A Cognitive Behavioral Modelling for Coping with Intractable Complex Phenomena in Economics and Social Science: Deep Complexity

Please cite the paper as:
Robert Delorme, (2017), A Cognitive Behavioral Modelling for Coping with Intractable Complex Phenomena in Economics and Social Science: Deep Complexity, World Economics Association (WEA) Conferences, No. 2 2017, Economic Philosophy: Complexities in Economics, 2nd October to 30th November 2017


It is argued in this paper that there is an issue of complex phenomenal intractability in economics, in particular, and in social science in general, and that it is unduly neglected in theorizing in these areas. This intractability is complex because it is an offspring of certain complex phenomena. It is phenomenal because it relates to empirical phenomena, which distinguishes it from conceptual and computational approaches to intractability and complexity. Among the possible reasons for this neglect, one is, in established complexity theory, the focus on computer simulations which seemingly solve for analytical sources of intractability. Another one is the relegation of intractability proper to theoretical computer science. Yet the empirical inquiries that originated this research reveal significant cases of intractable complex phenomena that are accommodated neither by existing complexity theory nor by the theory of computational intractability. The task ahead is therefore to construct a theory of complexity with phenomenal intractability. A reflexive cognitive behavioral modelling is developed and tested through its application. It results in what may be called a Deep Complexity Procedure. Its implications for economic and social theorizing are discussed.

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5 comment

  • Stephen DeCanio says:

    The review of the literature of complexity in the social sciences should include:

    DeCanio, Stephen J., 2014. Limits of Economic and Social Knowledge. Palgrave Macmillan.

    This book treats both computational and conceptual complexity issues.

  • David Harold Chester says:

    Intractability means that it cannot be moved. How this applies to an complex system is unclear. In my view, the complex nature of our economics or social system does not mean that no further progress is possible, which is what this word “intractability” indicates. Instead we need to better analyze the nature of the system as a whole. I have shown how to do this by developing a model of the most simple kind (that is not over-simple) and does indeed cover the whole of the Big Picture. My work can be viewed in SSRN 2695571 “Einstein’s Criterion Applied to Logical Macroeconomics Modeling” which is an alternative to the proposal at the end of this paper. It is much easier too!

  • Basil Al-Nakeeb says:

    The grim problem facing economics today is an unwarranted mathematical complexity that ignores Leonardo da Vinci’s wise advice: Simplicity is the ultimate sophistication. Leonardo da Vinci, an undisputed genius, had the mental capacity to take the most complex problem and reduce it to its simple essence. Yet, there are too many who, in grappling with a problem, end up adding to its complexity.
    Complexity has been the fashion for some time; its practitioners are typically the first to get lost in the intricate math they weave, arriving at wrong conclusions and misguided policy recommendations. They fail to observe two universal tests for any fruitful endeavor: relevance and common sense. The economic muddle in the West today is testimony to the confusion that was seeded by mathematical complexity. Voltaire’s notion that “common sense is not so common” is especially pertinent here. The risk to the majority of people and the economy is the dearth of good economists not mathematicians.
    Mainstream neoclassical macroeconomists has used complex methods to conclude that the deregulation has rendered markets so efficient that fiscal intervention has become unnecessary to counter recessions. During the late 1990s, the then chief economist at the World Bank, Joseph Stiglitz, observed that this misconception at the US Treasury and the International Monetary Fund (IMF) made the East Asia crisis worse, yet they were still clinging to it by the time the Great Recession hit.
    By contrast, Viscount Takahashi revolutionized macroeconomics by making Japan the first country in the world to recover from the Great Depression back in 1931, instinctively, profoundly and without any fancy math. The math best follows from a distance to add finer touches and rigor to an economic concept once common sense, rationality, qualitative analysis, and observation have established its validity. Mathematical economics is not a substitute for these essential tools. The careless application of mathematical economics has produced misconceptions that have become accepted truths, leaving young economists the unenviable but critical task of cleansing economics of many misguided hypotheses.
    John Maynard Keynes comments are most penetrating, “…good, or even competent, economists are the rarest of birds. An easy subject, at which very few excel…He [the master-economist] must reach a high standard in several different directions and must combine talents not often found together. He must be mathematician, historian, statesman, philosopher—in some degree…He must study the present in the light of the past for the purposes of the future.”
    In conclusion, the path toward more relevant economics is to accept Leonard’s judgement by making complex problems simple, the ultimate sophistication. The alternative will spell the end of economics as a useful instrument and a social science.

