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Complexity and institutional evolution

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Abstract

This paper shows which portions of complexity theory are most relevant for studying economic institutional evolution. This question leads to both the two main competing economics complexity theories: dynamic and computational complexity. Central to this is the concept of cumulative causation, invented by a main founder of institutional economics, Thorstein Veblen, a fact not widely recognized. This provides the basis for increasing returns, multiple equilibria, and bifurcations in the evolution of institutions. A central issue in institutional evolution involves both hierarchical emergence, with the interaction of spontaneous emergence with natural selection a central point. Recognizing these points may provide a new synthesis between the old and new institutional economics.

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Notes

  1. In the USA, this society has been closely associated with the so-called “old institutional economics”, whereas we shall argue that an evolutionary approach taking into account complexity can unite the old and new approaches.

  2. It is an open debate whether or not Veblen viewed cumulative causation as necessarily implying economies of scale, although he was aware of the importance of economies of scale in industrial systems. Setterfield (1997) recognizes Veblen’s priority in introducing the concept, but argues that Young (1928) and Kaldor (1972) more clearly tied it to the phenomenon of increasing returns.

  3. Veblen was not the first economist to advocate the usefulness for economics of evolutionary theory, with both Marx and Marshall doing so before he did, even as they did so from very different perspectives. The more complicating factor in all this is the fact that Darwin himself was crucially influenced by Malthus’s work on population when he developed his theory of natural selection (Rosser 1992).

  4. That this is not widely known can be seen in that Business Dictionary identifies the originator of the term as Young (1928) [http://www.businessdictionary.com/definition/cumulative-causation.html] and Wikipedia identifies its originator (actually “Circular cumulative causation”) as being Myrdal (1957) [https://en.wikipedia.org/wiki/Circular_cumulative_causation]. Certainly, Myrdal’s use of the term received widespread attention.

  5. See Rosser (2011, Mathematical Appendix) for such a discussion.

  6. The importance of increasing returns in dynamic complexity has been especially emphasized by Arthur (1994).

  7. The question of the relationship between polynomial or P programs and non-polynomial or NP programs, usually posed as the P! = NP problem, is one of the deepest in computer science and remains unsolved, although most mathematicians and computer scientists believe that they do not equal each other. For recent developments on this and connections with economics, see da Costa and Doria (2016).

  8. Clearly, there is no definitive boundary in observing what is essentially discrete data between what is continuous and discontinuous. In biological evolution, one observes different individuals across generations, and, with the exception of identical twins or clones, each individual’s genotype is discretely distinct from every other’s. Likewise with phenotypes, one might depict the possible variations of a certain physical characteristic on a continuous scale, but individuals will still have discrete differences from other individuals on such characteristics, even if these are very small. Thus, the distinction becomes arbitrary. At the lowest level, we see a discontinuous granularity, but higher level defenders of continuity see only gradual changes, especially in population averages, with it completely open to debate how rapid such changes must be before one can call them discontinuous (see Rosser 2000, Chap. 1, for further discussion of distinguishing continuous from discontinuous forms).

  9. The original formulation of Arthur’s model was from Arthur et al. (1987) and their study of Polya urns.

  10. Curiously, Sewall Wright also focused on animal breeding due to his working for the US Department of Agriculture in the 1920s, where his thinking about this led him to certain of his ideas such as random drift, also known as “the Sewall Wright Effect”, sometimes seen as a violation of strict natural selection in how new species might form, although the separation of genetically distinct sub-groups of a population may happen either randomly in nature or through the conscious control and direction by humans as in animal breeding.

  11. The term “meme” as the locus of evolution is due to Dawkins (1976), who also first proposed the idea of “universal Darwinism”.

  12. How evolution of habits and norms determines tax behavior in societies is studied by Torgler (2016).

  13. Sethi and Somanathan (1996) show that in such games there are multiple Nash equilibria, with some supportive and some destructive of the cooperative equilibria that are consistent with sustainable development. Rosser and Rosser (2006) extend this argument.

  14. See also Stokes (1995).

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  1. James Madison University, Harrisonburg, USA

    J. Barkley Rosser Jr. & Marina V. Rosser

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  1. J. Barkley Rosser Jr.
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Correspondence to J. Barkley Rosser Jr..

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Rosser, J.B., Rosser, M.V. Complexity and institutional evolution. Evolut Inst Econ Rev 14, 415–430 (2017). https://doi.org/10.1007/s40844-016-0060-3

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