Travel in the country of Programming

(In French : Voyage au pays des programmeurs)

I have a bad habit: when I do something, half of my attention is spend in watching the action in progress. This earned me a bad ranking on these tests which evaluates the intellect as the timeliness of responses.

Enjoying a little free time in August, I went back to programming and it allowed me to do a few observations on myself. It is good to remember the episodes where one encounters obstacles (see mon apprentissage de LaTeX): it helps to avoid making the same mistake repeatedly.

Experienced readers will find me ridiculous because I'm neither a professional programmer nor even a good programmer, but I don't mind. Those who believe that programming is an ancillary activity will stop reading me if they never read me, but I don't mind.

*     *

I confess: I love programming. I am a clumsy, inexperienced freshman, but this experience gives me the joy of exploration. When it works, I am delighted to have been able to bend the computer to do what I wanted him to do - it's much more satisfying than using a program written by someone else.

So I decided to program the methods of data analysis that I taught at ENSAE during the 70s. At the time I did not know programming, so I used the programs written by others and it annoyed me. I had to take revenge on this ignorance.

I know that there are excellent software for data analysis and that what I would do would bring nothing new, but my goal was not to launch a new product on the market.

Here, highly condensed, the story of this adventure.

The supidity of intelligence

French version : "L'imbécillité de l'intelligence"

Le Monde has published an enlightening article on the Snowden case (Aymeric Janier, " target=”_blank”>Keith Alexander, le « pacha » de la NSA", Le Monde, July 15, 2013).

37,000 employees, a budget of about $ 10 billion, ultra-powerful computers, the ambition of "intercept everything about everything, everywhere" ... We guess in Alexander's behavior a bureaucratic delirium : who could refuse him budget and computation power after September 11 2001, which has spread this obsessive fear that is the victory of the terrorists ?

Checklist of the information system

French version : Check-list du système d'information

To troubleshoot an engine one must seek first the most frequent failures, then move gradually towards the rarest (first ignition, then the air filter and fuel supply, then the carburetor, etc.). This allows on average to troubleshoot faster.

The "checklist of the information system" allows us to assess the quality of an existing system and prescribe the measures that will "help out". It begins by examining what happens on the workstation of the users, then it jumps to the other end of the information system to review the decision support system. Having taken the SI in a pincer, it moves on its architecture (organization of responsibilities, semantics). Finally, it considers the control of the information system from the point of view of the functional evolution, the technical platform and the economy.

1) Workstation

Operational information system

Have agents often to do manual re-keying? should they, in the same operation, connect and disconnect for various applications?

Are clearances clearly defined? Are the access rights of each agent automatically assigned when he identifies and authenticates its identification? must the agent identify several times in a day?

Is impression management effective? Are the mails sent by the company of good quality?

Are the agents adequately supported by the information system in the performance of their duties?

Does the company perform a periodic survey on the satisfaction of the users of the information system? Are decisions taken following the results of this survey?

An essay on industrial classifications

(Article by Bernard Guibert, Jean Laganier and Michel Volle in Économie et statistique No 20, February 1971)

French version: Essai sur les nomenclatures industrielles.

There can be no economic analysis without a classification. Only a classification can give precise enough meaning to the terms that crop up so often in economic reports - "textile industry", "furniture", "steel industry" and the rest. Classifications play an absolutely crucial role, but they tend to be dismissed as tedious. They consist of tiresome lists with only the occasional intriguing oddity to break the boredom. A classification specialist is seen as a real technology geek, and has to be exactly that to answer the seemingly hair-splitting questions (s)he is faced with every day: should the manufacture of plastic footwear come under footwear manufacture or under manufacture of plastic products? What is the distinction between shipbuilding and the building of pleasure boats? Should joinery be classed as manufacture of wood and wood products or as building construction?

The limit of statistics

In French : La limite de la statistique.

We know that statistics is not appropriate to describe a small population. We can certainly count the individuals who compose it, but it will be virtually impossible to go from description to explanation.

