All Things Complexity Adaptive Systems (CAS)

by Matthew Oldham


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Complexity Economics

“It is wiser to find out than to suppose” - Mark Twain

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Background

My initial questioning of neo-classical economics started after spending most of my microeconomics honors class working through the proofs as to why a demand curve slopes downwards (well, at least in most cases). Apart from the fact that undergraduate and business school students sail off into business without understanding this work, many of the assumptions pertaining to the rational representative agent seemed onerous and unlikely to be within the grasp of the standard consumer. Unknown to me at the time, Simon (1955) had already questioned whether humans possessed these capabilities, and offered the alternative of humans acting with bounded rationality.

The first line of defense against these queries has always been to cite Friedman’s doctrine of positive economics, with the implication being to assume agents act “as if” they were performing these calculations and successfully arguing that economics should be the study of “what is” rather than “what ought to be.” Friedman’s argument laid the foundation for the rational expectation phenomena that underwrote most of the mainstream economic theory of the latter half of the 20th century.

Again, unknown to me at the time, objections to the representative agent were gathering steam, with Kirman (1992), stating that the utilization of a representative agent is a “convenient fiction” used to provide micro foundations for aggregate behavior with the sole intent of solving for stable and unique equilibria. Kirman also provided the impetus to consider the economy as a complex system, capable of delivering emergent outcomes, when he made the point that “the sum of the behavior of simple economically plausible individuals may generate complicated dynamics.”

After re-commencing my research into the drivers of innovation I came across Eric Beinhocker’s The Origins of Wealth which opened my eyes to the growing field of complexity economics (i.e., the economy operates as a complex system). As discussed in the book complexity economics addresses many of the concerns relating to the rational expectations approach. The essence of complexity economics is to assume that the economy is not in equilibrium, but is in perpetual motion as economic agents (firms, consumers, investors) constantly change their actions and strategies in response to the outcome they mutually create. In turn, with a lack of a stable equilibrium, the use of static analytical frameworks becomes problematic. However, with ABM, the dynamics of the economy can be revealed, as they are not constrained by equilibrium conditions, and they allow for the interaction of agents within a system.

Consideration of the economy as a complex system has gathered considerable steam, and covers most topics in economics including: macro, innovation, finance, etc., with the intent being to study how non-intuitive results at the macro-level of a system can emerge from simple interactions at the micro-level. Some of the more interesting outcomes have been path-dependence, increasing returns to scale and inductive reasoning, as opposed to the rational agent models that employ deductive reasoning.

Rather than bog everyone down with the inner workings of complexity economics, I provide a link to a paper by the godfather of complexity economics, Brian Arthur (a review of his book is also provided in the book review section, and as well as an interview. A good explanation of complexity economics can be found at this website, or watch this video.

As mentioned previously the use of ABMs has been the primary analytical tool utilized to research the economy as a complex system. The website of Leigh Tesfatsion is a great resource. The newest textbook in the field is from Hamill & Gilbert (2015). In addition, while a little dated, the Handbook of Computational Economics Volume 2 is an excellent resource, with the various chapters providing overviews of how ABMs have been utilized in the various fields of economics.

My focus

The utilization of complexity economics for the goal of understanding the process of innovation is of great interest to me, and in particular the process of “creative destruction” (Schumpeter, 1942). There is extensive research and theory relating to the way firms interact with other firms. However, the evolutionary aspect of these relationships remains outside the mainstream of economic research.

The process of “creative destruction” involves any existing market structure being “destroyed” because of the successful innovation of a firm, with its significance being that it explains why firms are motivated to undertake new “actions” to either improve their position or retain their position in response to the actions of their competitors. The ramification is that the system is in a perpetual state of change, making it vital to understand the dynamics involved in the process of innovation.

While there are many parts to the evolutionary process, my interest has been in Red Queen hypothesis (Van Valen, 1973). The original hypothesis, as proposed by Van Valen (1973), was focused on evolutionary biology, with the key implication stating that due to the continuous and escalating actions of competitors within an ecosystem, organisms must continually evolve if they wish to survive. The direct relevance to the innovative policy of a firm is that its performance will depend on its ability to successfully react to the actions of its competitors.

I have a paper in the Intelligent Systems in Accounting, Finance and Management that covers my work so far in the area. The paper reports on the results creating a market eco-system, via an agent-based model (ABM), that combines the dynamic features of the Red Queen effect with well-accepted business world performance indicators. Essentially, firms are tasked with remaining “alive” by adapting to their environment via implementing their competitive response, a decision to innovate or imitate. An analysis of the firms’ behaviors delivers a deep understanding of the drivers of innovative behavior within the economy. The key findings of the paper are 1) that concentrated markets are not entirely detrimental to innovative behavior, with the blend of firm type being a more important consideration, and 2) that the rate at which an innovation impairs existing markets affects the activity levels of the firms within the population. The model’s results are validated against a matching study based on real- world data.

References

Arthur, W. B. (2013). Complexity Economics: A Different Framework for Economic Thought. SFI WORKING PAPER, 2013-04–012.

Friedman, M. (1953). The Methodology of Positive Economics. In Essays in Positive Economics (Vol. 3). Chicago, Ill.: University of Chicago Press.

Hamill, L., & Gilbert, N. (2015). Agent-Based Modelling in Economics: Hamill/Agent-Based Modelling in Economics. Chichester, UK: John Wiley & Sons, Ltd. https://doi.org/10.1002/9781118945520.

Kirman, A. P. (1992). Whom or What Does the Representative Individual Represent? Journal of Economic Perspectives, 6(2), 117–136. http://doi.org/10.1257/jep.6.2.117

Schumpeter, J. A. (1942). Capitalism, Socialism and Democracy. New York: Harper and Row.

Simon, H. A. (1955). A Behavioral Model of Rational Choice. The Quarterly Journal of Economics, 69(1), 99. http://doi.org/10.2307/1884852.

Tesfatsion, L., & Judd, K. L. (Eds.). (2006). Handbook of Computational Economics. Amsterdam Boston: Elsevier.

Van Valen, L. (1973). A New Evolutionary Law. Evolutionary Theory, 1, 1–30.

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