Agent-based computational economics
|Leigh Tesfatsion (2007), Scholarpedia, 2(2):1970.
|revision #66091 [link to/cite this article]
Agent-based computational economics (ACE) is the computational study of economic processes modeled as dynamic systems of interacting agents. Here agent refers broadly to a bundle of data and behavioral methods representing an entity constituting part of a computationally constructed world.
Examples of possible agents include individuals (e.g. consumers, producers), social groupings (e.g. families, firms, communities, government agencies), institutions (e.g. markets, regulatory systems), biological entities (e.g. crops, livestock, forests), and physical entities (e.g. infrastructure, weather, and geographical regions). Thus, agents can range from active data-gathering decision makers with sophisticated embodied cognitive capabilities to passive world features with no cognitive function.
Moreover, agents can be composed of other agents, thus permitting hierarchical constructions. For example, a firm agent might be composed of worker agents, manager agents, and shareholder agents.
ACE Research Areas
Current ACE research divides roughly into four strands differentiated by objective.
One primary objective is empirical understanding: Why have particular observed regularities evolved and persisted despite the absence of top-down planning and control? Examples of such regularities include interaction networks, financial market stylized facts, protocols for market operation, and the common adoption of technological innovations. ACE researchers seek causal explanations grounded in the repeated interactions of agents operating in realistically rendered worlds. Specifically, they try to understand whether particular types of observed regularities can be reliably generated from particular types of agent-based worlds.
A second primary objective is normative understanding: How can ACE models be used as laboratories for the discovery of good economic designs? ACE researchers pursuing this objective are interested in evaluating whether designs proposed for economic policies, institutions, or processes will result in socially desirable system performance over time. The general approach is akin to filling a bucket with water to determine if it leaks. An agent-based world is constructed that captures the salient aspects of an economic system operating under the design. The world is then populated with privately motivated agents with learning capabilities and allowed to develop over time. The key issue is the extent to which the resulting world outcomes are efficient, fair, and orderly, despite attempts by agents to gain individual advantage through strategic behavior.
A third primary objective is qualitative insight and theory generation: How can ACE models be used to gain a better understanding of economic systems through a better understanding of their full range of potential behaviors over time (equilibria plus basins of attraction)? Such understanding would help to clarify not only why certain types of regularities have evolved and persisted but also why others have not. One quintessential issue is the old but still unresolved concern of economists such as Adam Smith, Friedrich von Hayek, John Maynard Keynes, and Joseph Schumpeter: What are the self-organizing capabilities of decentralized market economies? A related issue is the evolution of institutions and social norms. For example, how do institutions and the behavioral dispositions of cognitive agents co-evolve over time, and what is the general likelihood that efficient social institutions will emerge without top down intervention?
A fourth primary objective is methodological advancement: How best to provide ACE researchers with the methods and tools they need to undertake theoretical studies of economic systems through systematic computational experiments, and to examine the compatibility of experimentally-generated theories with real-world data? ACE researchers are exploring a variety of ways to address this objective ranging from careful consideration of methodological principles to the practical development of programming, visualization, and validation tools.
- Filippo Castiglione (2006) Agent based modeling. Scholarpedia, 1(10):1562.
- Edward Ott (2006) Basin of attraction. Scholarpedia, 1(8):1701.
- James Meiss (2007) Dynamical systems. Scholarpedia, 2(2):1629.
- Eugene M. Izhikevich (2007) Equilibrium. Scholarpedia, 2(10):2014.
Linked below are websites of possible interest to ACE researchers and, more generally, to social science researchers interested in the development and use of agent-based modeling tools.