There are many ways of having fun with that experiment

My editorial

I am designing an experimental environment for products based on digital technologies. I am coalescing my thoughts around the type of digital products that I am particularly focused on those last weeks, namely on the FinTech. I started sketching the contours of that experimental environment in my last update in French (see Une boucle de rétroaction qui reviendra relativement pas cher ). Now, I am rephrasing the idea in English and giving it a bit of an extra spin. There are two groups, the geeks and the ordinaries, I mean a group of k software engineers – or k teams of them, the unit is to settle later – and a group of n users, allegedly prone to being more or less enthusiastic about FinTech products. The first round of the experiment starts as the users are asked to make a set of financial decisions in a controlled environment. By controlled environment I mean a standardized budget, e.g. €10 000, and a standardized set of possible uses for that small fortune. It would be good to have pretty a wide range, as uses come, covering typical saving (holding liquid capital for future, unspecified uses), investment (buying future possible gains with the present allocation of liquid capital), and typical consumption (e.g. sex, drugs, and rock’n roll).

The users make their financial decisions in that controlled environment. The software engineers are then being presented with various types of data about those decisions. ‘Various’ can range from simple reports like ‘50% of the users decided to invest not less than 40% of their budget into the Smartest President’s next business’, through more and more elaborate data on individual decisions, taking a slight turn by a detailed database of those individual decisions, all the way to the data from behavioural observation of the users (i.e. sequence of clicks, sequencing of the decision-making process, speed and pace of the corresponding sequence, eye-tracking, pulse, breathing, don’t-put-that-thermometer-where-we-can-both-regret-it etc.). Yes, you guessed correctly: we have been just experimenting with the users, and now we are experimenting with the software engineers. What type and what amount of data will be actually useful for them? If they can choose the type and the scope of information provided about the users, what their choice is going to be? From then on, what their response is going to be? ‘Cause now, the software engineers are to present prototypes of FinTech products, as well adapted as possible to what they know about the users.

Here’s the little caveat: the users are not supposed to tell the engineers what they want. There is just objectified observation. Why? Well, what I want is to simulate, in that experimental environment, the real working of a market, only faster. In real markets of standardized digital products, engineers seldom speak in person to the end users. They have reports, and they base their invention on it. In my experimental environment, each engineer can pick and choose the parcel of information for takeaway, and then calibrate their engineering response accordingly.

In that first round of the experiment, I arrange an interaction between the users of FinTech products and the engineers supposed to create those products. At this stage, the experiment is already informative at many levels. At the first and most basic level, it tells is about the patterns of financial decisions observable in users, and about the way they make those decisions. One level higher, I encompass both the data on users and the data on the engineering response, and what I get is knowledge about the interactive engineering process itself. In the presence of the given set of data on users, their decisions, and their decision making, how quickly did the engineers respond by producing prototypes of FinTech products? What is the typical pattern of utility in those products, and what are the idiosyncrasies in individually created FinTech solutions? I go one level higher in the generality of observation, and I am connecting the type and scope of data on users with the pattern of engineering response. Did richer a set of information on the users’ behaviour contribute to create more sophisticated FinTech products, i.e. did those engineers, who chose to work with more extensive an information about users, create something clearly sticking out of the crowd as FinTech solutions come? Maybe there is a golden path of wisdom, as regards the amount of data on users, somewhere between a cursory report about what percentage of them chose to buy government bonds, and extensive a report on how was their body temperature changing during the decision-making process.

Besides the amount of data collected about the users’ behaviour, there are other dimensions in that experiment. The specific profiling of users is, of course, one of them. How does the whole process work with users of different age, economic status, sex, education, professional walk of life and whatnot? Another dimension is the relative complexity of the users’ behaviour, on the one hand, and the complexity in engineering response, on the other hand. Up to a point, the more people we have having a certain type of idea, the more distinct ideas are being crystallized. In a sample of 100 users we can probably come up with greater a number of distinct patterns in the making of financial decisions than in a sample of 10 users. Still, diversity in users is not just their sheer number, it is also the relative disparity of their characteristics. If, in a sample of users, we put people with high school degree, and those with their college diploma hung on the wall, we come with a certain diversity in decision-making. Now, we add those with Master’s degrees. What has changed? Did the addition of one more level in education change the diversity in the making of financial decisions? Now, we can experiment at the same level with other characteristics, and each time the question is the same: how does the relative complexity in the population of users reflect in the relative diversity of the observable patterns in their financial decisions, and in the process of making those decisions?