In fact, explanation requires that we find in the statistical observation clues that guide us to causal hypothèses, between which we will choose based on the accumulation of past interpretations provided by the theory.

We find these clues in comparing the distribution of a character between different populations (eg, comparing the structure of age between two countries or between two times in the same country) and in looking at the correlation between characters within the same population.

One can always extract a representative sample in a large population, that is to say that distributions and correlations observed in this sample are not substantially different from those that could be observed on the entire population because the clues they provide lead to the same hypotheses.

Here is the test that will tell if the size of a population is sufficient to interpret its statistical description: that population must be able to be considered a representative sample drawn from a virtual population of infinite size whose structure is explained by the same causes that the population considered.

*     *

Some populations are therefore not "statistisable" (please forgive this neologism). For sure we can count their individuals and calculate totals, averages, dispersions and correlations, then publish it all in tables and graphs: but this morass will be impossible to interpret, we cannot move from this description to an explanation.

This is the case, for example, for much of business statistics: it often happens that the production of a branch or sector is concentrated in a few large companies whose number is too low for this population being “statistisable".

There is a remedy: if it is impossible to interpret a statistical description, we will use the monograph. The search for causal relationships at work in the population will no longer consider distributions and correlations, but consider each individual case in its particular history.

Of course history never provides more than assumptions, because the past is essentially enigmatic, but after all statistics also provides in the best case only assumptions... but they are not of the same nature, and the monograph requires a depth of investigation which statistics does not require.

The world of nature is ultra fractal

In French : Le monde de la nature est ultra fractal

Whatever the scale at which they are considered, fractals have the same degree of complexity. This is for example the case of the coast of Brittany: whatever the scale of the map, it is shredded. Magnify the detail of a fractal reveals a pattern similar to that of the entire drawing.

Examination of a natural object - whether the whole universe or a speck of dust - let appears, when the scale is changed, a series of views of the same complexity - but unlike Fractals they are not similar.

Consider the universe. Its geometry is non-Euclidean (space is curved). At the level of everyday experience the geometry is Euclidean. We find clusters of molecules in the grain dust examined by electron microscopy. Later we will encounter atoms, then the probability waves of quantum mechanics. Farther particles appear. We could go on, we could also select other scales ...

In the thinnest detail of nature lies, as in a fractal, a complexity which is equivalent to that of the whole. However each of the scales follows a peculiar geometry. This adds another kind of complexity to the complexity of the fractal. Nature, being essentially complex, is "ultra-fractal."

*     *

The examination of any physical object - your hand, a pencil, a speck of dust - can never achieve a detailed and complete description. This despairs those whose only absolute knowledge can satisfy the thirst for knowledge, but "absolute knowledge" is a mirage consisting of words that should not be juxtaposed.

The destiny of every human being is the both comic and tragic theater of the dialectic between the ultra-fractal world of physical, human and social nature and the world of values that animate his intentions. . This dialectic is the action.

To act he doesn't need an absolute knowledge: he needs only a relevant knowledge, that is to say a knowledge that is adequate to the action he intends to achieve. This knowledge, being expressed in a finite number of concepts, will always be simple compared to the complexity of nature.

The concepts that are necessary to driving - identifying obstacles and signals, anticipating the behavior of other drivers - select for example, in the complexity of the visual spectacle, a finite number of events. It is the same for all our actions: explicit thought is always simple and it is best to reserve to nature the qualifier "complex" ("complex thought", expression dear to Edgar Morin, is an oxymoron) even if a thought can be complicated in the sense that its acquisition requires a long apprenticeship. By cons the process of elaboration of thought is complex because the brain belongs to the world of nature.

*     *

The contrast between the simplicity of thought and the complexity of nature invites to postulate that this complexity is unlimited (that is to say not only infinite as a right line, but without limit). This hypothesis is an axiom because we can neither prove it nor demonstrate the opposite.

