GDP, final consumption of energy per capita, share of renewable energies in said final consumption, aggregate depreciation as % of the GDP, and the genetic code of institutions. What I can do with those observables, is to measure their levels, growth rates, cross-sectional distributions, and, at a more elaborate level, their correlations, cointegrations, and their memory. The latter can be observed, among other methods, as their Gaussian vector autoregression, as well as their geometric Brownian motion. This is the first big part of my educational product. This is what I want to teach my students: collecting that data, observing and analysing it, and finally to hypothesise on the grounds of basic observation.

[1] Turchin P., Currie, T.E.,  Turner, E. A. L., Gavrilets, S., 2013, War, space, and the evolution of Old World complex societies, Proceedings of The National Academy of Science, vol. 110, no. 41, pp. 16384 – 16389

[2] Turchin P., Currie, T.E.,  Turner, E. A. L., Gavrilets, S., 2013, War, space, and the evolution of Old World complex societies, Proceedings of The National Academy of Science, vol. 110, no. 41, pp. 16384 – 16389

My individual square of land, 9 meters on 9

My editorial

And so I am building something with a strategy. Last year, 365 days ago, I was just beginning to play with scientific blogging. Now, I have pretty clear a vision of how I want to grow over the next 365 days. My internal bulldog is sniffing around two juicy bones: putting up a method of and pitching a product, relevant to the teaching of social sciences by participation in the actual doing of research, and, on the other hand, putting together an investment project in the domain of smart cities. In this update, I start developing more specifically on that second one, and I focus on two things. Firstly, I perceive smart cities as both technological and social a change, which develops through diffusion of innovation. Very nearly axiomatically, the phenomenon of diffusion in innovations is represented as a process tending towards saturation. I want to find a method, and, hopefully, the metrics relevant to measuring the compound size of the market in the phenomenon called ‘Smart Cities’, mostly in Europe. In the same time, I want to test the tending-towards-saturation approach in forecasting the size of this market.

The emergence of smart cities, as both an urban concept and a business model, is made of smaller parts. There is investment in the remodelling and rebuilding of infrastructure. On the other hand, there is the issue of energy, both in terms of efficiency in its use, and in terms of its renewable sourcing. Finally, there is the huge field of digital technologies, and, looming somehow at the horizon, the issue of Fintech: the use of digital technologies to create local, flexible monetary systems. I am collecting data, step by step, to acquire a really sharp view of the situation, and so my internal curious ape comes by that report ‘The State of European Cities’ , as well as by that article ‘Smart Cities in Europe’ , and finally it swings to that interesting website: ‘Organicity’ . There seem to be two common denominators to all the reports and websites on this topic: experimentation and teaming up. Cities build their smart cities in consortiums rather than single-handedly. Each project is an experiment, to the extent that ‘established technologies’ are essentially the opposite of what business people expect to invest in when they invest in a smart city.

When I teach my students the fundamentals of business planning, I ask them frequently to look at the business concept from two sides: that of the enthusiastic founder, and that of a conservative investor. The balance sheet of the new business is likely to take shape at the intersection of those two approaches. I am adopting the same approach with my business concept. If I were a truly conservative an investor, I would ask, among other questions, what type of business am I supposed to put capital into, and what is the workable business model. The main types of business that come to my mind, regarding smart cities, are: development of real estate, construction, and technical services. However smart a city is becoming, it is made of architectural structures: buildings, roads, rails etc. Someone owns them before the smart city starts burgeoning, and someone owns them when the smart city is already running. Question: are the someones who own those structures afterwards the same someones who owned them beforehand, or are they different someones? What does the transfer of property in real estate look like in those smart cities? Another thing that I teach my students is ‘When you know nearly nothing about a market or a business, look at the prices and the demographics in the first place. Is the market conflating with people, is it stationary, or is it deflating? Are there any prices, which you can qualify as equilibrium prices, i.e. prices virtually exogenous to the bargaining power of individual market players, and clearly sensitive to other economic variables? If you can spot such prices, what trends do they display?’. Right, so now, I am applying my wisdom for sale to my own plans, and I try to figure out those things for selected cases of smart cities, just to make my hand.

