Agent-based models have been used since the mid-1990s to solve a variety of business and technology problems.

The three ideas central to agent-based models are agents as objects, emergence, and complexity.

Agent-based models have also been used for developing decision support systems such as for breast cancer.

Agent-based models are less concerned with predictive accuracy and instead emphasize theoretical development.

In some ways, agent-based models complement traditional analytic methods.

Agent-based models are thus effective in modeling bio-inspired and nature-inspired systems.

One class of computational models that is becoming increasingly popular are the agent-based models.

Agent-based models have many applications in biology, primarily due to the characteristics of the modeling method.

Several of the characteristics of agent-based models important to biological studies include:

Modular structure: The behavior of an agent-based model is defined by the rules of its agents.

However, most models work with the Lagrangian approach, which is an agent-based model following the individual agents (points or particles) that make up the swarm.

Before the agent-based model can be developed, one must choose the appropriate software or modeling toolkit to be used.

Listed below are summaries of several articles describing agent-based models that have been employed in biological studies.

These approaches include both compartmental models and agent-based models.

Most of the criticism seems to be aimed at agent-based models and simulation and how they work:

This is an agent-level model system, but unlike most agent-based models, it does not focus exclusively on the interactions of adjacent agents.

In corresponding agent-based models, the "agents" are "computational objects modeled as interacting according to rules" over space and time, not real people.

The birth of the agent-based model as a model for social systems was primarily brought about by a computer scientist, Craig Reynolds.

These include fields like sociological social psychology, social networks, dynamic network analysis, agent-based model and microsimulation.

Cas are occasionally modeled by means of agent-based models and complex network-based models.

Agent-based models of social networks, java applets.

In addition to the probabilistic approach, agent-based models exist that capture the spatial dynamics of the neural system under scrutiny.

Agent-based models of financial markets often assume investors act on the basis of adaptive learning or adaptive expectations.

The agent-based model developed for the study considered three types of agents: invasive species, importers, and border enforcement agents.

Agent-based models consist of dynamically interacting rule-based agents.