This axiom has the consequence that any mathematical theory, that is to say any logical structure based on a non-contradictory battery of axioms, is the model of a phenomenon belonging to the world of nature: thus the non-Euclidean geometries, created as an exercise in pure logic, provided subsequently a model for the geometry of the cosmos). If this was not the case, this theory would be a limit to the complexity of nature.

A mathematical theory can wait a long time or even never encounter the phenomenon it models because, being not revealed by experiment has revealed, this phenomenon remains is buried in the complexity of nature: but we are sure it exists. This confers to the math a radical realism, with an obvious caveat: if any mathematical theory models a phenomenon, it does not model all phenomena. It follows that the ambition of a "Theory of Everything" in physics is a mirage.

Two utility functions

In French: Deux fonctions d'utilité

The classical economic theory (Debreu, Theory of Value, 1959) is based on three elements: the utility function, the production function, the initial endowment that divides ownership of the goods among actors. Starting from these elements one can deduce the vector of relative prices that, directing the exchange, guides the economy to a Pareto optimum.

The elegant simplicity of this model provides it a great power. The general equilibrium is detailed in partial equilibrium, each market being the scene of a supply and a demand. This can be completed by introducing the time: the production function is then modified by the investment.

In "A Suggestion for Simplifying the Theory of Money" (Oxford University Press, February 1935) John Hicks proposed however to fill a gap in this theory. He noted that each agent has actually two utility functions: one describes the satisfaction that comes from consumption, and the other one describes the satisfaction that gives him the possession of a patrimony.

Assets can be classified according to their liquidity. Money, which is the pure liquidity, is readily usable and implies no risk but provides no income. Illiquid assets provide an interest or a rent, but their price is changing: their possession presents a risk of depreciation.

Creative intelligence

(Tranlated from: L'intelligence créative)

Creativity is a mystery. As we tend to spontaneously reproduce our conditions of existence we are fundamentally conservative, even those who call themselves "leftists." How is it then that we may yet evolve?

In any business, in any institution, conservative forces are struggling to ensure the sustainability of the organization and, as we well know, the leaders never understand anything new. The economic reasoning is not enough to explain why so many innovations happen: for an enterprise embarks on a new project it is not enough that innovation seems profitable, the potential profitability must also have been understood or at least glimpsed. How leaders "who never understand anything new" can nevertheless understand finally the value of an innovation?

These two mysteries are similar to the one the evolution of species confront us. If parents pass their genes to their children, how is it that one species can evolve, that forms life takes can diversify? The answer, as we know, lies in the mutations: genes are not transmitted all the same.

Mutations are random, most of them are harmful and their carriers disappear. Some, however, are so positive that they will benefit their holders in the competition for reproduction: hence evolution.

Does not happen in our minds, in our institutions, a phenomenon similar to this one that would explain the creative thinking in the individual and the innovation in the enterprise?

e-conomy

(Translated from e-conomie).

This book was published in 2000 by Economica, 49, rue Héricart, 75015 Paris, ISBN 2-7178-4073-7

The "e-conomy" is based on the synergy between microelectronics, software and network. It is also called "new economy" because it changes the competition on the market, the internal organization of firms and their cooperation.

This is certainly an "immaterial economy" but this characteristic stems from another, more fundamental: the cost of production being virtually independent of the volume produced, it is paid from the initial investment: e-conomy is a "sunk cost economy."

This has profound consequences on the economic equilibrium. The plants are automated, employment lies in design and distribution. The distribution of income is not connected to employment. Enterprises are organized around their information system and differentiate their products to build niche monopoly. Trade occurs through electronic intermediation. The investment being risky, competition is global and extremely violent.

Modeling the "e-conomy" enlightens the game of competition in microelectronics, software, network and in the sectors that use these technologies: broadcasting, airlines, trade. This allows to interpret the evolution of information systems and to diagnose some obstacles.

The "e-conomy" is highly effective but its power can lead to disaster if it is treated in the manner of "laissez-faire". It is therefore necessary to go beyond the economic dimension to consider the requirements of ethics and social cohesion.

Table of Contents: full text access.