I have recently read a lot about a project of smart city, namely the ‘Confluence’ project in Lyon, France. The place is dear to my heart, as I spent quite a chunk of my youth in Lyon, and I am glad to return there, whenever I can. The Confluence Project is located in the 2nd district of Lyon, at what the locals call ‘Presqu’Ile’, i.e. ‘Nearly an Island’, and the confluence of two rivers: Rhone and Saone. From the bird’s view, it is like an irregular, triangular wedge, with its top pointing South at the exact confluence of the two rivers, and its base resting, more or less, on the Perrache rail station. I am having a look at the local prices of real estate. As I visit the website ‘Meilleurs Agents’  , I can see an almost uninterrupted growth in the price per 1 m2, since 1994. Surprisingly, even the burst of the housing bubble in 2007 – 2009 didn’t curb much that trend. Over the last 10 years, it means almost 31% more in the average price of square meter. I focus on the prices of flats. Right now, the average price in Lyon is 3 690 € per square meter, and that average is expected in a general span from 2 767 € to 5 535 €. Against this general background, I take a few snapshots at different addresses. First, I have a glance at a long street – Cours Charlemagne – which almost makes the longitudinal backbone of the Confluence wedge. The average price per 1 m2 is 3 783 €, in a range from 2 664 € to 5 040 €. That average is slightly higher than the whole city, but the range of prices has slightly lower extremities.

Cours Charlemagne connects the posh neighbourhood of Perrache, in the North, to really industrial a place, at the Southern junction of the two rivers. Thus, I take a closer focus, and I target those different environments. Angle of Quai Rambaud and Rue Suchet, a truly posh place in the Northern part of Confluence, displays an average price of 4 725 € per 1 m2, in a range from 3 186 € to 6 207 €. Yes, baby, it just rockets up. Now, I take a little stroll to the South, apparently advancing towards lower prices, and I call by Rue Paul Montrochet. It should be cheaper than up North, and yet it is not: the average price is 5 144 € per square meter, in a range from 3 461 € to 6 483 €.

As usually, observing reality has been of some value. Provisional hypothesis, based on the case of Lyon-Confluence: smart cities grow where the prices of real estate grow. Now, a bit of a bow to reverend Malthus: I check the demographics, with The World Population Review , and I show those numbers in Table 1, below. There has been, and there still is, quite a consistent demographic growth. Basically, if you calculate the annual average growth rates in, respectively, the price of 1 m2 in residential space, and the local population, those two rates look almost like twins: around 3% a year. My provisional hypothesis puts on some ornamentation: smart cities grow where the prices of real estate grow, and where population grows.

Table 1 The population of Lyon, France (urban area)

Year  Population Growth Rate (%) Growth
2030  1 814 000 3,72%  65 000
2025  1 749 000 3,98%  67 000
2020  1 682 000 2,81%  46 000
2017  1 636 000 1,68%  27 000
2015  1 609 000 3,74%  58 000
2010  1 551 000 3,68%  55 000
2005  1 496 000 3,67%  53 000
2000  1 443 000 2,78%  39 000
1995  1 404 000 2,48%  34 000
1990  1 370 000 2,54%  34 000
1985  1 336 000 4,54%  58 000
1980  1 278 000 8,49%  100 000
1975  1 178 000 5,56%  62 000
1970  1 116 000 8,67%  89 000
1965  1 027 000 13,61%  123 000
1960  904 17,71%  136 000
1955  768 5,06%  37 000
1950  731 0,00%  –

source: http://worldpopulationreview.com/world-cities/lyon-population/ , last accessed January 11th 2018

