Agent-based model

Agent-based Model

An agent-based model (ABM) is a class of computational models for simulating the actions and interactions of autonomous agents (both individual or collective entities such as organizations or groups) with a view to assessing their effects on the system as a whole. It combines elements of game theory, complex systems, emergence, computational sociology, multi-agent systems, and evolutionary programming.

Overview

Agent-based models are used to simulate the behaviors of agents in a given environment. These agents can represent individuals, groups, or entities that interact with each other and their environment according to a set of rules. The goal of an ABM is to explore the dynamics of the system and to understand how individual behaviors lead to emergent phenomena at the macro level.

Components of Agent-based Models

An agent-based model typically consists of the following components:

• Agents: These are the individual entities with defined behaviors and characteristics. Agents can be heterogeneous, meaning they can have different attributes and rules of behavior.

• Environment: The space in which agents operate. The environment can be spatial, network-based, or abstract, and it can influence agent behavior.

• Rules: The set of instructions that dictate how agents interact with each other and with the environment. These rules can be deterministic or stochastic.

• Interactions: The ways in which agents communicate or affect each other. Interactions can be direct (e.g., communication) or indirect (e.g., through the environment).

Applications

Agent-based models are used in a variety of fields, including:

• Economics: To model markets and consumer behavior.
• Epidemiology: To simulate the spread of diseases and the impact of interventions.
• Ecology: To study ecosystems and animal behavior.
• Social Sciences: To explore social phenomena such as cooperation, competition, and social norms.

Advantages

• Flexibility: ABMs can model complex systems with heterogeneous agents and interactions.
• Emergence: They can capture emergent phenomena that arise from simple rules.
• Scalability: ABMs can be scaled to include large numbers of agents and interactions.

Challenges

• Computational Cost: Simulating large numbers of agents can be computationally expensive.
• Validation: Ensuring that the model accurately represents the real-world system can be difficult.
• Complexity: The complexity of the model can make it difficult to understand and interpret results.

See Also

• Complex systems
• Multi-agent system
• Emergence
• Computational sociology

References

• Bonabeau, E. (2002). "Agent-based modeling: Methods and techniques for simulating human systems." Proceedings of the National Academy of Sciences, 99(suppl 3), 7280-7287.
• Macal, C. M., & North, M. J. (2010). "Tutorial on agent-based modelling and simulation." Journal of Simulation, 4(3), 151-162.

External Links

• CoMSES Net: Computational Modeling for Socio-Ecological Science