Just to return to real life, which is sometimes useful in science, I remind: in any type of business, the supplier has to deal with various degrees of complexity in their target market. I am a software company from Romania, I had come up with a super-cool payment platform in the Romanian market, and now I am trying to see the big picture, i.e. the Chinese market. I will be interacting with much bigger, and potentially more diverse a population of customers. How should I change my engineering processes in order to cope efficiently with the situation? This is precisely the kind of questions that my experimental environment can give an answer to.

We go one step further, and we can test the link between the above-mentioned complexity in the set of users’ characteristics, and the complexity of the solutions prototyped by engineers. If I add one more social stratum to the sample of users, does a constant population of engineers come up with more of distinct prototypes of FinTech products? There is another path going (somewhere) from this point: if I keep the complexity observable in users constant, and I change the diversity in engineers, how will it affect the creation of prototypes? In the presence of a constant sample of 100 users, with their descriptive data, will 50 engineers, each working independently, generate greater a diversity in prototypes than 10 engineers? Once again, is there any golden path of optimization in that proportion? What if, besides varying the number of engineers, I shift between various types of diversity in their characteristics, e.g. the number of years spent in the professional engineering of software?

In all that experimenting, and shifting diversities, an economic equilibrium is timidly raising its head. What does an economic equilibrium’s head look like? Well, you can take the Marshallian equilibrium as a good example: two convex curves, crossing at one point, so it is like a Guinea pig with a pair of pointy ears, pointing slightly outwards. This could be something like a rabbit, as well. Anyway, each given set of characteristics in the sample of users, combined with a set of characteristics in the sample of engineers, produces an outcome in the form of a set of prototypes in FinTech products. A set of FinTech products, in turn, generates a certain stream of financial gains for users – like the money they save on paying for electricity or the money they gain from an investment portfolio – and a stream of revenue for the supplier of FinTech. The latter earns money in two basic patterns: either by collecting a small margin on each transaction performed by the user, or by charging the user a fixed monthly fee for the access to the given utility. I suppose that a well-rounded prototype, in a FinTech utility, should contain those components.

Thus, in my experiment, I have the following, compound variables, modifiable in my experimental environment:

  1. a vector UR = {ur1, ur2, …, urm} of m characteristics observable in users;
  2. a vector EN = {en1, en2, …, enk} of k characteristics observable in engineers;
  3. a vector EN(UR) = {en(ur)1, en(ur)2,…, en(ur)l} of l users’ characteristics actually used by the engineers in their work; in the experimental environment this vector can be either exogenously constrained or freely shaped by the engineers themselves;
  4. a vector FU(UR) = {fu(ur)1, fu(ur)2, …, fu(ur)p} of p functional utilities available in those FinTech prototypes; you know, the number of clicks (or screen touches) you need to transfer all your money to the Caiman Islands etc.;
  5. an aggregate stream PF(UR) of users’ payoffs from those FinTech prototypes presented; it can be further decomposed as a vector, namely PF(UR) = {pf(ur)1, pf(ur)2, …, pf(ur)z} of z users’ payoffs, but in my basic scheme I want to use it as an aggregate stream;
  6. an aggregate stream P*Q of revenues that the suppliers of those prototyped FinTech products can derive from their exploitation; once again, P*Q can be also represented as a vector P*Q(UR) = {p*q(ur)1, p*q(ur)2, …, p*q(ur)ß} of ß specific streams per user or per utility, but in my basic model I want to keep it simple;
  7. an aggregate stream EC of engineering and maintenance costs, in suppliers, connected to the basket of FinTech prototypes proposed; again, translation into a vector is possible, although not always necessary;

