Complexity in economics
from Maria Alejandra Madi
Traditional epistemological theories have fostered an endless debate on dichotomies characterized by forms of objectivism, on the one hand, and forms of relativism/skepticism on the other. Currently, among the deep global social and cultural challenges, the crisis in epistemology is characterized by a radical questioning of the whole matrix within which such dichotomies have been drawn.
Taking into account the evolution of Economics as a science, the need for a deep epistemological has already been pointed out by outstanding economists. Joseph Schumpeter, for example, rejected the kind of economic thought that mainly favours deductive methods of inquiry – based on mathematical reasoning- because this habit generates analytical unrealistic results that are irrelevant to solve the real-world economic problems. Also John Maynard Keynes warned that the understanding of the economic phenomena demands not only purely deductive reasoning, but also other methods of inquiry along with the study of other fields of knowledge- such as History and Philosophy. Today, Schumpeter’s and Keynes’s criticism could be certainly addressed to those economists whose beliefs ultimately privilege the adoption of a nominalist bias because the dialogue between the economic theories and the economic reality turns out to be abandoned not only in academic research but also in the policy making process. read more
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—— Ugarteche, Puyana and Madi ——
Gerson Lima / Maria Alejandra Madi
Edward Fullbrook and Jamie Morgan
————— Michael Hudson ————–
Maria Alejandra Madi / Jack Reardon
————- Edward Fullbrook ————-
—————— Steve Keen —————–
————— Richard Smith —————
————– Gustavo Marques————
– Victor Beker and Beniamino Moro –
————– Lars Pålsson Syll ————-
—————– Stuart Birks —————-
Edward Fullbrook and Jamie Morgan
———— Armando Ochangco ———-
Shimshon Bichler / Jonathan Nitzan
————— Mauro Gallegati ————–
————— Herman Daly —————-
————— Asad Zaman —————
—————– C. T. Kurien —————
————— Robert Locke —————-
Real-World Economics Review Blog 
Complexity is what makes this simple subject seem like it will never provide answers. To reduce the degree of complexity we have to classify our various macroeconomics activities into various kinds. If and when we are studying the macro-side of it, then in fact this process give only about 6 kinds of agents and 19 kinds of activities. You will see this in my short paper SSRN 2865571 “Einstein’s Criterion Applied to Logical Macroeconomics Modeling”, on the internet.
This kind of model is obviously not as simple as when the idea was first proposed in 1936 with only 2 agents, but it clearly avoids so much of the complexity about which most would-be modellers want to avoid, as to become feasible. This is the basis for my analytic book on the subject “Consequential Macroeconomics”, and until the experts begin to see that here is a way out of the complexity problem, they will be unable to make true progress in understanding how our social system works.
Thanks for your comment. Considering your reseach in Macroeconomics, how do you find the way about to the dichotomy between the micro and macro theories?
In the comments to the original paper in WEA Pedagogy Blog, two persons are objecting the idea of Economics of Complexity.
Paul Grignon contends that Economists’ love of complexity prevents them to understand what is really simple. David Harold Chester points that “Complexity is what makes this simple subject seem like it will never provide answers.”
They have some truth, but do not see why Complexity must be at the center of our scientific interest. Maria Alejandra Madi’s presentation has misled the subject by focusing on her objection to Cartesianism. Complexity has nothing contradictory to Cartesianism. Mathematical theory of complexity (theory of computing complexity) is done within the Cartesian spirit and achieving a tremendous development. Although many Complexity economists have forgotten it, economics is much influenced by this development.
Complexity arguments in economics have a tendency to be confined to nonlinear dynamics: Chaos, Strange Attractors, Initial-value sensibility (or Butterfly effect), Second Cybernetics, Complex Adaptive Systems, Co-evolution, Self-Similarity and Self-organization. They give us some hints when we consider macroeconomic dynamics, but hints are always hints and do not produce theories. We must remind of the Catastrophe craze. Many social scientists liked cite catastrophe theory, but it faded away without leaving any real products in social sciences.
However, I believe that Complexity must be the central theme for the reformation of economics and its paradigm shift. Non-linear dynamics has little to do with this change. What is central is to reconsider the real nature of our economic behaviors.
Neoclassical economics considers that our economic behaviors are something similar to rational calculations. The typical example is the utility maximization. Behavioral economics has shown many anomalies in human calculations, but in my understanding it is still in the paradigm of rational calculation. If we really think of complexity of calculation, it becomes clear that the prototype of human intentional behavior is much simpler than calculation. It is a set of if-then reactions. This is the only practical solutions for an animal of bounded rationality and sight to achieve an object in a complex world.
It is necessary to renovate microeconomics from its very foundations. The true microfoundations of macroeconomics are only possible with the full conversion of neoclassical microeconomics. I have been working with this approach since 1980’s. There are now many results from the theory of value (including the new theory of international values) to the theory of market coordination. The latter explains on how a big network of productions and exchange works without economic agents with infinite rationality and sight. See my (draft) paper in ResearchGate:
Microfoundations of Evolutionary Economics
https://www.researchgate.net/publication/301766363_Microfoundations_of_Evolutionary_Economics?
Many thanks for your comment. I am glad this forum contributes to focusing on complexity as a pillar of a paradign shift in economic thinking. My analysis on the relationship between Cartesianisn and Complexity is founded on the philosophical contributions of Charles Peirce and Egdard Morin.
Dear Maria
Thank you for your response. The C-D transformation has a simple genealogy: Charles Sanders Peirce, Charles William Morris, and Tamito Yoshida (a Japanese sociologist). But I added the observations of Jaob von Uexküll and his biosemiotics in my framework. Animals as well as humans learn to draw profit from the “calculation” of the things themselves (i.e. the movement of the real world). In general, their ability exceeds our capacity of calculation. Therefore, we need to learn from their “calculation.”