The decision makers of the Lyon-Confluence project claim they are in some sort of agreement with two other initiatives: Vienna and Munich. I quickly perform the same check for Vienna as I did for Lyon. In this case, the initiative of smart city seems to be city-wide, and not confined to just one district. As for the prices of apartments, I start with the Global Property Guide . Apparently, the last six years brought a sharp rise in prices (plus 39%), still those prices started curbing down a bit, recently. A quick glance at Numbeo shows an average price of 7 017,18 € per 1 m2 in the city centre, in a range from 4 800 € to 10 000 €, and further out of the centre it makes like 3 613,40 € per square meter on average, comprised between 3 000 € and 5 000 €. On the whole, Vienna looks a shade more expensive than Lyon. Let’s check the demographics, once again with The World Population Review (Table 2, below). Quite similar to Lyon, maybe with a bit more bumps on the way. Interestingly, both initiatives of smart cities started to take shape around 2015, when both cities started to flirt with more or less 1,5 million people in the urban area. Looks like some sort of critical mass, at least for now.

Table 2 The population of Vienna, Austria (urban area)

Year Population Growth Rate (%) Growth
2030 1 548 000 0,98% 15 000
2025 1 533 000 2,06% 31 000
2020 1 502 000 2,32% 34 000
2017 1 468 000 2,09% 30 000
2015 1 438 000 6,28% 85 000
2010 1 353 000 7,89% 99 000
2005 1 254 000 4,33% 52 000
2000 1 202 000 -3,14% (39 000)
1995 1 241 000 1,89% 23 000
1990 1 218 000 -3,87% (49 000)
1985 1 267 000 -2,46% (32 000)
1980 1 299 000 0,23% 3 000
1975 1 296 000 0,15% 2 000
1970 1 294 000 10,13% 119 000
1965 1 175 000 10,85% 115 000
1960 1 060 000 13,25% 124 000
1955 936 12,64% 105 000
1950 831 0,00% –

source: http://worldpopulationreview.com/world-cities/munich-population/ , last accessed January 11th, 2018

Good. As my internal curious ape turns and returns those coconuts, ideas start taking shape. At least one type of socio-economic environment, where that curious new species called ‘smart cities’ seem to dwell, is an environment where them growth rates in housing prices, and in population, are like 3% or more. One million and a half people living in a more or less continuous urban area seem to make like a decent size, in terms of feeding grounds for a smart city. Prices of residential real estate, associated with the emergence of smart cities in Europe, seem hitting like 4 500 € or more. This is probably just one type of environment, but one is already better than saying ‘any environment’. The longer I do social sciences, the more I am persuaded that we, humans, are very simple and schematic in our social structures. Theoretically, with the individual flexibility we are capable of, the science we have, and with Twitter, we could form an indefinitely diverse catalogue of social structures. Yet, it is more like in a chess game: there are just a few structures that work, and others just don’t, and we don’t even full comprehend the reasons for them not working at all. When we talk business and investment, there are some contexts that allow the deployment of a business model, whilst it just doesn’t work in other contexts. Same thing here: the type of environment I am casually sketching is the one where smart cities work in terms of business and investment.

My business plan for investing in smart cities has certainly one cornerstone, namely that of gains in the market value of real estate involved. One cornerstone is not bad at all, and now I am thinking about putting some stones under the remaining three corners. In that report which I mentioned earlier, namely report ‘The State of European Cities’ , I have already spotted two interesting pieces of information. Firstly, the sustainable density of population for a smart city is generally the same as for sustainable public transport: 3000 people per km2 or more. Secondly, the dominant trend in the European urbanisation is the growth of suburbs and towns, rather than cities strictly spoken. It pertains to my home country, Poland, as well. Thus, what we have as market, is a network of urban units moderate in size, but big in connections with other similar units. Two classes of business prospects emerge, then, regarding the investment in smart cities. Following my maths classes at school, I call those prospects, respectively, the necessary context, and the favourable context. The necessary is based on the density of population: the more we are per square kilometre, the more fun we are having, and the special kind of fun we can have in a smart city requires at least 3000 people per km2, or, in other words, each individual person having for their personal use no more than a square of 18 meters on 18. The favourable is made of real estate prices, and demographic growth, the former hitting above 4 500 € per 1 m2, and growing at 3% per annum, on average; the latter needs to make the same 3% a year.

