The current issue of The Economist carries an article titled, “Riders on a swarm.”  The article describes the use of swarm intelligence–the collective behavior that results from the individual actions of many simple “agents”–that is inspired by the behavior of insects like ants and bees or flocks of birds.  Although–unlike a column that appeared in a previous issue –“agent-based simulation” is not mentioned by name, these models have all of the relevant attributes of agent-based simulations, and you can find example models of collective insect and flocking bird behavior in agent-based toolkits such as NetLogo

As noted in the article, these models have found some business applications in logistics and problems like traffic control.  Ant-based foraging models, for example, have been applied to solving routing problems for package delivery services.  Route optimization, given a set of delivery locations, is a fixed problem with a large number of potential solutions that probably can be solved analytically (or by simple brute force) with enough computing power.  Swarm models have the advantage that they can arrive at a good and often optimal solution without needed to specify and solve a linear programming problem.  By programming simple individual agents, such as artificial ants, with a simple set of rules for interacting with their environment and a set of goal-directed behaviors, the system can arrive at an optimal solution, even though no individual agent “solves” the problem. 

Something that was new to me in this article is “particle swarm optimization” (PSO) which is inspired by the behavior or flocking birds and swarming bees.  According to the article, PSO was invented in the 1990’s by James Kennedy and Russell Eberhart.   Unlike the logistics problems, there may be no closed form or analytically tractable solution to problems such as finding the optimal shape for an airplane wing.  In that case, a simulation in which thousands of tiny flowing particles follow a few simple movement rules may be just the ticket.

This stuff is fascinating, but it’s not clear that there are many useful applications for this type of modeling in marketing or marketing research, at least as long as the unit of analysis is the intersection of an individual “consumer” and a specific purchase or consumption occasion.   Of course, if imitation and social contagion are at least as important in our purchase decisions as the intrinsic attributes of t products and services (as research by Duncan Watts and his collaborators has shown in the case of popular music), then agent-based simulations may turn out to be one of the best ways to understand and predict consumer behavior.

Copyright 2010 by David G. Bakken.  All rights reserved.

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