List of my books

Table of contents of e-conomy

This book was published by Economica in October 2000. Some details would of course need to be updated, but the structure of the model and of the reasoning remains relevant.

Most of the texts are in French. I shall translate them progressively into English.

Foreword
Acknowledgments
Preface

Part I: The model
1 - Overview (in English)
2 - Increasing Returns
3 - Contemporary technical system
4 - Monopolistic competition
5 – Layer Model

Part II: Areas
6 - Computing
7 - Audiovisual
8 - Dimensioning
9 - Telecommunications
10 - Airlines

Part III: Uses
11 - Information System
12 - Obstacles
13 - E-commerce
14 - Relationship between nations
15 - Putting in perspective

e-conomy, Chapter I : Overview

(Translated from Vue d'ensemble).

Nota Bene: we use here the term "model" in a sense different from that given to it by econometricians. We want to draw the consequences of a few simple principles (CTS, production function with fixed cost) and compare them with observational data: this allows us to use some mathematical tools and terms such as "exogenous" and "endogenous". By cons we have not built a quantitative model involving a detailed and complete formalization of assumptions and allowing to test them systematically: this work is a preliminary to such a formalization.

CTS, automation and fixed costs

Following an approach inspired by the work of Bertrand Gille¹, we consider the synergy proper to the "contemporary technical system" or CTS (microelectronics, automation, computer) that characterized the economies of rich countries from the 70s.

This synergy gives the production function a "fixed cost" structure, which means that the cost (almost) does not depend from the quantity produced: it is easy to check that in the case of electronic chips or of software which are essential and fundamental products of the CTS.

Differentiation and monopolistic competition

The “fixed cost production” function would imply for the market of each product a natural monopoly structure, where only one company can survive. In order to avoid this risk companies seek to differentiate the product as much as they can, that is to say as much as the demand can handle (for a product to be differentiated, it is obviously necessary that there exist a demand for different varieties of this product).

The existence of cross-trade between countries reflects this differentiation. Let us quote one of the favorite examples economists use: if the cars were not differentiated, trade in cars would not exist between France and Germany because for the same price clients would not go looking abroad a model identical to the one they can find at home. Undifferentiated goods (pig iron etc..) are not subject to cross-trade.

Companies enjoy on each variety a niche monopoly on the border of which they compete with suppliers of other varieties. Monopolistic competition is endogenous to the model: at the equilibrium the number of varieties produced is determined, as well as their price and the quantity sold of each variety.

The peaceful symbolic of any equilibrium model should not conceal the potentially violent phenomena of creation and destruction that renew the actors. These phenomena will be illustrated by sectoral examples.

Critique of correlative reason

(Translated from Critique de la raison corrélative).

Statistics provides counts, averages and totals; it also provides a measure of dispersion for quantitative variables, the standard deviation; finally it provides a measure of the relationship between quantitative variables, the correlation (for qualitative variables, the equivalent of the correlation is the chi2).

I spare the reader the mathematical expressions of these concepts: they are found in textbooks.

When there is an affine relationship (Y = aX + b) between two variables X and Y the absolute value of their correlation coefficient is equal to 1: they are "correlated".

When no such relationship exists, the correlation coefficient is equal to zero: the two variables are not correlated. When the relationship exists but is fuzzy, the absolute value of the correlation coefficient lies somewhere between 0 and 1.

*     *

Confronted to the descriptions statistics provide, we are like those children who always want to know why things are as they are: we want to know the causes. Felix qui potuit rerum cognoscere causas [1]!

How to use Big Data

(Translated from Comment utiliser le Big Data)

Stéphane Grumbach and Stéphane Frénot have published in Le Monde on January 7, 2013 an article that develops what is often said about Big Data: "Les données, puissance du futur."

It is true that the Internet provides powerful editorial means to the institutions that produce statistics, it is also true that the observations collected by computer processes allow novel usages. One should of course be aware of the new possibilities and new dangers that entails.