Now, my basic economic equilibrium, if I keep it more or less Marshallian (you know, the rabbit), would be something like: [P*Q – EC] -> PF(UR). It means that there could be some combination of my experimental variables, which makes the aggregate profits, on the part of suppliers, tend to equality with the aggregate payoffs in users. That Marshallian approach has some catches in it. It supposes the existence of an equilibrium price in FinTech products, i.e. a price that clears inventories and leaves no customer with unsatisfied demand for FinTech utilities. This is tricky. On the one hand, in finance, we assume the existence of at least those most fundamental equilibrium prices, like the interest rate or the discount rate. It was observed already in the 18th century, by Adam Smith, among others, but, interestingly enough, not earlier. When I read that book by Jacques Savary, published in 1675, entitled ‘Le Parfait Négociant ou Instruction Générale Pour Ce Qui Regarde Le Commerce’ (in English: ‘The Perfect Merchant or General Instructions as Regards Commerce’), equilibrium rates in finance tend to be considered as a comfortable exception than as the general rule of the market. Still, here, in FinTech, we are at the junction of finance and digital technologies. In IT, people don’t really like talking about equilibrium prices, and I can hardly blame them, ‘cause the actual diversity in IT utilities is so great that we can throw the assumption of homogenous supply through the (metaphorical) window as well.

Anyway, I can optimize my experimental environment regarding the Marshallian equilibrium, or any other economic equilibrium, actually. I can make a function [P*Q – EC] = f[UR], i.e. a function linking the aggregate profits in the suppliers of FinTech products with the observable complexity in the population of users. I can do the same with the complexity observable in the population of engineers. There are many ways of having fun with that experiment, at this stage, and this is just the first stage. There is more to come. The engineers come up with a range of prototypes in FinTech, and they present them for testing. The users test those prototypes, in an experimental environment, and once again we apply the same philosophy: the users are supposed to be informative by their behaviour, not so much to talk about their impressions.

An interesting question arises, namely that of learning. If I am one of those users, and I am supposed to test those prototypes devilishly, I mean like 6 prototypes tested over 6 days, with 6 cups of coffee in terms of neuro-logistic support, I can’t help learning. At my 3rd prototype tested, I will be using, consciously or not, whatever, the experience accumulated with the testing of prototypes 1 and 2. Thus, what I will be really testing, will not be each prototype separately, but a sequence of prototypes, associated with a process of learning. Someone could say that learning creates a harmful bias (believe me, there are people who think so), and so the edge of learning can be blunted in two basic ways.

Firstly, the experimental environment can take away from me any feedback on the financial decisions I make with a given FinTech utility. When we do something about money, we like seeing the outcomes, it is kind of attached to money in its cultural code. You take away the feedback on results, and, by the same means, you take away the incentive to learn. We could think about such an experimental environment, and yet, I have like a little voice in the back of my head (no, I am perfectly sane) saying that financial decisions without feedback are not really financial decisions. That would be an experiment biased by the absence of bias. Secondly, I can make each user test just one prototype. There is no opportunity for learning in this case. Still, we need to split the sample of users into as many subsamples as we have prototypes to test, and each has to somehow representative.

Now, do I really want to take learning out of the equation? Do I expect the users out there, in the big, wild market of FinTech, using those apps without learning anything? I just think it could be safer to assume that people learn things. What we should be really testing, at this stage of the experiment, could be precisely the phenomenon of learning, with respect to FinTech.

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?

Experimenting with new structures

My editorial

I return to doing my review of literature in order to find accurate distinctions as for historical changes. Besides those articles on the history of technology as such, I found an interesting paper by Bert J.M. de Vries, , Detlef P. van Vuuren, and Monique M. Hoogwijk (de Vries et al. 2007[1]). It is interesting because the authors attempted to assess the potential for producing renewable energies for the first half of the 21st century, and they did it in 2007, so exactly at the moment when, according to my calculations, something really changed in the sector of renewable energies and it started developing significantly faster than before. It is interesting to see how did other researchers see the future at a moment, which is already the past, and quite a significant bit of past. As usually, I start from the end. The kitchen door I can poke my head through, regarding this paper, is Appendix B, presenting in a concise, tabular form, the so-called four scenarios developed by the authors. So I rummage through those scenarios, and one thing instantaneously jumps to my eye: the absence of the solar-thermal technology. Today, the solar-thermal is probably the most powerful technology for producing renewable energy, yet, when this paper was being written, so not later than in 2007, the solar-thermal technology was still in its infancy. It was precisely in 2007 that the World Bank declassified and published first reports about the big Ouarzazate project in Morocco, and only in 2011 it became more or less clear how to finance those projects. As I am engaged in research on renewable energies, I will probably mention that solar-thermal technology more than once, and for now I am content with noticing that solar-thermal changed a lot in the way we perceive the future of renewable energies.

