Case Study: Nasdaq - ' Traffic Jam '

It took a bit of a flyer for Nasdaq to turn to agent-based modeling in 1998. At the time, agent-based modeling was a promising but far from perfected technology that had its beginnings in complexity science, a popular area of research for computer and cognitive scientists during the past couple of decades. The central tenet of this theory is that complex systems emerge from a series of seemingly random actions by individual "agents" responding to each other. For instance, the flow of traffic on a freeway is the result of the different driving styles of hundreds of people behind the wheel; some are timid and hit the brakes frequently, others tailgate and go 20 miles over the speed limit, still others are alert or drowsy. Complexity theorists argue that it's impossible to understand or predict traffic patterns without examining those patterns from the bottom up—from the interactions among drivers, and not from the overall, visible movement on the freeway.

Businesses, too, are made up of numerous complex systems. A supply chain, for example, is composed of factories, warehouses, trucks, raw materials, inventory and retailers, managers and line employees working at the various jobs through the network. By their actions, each of these "low-level" agents determine the emergent phenomena—or the performance—of the larger system, according to complexity scientists. Agent-based modeling is a computerized rendering of complexity science. It's a way to simulate complex systems by using mathematical algorithms to create models of agents—virtual people and virtual objects—that represent individual components in a system. By setting these agents in motion, managers can watch and measure how their organizations operate—and thus determine, for instance, specific points of inefficiency as well as the origin of such weaknesses. And by throwing in new wrinkles for the agents to react to—such as a regulatory change, a competitor with a similar, less expensive product, or an unlikely real-world decision such as moving trucks with less than full loads—it's possible to see how the organization will react or be affected.

"An agent can be anything," says Stuart Kauffman, a BiosGroup founder and one of the first complexity scientists at Santa Fe Institute, a research center with an emphasis on understanding how complicated patterns and systems emerge from simple randomness. "It can be a pallet, it can be an SKU, it can be a truck, it can be a cross-loading dock. It can be any part of the system. Some agents make decisions and some agents only have a certain amount of time to decide what to do. Whatever they are, their interaction and how it changes the dynamics of the complex system we're studying are what we're interested in."

The results of agent-based modeling can be surprising. Consider the model built by Sainsbury's, the British supermarket chain. Based on bar-coded data, video camera studies and expert knowledge, the model—called SimStore—duplicated shopping behavior down to such details as the percentage of shoppers who turn right after entering the store and the average time a consumer spends in different supermarket departments. Rules were incorporated into the model that mimicked real-life behavior: Some shoppers would always move from wherever they are to the nearest item on their list; others would go, instead, to the next item on the list.

Perhaps the most revelatory insight that Sainsbury's took away from SimStore, says Eric Bonabeau, chairman and chief scientific officer at Icosystem Corp. (see Expert Voices), a complexity theory consultancy in Cambridge, Mass., was that when there were more shoppers in the supermarket, sales of wine, displayed in the back of the stores, dropped. The reason, Bonabeau posits, is that as the store becomes crowded, there are more clusters of customers throughout the supermarket. That discourages people from fighting their way through these groups to reach the wine. This is extremely useful knowledge for Sainsbury's executives, which could lead to creative decisions that they may not have dared make without the agent-based model. For instance, the supermarket's managers may decide to limit discounting—typically viewed as a winning strategy that brings more customers into the store—because while it potentially increases sales of low-margin products, the crowds it attracts are hindering more valuable high-margin wine purchases.