By the way, I made a quick calculation for my family and our house. We live in a terraced house, located on a plot of land of 250 m2. We are three, which makes 83,6 m2 per capita, which, in turn, means that each capita has an individual square of land the size of 9,14 meters on 9,14. We are double the density of population required for a smart city. There is no other way: I have to go for it.

Une courbe élégante en « S » aplati

Mon éditorial

Hier, dans ma mise à jour en anglais (“The dashing drip of Ketonal, or my fundamental questions for the New Year”  ), je me suis permis de formuler une sorte de compte rendu de tous les projets sur lesquels j’ai l’intention de travailler en 2018. D’une part, c’est comme une liste de résolutions pour le Nouvel An mais d’autre part c’est un premier pas sur mon chemin vers un peu de mise en ordre dans ce p****n de bordel – créatif, certes, bordel quand même – qui est tout à coup apparu dans ma vie professionnelle. J’ai découvert – tout récemment, en fait – que je suis capable d’achever quelque chose comme du développement personnel lorsque je combine du chaos avec de la cohérence. En ce qui concerne le chaos, la vie donne tout ce qu’il me faut. En ce qui concerne la cohérence, l’écriture est une bonne stratégie. Ecrire, dans une forme proche de celle d’un journal personnel, m’aide à mettre de l’ordre dans des idées échevelées. En plus, j’ai découvert que l’écriture marche le mieux dans mon cas lorsque je m’adresse à un public, par exemple à vous, les lecteurs de mon blog. Cette fois aussi, ça a marché. Dans cette mise à jour d’hier, comme je divaguais (oui, je le dis franchement, je divaguais un peu) sur ces différents projets que j’ai sur la planche à présent, mes idées avaient tout à coup commencé à prendre une cohérence respectable.

Alors, mon ordre personnel dans du chaos personnel s’articule autour de deux projets principaux : d’une part, je développe un site Internet éducatif, dévoué à l’éducation en sciences sociales à travers la participation dans de la recherche réelle, et d’autre part, je développe un projet d’investissement ciblé sur les soi-disant cités intelligentes, ou « smart cities » en anglais. La première de ces deux idées, le site éducatif, est quelque chose que j’avais en tête pour pas mal de temps déjà, et que je suis en train d’amorcer à travers ce blog-même. Ce qui me manquait, c’était une sorte d’idée distinctive de base. Comme élève d’abord, étudiant ensuite, j’avais toujours une réserve profonde vis à vis des manuels classiques. Depuis que je suis devenu prof et chercheur, je pris une conscience même plus aiguë de la superficialité des manuels, en comparaison du savoir scientifique à proprement dit. Bien sûr, l’étape suivante après les manuels, c’est l’éducation interactive. C’est intéressant, mais là, je me sens un tout petit peu mal à l’aise. Etre interactif dans le cadre d’un contenu typiquement standardisé pour l’éducation, n’est pas encore tout à fait ce que je veux faire avec les étudiants. Le terrain où je me sens vraiment dans mon élément, c’est l’éducation à travers la participation dans la solution des problèmes réels à 100%. Les sciences sociales sont mon champ de recherche et l’éducation la plus réaliste qu’on peut y avoir c’est l’application de l’outillage scientifique au développement des projets d’investissement, d’action sociale, ou encore des projets politiques.