Returning to this paper by Bert J.M. de Vries, , Detlef P. van Vuuren, and Monique M. Hoogwijk, and to their scenarios, I see four basic technologies being taken into account: wind, solar photovoltaic, biomass electric, and liquid biomass. As a matter of fact, we have another technology, besides the solar-thermal, which strikes by its absence at this precise party: the hydro, in its many shades. Right, probably the authors did not have access to the relevant data. I continue studying this table in appendix B, and what strikes me as odd is that each technology is described with a different set of quantitative variables. It is easy to see that the authors had hard times to drive different technologies to common denominators. As I stop clinging to that Appendix B and browse through the whole paper, I can see a lot of maths but very few numbers. It is hard to say, what was exactly the empirical basis for the scenarios presented. On the whole, this paper by de Vries et al. is interesting as for some methodological insights, but a little disappointing on the whole. I can see nothing that looks like real discovery.

And so I am drifting towards other sources, and I come by an interesting author, David Edgerton and his book ‘Shock of the old: Technology and global history since 1900’ (Edgerton 2011[2]). I am fishing the most interesting bits out of this study, and I find some insights worth stopping by and think. First of all, David Edgerton points out that we commonly live in an illusion of constant technological progress, i.e. of a process, which consistently brings improvement in the conditions of living, as technology changes. Edgerton shows, quite convincingly, that technological change is not necessarily to put at equality with technological progress. According to his findings, there were just a few periods of real technological progress since 1900: between 1900 and 1913, followed by another between 1950 and 1973. Save for those short windows in time, the link between technological change and the conditions of living is really ambiguous.

In Section 4 of his book, entitled ‘Maintenance’, David Edgerton brings forth that intuition that I very much share: that the way we deal with both the physical wear and tear of our technology, and with its moral obsolescence, is deeply significant to our overall well-being due to technology. There is that interesting thing I noticed in Chinese cities, this summer: Chinese people do hardly any renovation in buildings. Save for those occasions when a store is being replaced by another and the facade needs some obvious patching, buildings in China seem to live a life similar to that of clothes: when they are used up, they are being demolished and something new is being built in the same location (they do put a lot of effort into maintaining infrastructure, mind you). In Europe, we are almost obsessively attached to maintaining our buildings in good condition, and we spend a lot on renovation. Two cultures, two completely different roles of maintenance in the construction business. At this point, the research presented by David Edgerton gets strong support in earlier work, by the eminent French historian Fernand Braudel (Braudel 1981[3], 1983[4]) : real technological revolutions happened, in many fields of human ingenuity, when the technology in place allowed providing for more than simple maintenance. Both authors (Edgerton and Braudel) point on a turning point in the history of agriculture, probably by the end of the 19th century, when the technology of farming allowed to spend relatively less effort on rebuilding the fertile power of the soil.

Another great insight to find with David Edgerton is the importance of killing in technological change. Warfare, hunting, whaling, combatting pathogens, eradicating parasitic species from our agricultural structures – it all means killing, and it apparently has had a tremendous impact on technological change in the 20th century. This dramatic and deep reference to one of our fundamental functions as organisms (yes, my dear vegans, we all kill someone or something, even without knowing we do) makes me think about this elaborate metaphor of struggling civilisations, to find in Arnold Toynbee’s ‘Study of History’[5]. Development of societies is not an easy game: it is struggle, risk, sometimes stagnation. We, in our civilisation, as it is now, at the beginning of the 21st century, have no grounds to assume we are different than those who struggled before us. Arnold Toynbee coined this metaphor of struggle in an even broader context: how can we distinguish between civilisations? His point was that criteria like race, skin colour or even religious beliefs are misleading in classifying cultures and societies. To him, the real distinction pertained to the state of the struggle, so to say: is there a visible breakthrough in the past of the given social group, a kind of dramatic threshold which made those people take a completely different turn? If the answer is ‘yes’, then we could very well have two different civilisations, before and after that pivotal point in time. If no, we have, in fact, the same cultural paradigm, just dressed differently according to the current fashion.

