Individual-Based Models
an annotated list of links
by Craig Reynolds
Individual-based models are simulations based on the global consequences
of local interactions of members of a population. These individuals
might represent plants and animals in ecosystems, vehicles in traffic,
people in crowds, or autonomous characters in animation and games. These
models typically consist of an environment or framework in which
the interactions occur and some number of individuals defined in terms
of their behaviors (procedural rules) and characteristic parameters.
In an individual-based model, the characteristics of each individual are
tracked through time. This stands in contrast to modeling techniques where
the characteristics of the population are averaged together and the model
attempts to simulate changes in these averaged characteristics for the
whole population. Individual-based models are also known as entity
or agent based models, and as individual/entity/agent-based simulations.
Some individual-based models are also spatially explicit meaning
that the individuals are associated with a location in geometrical space.
Some spatially explicit individual-based models also exhibit mobility,
where the individuals can move around their environment. This would be
a natural model, for example, of an animal in an ecological simulation.
Whereas plants in the same simulation would not be mobile. Some individual-based
models are not spatially explicit, for example a simulation of a computer
network might be based on individual models of the networked computers,
but their location would be irrelevant. Spatially explicit models may use
either continuous (real valued) or discrete (grid-based,
integer valued) space.
Individual-based models are a subset of multi-agent systems which
includes any computational system whose design is fundamentally composed
of a collection of interaction parts. For example an "expert system" might
be composed of many distinct bits of advice which interact to produce a
solution. Individual-based models are distinguished by the fact that each
"agent" corresponds to autonomous individual in the simulated domain.
There is an overlap between individual-based models and cellular
automata. Certainly cellular automata are similar to spatially-explicit,
grid-based, immobile individual-based models. However CAs are always homogeneous
and dense (all cells are identical), whereas a grid-based individual-based
model might occupy only a few grid cells, and distinct type of individuals
might live on the same grid. (Of course a CA can have cells in various
states, and so represent concepts like "empty" or "occupied by type 3".
Perhaps the only difference is whether the simulation's inner loop proceeds
cell by cell, or individual by individual.) The philosophical distinction
is whether the simulation is based on a dense and uniform dissection of
the space (as in a CA), or based on specific individuals distributed within
the space.
Of course, note that everyone uses terminology differently, so take
the definitions above with a grain of salt. ("Your mileage may differ.")
Another taxonomy of individual-based models can be found at this page on
multiple
autonomous agent technology.
My interest in this area began when I made a model of bird
flocks and related group motion. As a result I am particularly interested
in individual-based models using spatially explicit mobile agents in continuous
space. This bias may be reflected in the selection of resources listed
below.
Online resources
-
Swarm is a software
package for multi-agent simulation of complex systems being developed at
The Santa Fe Institute. See this
preliminary version of the Swarm FAQ
and these example
applications built using the Swarm system.
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Ecology and Biology:
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Scaling
from Trees to Forests: Analysis of a Complex Simulation Model by Doug
Deutschman, Simon Levin,
Catherine Devine and Linda
Buttel. (A very nicely produced multimedia presentation in Science
Online.) Spatially explicit forest growth models using SORTIE, a stochastic
individual-based simulation model of forest dynamics in which trees compete
for light.
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ATLSS
Across Trophic Level System Simulation for the Everglades/Big Cypress
Region of South Florida, a very large-scale ecological simulation effort
lead by Donald DeAngelis
and a cast
of thousands. See the related paper: Computational
Models of White-Tailed Deer in the Florida Everglades, and this report
from Science
Alliance News.
-
Facilitating
Mobile Objects within the Context of Simulated Landscape Processes
by James D. Westervelt
and Lewis D. Hopkins
this 1996 paper describes modeling carnivore and herbivore populations
as they interact with vegetation in the context of a landscape described
with a geographic information system.
-
The Tragedy
of the Commons Java applets and commentary by Walter Korman (from the
now defunct weekly column Deep
Magic). Based on Garrett Hardin's 1968 paper.
-
Parallel Software Tools
for Ecological Simulation including the Java-based GUST
which runs an interactive version of their Szymanski-Caraco cellular automata
model. See their Guide
to Related Research.