  • Yoshinori Shiozawa says:

    A comment on Delorme’s keynote paper: A Cognitive Behavioral Modeling for Coping with Intractable Complex Phenomena in Economics and Social Science: Deep complexity

    Yoshinori Shiozawa

    This was a paper hard to read. It does not mean that the paper was badly written. The difficulty of the task that the author sought enforced him to write this difficult paper. After struggling a week in reading the paper, I am rather sympathetic with Delorme. In a sense, he was unfortunate, because he came to be interested in complexity problems by encountering two problems: (1) road safety problem and (2) the Regime of Interactions between the State and the Economy (RISE). I say “unfortunate,” because these are not good problems with which to start the general discussion on complexity in economics, as I will explain later. Of course, one cannot choose the first problems one encounters and we cannot blame the author on this point, but in my opinion the good starting problems are crucial to further development of the argument of complexity in economics.

    Let us take the example of the beginning of modern physics. Do not think of Newton. It is a final accomplishment of the first phase of modern physics. There will be no many people who object that modern physics started by two (almost simultaneous) discoveries: (1) Kepler’s laws of orbital movements and (2) Galileo’s law of falling bodies and others. The case of Galilei can be explained by a gradual rise of the spirit of experiments. Kepler’s case is more interesting. One of crucial data for him was Tycho Brahe’s observations. He improved the accuracy of observation about 1 digit. Before Brahe for more than one thousand years, accuracy of astronomical observations was about 1 tenth of a degree (i.e. 6 minutes in angular unit system). Brahe improved this up to an accuracy of 1/2 minute to 1 minute. With this data, Kepler was confident that 8 minutes of error he detected in Copernican system was clear evidence that refutes Copernican and Ptolemaic systems. Kepler declared that these 8 minutes revolutionize whole astronomy. After many years of trials and errors, he came to discover that Mars follows an elliptic orbit. Newton’s great achievement was only possible because he knew these two results (of Galilei and Kepler). For example, Newton’s law of gravitation was not a simple result of induction or abduction. The law of square-inverse was a result of half-logical deduction from Kepler’s third law.

    I cite this example, because this explains in which conditions a science can emerge. In the same vein, the economics of complexity (or more correctly economics) can be a good science when we find this good starting point. (Science should not be interpreted in a conventional meaning. I mean by science as a generic term for a good framework and system of knowledge). For example, imagine that solar system was composed of two binary stars and earth is orbiting with a substantial relative weight. It is easy to see that this system has to be solved as three-body problem and it would be very difficult for a Kepler to find any law of orbital movement. Then the history of modern physics would have been very different. This simple example shows us that any science is conditioned by complexity problems, or by tractable and intractable problem of the subject matter or objects we want to study.

    The lesson we should draw form the history of modern physics is a science is most likely to start from more tractable problems and evolve to a state that can incorporate more complex and intractable phenomena. I am afraid that Delorme is forgetting this precious lesson. Isn’t he imagining that an economic science (and social science in general) can be well constructed if we gain a good philosophy and methodology of complex phenomena?

    I do not object that many (or most) of economic phenomena are deeply complex ones. What I propose as a different approach is to climb the complexity hill by taking a more easy route or track than to attack directly the summit of complexity. Finding this track should be the main part of research program but I could not find any such arguments in Delorme’s paper.

    In the next post, I am planning to discuss the track I propose.

    • Dave Taylor says:

      Yoshinori, I like your introducing the three-body problem. May I suggest the following handles on it: the first, that the two-body human family becomes a three body family when it has children, but these in turn join with others to form different two-body families. The second, described by James Gleick on p.90 of the Cardinal edition of “Chaos”, describes how “To make a Cantor set, you start with the interval of numbers from zero to one, represented by a line segment. Then you remove the middle third. That leaves two segments, and you remove the middle third of each. … That leaves four segments, and you remove the middle part of each; and so on to infinity. … Mandelbrot was thinking of transmission errors as a Cantor set arranged in time”. By p.96 Mandelbrot was thinking of this in turns of dimensions; by p.108 geologist Scholz is seeing two-dimensional space up close as around 2.7 dimensions: its chaotic appearance arising not from a three-BODY problem but from the dimensionality of information feedback approaching 3. The solution to this transmission error problem is Shannon’s error-correction logic, but in the cybernetic macro form of PID control systems, changing course in response to D feedback to avoid errors can cause chaos if the course is not reset once the problem is avoided. This macro form applies to household economics when one considers sexual types (genders) rather than individual people, with insanity being a familiar outcome of incest.

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