This post continues a sequence of posts on the numerous changes required to create a Macroeconomics for the 21st Century. The previous post was Ecological Economics For the next post in this sequence, see Economics as Moral Philosophy
Classical Physics, the model for modern economics, was based on the ideas of stability and permanence of astronomical orbits; see Mirowski (1992). Deeper examination of astrophysics led to the replacement of this view by big bang which gave birth to the universe, and increasing entropy, which will lead to its heat death. “Equilibrium” just appears as a temporary and local phenomenon in an evolving and chaotic universe.
Complexity economics takes non-equilibrium seriously. Constantly evolving systems may never be in equilibrium and may even lack a tendency towards equilibrium. The mode of analysis required for such systems cannot be based on the kind of mathematic currently dominant in the economics profession. Complex systems theory was pioneered in the 1980s and 1990s by a small team at the Santa Fe Institute led by W. Brian Arthur. Some of the key elements of this approach to economics can be described as follows:
- Computational Aspects: It is essential to understand how closely our analytical methods correlate with our computational abilities. Just as tensor calculus enabled the discovery of relativity by Einstein, the computer power today enables us to explore consequences of behavioral assumptions far beyond the capabilities of mathematical formulae. Even the mechanics of human motion cannot be captured in formulae, but can be expressed and represented as a linked network on computers. Similarly, computers today allow us to capture and analyze dynamics of extremely complex systems.
- Complex Features: Brian Arthur et al. (1997) describe several features of complex systems which create models of types not easily accessible by conventional analytical tools and techniques. These include:
- No global controller—Controls are provided by mechanisms of competition and coordination between agents. Economic actions are mediated by legal institutions, assigned roles, and shifting associations. No global entity controls interactions. Contrast with DSGE models, where decisions are supposed to optimize welfare function for the economy as a whole.
- Cross-cutting hierarchical organization—The economy has many levels of organization and interaction. Units at any given level behaviors, actions, strategies, products typically serve as “building blocks” for constructing units at the next higher level. The overall organization is more than hierarchical, with many sorts of tangling interactions (associations, channels of communication) across levels. This type of modeling is essential to capture the effects of institutions, which conventional economics fails to model.
- Ongoing adaptation—Behaviors, actions, strategies, and products are revised frequently as the individual agents accumulate experience. The possibility for agents to learn about their environment and change their behavior in response does not exist in conventional economic theory, because everyone is supposed to know everything and automatically act in the best possible way. Trial and error, and dynamics of learning play no role.
- Out-of-equilibrium dynamics—Because new niches, new potentials, new possibilities, are continually created, the economy functions without attaining any optimum or global equilibrium. Improvements occur regularly. Again, this is in dramatic contrast with conventional economic theory, which is blind to disequilibrium behavior.
- Interactions between Groups and Individuals: Individual behaviors create group responses, but individuals also respond to group dynamics. There is two-way interaction between groups and individuals. In response to a price change, the individual response of a firm is conditions on its assumptions about the group response. In turn the group response aggregates individual responses, creating complexity and emergent behaviors. Radical uncertainty and black swans enter the picture because we cannot anticipate how others model the system and make decisions, but these decisions affect our decisions and outcomes. In addition, even though families, communities, and friends are central to our lives, they do not exist in the models of economists. These can be accounted for realistically in complex models.
Some of the successful applications of complexity include the economic complexity index (ECI) introduced by Hausmann et al. (2011), which is highly predictive of future GDP per capita growth. Arthur (2018) describes some of the striking successes of complexity economics as follows: “the increasing-returns work done in the 1980s … shows how network effects lead to lock-in and dominance of one or a few players. This can’t be done by equilibrium economics — it’s not an equilibrium phenomenon. Now all of Silicon Valley accepts this theory and operates by it. Another major contribution is to asset pricing, where all rational market theories were proven failures in the GFC 2007. Complexity doesn’t assume there is an equilibrium and set out to find it. It assumes investors don’t know what the market is doing and must learn for themselves what works — which itself changes the market. The results show phenomena seen in real markets: technical trading, correlations among price and volume, and periods of high volatility followed by low volatility (GARCH behavior). Thus, unlike rational expectations, complexity theory explains real world financial phenomena.”
Hausmann, R., C. Hidalgo, S. Bustos, M. Coscia, A. Simoes and M. A. Yildirim (2011). The Atlas of Economic Complexity: Mapping Paths to Prosperity. Cambridge: Center for International Development, Harvard University.
Arthur, W. B. (2018). Complexity Economics. Santa Fe Institute webpage, http://tuvalu.santafe. edu/~wbarthur/complexityeconomics.htm (accessed April 17, 2020).