
# 9.1: What Are Agent-Based Models?

At last, we have reached the very ﬁnal chapter, on agent-based models (ABMs). ABMs are arguably the most generalized framework for modeling and simulation of complex systems,whichactuallyincludebothcellularautomataanddynamicalnetworksasspecial cases. ABMs are widely used in a variety of disciplines to simulate dynamical behaviors of systems made of a large number of entities, such as traders’ behaviors in a market (in economics), migration of people (in social sciences), interaction among employees and their performance improvement (in organizational science), ﬂocking/schooling behavior of birds/ﬁsh (in behavioral ecology), cell growth and morphogenesis (in developmental biology), and collective behavior of granular materials (in physics). Figure 19.1 shows a schematic illustration of an ABM. It is a little challenging to deﬁne precisely what an agent-based model is, because its modeling assumptionsarewideopen,andthustherearen’tmanyfundamentalconstraints thatcharacterizeABMs. ButhereiswhatIhopetobeaminimalisticdeﬁnitionofthem:

Agent-based models are computational simulation models that involve many discrete agents.

There are a few keywords in this deﬁnition that are important to understand ABMs. The ﬁrst keyword is “computational.” ABMs are usually implemented as simulation models in a computer, where each agent’s behavioral rules are described in an algorithmic fashion rather than a purely mathematical way. This allows modelers to implement complexinternalpropertiesofagentsandtheirnontrivialbehavioralrules. Such representations of complex individual traits are highly valued especially in social, organizational
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Agents
Environment
Agent’s attributes • position • age • gender • knowledge • memory • experience • emotion       etc…

Figure 19.1: Schematic illustration of what an agent-based model (ABM) looks like.

and management sciences, where the modelers need to capture complex, realistic behaviors of human individuals. This is why ABMs are particularly popular in those research areas. This, of course, comes at the cost of analytical tractability. Since agents can have any number of complex properties and behavioral rules, it is generally not easy to conduct an elegant mathematical analysis of an ABM (which is why there is no “Analysis” chapter on ABMs after this one). Therefore, the analysis of ABMs and their simulation results are usually carried out using more conventional statistical analysis commonly used in social sciences, e.g., by running Monte Carlo simulations to obtain distributions of outcome measurements under multiple experimental conditions, and then conducting statistical hypothesis testing to see if there was any signiﬁcant difference between the different experimental conditions. In this sense, ABMs could serve as a virtual replacement of experimental ﬁelds for researchers. The second keyword in the deﬁnition above is “many.” Although it is technically possible to create an ABM made of just a few agents, there would be little need for such a model, because the typical context in which an ABM is needed is when researchers want to study the collective behavior of a large number of agents (otherwise it would be sufﬁcienttouseamoreconventionalequation-basedmodelwithasmallnumberofvariables). Therefore, typical ABMs contain a population of agents, just like cells in CA or nodes in dynamical networks, and their dynamical behaviors are studied using computational simulations. The third keyword is “discrete.” While there are some ambiguities about how to rigorously deﬁne an agent, what is commonly accepted is that an agent should be a discrete individual entity, which has a clear boundary between self and the outside. CA and networkmodelsaremadeofdiscretecomponents, sotheyqualifyasspecialcasesofABMs. In the meantime, continuous ﬁeld models adopt continuous spatial functions as a representation of the system’s state, so they are not considered ABMs. There are certain properties that are generally assumed in agents and ABMs, which collectively deﬁne the “agent-ness” of the entities in a model. Here is a list of such properties:

Typical properties generally assumed in agents and ABMs

• Agents are discrete entities.
• Agents may have internal states.
• Agents may be spatially localized.
• Agents may perceive and interact with the environment.
• Agents may behave based on predeﬁned rules.
• Agents may be able to learn and adapt.
• Agents may interact with other agents.
• ABMs often lack central supervisors/controllers.
• ABMs may produce nontrivial “collective behavior” as a whole.