-
Ecomachines and
Spatial Modeling in Ecology and Biology was a workshop held January
13-16, 1996 at the Santa Fe Institute.
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Evolution and
Spatial Structure Interact to Influence Plant-Herbivore Population and
Community Dynamics by Gregg
Hartvigsen and Simon
Levin. An individual-based model of plant-herbivore interactions and
coevolution.
-
Development
of a spatially explicit, individual-based model of marine fish early life
history by Hinckley S, Hermann
AJ, and Megrey BA, published in Marine
Ecology Progress Series. See also animation links on A
Biophysical Model of Shelikof Strait
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Gorilla
Simulation work by Mark
Scahill including this update
and a draft paper called Modeling
Mountain Gorillas from around 1995.
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Gecko, a
spatial individual-based simulator for modeling ecosystem dynamics, by
Oswald Schmitz
and Ginger Booth.
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Papers from the Third
International Conference/Workshop on Integrating GIS and Environmental
Modeling January 21-25, 1996, Santa Fe, New Mexico:
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Ship Fouling
by Yosef Cohen.
A Java applet demonstrating the interaction of barnacles settling on the
hull of a ship and limpets, used as a biological control, which can bulldoze
away young barnacles.
-
Spatially
Explicit Population Dynamics and the Snowshoe Hare by Jason
A. Thomas. See also his list of links on Spatially-Explicit
Population Modelling.
-
Multi-Agent Simulation
of Honey Bee Colonies by David
Sumpter uses a Swarm-based model to investigate colony activities (particularly
thermoregulation)
by simulating bee behavior.
-
Manta by Alexis
Drogoul is an ethological simulation of ant colony behavior modeled
at the level of individual ants. Several related papers
are available online. See also this review
of Manta by Howard Gutowitz.
-
Model
of Animal Behavior (MOAB) a description of software for a spatially
explicit, individual based model of animal movement and foraging behavior
by Jacoby Carter and colleagues at
the U.S. Geological Survey.
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ECOTOOLS
uses individual-based models to study animal behavior and ecological issues.
See descriptions of various ECOSIM
reimplemented models: schooling, flocking, storks, dragonflies, crowns,
largemouth bass, northern cod.
-
The non-linear
dynamics of survival and social facilitation in termites by Octavio
Miramontes and Og DeSouza.
Uses a "mobile cellular automata" (a mobile, spatially explicit individual-based
model in discrete space) to simulate a colony of termites. See also Miramontes'
Complexity and
Social Behaviour
-
PUMA software by Paul
Beier was developed to predict the risk of extinction in cougar populations
under various development scenarios. The model is individually-based but
not spatially explicit
-
Deer Management Simulator
by Ken Risenhoover (et al.?)
is a spatially explicit modeling environment for evaluating deer management
strategies. See also these summaries
of related research at the same lab.
-
The Weaver Project
by Matt
Hare, Alan Sibbald, and Alistair Law, is a spatially explicit, individual-based
model of the red
grouse in Scotland's heather moorland. It seeks to provide wildlife
managers with advice on appropriate strategies to restore grouse populations.
-
Individual-Based Fish
Population Model Applied to Management Issues (1991) Deangelis
et al., individual-based population model for smallmouth bass, used
to investigate the impact of varying the opening date of the fishing season.
-
Fire
Scale, Fire Pattern, and Winter Severity Influence Simulated Ungulate Survival
in Northern Yellowstone National Park (1992) by M. G. Turner, Y. Wu,
W. H. Romme, and L. L. Wallace. A spatially explicit individual-based model
of the effects of fire on winter foraging and survival of elk and bison
in Yellowstone National Park.
-
Instructional Tools:
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Archaeology, Anthropology, Sociology and Psychology:
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Swarm-based
Modeling of Prehistoric Settlement Systems in Southwestern North America
(1997) by Tim
Kohler and Eric
Carr, describes an agent-based simulation constructed with the Swarm
system. See also this 1995 paper: Agent-Based
Modeling of Anasazi Village Formation in the Northern American Southwest.
-
Sugarscape (and the book
Growing Artificial
Societies: Social Science From the Bottom Up) by Joshua
M. Epstein and Robert
Axtell. Describes experiments with an artificial society, a
computer model consisting of a population of autonomous agents and a separate
environment in which the agents live. See also this feature
at Discovery Online.
