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.
I’ll be speaking at the upcoming ESOMAR Congress (Athens, Greece, 12-15 September 2010). You can find an abstract of my presentation, “Riding the Value Shift in Market Research: ‘Only the Paranoid Survive'” by clicking here. Click here to see the full conference program.
The “economic focus” column in the July 24th-30th (2010) issue of The Economist is titled “Agents of change.” As many of us have come to believe over the past couple of years, the “dynamic stochastic general equilibrium” (DSGE) economic models used by central banks and other economists more or less fell apart when it came to predicting or anticipating the credit-fueled meltdown that we are just now beginning to recover from. The Economist reports on a June workshop sponsored by the National Science Founcation and attended by central bank economists (from the Fed and the Bank of England), policy advisors, and computers scientists who convened to explore the potential of agent-based models of the economy.
Agent-based models have emerged from the intersection of computer science and social science and have been applied to population dynamics, epidemiology, species extinction, wealth creation, the formation of communication networks, and a host of other problems not well served by traditional economic models. In contrast to the DSGE approach, which represents the economy as a series of equations to be solved using highly aggregated data as inputs, agent-based models of economic systems are “bottom up”–they generate complex behavior by creating populations of autonomous agents, giving them simple behavioral rules, and then simulating (over thousands of iterations in many cases) the interactions of these agents. Under some starting conditions, an agent-based simulation may produce results similar to a DSGE model. For example, Joshua Epstein and Robert Axtell (one of the NSF workshop organizers) found that agents operating under rules that permitted bargaining for the exchange of two commodities arrived at prices that fluctuated around a sort-of equilibrium point. By the way, their book, Growing Artificial Societies: Social Science from the Bottom Up (1996), is one of the best introductions to agent-based modeling.
In some ways, agent-based models of the ecomony are generating new interdisciplinary thinking. In an Op-Ed piece in the New York Times in October of 2008, Mark Buchanan (a theoretical physicist) titled “The Economy Does Not Compute,” we learn about an agent-based model developed by Yale economist John Geanakoplos and two physicists, Doyne Farmer (another of the NSF workshop organizers) and Stephan Turner designed to explore the influence of the level of credit or leverage in a market on the market’s overall stability. A typical objective for an agent-based model is to develop an understanding of the sensitivity of a complex system to changes in one or more of the model variables, and these researchers found that greater levels of credit leads to greater interdependence among the actors (or agents) and this pushes the market toward instability. The DSGE models are not very good at capturing this kind of process. And this model revealed something even more striking, but perhaps not surprising to those who have used agent-based models to capture the non-linear nature of complext adaptive systems. What Geanakopolos, Farmer and Turner found is that the leverage-induced market instability does not develop gradually but arrives suddenly–with the economy essentially falling off a cliff.
Buchanan goes on to cite two other applications of agent-based modeling. One involved testing the impact of small transaction taxes in foreign exchange markets, and the second looked at deregulation in a state’s electricity market. In both cases, the simulations provided insight that challenged the prevailing ideology-based assumptions, and could lead to better policy outcomes.
I’m not sure how readily mainstream economists or central bankers will embrace the agent-based way of thinking, but it’s pretty clear that at a minimum, agent-based approaches to understanding complex systems like the economy can only add to our ability to make better policy decisions.
Copyright 2010 by David G. Bakken. All rights reserved.
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