Avec tout le côté excitant et (un peu) grandiose de confronter des étudiants des sciences sociales directement avec des cas réels, je suis conscient du fait que cette idée peut aussi bien s’enliser dans un phénomène bien connu : si vous apprenez aux gens à nager en les jetant directement dans de l’eau profonde, beaucoup d’entre eux vont tout simplement se noyer. L’éducation, dans quel domaine que ce soit, consiste à créer, pour l’apprenti, un sentier fait de tâches qu’il est objectivement capable d’accomplir, donc de tâches où son système nerveux peut enregistrer un succès qu’il serait intéressant de reproduire, encore et encore. Je sais par expérience que la recherche scientifique, au moins si on l’approche de façon vraiment sérieuse, est une tâche ardue. Même des chercheurs bien rôdés ont des crises de découragement. Alors le vrai défi éducatif consiste à prendre des cas réels, et des objectifs de recherche cent pourcent ancrés dans la vie réelle, et en même temps, les diviser en des petits pas scientifiques abordables pour les novices.

Ce que je mijote est donc un chemin éducatif, dans le cadre duquel je vais guider les étudiants dans une approche scientifique des situations réelles. C’est alors que mon second projet pour cette année entre en scène : l’investissement dans les cités intelligentes. Tout d’abord : pourquoi ? Tout d’abord : parce que ça m’intéresse. Ensuite, j’ai récemment eu la preuve du bien-fondé de ces intuitions que j’avais partagées avec mes lecteurs à propos d’une monnaie virtuelle reliée au marché d’énergies renouvelables. Si vous daigniez de jeter un coup d’œil chez https://www.wepower.network , vous verrez pratiquement la même chose que mon idée de Wasun, que j’avais décrite plusieurs fois sur ce blog ( regardez, par exemple : ‘Being like silk, espousing the protrusions of Vatenfall’ ou bien ‘Les moulins de Wasun’ ). Finalement, si je veux jouer le rôle d’un mentor dans l’application des sciences sociales à des entreprises réelles, eh bien, il faut que je tienne la forme, moi-même, en ce qui concerne l’étude de tels projets.

Je commence par consulter professeur Google sur le sujet. Voilà qu’il me retourne quelques adresses utiles : http://www.smart-cities.eu , par exemple, ou bien http://ec.europa.eu/eip/smartcities/index_en.htm , ou encore http://www.smartcitiesineurope.com . Comme d’habitude, j’effectue une lecture superficielle de ces sites juste pour avoir une idée de ce qui se passe. Là, une précision est due : lorsque j’écris « une idée de ce qui se passe », je l’entends dans le sens le plus fondamental. Ce qui se passe c’est un ensemble des phénomènes qui se passent, c’est-à-dire qui démontrent du changement dans le temps. Avoir une idée de ce qui se passe au sujet des cités intelligentes veut dire identifier des variables, objectivement observables et mesurables, qui montrent du changement. La première variable que j’ai pu observer est le nombre des villes en Europe qui mettent en place, à des échelles différentes, des projets de cité intelligente. Ce nombre est en train de croître. C’est une expansion qui – en des termes théoriques – a tout l’air d’un phénomène de contagion. En sciences économiques, nous modelons des trucs comme ça avec des fonctions qui tendent vers un niveau hypothétique de saturation. D’habitude, c’est une fonction logistique ou bien la densité cumulative de probabilité de l’occurrence d’un phénomène donné dans des cellules séparées d’un marché. Dans les deux cas, on a une courbe élégante en « S » aplati qui tend asymptotiquement vers le niveau de saturation. Avec un peu d’Excel, un peu de temps à tuer et un peu d’imagination mathématique on peut donner à cette courbe des contorsions intéressantes, mais ce qui m’intéresse vraiment à ce point-ci est ce niveau hypothétique de saturation. Quelle est la taille du marché des cités intelligentes en Europe ? Ce marché est composite : il englobe des travaux de construction, de la mise en place d’infrastructure(s), des plateformes du type Fintech (finance plus utilité en ligne) et même des logiciels pour les téléphones portables. Je me demande combien des gens et combien de capital peut joindre ce mouvement.

Alors voilà mon premier challenge de recherche dans ce projet des cités intelligentes : prédire la taille du marché et en même temps imaginer un chemin éducatif pour apprendre aux étudiants en sciences sociales comment formuler une telle prédiction.

