kami/docs/abm.rst

About Agent-Based Models
========================

Agent-based models (ABM) are a type of computational model used
to simulate the behavior of autonomous agents within a system. These
agents can be individuals, groups, organizations, or other entities
that interact with one another and with their environment.

One of the key features of ABMs is that they focus on the micro-level
interactions between individual agents, rather than aggregating
data to study macro-level phenomena. This allows for the examination
of complex behaviors that emerge from the interactions between
agents, such as the spread of a disease or the formation of social
networks.

ABMs are often used in fields such as economics, sociology, and
biology to study the behavior of individuals and groups. They can
also be used to simulate the effects of different policies or
interventions on a system.

In order to create an ABM, the researcher must first define the
agents and their characteristics, such as their behavior, beliefs,
and goals. They must also define the rules of interaction between
the agents and their environment. Once these parameters are set,
the model can be run to simulate the behavior of the agents over
time.

ABMs are a powerful tool for understanding complex systems, but
they also have their limitations. Because they focus on micro-level
interactions, they may not accurately capture macro-level phenomena.
Additionally, they often require a significant amount of computational
resources and can be difficult to validate.

Overall, agent-based models are a valuable tool for understanding
the behavior of complex systems and the emergence of complex behaviors
from the interactions between individuals. However, it is important
to use them in conjunction with other methods to fully understand
the system being studied.

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