The authors of this article, however, handle with too few precautions the semantic bombs that are the words "data" and "information." Phrases such as "to digitize everything", "information society", "mass of data", "a resource little different from commodities such as coal and iron ore" are actually deceptive, because by encouraging to consider data according to their volume they slide down the slope of the "information theory."

What is a "concept"?

(Translated from: Qu'est-ce qu'un concept ?)

"What is a concept? " asked once a friend of mine. I had used this word while we were talking about economy.

 This friend has suffered like me philosophy in the last year in high school. She may not have been paying attention, it may be that his teacher was a mediocre philosopher or poor teacher. To understand the philosophy, told me a philosopher who thought of his own experience, one must have reached at least the age of thirty years, have formed a family, have children and practice a profession ... What can we understand at the age of eighteen?

 I admire the choice and depth of the readings of this friend, if not their extent, but this fine and intelligent person kept so unpleasant memories of the course of philosophy that she close her ears when she hears one of the terms of the technical vocabulary, in particular "concept".

It is therefore normal that she does not know what it means. I begun to understand it when, working at INSEE, I questioned the relevance of the classification of activities and the relationship between statistics and economic theory.

*     *

"A concept, I said, that's an idea associated with a definition." As it meant nothing to her, I had to develop.

An essay on industrial classifications

Article by Bernard Guibert, Jean Laganier and Michel Volle, published in Économie et statistique No 20, February 1971

(Translated from: Essai sur les nomenclatures industrielles)

There can be no economic analysis without a classification. Only a classification can give precise enough meaning to the terms that crop up so often in economic reports - "textile industry", "furniture", "steel industry" and the rest. Classifications play an absolutely crucial role, but they tend to be dismissed as tedious. They consist of tiresome lists with only the occasional intriguing oddity to break the boredom. A classification specialist is seen as a real technology geek, and has to be exactly that to answer the seemingly hair-splitting questions (s)he is faced with every day: should the manufacture of plastic footwear come under footwear manufacture or under manufacture of plastic products? What is the distinction between shipbuilding and the building of pleasure boats? Should joinery be classed as manufacture of wood and wood products or as building construction?

Understanding the iconomy

(Translated from: Pour comprendre l'iconomie)  

Notice to the Reader: This text is well suited for those who accept the austerity of abstraction. Others will deem it probably poor and too assertive.

*     *

To understand today's economy, which is obviously complex, one must define a few simple concepts that will allow to build an argument (see What is a "concept"?).

To generate these simple concepts requires a complex meditation fed by experience, conversations and readings. The long journey of this meditation leaving no trace in the dryness of the concepts, only the comment can provide them a little heat.

We will therefore proceed more geometrico. We first present six concepts that provide a theoretical framework for modeling today's economy, then nine concepts that give a content to this framework. Then we cite each concept followed by the comment that explicit it.

*     *

Six economic concepts

1. the purpose of economy is the material well-being of the population;
2. any action deemed necessary or advisable by society is carried out by an institution of which it is the mission;
3. the mission of the enterprise, economical and industrial institution, is to effectively produce useful things; it ensures in the biosphere the interface between society and nature;
4. the fulfillment of the mission of an institution requires an organization that has a dialectical relationship with the mission;
5. the State, institution of the institutions, define their missions, instigates their creation and regulates the dialectic of the mission and the organization;
6. an industrial revolution transforms the nature, and therefore the mission of the institutions and the practical conditions for their organization.

Nine concepts for understanding the contemporary economy

1. the production system is based on the contemporary technical system (CTS), whose fundamental techniques are microelectronics, software and the Internet; the CTS succeeded in 1975 to the modern technical developed system (MTDS);
2. the emergence of the CTS sparks a cascade of anthropological consequences;
3. repetitive work is automated;
4. the bulk of the effort required by the production is achieved during the initial investment;
5. market obeys the regime of monopolistic competition;
6. products are packages of goods and services, each developed by a network of partners;
7. the material well-being of the consumer depends on the quality of its consumption;
8. predators are skilled users of CTS;
9. the crisis is due to the inadequacy of the behavior of economic agents to the productive system that the CTS brings out.