Complexity economics

Complexity economics is the application of complexity science to the problems of economics. It sees the economy not as a system in equilibrium, but as one in motion, perpetually constructing itself anew. It uses computational and mathematical analysis to explore how economic structure is formed and reformed, in continuous interaction with the adaptive behavior of the 'agents' in the economy.
Models.
The "nearly archetypal example" is an artificial stock market model created by the Santa Fe Institute in 1989. The model shows two different outcomes, one where "agents do not search much for predictors and there is convergence on a homogeneous rational expectations outcome" and another where "all kinds of technical trading strategies appearing and remaining and periods of bubbles and crashes occurring".
Another area has studied the prisoner's dilemma, such as in a network where agents play amongst their nearest neighbors or a network where the agents can make mistakes from time to time and "evolve strategies". In these models, the results show a system which displays "a pattern of constantly changing distributions of the strategies".
More generally, complexity economics models are often used to study how non-intuitive results at the macro-level of a system can emerge from simple interactions at the micro level. This avoids assumptions of the representative agent method, which attributes outcomes in collective systems as the simple sum of the rational actions of the individuals.
Measures.
Economic complexity index.
MIT physicist César Hidalgo and Harvard economist Ricardo Hausmann introduced a spectral method to measure the complexity of a country's economy by inferring it from the structure of the network connecting countries to the products that they export. The measure combines information of a country's diversity, which is positively correlated with a country's productive knowledge, with measures of a product ubiquity (number of countries that produce or export the product). This concept, known as the "Product Space", has been further developed by MIT's Observatory of Economic Complexity, and in The Atlas of Economic Complexity in 2011.
Relevance.
The economic complexity index (ECI) introduced by Hidalgo and Hausmann is highly predictive of future GDP per capita growth. In Hausmann, Hidalgo et al., the authors show that the List of countries by future GDP (based on ECI) estimates ability of the ECI to predict future GDP per capita growth is between 5 times and 20 times larger than the World Bank's measure of governance, the World Economic Forum's (WEF) Global Competitiveness Index (GCI) and standard measures of human capital, such as years of schooling and cognitive ability.
Metrics for country fitness and product complexity.
Pietronero and collaborators have recently proposed a different approach. These metrics are defined as the fixed point of non-linear iterative map. Differently from the linear algorithm giving rise to the ECI, this non-linearity is a key point to properly deal with the nested structure of the data. The authors of this alternative formula claim it has several advantages:
The metrics for country fitness and product complexity have been used in a report of the Boston Consulting Group on Sweden growth and development perspectives.
Features.
Brian Arthur, Steven N. Durlauf, and David A. Lane describe several features of complex systems that deserve greater attention in economics.
Contemporary trends in economics.
Complexity economics has a complex relation to previous work in economics and other sciences, and to contemporary economics. Complexity-theoretic thinking to understand economic problems has been present since their inception as academic disciplines. Research has shown that no two separate micro-events are completely isolated, and there is a relationship that forms a macroeconomic structure. However, the relationship is not always in one direction; there is a reciprocal influence when feedback is in operation.
Complexity economics has been applied to many fields.
Intellectual predecessors.
Complexity economics draws inspiration from behavioral economics, Marxian economics, institutional economics/evolutionary economics, Austrian economics and the work of Adam Smith. It also draws inspiration from other fields, such as statistical mechanics in physics, and evolutionary biology. Some of the 20th century intellectual background of complexity theory in economics is examined in Alan Marshall (2002) The Unity of Nature, Imperial College Press: London. See Douma & Schreuder (2017) for a non-technical introduction to Complexity Economics and a comparison with other economic theories (as applied to markets and organizations).
Applications.
The theory of complex dynamic systems has been applied in diverse fields in economics and other decision sciences. These applications include capital theory, game theory, the dynamics of opinions among agents composed of multiple selves, and macroeconomics. In voting theory, the methods of symbolic dynamics have been applied by Donald G. Saari. Complexity economics has attracted the attention of historians of economics. Ben Ramalingam's Aid on the Edge of Chaos includes numerous applications of complexity economics that are relevant to foreign aid.
Complexity economics as mainstream, but non-orthodox.
According to , , and contemporary mainstream economics is evolving to be more "eclectic", diverse, and pluralistic. state that contemporary mainstream economics is "moving away from a strict adherence to the holy trinity – rationality, selfishness, and equilibrium", citing complexity economics along with recursive economics and dynamical systems as contributions to these trends. They classify complexity economics as now mainstream but non-orthodox.
Criticism.
In 1995-1997 publications, "Scientific American" journalist John Horgan "ridiculed" the movement as being the fourth "C" among the "failed fads" of "complexity, chaos, catastrophe, and cybernetics". In 1997, Horgan wrote that the approach had "created some potent metaphors: the butterfly effect, fractals, artificial life, the edge of chaos, self organized criticality. But they have not told us anything about the world that is both concrete and truly surprising, either in a negative or in a positive sense."
Rosser "granted" Horgan "that it is hard to identify a concrete and surprising discovery (rather than "mere metaphor") that has arisen due to the emergence of complexity analysis" in the discussion journal of the American Economic Association, the "Journal of Economic Perspectives". Surveying economic studies based on complexity science, Rosser wrote that the findings, rather than being surprising, confirmed "already-observed facts." Rosser wrote that there has been "little work on empirical techniques for testing dispersed agent complexity models." Nonetheless, Rosser wrote that "there is a strain of common perspective that has been accumulating as the four C's of cybernetics, catastrophe, chaos, and complexity emerged, which may now be reaching a critical mass in terms of influencing the thinking of economists more broadly."