All the review of literature about the history of technology, and about renewable energies, makes me ask once more that fundamental question: what exactly happened in 2007 – 2008, when the market of renewable energies suddenly moved its lazy ass and started growing much faster than before? Hypothesis #1: it was mostly technological a change, possibly connected to the banalisation of photovoltaic modules, or to the concurrent emergence of the solar-thermal technology. Hypothesis #2, just a shade away from #1 is that we are talking about technological change in the immediate vicinity of renewable sources of energy. Maybe something about electronics, nanotechnology, transport? Here, although the hypothesis is really interesting, I am very much at a loss. I will have to do some book-worming about it. Hypothesis #3: what happened in 2007 – 2008 was mostly social a change. Something shifted in our patterns of being together in society, which made people turn towards renewable energies more than before. That something could pertain to urbanisation, density of population, food deficit, velocity of money and whatnot. I have already done a lot of empirical testing in this respect.

Now, I am shifting my focus slightly, onto my personal experience in innovating, namely in learning the Python. As I have been wrestling with the Python, those last four days, I became aware of a pattern in my behaviour: I start with trying to make the new technology do exactly the same things that the old technology used to do for me, then I progressively become aware that I need to learn new skills in order to get those results (so I have that strange connection: new technology, old expectations regarding the outcomes, new skills to learn), and I also become aware that I need to reshuffle my resources, namely information, so as to squeeze it into new structures. Now, I am advancing one step further and I try to go the other way round: I had a look at what the logical structures of the Python look like and what I am trying to do now is to discover what can possibly the Python do for me. In other words, I am progressively passing from using Python as just a harder-to-tame version of the Excel towards discovering what it can do for me.

I am playing with one of the first things I found in the ‘Tutorial’ textbook, available at, namely with the structure ‘for n in range(x, y):’ and then you specify what do you want the Python to do while n stays in a given range. Here below is a simple try from my part:

>>> for x in range(1, 10):

            structure={‘x’:(x**2, x**0,5)}











# Honestly, I don’t even know why I formed this precise function. It just came like that. I imagined a structure when a cardinal order is established, from 1 to 10, and each point in the order has two coordinates derived from its position: square power and square root. It is nice and it didn’t exactly work as I planned. I got a ten-time repeated [‘x’]. As I put that ‘x’ in single quotes, it has been considered as a word, or a piece of content. Now, I modify slightly my structure: I took off those single quotes. Here is the result: 

>>> for x in range(1, 10):

            structure={x:{x**2, x**0,5}}











#Better a small step forward than no steps at all, as they say. After having removed those single quotes from around the x, I got a cardinal order in response. Still, the function did not yield the coordinates I expected to have out of it. Now, I replace the command ‘list()’ by the command ‘print()’. So far, I did not quite grasp the distinction between those two. I decided to experiment in order to acquire some knowledge, and here comes what I got:

>>> for x in range(1, 10):

            structure={x:{x**2, x**0,5}}


{1: {1, 5}}

{2: {1, 4, 5}}

{3: {9, 5, 1}}

{4: {16, 1, 5}}

{5: {25, 5, 1}}

{6: {1, 36, 5}}

{7: {49, 5, 1}}

{8: {64, 1, 5}}

{9: {81, 5, 1}}

#Now, this is most interesting. As I replaced ‘list()’ with ‘print()’, the function finally yielded the coordinates. There is just one tiny little problem: instead of having just two coordinates, in 8 cases on 9, starting from the cardinal 1, I have three coordinates instead of two. Why? Oh, I see. I put ‘0,5’ instead of ‘0.5’. For the Python, decimal values are accepted as such if the decimal point is literally the point, not the coma.  The expression ‘x**0,5’, which I meant to be x power 0,5, has been read by the Python as x power 0, followed by a 5. OK, I understand. My mistake. I replace the ‘0,5’ by ‘0.5’, by the book, and I launch it again. 

>>> for x in range(1, 10):

            structure={x:{x**2, x**0.5}}


{1: {1}}

{2: {1.4142135623730951, 4}}

{3: {9, 1.7320508075688772}}

{4: {16, 2.0}}

{5: {25, 2.23606797749979}}

{6: {2.449489742783178, 36}}

{7: {49, 2.6457513110645907}}

{8: {64, 2.8284271247461903}}

{9: {81, 3.0}}

#Finally, it worked. Now, I push my experiment further and I introduce a third coordinate, the natural logarithm of the cardinal position.  

