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Why do traffic jams develop out of seemingly free-flowing cars? How can a simple clerical error mushroom into a corporate catastrophe? And why does a suddenly crowded supermarket lead to a significant drop in wine sales? Welcome to the art and science of studying so-called emergent phenomena, the seemingly unpredictable group behavior that emerges from the interactions of individuals.
In this excerpt from Harvard Business Review, the author looks at how a better understanding of emergent phenomena using computer agent-based modeling can help employers improve incentive programs, predict changes to corporate culture, and monitor operational risk.
Motivating employees
Companies have been using agent-based technology to model the actions not only of their customers but also of their employees. A consumer goods corporation has used the technology to design a better incentive structure for its country managers in Europe. The company had been rewarding them based on their proportion of "shorts" (when a product sells out)—the lower the better. But that encouraged managers to order more than they needed—a particularly costly practice when the products were perishable. To prevent spoilage, the company often had to quickly relocate huge quantities of stock from, say, Denmark to Italy if the Danish country manager had overestimated his requirements. Thus a new incentive system was needed, one that would motivate the country managers to act in the best interest of the overall company.
The problem is trickier than it might first appear. Obviously, the current system encouraged hoarding, but incentives tied to just the company's overall performance were not viable because people dislike having their bonuses linked to factors over which they have little influence. So what local behavior should the company reward, and how should it ensure that the new system would not ultimately lead to any counterproductive actions, like hoarding? Agent-based modeling helped uncover the answer: Tie the country managers' bonuses directly to their storage costs in addition to their shorts. This change alone could reduce supply-chain costs by several percent, resulting in annual savings of millions of dollars. In essence, agent-based modeling helped connect the local behavior of country managers to the global performance of the organization.
Other companies have used agent-based modeling to investigate radically new ways of doing business. In the pharmaceutical industry, the cost of developing new drugs has surged, forcing many companies to rethink their R&D operations. Part of the problem is the so-called selfish-team syndrome, in which a group that is developing a particular drug makes biased decisions—for example, trying to save the project when it should be killed—because the team's reputation is tied to the drug's success or because the team members have become emotionally attached to the project. Such counterproductive behavior can slow drug development and increase its cost. Concerned by such issues, a major pharmaceutical company thought of a possible solution—creating a marketplace to subcontract some of the drug development in the early phases of human clinical trials.
| Agent-based modeling can also help predict how changes in an organization's recruiting strategy might ultimately affect its corporate culture. |
| — Eric Bonabeau |
To explore that and other alternatives, my colleagues at Icosystem and I developed an agent-based model of the various players, both the company's employees as well as potential contractors, including contract research organizations (companies that specialize in managing clinical trials), academics who do consulting work, and even experts at competing firms. We found that because of the diversity of the players (their different motivations, aversions to risk, cost structures, and so on), our pharmaceutical client could not possibly coordinate all of that activity profitably in an open marketplace.
Our client then suggested creating a network of participants, both internal and external, using incentives that encouraged better decision making (such as bonuses tied to the success of the entire portfolio of drug molecules). Through further modeling, we found that this solution could help our client more than double the risk-adjusted value of its portfolio of recently discovered molecules. Based on these results, the company has decided to test in the real world this new way of organizing early clinical development.
Agent-based modeling can also help predict how changes in an organization's recruiting strategy might ultimately affect its corporate culture. For instance, in an experimental project, Cap Gemini Ernst & Young's Center for Business Innovation developed an agent-based model of Hewlett-Packard's employees. For decades, HP had a strong tradition of hiring people for their loyalty and not necessarily for their experience. The company focused its efforts on finding people, often recent college graduates, who would fit into its culture, and many employees spent their entire careers at HP. But as the labor market began moving toward free agency, HP became concerned about how that change would affect the company. In addition, as the company shifted its focus toward services, it became increasingly interested in hiring high-powered, experienced consultants, who were typically much less loyal than HP engineers.
The simulation results confirmed some of HP's suspicions. Hiring free agents, for example, would eventually result in higher turnover costs, as employees (even those who were originally loyal) would begin to leave at a higher rate. A more surprising finding was that hiring experienced but less loyal people would eventually lead to an overall decrease in HP's total level of knowledge. That result would be particularly pronounced if the hiring strategy was changed abruptly. A better alternative was a gradual transition over the course of a year or two. Furthermore, the agent-based model suggested that HP could greatly mitigate the negative effects of such a change by simultaneously investing heavily in knowledge capture, such as a repository and IT systems that could retain some of the expertise of employees before they left. To do so would be a marked shift from HP's traditional focus on the development of each individual employee (for instance, by encouraging job rotation across businesses and functions)—a strategy that makes less sense when turnover is high.
| Organizations do not have a clear understanding of exactly how an error (or act of fraud) can cascade through a system. |
| — Eric Bonabeau |
An exciting new area of agent-based research is in the field of operational risk, which is a growing concern at many financial institutions because of the huge losses suffered over the years by Daiwa, Sumitomo, Barings, Kidder Peabody, and others. Although banks have developed efficient and sophisticated ways of assessing their market and credit risks, they are still in the early stages of figuring out just how to measure and monitor their operational risk. The task is extremely difficult because organizations do not have a clear understanding of exactly how an error (or act of fraud) can cascade through a system, causing a catastrophic loss, like a tree that falls on a power line and disrupts the electrical power grid of several states.
