Complexity economics

Complexity economics is the application of complexity science to the problems of economics. It is one of the four C's of a new paradigm surfacing in the field of economics. The four C's are complexity, chaos, catastrophe and cybernetics. This new mode of economic thought rejects traditional assumptions that imply that the economy is a closed system that eventually reaches an equilibrium. Instead, it views economies as open complex adaptive systems with endogenous evolution. Complex systems do not necessarily settle to equilibrium — even ideal deterministic models may exhibit chaos, which is distinct from both random (nondeterministic) and analytic behavior.

Introduction

Complexity economics is built on foundations which draw inspiration from areas such as behavioral economics, Marxian economics, institutional economics/evolutionary economics, Austrian economics and the work of Adam Smith. Complexity incorporates components from each of these areas of economic thought, though the complex perspective includes many more characteristics to describe a dynamic system such as emergence, sensitive dependence on initial conditions, red queen behavior, and complex systems usually incorporate a selection mechanism as described by most general evolutionary models. There is no widely accepted specific definition for complexity as it pertains to economic systems. This is largely because the field as a whole is still under construction.

Complexity economics rejects many aspects of traditional economic theory. The mathematical models used by traditional economics were formulated in an analogy with early models of thermodynamics. These mathematical models of economics were substantially based on the first law of thermodynamics, equilibrium. Later, the second law of thermodynamics, concerning the growing amount of entropy in any spontaneous physical process, was formulated by Rudolf Clausius. Proponents of complexity economics claim that traditional economic models never adapted to the latter discovery and thus remain incomplete models of reality, and that mainstream economists are yet to introduce information entropy to their models. Information entropy was developed in 1949 by C. Shannon and W. Weaver, based on Boltzmann's statistical thermodynamics, as "information uncertainty", associated with any probability distribution. Entropy has been used at least since 1988 to formulate the important concepts of organization and disorder, viewed as basic state parameters, in describing/simulating the evolution of complex systems (including economic systems).

In the light of the new concepts introduced, economic systems shall no more be considered as "naturally" inclined to achieve equilibrium states. On the contrary, economic systems - like most complex and self-organized systems - are intrinsically evolutionary systems, which tend to develop, prevailingly toward levels of higher internal organization; though the possibility of involution processes - or even of catastrophic events - remains immanent. Traditional economic models have been constructed by allowing only a very small number of degrees of freedom, in order to simplify models. For example, the relation of unemployment and inflation is traditionally considered to be a simple function with one degree of freedom, allowing for very little entropy.

As to the practicability of theoretical instruments, there is also a crucial difference to allow for: traditional economics was conceived before computers had been invented. Computational simulations have made it possible to demonstrate macro-level rules using only micro-level behaviors without assuming idealized market actors. For example, Pareto's law can be demonstrated to arise spontaneously.

Behavior of complex systems

Brian Arthur, Steven N. Durlauf, and David A. Lane, of the Santa Fe Institute define six features of complex systems that have presented significant trouble for traditional mathematics.

1. Dispersed Interaction — What happens in the economy is determined by the interaction of many dispersed, possibly heterogeneous, agents acting in parallel. The action of any given agent depends upon the anticipated actions of a limited number of other agents and on the aggregate state these agents co-create.
2. No Global Controller — No global entity controls interactions. Instead, controls are provided by mechanisms of competition and coordination between agents. Economic actions are mediated by legal institutions, assigned roles, and shifting associations. Nor is there a universal competitor—a single agent that can exploit all opportunities in the economy.
3. 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.
4. Continual Adaptation — Behaviors, actions, strategies, and products are revised continually as the individual agents accumulate experience—the system constantly adapts.
5. Perpetual Novelty Niches — These are continually created by new markets, new technologies, new behaviors, new institutions. The very act of filling a niche may provide new niches. The result is ongoing, perpetual novelty.
6. Out-of-Equilibrium Dynamics — Because new niches, new potentials, new possibilities, are continually created, the economy operates far from any optimum or global equilibrium. Improvements are always possible and indeed occur regularly.

Comparison with traditional economics

The table below illustrates the differences between the complexity perspective and classical economics. Eric Beinhocker proposes five concepts that distinguish complexity economics from traditional economics. The first five categories are Beinhocker's synthesis, the last four are from W. Brian Arthur as reprinted in David Colander's The Complexity Vision.

	Complexity Economics	Traditional Economics
Dynamic	Open, dynamic, non-linear systems, far from equilibrium	Closed, static, linear systems in equilibrium
Agents	Modelled individually; use inductive rules of thumb to make decisions; have incomplete information; are subject to errors and biases; learn to adapt over time; heterogeneous agents	Modelled collectively; use complex deductive calculations to make decisions; have complete information; make no errors and have no biases; have no need for learning or adaptation (are already perfect), mostly homogeneous agents
Networks	Explicitly model bi-lateral interactions between individual agents; networks of relationships change over time	Assume agents only interact indirectly through market mechanisms (e.g. auctions)
Emergence	No distinction between micro/macro economics; macro patterns are emergent result of micro level behaviours and interactions.	Micro-and macroeconomics remain separate disciplines
Evolution	The evolutionary process of differentiation, selection and amplification provides the system with novelty and is responsible for its growth in order and complexity	No mechanism for endogenously creating novelty, or growth in order and complexity
Technology	Technology fluid, endogenous to the system	Technology as given or selected on economic basis
Preferences	Formulation of preferences becomes central; individuals not necessarily selfish	Preferences given; Individuals selfish
Origins from Physical Sciences	Based on Biology (structure, pattern, self-organized, life cycle)	Based on 19th-century physics (equilibrium, stability, deterministic dynamics)
Elements	Patterns and Possibilities	Price and Quantity

Examples

Another example for a complex system may possibly the German Hyperinflation 1922/23, which the Traditional Economics fruitless describes. This colossal and devastating event of hyperinflation with law and order vanished, the breakdown of the social and economic order and the establishment of the general behaviour "everyone for himself" may be explained through rational reasoning, for example through the social science’s mass hysteria or bandwagon effect, through the medical discovery of the social brain or mirror neurons. Nevertheless the immense acceleration process in hyperinflations still remains unclear and unpredictable. The transformation of an inflationary development into the hyperinflation as a very complex phenomenon could be a further advanced research avenue of the complexity economics.

See also

• Computational economics

External links

• Santa Fe Institute Birthplace of complexity science
• What Should Policymakers Know About Economic Complexity (PDF) Summary of key ideas in complexity economics