>>> import math   #I turn on the module with mathematical functions

>>> for x in range(1, 10):

            structure={x:{x**2, x**0.5, math.log(x)}}


{1: {0.0, 1}}

{2: {0.6931471805599453, 1.4142135623730951, 4}}

{3: {9, 1.0986122886681098, 1.7320508075688772}}

{4: {16, 1.3862943611198906, 2.0}}

{5: {25, 2.23606797749979, 1.6094379124341003}}

{6: {1.791759469228055, 2.449489742783178, 36}}

{7: {49, 2.6457513110645907, 1.9459101490553132}}

{8: {64, 2.8284271247461903, 2.0794415416798357}}

{9: {81, 2.1972245773362196, 3.0}}

Good. It seems to work. What I have just succeeded to do is to learn a typical, very simple structure in Python and this structure does something slightly different than the Excel: it generates a sequence of specific structures out of a general, logical structure I specified. I am wrapping up my learning from my learning: it took me four days to start looking actively for new possibilities offered by the Python. If I extrapolate to the scale of collective behaviour, we have those four patterns of innovating: a) trying to obtain old outcomes with the new technology b) making mistakes, seeing my efficiency plummeting, and learning new skills c) rearranging my resources for the new technology d) experimenting and exploring the new possibilities, which come with the new technology. As I refer this generalized account of my individual experience to the literature I quoted a few paragraphs earlier. How does a breakthrough occur in these specific patterns of behaviour? I can assume there is a critical amount of learning and adaptation, required in the presence of a new technology, which can possibly, referring once more to Arnold Toynbee’s metaphor of struggling civilisations, make a technological transition risky, impossible or null in its balanced outcomes.

[1] de Vries, Bert J.M., van Vuuren, Detlef P., Hoogwijk Monique M., 2007, Renewable energy sources: Their global potential for the first-half of the 21st century at a global level: An integrated approach, Energy Policy, vol. 35 (2007), pp. 2590–2610

[2] Edgerton, D. (2011). Shock of the old: Technology and global history since 1900. Profile books

[3] Braudel, F., 1981, Civilization and Capitalism, Vol. I: The Structures of Everyday Life, rev.ed., English Translation, William Collins Sons & Co London and Harper & Row New York, ISBN 00216303 9

[4] Braudel, F., 1983, Civilisation and Capitalism. Part II: The Wheels of Commerce, trans. Sian Reynolds, Book Club Associates, William Collins Sons & Co,

[5] Toynbee, J. Arnold. Study of history. University press, 1946, pp. 69

Theorizing and learning by doing, whilst wrestling with Python

My editorial

And so I am experimenting with my thinking. I am writing about choices we make regarding new technologies whilst placing myself in a situation of absorbing a new technology: The Python. I am talking about that programming language, reputed for being particularly useful in studying quantitative data. I thought it would be interesting to write about people’s choices whilst making a choice about new technology. Just to be precise: the variety of Python I am enduring my pains of neophyte with is Python 3.6.2, and besides using the materials provided at the standard library of the Python I am using a book by Toby Segaran, entitled ‘Programming Collective Intelligence’, Published by O’Reilly Media, Inc. (ISBN-10: 0-596-52932-5; ISBN-13: 978-0-596-52932-1). I want to discover a few things. Firstly, I want to rediscover the process of discovering something new. It has been a long time since I really had to absorb some brand new set of skills and understandings. I recently became aware of that: I am writing about innovation and technological change, about whole communities switching to renewable energies but with all that, I am not absorbing any true novelty. Secondly, data science becomes kind of adjacent a discipline for social sciences, and I wanted to discover what can I get more out of Python that my statistical software (i.e. Wizard for MacOS) does not provide. So I want to discover new possibilities, and I want to rediscover what is it to discover new possibilities.