-
Theory in
a Complex World: Computational Laboratories in Economic Geography by
Catherine Dibble explores
the gap between simulation experiments and a understanding of the underlying
phenomena.
-
An Agent
Based Simulation Environment for Public Order Management Training by
Roderick Williams
is a tool to help train training police officers to manage large public
gatherings (crowds, demonstrations, marches). See also the page for the
CACTUS
(Command And Control Training and Planning Using Knowledge Based Simulation)
system.
-
Coordinating
Turn-Taking with Gaze by David
G. Novick, Brian Hansen
and Karen Ward. Uses an individual-based
model to validate the proposed mechanism.
-
An Agent-Based
Model of Seating In A Theater a Java-based class project by Yale Wang.
See also his version of Schelling's
Segregation Model and the El
Farol problem: how the appropriate number of people decide to show
up for an event.
-
Modeling
Audience Group Behavior by Nuria
Oliver and Stephen
Intille describes an agent based model (spatially explicit, discrete
space, non-mobile) of synchronization and other decentralized collaborative
behaviors of a group audience.
-
Simulation:
a emergent perspective, text of a lecture advocating use of individual-based
models in sociology and related fields, by Nigel
Gilbert. See related material at The
Computer Simulation of Societies site.
-
Spatially-Explicit
Autonomous Agents for Modelling Recreation Use in Complex Wilderness Landscapes
by Randy Gimblett,
Bohdan Durnota and Bob Itami, uses autonomous agents to assist natural
resource managers in assessing and managing dynamic recreation behavior,
social interactions and resulting conflicts in wilderness settings. See
also the Recreation
Behavior Simulator (RBSim) which simulates the behavior of human recreators
in high use natural environments.
-
Agency and Interaction
by Peter J. Burke
considers the connection between macro-level group characteristics (social
structure) and the micro-level interaction between individuals.
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Economics
-
Playing the
game of life by Rita Koselka (an April 7, 1997 article from Forbes)
covers individual-based models of the music CD business by Coopers
& Lybrand ESG, the stock market by W.
Brian Arthur and John Holland, the Sugarscape
model by Joshua Epstein and Robert Axtell, and Challenge from Thinking
Tools.
-
Aspen,
a microanalytic model to simulate the U.S. economy. Aspen uses economic
agents to represent the various decision-making segments, and the microanalytic
simulation process models each agent individually.
-
Agent-Based Computational
Economics (ACE) by Leigh
Tesfatsion, a computational study of economies modeled as evolving
decentralized systems of autonomous interacting agents. Which seeks to
explain these global regularities in economic processes from the bottom
up.
-
Agent
based simulation of artificial electricity markets by Raimo
P. Hämäläinen et al. models how customers respond
to different price patterns for electrical power.
-
A Market Simulator
from Systems View attempts to
provide sales and revenue forecasts based on various marketing strategies
-
Artificial
Life Simulation of the Textile/Apparel Marketplace: An Innovative Approach
to Strategizing about Evolving Markets by Evelyn
L. Brannon, Lenda Jo Anderson, R. Alan Donaldson, Thomas E. Marshall,
Pamela V. Ulrich. See also.
-
Economic Modeling
of Global Innovation Diffusion, Diploma Thesis of Johannes
Kottonau and Friedemann Buergel
(aka Bürgel) uses an agent based simulation model called LEM 1.1 to
visualize cultural, institutional, economic and legal key factors of spacio-temporal
diffusion of new technologies (specifically Light Electric Vehicles (LEVs)).
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Agent
Based Simulation of the Hotelling Game by Michael Friedlander and David
Sumpter a spatial variation on a model of the pricing of identical
goods by the only two shops in a town.
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Traffic and vehicle simulations
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Animation and Interactive Multimedia
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Related topics
Offline resources
Laboratories and Groups
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Academic
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Commercial
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The Emergent Systems Group
of Coopers & Lybrand uses multiple agent systems to model decision
making and trends in various simulations of real world marketplaces.
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Thinking Tools makes agent-based
simulation games to help train business decision-makers.
Conferences
Send comments to Craig Reynolds <cwr@red.com>
visitors since
9-28-97
Last update: November 7, 1997