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Economics and Evolution

G. R. Hodgson

posted on 15 September 2001

reviewed by Joe McCauley

Nice, but with some confusion over reductionism, and mechanism vs. organism.

People just do not maximize (utility) on a selfish basis every minute the system would not work if they did a consequence of that hypothesis would be the end of organized society.

K. Arrow

The author begins with the key question: why, in orthodox economic theory (neo-classical economics ala Walras, Fisher, Samuelson et al) is math (superficially) raised to such unwarranted heights while attempts to face empirical reality are so weak? Hodgson presents a stimulating if too philosophic (and nonempiric) discussion of the ideas of Darwin, Smith, Spencer, Marx, Veblen, Marshall, Mayer, Schumpeter, and especially Hayek. A main question is: in a biologic analogy what, if any, are the economic anaogs of genes, the relatively invariant elements, the 'elementary particles? of a local economic system from which 'mutations? and 'selection? by the environment (the rest of the world economic system) can be understood.

Connected with the lack of obvious invariants in economic reality is the question how one can meaningfully divide the global system into 'system studied plus environment.? If nothing can be isolated then it is impossible to discover any rule of mathematical law of time evolution (1). In particular, we cannot then treat the environment as an external field that 'selects? economically, unless we can empirically-accurately make the arbitrary division into 'system with initial conditions, plus environment?.

Another problem is the author?s confusion over the idea of 'evolution?. For evolution to occur, organic behavior is not required. Every dynamical system has a time evolution operator that generates the trajectory (2). That analogs of mutations can be built into continuous time dynamical systems is likely since certain Newtonian dynamical systems may be capable of universal computational capacity (maximum computational complexity, equivalent to a Turing machine (3)). Therefore it is, at this stage, superficial to believe that the idea of organism is in conflict with the notion of mechanism. Every mathematical model is an example of 'mechanism? (1), albeit not necessarily Newtonian mechanism.

Third, (and correctly) implicit in the discussion is that complete reductionism (explanation of biology and economics in terms of quarks or atoms, e.g.) is mathematically impossible (in quantum theory, an Ohm meter has no definite reading, while DNA behaves like a classical computer) and that some idea of hierarchy of description is necessary. But, how to identify empirically the necessary approximately invariant elements? Dawkins?s idea of the reduction of behavior to genes is badly treated, but we really cannot rule out at this stage that some forms of mental behavior, at least, might be genetically determined, just as some known illnesses are genetically determined. Genetics 'looks like? physics precisely because genes are approximately invariant, like inertial masses. This analogy is not an accident. Mendel was trained in physics in Vienna, not in biology or philosophy (1).

Hayek?s extreme free market ideas, usually not to be found in texts in the US, are given two chapters. One chapter is dedicated to his idea of the emergence of spontaneous order, which presumes (without any empirical evidence whatsoever) the existence of a stable, optimal outcome of an unregulated free market (he sketches the history of the emergence of the hierarchy of markets as a possible example). If such an outcome were guaranteed, as Hayek assumed, then we could do away with central banks (and forget Greenspan!), and denationalize money as The Austrian School of Economics advocates. The whole problem of the idea of preferences (utility functionals), however, is that they are neither time- nor path-independent (4).

In general, the orthodox school (and also Hayek) optimizes something or other (utility, or something else) whereas biological systems seem not to optimize anything, rather they are error-prone and error-correcting via gross redundancy (5). Maybe biological systems do not optimize anything.

The book is excessively philosophic, in the pre-scientific Aristotelian tradition. C. S. Pierce and John Dewey (who guaranteed that US public schools would be anti-academic, in part by making high schools a continuation of primary education instead of a transition to secondary education (6)). The author is unfriendly to Descartes and Galileo, but based on a lack of understanding of how scientific knowledge is discovered (for a book on modern bioinformatics that makes the necessity of reductionism in biology quite clear, see (7)). This misfortune is connected with the usual failure to understand the difference between mathematical models and mathematical laws of nature that are based on observed local invariance principles (1), like the principle of relativity (the first physical principle discovered by Galileo (2,8)). Popperian falsification is also given the short-schrift, praising Pierce against Descartes on the role of belief vs. skepticism! Finally, metaphors are only used in the early stages of a theory when one understands little or nothing. Newtonian and Maxwellian laws, when generalized to admit 'learning? ala Hopfield (9), are capable of universal computational capability (10). What can come out of these systems mathematically is therefore in principle unlimited. Whether they will be useful in understanding (macro-) biology and/or economics, or whether some simpler form of modelling will be more useful is still an open question.

I found the book to be enlightening. The main question connected with the book: is anything out there approximately invariant, or is the use of mathematics and/or biologic ideas in economics relatively hopeless?


1. J. L. McCauley, The New Science of Complexity, Discrete Dynamics in Nature and Society 1, 17, 1997.

2. J. L. McCauley, Classical Mechanics: Flows, Transformations, Integrability and Chaos, Cambridge, Cambridge, 1997.

3. C. Moore, Phys. Rev. Lett. 64, 2354, 1990; Nonlinearity 4, 199 and 727, 1991.

4. J. L. McCauley, The Futility of Utility: How Market Dynamics Marginalize Adam Smith, Physica A285, 506, 2000.

5. J. von Neumann, Probabilistic Logics and the Synthesis of Reliable Organisms from Unreliable Components, in Natural and Artificial Automata, ed. A. W. Burkes.

6. Hannah Arendt, Between Past and Future, Penguin, NY, 1993.

7. W. R. Loewenstein, The Touchstone of Life: mplecular Information, Cell communication, and the foundations of Life, Oxford, Oxford, 1999.

8. J. B. Barbour, Absolute or Relative Motion: the Discovery of Dynamics, Vol. 1, Cambridge, Cambridge, 1989.

9. J. Hopfield and D. Tank, Computing with Neural Circuits: A Model, Science 233, 625, 1986.

10. H. T. Siegelmann, Computing beyond the Turing Limit, Science 268, 545, 1995.


Reprint edition (January 1997)

Univ of Michigan Pr; ISBN: 0472084232

Amazon price: $25.95