I really started this experiment yesterday. As I am recollecting what I have been doing, yesterday and today, a few patterns emerge. As I mentioned it, I started under a twofold impulse: an idea connected to my research and writing, for one, and sort of a vague peer pressure, for two. Being my own guinea pig, I assume I am somehow representative, and so I peg down those two motivations: curiosity awoken by current activity, and peer pressure, also connected to the present activity. As a matter of fact, I started with implementing Python in my current activity, i.e. in research. I tried to use the basic notations for expressing deterministic choice (see ‘Qu’est-ce que ça fait d’être mon propre étudiant, avec Python’). Then, I used Python to meddle with quantitative data, the one in the CSV format. I played with building strings and sets of values out of that format. I am progressively getting acquainted with the distinction between names, defined in single quotes (i.e. ‘x’), and values without quotation marks. Anyway, I took something I already do with the tools I already have, and I am trying to do it with Python. It is awkward, it is slow, it involves a lot of trial and error. I have to solve new problems. I am confronted with something apparently smart (the Python), which returns dull, preformatted answers in the case of failed attempts from my part. Tons of fun, in other words. Here, I have a second pattern, important for understanding innovation: every time we absorb a new technology, we kind of insert it into our current life and we keenly observe what changes under the impulse. That change involves an initial drop in efficiency. What I am trying to do with Python now, I do much faster and more artfully with my Excel and my Wizard combined.

The drop in efficiency I am experiencing is very much connected to the logic I have to use, or, in other words, to the structure of information. In Excel, I have tabularized structures. In Wizard, I almost don’t pay attention to the raw data, previously imported from Excel, and I focus on analysis, which involves picking up, by clicking, from pre-formatted lists. Anyway, both in Excel and in Wizard, I have a two-dimensional structure, where I move mostly graphically, with a mouse or a touchpad. In Python, I have logical structures akin to typical writing. I have to type all those structures by myself, or copy from Excel and format into the written way. I am becoming aware, I mean really aware that what I used to perceive as adjacent columns or rows of data are, in fact, separate logical structures linked by some kind of function. I encounter two types of challenges here. The most elementary one is the shift from reversible to irreversible. In Python, in order to have any logical structure usable in subsequent operations, like a string of names or a set of values, I have to validate it with an ‘Enter’. Still, after I have validated it, I cannot alter it. If I made a mistake, I have to define a new structure, almost identical to the previous one, save for those corrected mistakes, and to replace it under the same name I gave to that previous one. Defining functions that link different logical structures is much harder for me, at least for the moment.

As I am wrestling with the Python, I recall my early experience with software like DBASE or Lotus, back in the early 1990ies. Those pieces of software were something between the Python and the modern software base on graphical interfacing. I am reconstructing the path of learning I have covered and I am forming another pattern regarding innovation. From DBASE or Lotus, the path has been towards simplification and greater a comfort on the user’s side, whilst creating an increasing complexity on the programming side. What I am doing now, as I am learning Python, is to deconstruct that comfort and simplicity and see what I can get if I rethink and relearn the technology of comfort and simplicity. Just to give you an idea of what I am working with right now (the initiated to Python, I implore magnanimity from your part), I give the example of what I have written kind of on the side, whilst writing what I am writing for the blog. Here it is:

# I want to define a set of people, a set of technologies, and to illustrate the fact of preferring some technologies more than others. So I define my set of ‘people’, as a string: 

>>> people=[‘Geogre’, ‘Chris’, ‘Eleonore’, ‘Kathy’, ‘Miron’, ‘Eva’]

# S***, I made a ‘Geogre’ out of ‘George’; I have to define people again

>>> people=[‘George’, ‘Chris’, ‘Eleonore’, ‘Kathy’, ‘Miron’, ‘Eva’]

>>> technologies=[‘photovoltaic on the roof’, ‘own water turbine’, ‘connection to smart grid’, ‘cycling more driving less’]

>>> scores=(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)

# Now, what I want is to connect somehow the scores to the people and to the technologies. I want to figure out some kind of function, which would put those three categories together. Here below you can see a failed attempt:

 >>> for n in people and for m in technologies return(scores [3])

SyntaxError: invalid syntax << # What did I tell you?

#Now, I am less ambitious. I am trying to create a logical structure from just two categories: people and technologies. I just want to put them in pairs, and label that matching ‘experiments with’. Here is what I did pretty intuitively:

>> for n in range(0, len(people)):

                  for m in range(0, len(technologies)):

                                   print(people [n], ‘experiments with’, technologies [n])

