Agent-based computational economics (ACE) is the computational modeling of economic processes (including whole economies) as open-ended dynamic systems of interacting agents.
Here "agent" refers broadly to a bundle of data and methods representing an entity residing within the dynamic system. Examples of possible agents include: individuals (e.g., consumers and producers); social groupings (e.g., families, firms, communities, and government agencies); institutions (e.g., markets and regulatory systems); biological entities (e.g., crops, livestock, and forests); and physical entities (e.g., infrastructure, weather, and geographical regions). Thus, agents can range from passive system features to active data-gathering decision makers capable of sophisticated learning and social behaviors. Moreover, agents can be composed of other agents, permitting hierarchical constructions.
ACE modeling is analogous to a culture-dish laboratory experiment for a virtual world. Starting from an initial world state, specified by the modeler, the virtual world should be capable of evolving over time driven solely by the interactions of the agents that reside within the world. No resort to externally imposed sky-hooks enforcing global coordination, such as market clearing and rational expectations constraints, should be needed to drive or support the dynamics of this world.
Important Note: A major misconception, still being expressed by some mainstream economists and bloggers uninformed about the powerful capabilities of modern software, is that ACE agents cannot exhibit forward-looking behavior. To the contrary, ACE agents can exhibit a broad spectrum of learning behaviors ranging from stimulus-response adaptation to anticipatory learning and intertemporal planning. For example, Q-learning developed by Watkins (1989) for the approximation and successive updating of dynamic programming value functions is a widely used method for the algorithmic representation of anticipatory agent learning. Q-learning is a special case of temporal-difference learning, which involves the use of changes (or differences) in predictions over successive time steps to update current predictions. Extensive annotated pointers to reference materials on these and other agent learning algorithms can be accessed at the following site:
ACE Research Area: Learning and the Embodied Mind.
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 trade networks,
socially accepted monies, market protocols, business cycles, and the common
adoption of technological innovations. ACE researchers seek causal
explanations grounded in the repeated interactions of agents operating in
realistically rendered virtual worlds. Specifically, they try to understand whether
particular types of observed regularities can be reliably generated within these worlds.
A second primary objective is normative understanding: How can
ACE models be used as computational 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. A virtual 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. One 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 second key issue is a cautionary concern for adverse unintended consequences.
A third primary objective is qualitative insight and theory
generation: How can ACE models be used to gain a better understanding of dynamic 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. A quintessential example is the old
but still unresolved concern of economists such as Adam Smith and Friedrich von Hayek: What are the self-organizing capabilities of decentralized market economies?
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 dynamic 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.
Linked below are materials of possible interest to ACE researchers as well as to researchers more generally who wish to explore the potential usefulness of agent-based modeling for social science purposes. These
materials are updated on a regular basis, and suggestions for additional materials to include are welcome.
As time permits, ACE news notes are posted below to let people know which ACE Website pages have been most heavily updated since the last news notes posting. Whenever these news notes are ready
for posting, a brief announcement giving a pointer to this
posting is emailed to all participants in a moderated Majordomo announcements-only ACE news list.
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if you have ACE-related news items that you would like included at the ACE website and announced in the ACE news postings. Only items of persistent interest (e.g., not conference announcements) can be handled, and only batched postings by the moderator are permitted.
Materials Linked to Date
A Big Picture Overview: "From Human-Subject Experiments to Computational-Agent Experiments (and Everything in Between)"
On-Line Guide for Newcomers to Agent-Based Modeling
in the Social Sciences (R. Axelrod and L. Tesfatsion)