The dashing drip of Ketonal, or my fundamental questions for the New Year

My editorial

Recently, I have been working on a few parallel projects, which all orient me towards business and management rather than economics. Of course, the three are related: you cannot expect to draft a decent business plan without understanding markets around you, just as you have meagre chances to understand markets if you ignore the way organisations work. Anyway, the business projects that I am currently working on orient me towards those most fundamental questions of social theory. It starts with a very practical issue, like ‘How can I know this precise project is going to be successful?’, and then, as I am articulating the answer, I feel as if I were ploughing through the entire field of social sciences. I have just finished preparing a business plan for an investment in real estate. At the first sight, it seemed simple as the blueprint of flail: you buy a residential building, you renovate it, in the course of renovation you consider rearranging the division of the building into apartments, then you sell the apartments one by one, and you rent the shops at the ground floor. A textbook of doing business, someone could say. Yet, as I started to estimate the profitability of the thing, questions popped up, initially very simple: will the Internal Rate of Return (IRR) be more suitable to estimate the return on investment, or should I rather consider the Net Present Value (NPV) of the project? Whatever I choose, what window of time should I take to gauge the profits: 5 years, 7 years, or maybe 12? Simple, almost stupid questions, and still they matter. For the mildly initiated: the IRR tends to be used mostly for low-risk, long-run projects, whilst the NPV looks better for short-termist, hit-and-run ventures.

By textbook rules, real estate is definitely the first kind: no technological life-cycle, not really much to handle in terms of manufacturing or logistics, sniper-like customer relations. The IRR should be better. Yet, as I did my homework about the local market of residential real estate, the sheer distribution of prices showed me a dilemma in the design of apartments in the course of renovation: you go into structure and design A, and you gain a modest return, not much above the average yield of sovereign bonds, but if you go into structure and design B, you can achieve way higher. The fork of disparity between the two scenarios was such that I decided to settle for NPV as my main metric of profitability. Absolutely against the textbook, but I intuitively guess this is a better measure. As I applied it, an interesting result appeared: whatever the design chosen, the project has any chance to bring a positive cash flow to the investor if and only if he leverages the thing with a mortgage, like 60% of the assets planned. You could ask: how can borrowing, with the obvious necessity to share your profits with the bank, at a fixed rate, enhance the net cash-flow? Well, yes, it can.

There is that other project I am working on, namely the practical implementation of CSR (Corporate Social Responsibility) in a real company. The company’s boss, who happens to be my friend, asked me to assess objectively whether it would pay him off to implement a code of CSR in his organisation. As a social scientist, I almost intuitively assume that what people think is one thing, what they do is another, what they think they do is still different story, and what they say they think they do is the ultimate smokescreen of human behaviour. For me, the current fashion for Corporate Social Responsibility is very much the Bullshido of management. Still, that friend of mine does not really care about theory. He is about to participate in a few serious tenders in his market, and in each of them he can score additional points if his company has a code of CSR effectively in place. Of course, as you can easily guess, the ‘effectively in place’ part is the trickiest one. There are pennies to collect, actually implementing CSR is the way to collect them, and the practical questions are: is it feasible at all, how to do it, and is it any good outside the formal conditions of those tenders? Just if you haven’t spotted it yet: it is about predicting complex human behaviour, in an organisation, and about assessing the economic outcomes of that behaviour. Tough s**t, actually.

There is still another venture I am progressively becoming involved into, in the Fintech. A group of business people is considering the issuance of cryptocurrencies in the stock-market fashion, i.e. they would like to issue cryptocurrencies in the way financial institutions issue various financial instruments: with an Initial Public Offering, and some respectable balance sheet to back up that IPO, with decent underwriting, with book building etc. The only difference in comparison to regular securities is that what will be issued are not securities as such but numerical codes corresponding to the units of cryptocurrency, and there has to be a technological platform to sustain the whole thing. The question those business people ask is very simple, once again: at the end of the day, after we account for the discount at issuance, and for the cost of maintaining the technology, what will be the return on investment? Intuitively, I am thinking about taking the history of Bitcoin (the best documented so far) as a case study, and try to build any kind of bridging to the world of classical financial instruments. Still, this is just an intuition. I am trying to find out the right scientific questions to ask, in order to answer the general business question.