My colleagues at Bios and Cap Gemini Ernst & Young and I have applied agent-based modeling to analyze and quantify the operational risk of the asset management business of Société Générale in France. In the simulation, we modeled the company's employees as virtual agents who continually interacted with one another as they performed their tasks. From past data, we knew that bank employees commonly make certain types of errors, such as writing down the wrong number of zeroes ($10,000 instead of $1,000) or confusing a local currency with the euro. But we found that such errors would almost never lead to catastrophic losses unless they occurred in certain types of situations—for example, when the financial markets are volatile in August. Detailed results from the agent-based model helped explain why.
Fluctuations in the market lead to an increase in the volume of transactions, which then results in a much higher number of errors because people are rushed and have little time to double-check their work. In France, the problem is exacerbated in August because that's when many employees—generally the more experienced ones who have earned seniority—take extended vacations. In one scenario, an inexperienced and overworked trader makes a mistake: Instead of selling a stock, he buys it, and nobody in his department, including his busy supervisor, spots the error. The paperwork for the order makes its way to the back office, where a summer intern also fails to detect the mistake and processes the order. By the time the gaffe is uncovered several days later, the stock has plummeted in value, resulting in a multimillion-dollar loss.
Not only did we uncover such potential vulnerabilities, we could also estimate the likelihood that they would occur in the real world, using historical data from the capital markets. Although catastrophic losses were extremely unlikely in the model, by running thousands of simulations we were able to generate the rare events that triggered such disasters, and those results helped provide statistics about the bank's true operational risk. From that information, Société Générale could test procedures for minimizing that risk (such as changes to its vacation policy) as well as calculate how much capital it should set aside to cover certain potential losses. Currently, financial institutions do not have an accurate way of determining their operational risk, so regulatory agencies force them to overestimate the amount of rainy-day cash they need to have in reserve. In the asset-management business, a financial institution that could determine its operational risk accurately could easily save millions of dollars each year, not only by freeing up some rainy-day capital (which can then be invested) but also by lowering the organization's insurance premiums.
The research into organizational behavior at HP, our pharmaceutical client, and Société Générale holds a larger lesson. A common criticism of agent-based modeling is that the technology often requires an understanding of the complex psychology of human behavior, and errors in quantifying such "soft factors" can lead to results that are grossly inaccurate. As the saying goes, "garbage in, garbage out." Of course, an agent-based model will only be as accurate as the assumptions and data that went into it, but even approximate simulations can be very valuable. HP, for example, used its model to gain a better qualitative understanding of how certain factors (the company's hiring strategy, employee turnover, total level of knowledge, and so forth) were related. By contrast, the simulation for our pharmaceutical client was much more detailed and complete, enabling the company not only to understand its business better but also to predict, shape, optimize, and control it. In other words, how a company uses an agent-based model should be directly related to the work and data that went into building it, and vice versa.
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What Are Emergent Phenomena?
Whenever I try to explain the concept of emergent phenomena to a group of business executives, I ask them to think about the following game: Imagine that we all started mingling, as if we were at a cocktail party. At random, you silently pick two other people (call them A and B) and then you always position yourself so that A is between B and you. If everyone else were to do the same, what would happen? Now, let's change the game slightly: You always position yourself so that you are between A and B. Again, if everyone else were to do likewise, what would happen?
If there are several dozen of us, in the first game everyone will continue moving around the room for hours as we continuously try to keep ourselves in the right position. An outside observer who was unaware of our game might think that the movement was random. In the second scenario, the result is markedly different. Within seconds, we will have clumped into a single, almost stationary cluster. The same uninformed observer might think that our goal was to join together. In either case, our collective behavior—the milling around or the clumping—is the emergent phenomenon that has arisen from our individual actions. (To see a simulation of this game, visit www.icosystem.com/game.)
The simple game holds three important lessons. First, emergent phenomena can be unpredictable and often counterintuitive. What would happen, for example, if half of us followed the rule of the first game while the rest obeyed the other rule? Second, a seemingly minor change in what we do individually within a group can radically alter our collective behavior. Third, a logical link does not necessarily exist between our individual actions and the resulting emergent phenomenon. That is, why should the second—and not the first—game result in clumping?
Indeed, emergent phenomena often have a life of their own that is separate and distinct from the behaviors of their constituent parts. A traffic jam, for instance, cannot always be understood by studying what each individual driver is doing. Examples of emergent phenomena in the business world include organizational behavior that is shaped (or misshaped) through employee bonuses and incentives, free markets in which prices are set through the myriad interactions of buyers and sellers, and consumer buzz that propels sleeper products into runaway successes.
— Eric Bonabeau