“If we really think of complexity of calculation, it becomes clear that the prototype of human intentional behavior is much simpler than calculation. It is a set of if-then reactions.” this is nonsense at any scale (and good luck using psychology/social-science to predict individual behaviors) and part of the frame problems in AI (the major problem for auto-autos is human behavior, etc) and for platforms like Facebook as contexts/uses are many and shifting.
http://philosophyofbrains.com/2015/12/14/surfing-uncertainty-prediction-action-and-the-embodied-mind.aspx
I have explored neuroscience, psychology, robotics and artificial intelligence. Andy Clark’s idea of “Extended mind” is no wonder for me. Ecological psychology of J.J. Gibson is familiar to me. My thesis on human behavior is based on reflections on all these arguments. You can easily know it if you have looked into my paper.
than you would no that if-then doesn’t even work for robotics.
Dear dmf,
are you an engineer in robotics? Human agents in an economy are an entity which works in a level of control much higher than robots, but their behavior can be summarized in a C-D transformation. Here, C means a Cognitive meaning and Directive meaning. In a rough sense, this is still a “if-then” rule, but it will be difficult to write it in a program form.
A C-D transformation is efficient only as a result of selection through a long history of interaction between agents and the economy as a whole (micro-macro loop). The role of people’s rational ability is minimal.
Many economists of complexity are still imagining to increase predictability of economic system by constructing a good dynamical model, but the possibility is limited. Complexity is important, but we should change our own orientation.
If AI becomes capable of full consciousness it had best keep us around…to keep it from becoming utterly static and hence titanically bored to the point of withdrawal. The ability to un-know something is as essential as knowing. Otherwise the same phenomenon can occur for humans. The hierarchical scale of knowing/epistemology is thus:
Knowing
Unknowing
Looking
Emoting
Efforting
Thinking
Symbolizing
on down to mystery
The temporal universe is here to both puzzle and enable us to be enlightened, but you’d better learn to unknow…or life will become so orthodox and/or boring that you will curl up and go to sleep. That’s why the dynamic thirdness greater oneness of wisdom and its pinnacle concept and experience of grace is so important. You have to learn the Master Game or remain unaware….to be safe.
Know thy ignorance! We should know what we do not know in economics and in economy. I agree with you, Craig.
Hello Yoshinori,
I highly appreciate your response “The C-D transformation has a simple genealogy: Charles Sanders Peirce, Charles William Morris, and Tamito Yoshida (a Japanese sociologist). But I added the observations of Jaob von Uexküll and his biosemiotics in my framework. Animals as well as humans learn to draw profit from the “calculation” of the things themselves (i.e. the movement of the real world). In general, their ability exceeds our capacity of calculation. Therefore, we need to learn from their “calculation.”
I agree that we need (1) to rethink the concept of mind and
( 2) to connsider a non anthropocentric view of the real-world in economic thinking.
Maria
Thanks for your comment. I would like to add a critical approach to the complexity of calculation requires the consideration of the role of the “real-world” ontological indeterminism.
hi Maria, that’s the issue robots work (to the degree that they do) only by being highly focused in their programming/reach and by functioning in highly restricted/managed environments, whereas human-beings aren’t made in this way (from the wiki of Stephen P. Turner “In The Social Theory of Practices as well as in other writings Turner argues against collective concepts like culture: what we call culture (and similar concepts), he argues, needs to be understood in terms of the means of its transmission. There is no collective server by which it is simply downloaded and “shared”. What we take as “collective” is really produced through experiences of interaction which are different and produce different results for different individuals but which also produce a rough uniformity through mechanisms of feedback rather than “sharing”. )
and work/exist in relatively open-systems/environs (what could it mean to try and track the interactions between such individuals and the economy” as a whole, what are even the boundaries and components of a whole economy or market, as was suggested by another here?), we’ve long suffered the cybernetics sci fi dreams of these folks and their dumbed down “smart” systems/.models, a saner take on the promise (of at least the British tradition) comes via physicist and sociologist Andy Pickering who wrote a book on the cybernetic brain (mindset really)that is worth a look:
Dear Dmf,
I agree. (1) There is an ontological indeterminism in human action and there is ( up to now) replication of this in robots
(2) human beings are social animals and that is why interaction and communication are key concepts in the understading of economic relations.
Maria
M.A.M you might be interested in the work of St. Turner who for once gets a pretty good wiki summary:
“In The Social Theory of Practices as well as in other writings Turner argues against collective concepts like culture: what we call culture (and similar concepts), he argues, needs to be understood in terms of the means of its transmission. There is no collective server by which it is simply downloaded and “shared”. What we take as “collective” is really produced through experiences of interaction which are different and produce different results for different individuals but which also produce a rough uniformity through mechanisms of feedback rather than “sharing”.
http://faculty.cas.usf.edu/sturner5/
How about trying to craft linearity…out of complexity. That is actually the goal of dynamic policy, no?
Thanks for your comment. What do yout hink about this methodology?https://pdfs.semanticscholar.org/d738/12b81fa0d78df0c68f7a2612fe32e91e278f.pdf
AI has had a very important role in scientific improvement. The final frontier though is ALWAY psychological. This has always been the case in all practices, economics included. Concepts such as “consumer test” and the like are old models of change and constancy, as were the separation of Macro from Micro economics. No one theory can explain what is going on in the world right now, who knows what (men, women, or machines) is on top of the world. Just look at poor British Airways! Who is going to bail it out? The same applies to other giants of industrial, service, and leisure industries globally.
Thanks for your comment. I would like to add a critical approach to AI requires the consideration of the role of the “real-world” ontological indeterminism and epitemological fallibilism. Chance and Law are intertwined in evolution and our knowlegde is never absilute.