I am nurturing my own business project on a small scale: an educational website in the field of social sciences. That scientific blog I keep, progressively more and more nested in the WordPress environment at https://discoversocialsciences.com , and still mirrored at http://researchsocialsci.blogspot.com makes some sort of core, which I want to build that project around. The blog serves me to make my hand as for the style, and the general drill of producing content. I am applying that principle, anecdotally attributed to Pablo Picasso, namely that when inspiration comes, it is likely to find me at work. For the moment, I have very loose ideas as for this project. I haven’t even formalized my paid content yet. I suppose I will start with some sort of crowdfunding, Patreon or other beast, and then move towards something more organized.

The questions I have in my mind, regarding this project, sum up to a loop between ‘who?’, ‘what?’, and ‘how?’. How do I want to interact with my customers? What exactly will be my product? Who will be my customer? Initially, I am thinking about providing additional, educational resources for students. By ‘additional’ I mean that what I will be offering will be a step further beyond textbooks, rather than the typical textbook content. Probably it is because I have always been bored to death with textbooks. I have always preferred to discover things by myself. Yes, it involves getting some dirt on my hands, but it is a lot more fun, and this is the kind of fun I want to provide my customers with: the fun with doing research in social sciences, where doing research is training, in the same time. Thus, the kind of interaction I want to have with my customers consists in involving them in doing research, where I will mentor them. Here, I come to the ‘what?’, and, honestly, I still don’t know what the ‘what?’ is going to look like, in details. I know, for sure, that I want to go for real stuff, i.e. the actual, important, social issues, not the distilled type of problems you can find in textbooks. If I want to learn how to hunt grizzly bears, I have to learn with someone who actually hunts them, not with a rat catcher.

Finally, there is that last one, and in the same time the biggest. I have been working, all the 2017-long, on technological change, and, more particularly, on renewable energies. I have been progressively becoming aware that there is an actual, huge business building up around the topic: smart cities. In Europe, the idea is really gathering speed. Recently, I did some research about the ‘Confluence’ project in Lyon, France. This is another smart city – or rather smart district in a city – being built in parallel with similar ones popping up in Vienna and Munich. As I had a provisional glimpse of the thing, the speed and magnitude of business and investment projects going on there is just appalling. I think I am going to devote much of my time until this summer to preparing and promoting a business plan for entering the business of smart cities. Here, once again, I am at the stage of figuring out what are the right questions to ask.

Those last months, I tend to think that fundamental questions are the best ones to ask. Additionally, I had that funny kind of New Year’s Night, at an emergency ward, in my district hospital, under an intravenous drip, with symptoms of acute food poisoning. The main suspect is a piece of herring with unclear curriculum. This is the thing with us, Polish people, and the herring: this is really tough love. Anyway, that night spent with a cannula stuck in my left hand’s veins, it made me really consider life under a different angle. The angle was really different. I asked my friends around: no one was seeing their drinks from underneath, only I did. Ketanol was the dashiest one. Such situations tend to push you back to fundamental questions, like: what is the point of being here and now? Now, you could legitimately wonder about the connection between the doings of a vicious herring, and social sciences. Well, I am wondering what are the most fundamental questions of social sciences, when said sciences are being applied to real-life, practical situations. This is the kind of questions I would like my students to know how to ask.

