There is no universal agreement on the precise definition of the term ‘agent’. From a pragmatic modeling standpoint there are several features that are common to most agents:

Autonomy: Agents are autonomous units (i.e. operating without the influence of centralized control), capable of processing information and exchanging this information with other agents in order to make independent decisions. They are free to interact with other agents, at least over a limited range of situations, and this does not (necessarily) affect their autonomy. In this respect, agents are active rather than purely passive (see below).

Heterogeneity: The notion of mean-individuals is redundant; agents permit the development of autonomous individuals. Groups of agents can exist, but they are spawned from the bottom-up, as amalgamations of similar autonomous individuals.

Active: Agents are active because they exert independent influence in a simulation. The following active features can be identified:

Pro-active/goal-directed: Agents are often deemed goal-directed, having goals to achieve (not necessarily objectives to maximize) with respect to their behaviors. For example, agents within a geographic space can be developed to find or follow a set of spatial paths to achieve a goal within a certain constraint (e.g. time), when exiting a building during an emergency.

Reactive/Perceptive: Agents can be designed to have an awareness, or sense of their surroundings. Agents can also be supplied with prior knowledge, in effect a ‘mental map’ of their environment, thus providing them with an awareness of other entities, obstacles, or required destinations within their environment. Extending the example above, agents could therefore be provided with knowledge of building exit locations.

Bounded Rationality: The dominant form of modeling in the social sciences is based upon a rational-choice paradigm. Rational-choice models generally assume that agents are perfectly rational optimizers with unfettered access to information, foresight, and infinite analytical ability (Parker et al., 2003). These agents are therefore capable of deductively solving complex mathematical optimization problems in order to maximize their well-being, thereby balancing long-run and short-run payoffs in the face of uncertainty. While rational-choice models can have substantial explanatory power, some of their axiomatic foundations are contradicted by experimental evidence, leading prominent social scientists to question their empirical validity. However, agents can be configured with ‘bounded’ rationality (through their heterogeneity) to circumvent the potential limitations of these assumptions (e.g. agents can be provided with fettered access to information at the local level). In effect, the aforementioned ‘perception’ of agents can be constrained. Thus, rather than implementing a model containing agents with optimal solutions that can fully anticipate all future states of which they are part, agents make inductive, discrete, and adaptive choices that move them towards achieving goals. For instance, an agent may have knowledge of all building exit locations, but agents will be unaware if all exits are accessible (e.g. some may have become blocked through congestion).

Interactive/Communicative: Agents have the ability to communicate extensively. For example, agents can query other agents and/or the environment within a neighborhood, via neighborhoods of (potentially) varying size, searching for specific attributes, with the ability to disregard an input which does not match a desirable threshold.

Mobility: The mobility of agents is a particularly useful feature, not least for spatial simulations. Agents can roam the space in which they are situated within a model. Juxtaposed with the agent’s ability to interact and their intelligence, this permits a vast range of potential uses.

Adaptation/Learning: Agents can also be designed to be adaptive, which can produce Complex Adaptive Systems (CAS; Holland, 1995). Agents can be designed to alter (limited to a given threshold if required) their states depending on their current states, permitting agents to adapt with a form of memory or learning, but not necessarily in the most efficient way possible. Agents can adapt at the individual level (e.g. learning alters the probability distribution of rules that compete for attention), or the population level (e.g. learning alters the frequency distribution of agents competing for reproduction).