Agent-based Modeling: A Brief Introduction Louis J. Gross The Institute for Environmental Modeling Departments of Ecology and Evolutionary Biology and Mathematics University of Tennessee

General Goal of this Modeling Approach? Provide a means to connect interactions between individuals and environmental and other influences, taking account of differences between individuals.

What is agent-based modeling? A methodology to track the actions of multiple "agents" which are defined to be objects with some type of autonomous behavior. Examples: individual animals or plants single cars or airplanes letters or packages football players

Key component: a set of rules which governs the actions of the agents. These agents need not be rational (letters, many car drivers), but need to have some explicit set of actions which will follow (perhaps according to some probability distribution) from their current state and the state of other agents

Difference between agent or individual-based models and models of agents: One is possibly a subset of the other - there are many papers published on models that describe individual-level processes and actions, for examples models for individual plant or animal growth that include bioenergetics. These do not focus on the interactions between individuals and it is these interactions which can greatly affect phenomena that occur due to aggregation of individuals.

The methodology which underlies agent-based approaches is "object-oriented programming" object-oriented languages (C++ and Java are two popular ones) provide data structures which naturally allow for efficient agent-based modeling they provide for "inheritance" whereby one data- structure (e.g. individual) is provided with the same underlying data-structure as all others of a particular type (e.g. species), allowing the coder to set up a class of objects (individuals all of the same species) with similar basic properties, but allowing for differences between them (perhaps due to sub-classes such as gender) This is a very modular and hierarchical approach to coding, unlike traditional procedural languages.

How does agent-based modeling relate to other standard modeling approaches? –Much of modeling in biology uses an aggregated approach: a single variable represents a property of a collection of objects (populations, cells, genes, etc.) –Agent-based models use the reductionist view that these aggregated variables should be able to be observed as a function of the actions and interactions of the individuals which make up the aggregation. –Objective is to describe the basic processes which control the actions of individuals, and aggregate these up to determine the resultant macrodescriptors which arise at higher levels of organization.

Are there other methods which account for individual-actions? Dynamic state-space approach: the state structure is that of a few characteristics of individuals which may be presumed to follow an optimization rule through time. Dynamic programming algorithm is applied to determine optimal dynamic behavior of individuals.

The major objectives of individual-based models are to: 1.Consider individual variations in factors such as sex, size, age, health, social status, etc. 2.Include spatially-explicit information on habitat, roads, topography, local resources, etc. and the effects these have on individual behavior. 3. Provide a mechanism for interactions to occur between individuals.

4. Allow for dynamic coupling of habitat components to organisms through direct feedback of organism behavior on appropriate habitat conditions, such as reducing available forage due to effects of individuals. 5. Provide mechanisms to take into account detailed behavioral and physiological information when available. 6. Estimate phenomena at different organizational levels (e.g. population/community) from the actions of individuals

Some Disadvantages of Individual-Based approaches 1. Requires detailed knowledge of behavior and physiology, thus is generally appropriate for large, charismatic species, but of limited use in other cases. 2. May require considerable coding expertise to develop as well as considerable computer time to run. 3. Typically requires many simulations to evaluate any particular situation as it is based upon an underlying stochastic model. 4. As with any model, typically requires assumptions about what aspects of behavior are important and what can be ignored.