I ask ‘What kinds of things are happening around me and how are they happening?’. In each of the situations I have just described, I am kind of tuning my brain on some specific distinctions: prices of real estate and their distribution over time and across space, cash flows, patterns in human behaviour regarding ethical norms, creation and evolution of monetary systems, ways of entering the general field of smart cities, and, finally, the number of drops that an intravenous drip produces per minute. When I am wrapping my mind around something new, the wrapping means finding the right categories to distinguish in the given situation. How can I know my distinctions are the right ones? There is only one way to know it, baby: by trial and error. Make some distinctions, apply them in the situation at hand, figure out whether they make you advance any further, draw a bottom line, repeat the whole sequence until you reach a subjective state of intellectual satisfaction. There is a useful scientific method of checking if my distinctions are accurate for the piece of real life I am currently dealing with. Just check if you can deconstruct, out of that piece of real life, meaningful sequences of things you have distinguished. If you can build a sequence of events, and that sequence is kind of relevant for the situation, the way you describe those events is probably accurate. Not necessarily accurate at 100% – it never is – but accurate just enough to set a path for further action. The scientific bottom line of this question is to transform experience into data, which, in turn, can be subject to mathematical modelling. Yes, I like maths. They are logic expressed without bullshit.

Then, in a second step, I ask ‘How is the way of happening in the thing A connected to the way of happening in the thing B?’. In terms of formal scientific tools, there is a cartload of statistics to apply here, and this is the right track. Still, my own path of thinking, and the path of thinking I would like my students to follow, is even more fundamental. When I study the connection between happenings, I want to know, for example, whether the thing A kind of pulls the thing B out of inexistence, or does it rather push the thing B out of the world? If reality had a DNA, and if that DNA reproduced itself by recombination (which requires female realities and male realities, and this is really interesting), my two things, A and B, would be like two genes, which can be switched on or off. In one possible scenario, when the gene A gets activated, the gene B follows, and it is like 1 -> 1. In another scenario, the activation of A automatically switches B off, or 1 -> 0. On the long run, the kind of gene B that gets deactivated frequently, gets kicked out of reality, into a dormant state, as there is no respectable, female reality who would like to give birth to baby realities with B in their genome. This is how some phenomena, like burning the alleged witches at the stake, get kicked into a blissfully dormant state, at least until some idiot has the idea of waking them up. Easier than you think, to wake those monsters up, by the way.

If, on the other hand, phenomenon B gets activated in noticeably many cases, it kind of gets knighted into the reality, and it acquires the privilege of activating or deactivating other genes in reality. This is how small, functional interactions between measurable occurrences can lead to structural changes in the social system (see, for example: Krugman 1991[1];  Krugman 1998[2]). That approach to functional connection between phenomena is slightly different from what you can learn in you class of statistics, yet there is a lot on common between the two ways of apprehending coincidental change. Statistically, we can talk about correlation between phenomena, when they swing away from their statistically expected states by a similar distance in the same time, or about their mutual cointegration, if they swing at the same rhythm, i.e. with the same length, amplitude, and frequency in their respective cycles. What I am the most interested in, in my research, are those specific cases of correlation and/or cointegration, when the functional connection at hand leads to one of the connected phenomena suddenly shifting into a qualitatively different state (into a different equilibrium, as we could say in economics).

Now, I ask my third BIG question: ‘So what? Does it matter at all? How does it matter?’. Things matter to me, when they change I do things. Things matter to the society, when they change the way people do things. Thus, in all the multitude of coincidentally changing things, in their correlations and cointegrations, in the way they affect the genetic code of reality, there are changes that involve our behaviour. These ones matter. If the given piece of reality switches its genes into the ON position, what will I do? How can some patterns of behaviour become dormant? How can they be revived (and, bloody hell, is it a good idea to revive them)? What should I do? What will other people do? What do I expect them to do? This is when social science becomes the real fun. I look for that tiny little correlation, which makes my behaviour wise or aberrant in the given situation. I look for those little signals, predictors of wonderful progress, or, in more complex a situation, of deplorable recess.

[1] Krugman, P., 1991, Increasing Returns and Economic Geography, The Journal of Political Economy, Volume 99, Issue 3 (Jun. 1991), pp. 483 – 499

[2] Krugman, P., 1998, What’s New About The New Economic Geography?, Oxford Review of Economic Policy, vol. 14, no. 2, pp. 7 – 17