On the limited applicability of statistical physics to economics
from Lars Syll
Statistical mechanics reasoning may be applicable in the economic and social sciences, but only if adequate consideration is paid to the specific contexts and conditions of its application. This requires attention to “non-mechanical” processes of interaction, inflected by power, culture, institutions etc., and therefore of specific histories which gives rise to these factors …
Outside of very specific cases, statistical physics is more likely to provide useful metaphors and ways of thinking than computational techniques and definite answers. Although statistical mechanical explanation can shed light on particular mechanisms that may be at play (e.g. explaining how distributions are shaped by the differences between the ways in which wages and profits may respectively evolve) these must be interpreted in a specific causal context … In the context of the human sciences, agency – not merely the exercise of individual choice but the shaping of the circumstances of collective life – is the central factor both in determining the properties of a system and in shaping individual choices. The exercise of human agency may bring a social system closer to an “equilibrium” in some circumstances, and disrupt it in others. It is among the specifically social factors that must be taken note of in the dialogue between statistical physics and the social sciences.
Generally speaking, economists know too little about physics, biology, meteorology and their scientific methods. Based on their very poor knowledge, economists often argue that we should reject to learn from natural sciences on the basis that natural and social sciences are fundamentally different. Of course, it is not wise blindly to imitate natural sciences. Also, it is true that there are so many such silly imitations, but it does not imply that we cannot or should not learn from experiences of natural sciences. The paper of Sanjay G. Reddt’s paper argues some concrete cases of successful (and failed) attempts from which we economists may learn. He teaches us kinds and aspects in which economics can learn from physics and in particular from statistical physics. Among these possibilities of learning, we have some negative ones, i.e. to learn how we should not learn from some past experiences.
These are some excerpts from Section 1, which are noteworthy for further arguments:
Some of the conclusions are also notable:
Although I do not deny the possibility to learn from statistical physics successfully (e.g. the entropy maximization), I believe we should pay more attention to new approaches that do not draw on statistics. See for example, two of my papers:
A new framework for analyzing technological change, Journal of Evolutionary Economics, 2020.
The principle of effective demand: a new formulation (open source) Review of Keynesian Studies, 2021.
The two papers are neither predictive or descriptive (in a narrow sense), they give a good explanation on what is happening in particular aspects of the economy. None of them assume equilibrium, perfect rationality, nor representative agent, but provides history-friendly microfoundations to Post Keynesian and evolutionary economics based on a totally different understanding on how market economy works.
The paper I read is the draft version of the published paper. I suppose there is no big difference between them.
In short you have to treat economy as a complex
system, just like climate.
Dear reallifeeconomics. You have a good vision of the economy. But, having a good vision does imply that we can easily obtain a good theory. Do you have any idea?
They can start by being useful economists rather than an idler, hobbiest, or literature-only armchair economist blinded by conceit and the false premise that for economics to be “a science” it must be about the dispassionate search for timeless truth which is for the sake of mathematical tractability reducible to simple “if-then” rules that tell us what even the “dimmest observer of real-existing capitalism already” knows from experience.
Thanks. I do not think that a good vision of economy requires theory, as it does for math or physics. There can not be (politics or value) neutral economics. May be that is the reason why classics called it political economy and objected to its being concieved as a theoretical science. We live in the age of much more advanced science, compared to the times of A. Marshall. Furthermore, we have computers that dramatically changed scientific methodology. So, the stubborn objection of the classics can be somewhat relaxed: In addition to political economy, these days we can also practice economics as a computational social science. This means model building. They may be closed and/or ad hoc (in economical sense) mathematical models or computational simulations. Such models are useful to represent relevant aspects of our economical lives. I believe that political economy must inform the theories behind these models, which are useful only if they are good surrogates of the economical reality in question.
In my above reply, I wrote
It seems that I missed to add the important word “not”, although it seems there were no big confusions. The above sentence must be read as
Yes.. It is a necessary but not a sufficient condition..
