Transcript Spatial Control and Individual-Based Modeling: Bears and

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 datastructure (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.
Provide a mechanism for interactions to
occur between individuals.
3.
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.