# It did not yield ‘SyntaxError’, which is already a plus. After an additional ‘Enter’, I had this:

George experiments with photovoltaic on the roof

George experiments with photovoltaic on the roof

George experiments with photovoltaic on the roof

George experiments with photovoltaic on the roof

Chris experiments with own water turbine

Chris experiments with own water turbine

Chris experiments with own water turbine

Chris experiments with own water turbine

Eleonore experiments with connection to smart grid

Eleonore experiments with connection to smart grid

Eleonore experiments with connection to smart grid

Eleonore experiments with connection to smart grid

Kathy experiments with cycling more driving less

Kathy experiments with cycling more driving less

Kathy experiments with cycling more driving less

Kathy experiments with cycling more driving less

# Oouff, it worked, for a change.

Simple lesson: coming up with structures that work takes experimentation. Experimentation takes time and energy, and still, in the same time, yields learning. Experimenting with new technologies is a trade-off between the time and energy devoted to experimentation, on the one hand, and the learning we have from it, on the other hand. The next step I am trying to achieve, unsuccessfully as for now, is to connect people and technologies to those numerical values from ‘scores’. Here below, you can find a short account of my failures:

>>> list(scores)

[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

>>> scores=[list(scores)]

>>> scores [3]

Traceback (most recent call last):

  File “<pyshell#12>”, line 1, in <module>

    scores [3]

IndexError: list index out of range

>>> scores[2]

Traceback (most recent call last):

  File “<pyshell#13>”, line 1, in <module>


IndexError: list index out of range

What did I do wrong? Where is the loophole in my thinking? I think I know: what I have been trying to do was to kind of pick up arbitrarily a value from the set of ‘scores’ and connect it somehow to people and technologies. I have been trying to do what I commonly do with Excel: I just sort of tried to match it, as if I were making a table. I was trying to work with the new technology (i.e. the Python) just as I am used to work with the old technology, namely Excel. It does not work. New technology requires new skills and habits. There is that thing about logical structures: the numerical ones are different from the strictly spoken logical ones. Numbers are of different nature than words and letters. If I want to express the preferences of my ‘people’ for particular ‘technologies’, I have to construct a function of preference, i.e. a function which yields a numerical result through a mathematically valid process. At that point, I am becoming aware that I have two alternative logics of constructing such a function: through positioning or through inference.

The first technique consists in ascribing, to my ‘people’ and my ‘technologies’, some kind of coordinates in a virtual space. It is very similar to the Bayesian rectangle: I represent phenomena as a geometrical structure. This structure has a frontier delimiting its expanse, and, possibly, some additional secants, which serve to fine-tune the position of a given point in this space. Positioning looks like the right way to construct a function of preference when position matters. Position is distance of the given point (person, technology etc.) from (to?) some peg points fixed in the given space. In that book by Toby Segaran, I found an interesting reminder about the application of the Pythagorean theorem when it comes to distance in an imaginary space: a2 + b2 = c2. If you have two points, and you ascribe two coordinates to each (actually, this would be a plan, not a space, but who cares?), you can measure the distance between those two points on each coordinate separately, as a difference, then square up those differences, add them up, and take the square root of the so-calculated sum. Summing up, the technique of positioning allows ascribing to people preferences, which result from their relative distance from your peg points or peg axes: conservatism, openness, liberalism etc. The technique of inference assumes that if I have some characteristics X, Y, Z, my preferences are the most likely to be A, B, C and usually those As, Bs, and Cs are the expected values in a statistical distribution. If I am Polish and my friend is French, and France has greater a share of renewable energies in their final consumption of energy than Poland, my Friend is supposed to have stronger a preference for putting photovoltaic panels on his roof, or something in those lines.

I am wrapping up this account of my personal experience with innovation. I use the new technology, in the first place, to reach the same outcomes I commonly achieve with the old technology. I basically squeeze that new technology into the social role I have now. When a lot of people do the same, it means that the new technology, in a first step of collective learning, adapts to the existing social structure. Then, I intuitively use old habits and skills with the new technology, which causes problems, and diminishes my efficiency, and still it is lots of fun, and it makes me learn something new. Once again, as I translate this personal experience into collective patterns of behaviour, the greater the relative distance between the new technology and the old one, the more trial and error, the more learning required, thus the deeper is the trough in efficiency, and the greater is the leap caused by learning for the new technology. Relating it to the old concept of technological determinism by Karl Marx, I can see the weakness of his thinking. We assumed that the greater the economic power of a technology, so the greater possible technical improvements it can bring, the greater power it has to bend the social structure around it. Still, my little experiment brings a new variable: the greater the transformative power of a new technology, the more learning it requires and the less we can predict about how the hell it will change the social structure around it.