I agree almost completely with Yoshinori”s comments.The modern economy, being a complex multifractal system with a Feigenbaoum attractor, needs statistical physics more than ever; otherwise we will be asking What is wrong with economics? for the next hundred years. Moreover,statistical physics is applicable to the whole Universe. Those interested should google the conclusions of Mohamed El Naschie in E-infinity Cantorian space-time.
Any understanding of an economic system will have to rely on the law of large numbers, I believe. Individuals are not automata and will be unpredictable without detailed knowledge of each one (which Amazon tries to obtain!). But group behaviour can be more predictable. That is why representative agent theorising and phoney “microfoundations” are such a waste of time. The analogy of brownian motion at the molecular level or even indeterminacy at the quantum level giving rise to deterministic physical laws is interesting. But it is only an analogy. It is not clear to me how far there is a formal equivalence with economic systems. I would be interested in hearing from C-ReneDominique why he is so confident about that.
Statistical physiscs works with particles that interact according to laws of physics. Wheras, in economy there are no such laws. Agents have variable behaviours, which may change during interaction with other agents or with the environment.
Gerald, you are generalizing too much. I agree with you if you say “some understanding of an economic system will have to rely on the law of large numbers.”
New technologies (new product and production techniques) comes quite “randomly” if we observe an economy from the outside. However randomly they may come, the real wage will generally go up if mangers of firms choose more profitable techniques and if wage and markup rates remain constant, See my paper on the new framework for analyzing technological change. To obtain this result, I use the law of large numbers nowhere.
The historical evidence shows Gerald, in his comments, has indeed frequently “generalized too much” the power of statistics and “the law of large numbers.” Everything is relative.
It is economic myth making to claim _real wages_ are determined by new technology and that this new technology leads to higher _real wages_. The devil is in the details. Only the few who are highly skilled benefit from these higher _real wages_ (~10% or less) while the lion’s share of these gains to to the .01% who own the monopoly capital. It is a myth that higher productivity leads to higher wages when reality shows real wages have become detached from productivity growth.
To Gerald and Reallifeec: Ask yourself Why income distribution, firms’ size,, returns, language ecology, stars’ sizes distribution, etc.follow a power law? Or why is economics a non ergodic construct?(contrarily to what we would wish). The power law can be broken by policy changes but it is an emergent universal law. Statistical physics allows us to figure out that the modern market lives in dimension between 2 and 3.
Power law is not universal, but it is true that quite a few economic phenomena follow power law distribution. You have to be careful though, because there are continous distributions that can be taken for a power law, like log-normal for example. It is also true that economic processes are not ergodic, or anything like it. These are all well known features of complex systems.
We should ask why various phenomena obey the power law. Many of power law distributions (fat or long tailed distributions) have a specific characteristics named stable distribution. (See “Stable distribution” in Wikipedia. Do not misunderstand that an phenomenon is stable in an ordinary sense of the word). French mathematician Paul Lévy investigated this type of distributions and determined the general form of stable distributions. Except the normal distribution that has a finite variance, all other stable distributions have no variance (or have unbounded variance). Physical systems normally obey the normal distribution, probably because aberrations are bounded within a certain energy level. In financial markets and others, we observe many distributions that are not bounded in the similar way. Many of them obey a stable distribution (at least asymptotically), probably because some “universality” like central limiting law works. (Do not confuse with “universal” laws like Newton’s law of universal gravitation.)
Stable distributions except the normal distribution have no simple algebraic formula, but they are expressed as a Fourier transform. It is known that these distributions are asymptotically proportional to a power of the random variable. Quite different from normal distributions, they exhibit an extremely fat density at points far from the “mean”. (Nota bene: If the index is smaller than 1, theoretically there are no means.)
Error: central limiting law -> central limit theorem (law of large numbers)
What assumptions underlie these claims that the “statistical mechanics of money” and do they hold in the real-world?