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 / Spring 2005 / Issue 38(originally published by Booz & Company)


Supermodels to the Rescue

Done properly, computer simulation represents a kind of “telescope for the mind” — it multiplies our powers of analysis and insight just as a telescope does our powers of vision. With simulations, leaders can surpass their ordinary abilities and discover relationships that the unaided human mind would never grasp.

Thought Experiments
In science, simulation proved its value long ago as an irreplaceable tool for exploration and discovery. With simulations, researchers have discovered new materials and tested theories of the early universe. The very best models of the human heart now run on supercomputers, and are so accurate that the Food and Drug Administration uses them to test drugs in “virtual experiments” that involve no patients.

As political scientist Robert Axelrod of the University of Michigan suggests, simulation can even be seen as a “third way” of doing science. Whereas deductive science derives the consequences that follow logically from basic assumptions, inductive science gathers empirical facts and tries to generalize to a pattern. Science by simulation does neither. Simulations begin with assumptions, yet they explore logic in an empirical or experimental fashion, producing output that reveals the consequences that are likely to unfold from the setting of a certain situation. Simulations are thought experiments.

Over the past decade, firms such as Cisco Systems, Nokia, Capital One, and Boeing have pioneered the use of advanced simulation to model and prototype both product and process innovations. By recasting designs or by altering logistics at the touch of a button, they can advance complex adaptations of key methodologies and goods, work that in the past would have taken months. Simulation-based process reengineering gives these and other firms an edge on their competitors. (See “Here Comes Hyperinnovation,” by Michael Schrage, s+b, First Quarter 2001.)

But businesses are also going further with simulations — using them to tame unruly fluctuations in complex production lines; to foresee and predict the consequences of organizational change; and to respond with intelligence and flexibility to myriad market shifts. With a technique called agent-based modeling, business leaders can now model entire business organizations quite effectively, creating full-scale virtual laboratories in which to test their organizations and try strategies “offline” before exposing themselves to the risks of the live competition.

Computer simulation in itself, of course, has been around for decades, and strategists and operations engineers have come to rely on it when exploring models of manufacturing processes, scheduling problems, and strategic business challenges. The computational approach — based on differential equations, linear programming, and other mathematical methods — has typically employed computers to do traditional mathematical analysis more powerfully. What makes agent-based modeling so different is a determined commitment to modeling organizations and business processes piece by piece from the “bottom up” — not so much analyzing operations as replicating them in silico.

These models begin with relatively simple component models of machines, products, employees, and customers, simulating their characteristics, behavioral habits, and aims. The computer then puts all these “agents” together within the realistic structure of the organization and its business environment, and lets them interact. Decision makers can run experiments — reorganizing employees or offering them differing incentives, devoting more effort to one customer rather than another — and can watch as outcomes emerge naturally, unconstrained by the decision makers’ own prejudices about what “ought” to happen. The technique offers insight into the unexpected.

Committing “Cybermistakes”
Decisions in the real world have consequences. Decisions in a virtual world do not. Hence the first reason for strategic exploration through simulation: Organizations can learn from their mistakes without paying the costs of making them.

Two years ago, a major multinational pharmaceutical company discovered a serious problem in its drug development process. The company, whose associates requested anonymity for themselves and the firm, organized R&D by assigning potential new drugs to individual development teams. Naturally, the success or failure of any specific project could affect a team’s reputation, and this meant that teams were often lured into making “selfish” decisions that were bad for the company; they might keep a project going longer than warranted, for example, to preserve the impression of possible success. Executives found themselves canceling projects in the third phase of clinical trials, with losses that were hundreds of millions of dollars higher than if development had been abandoned at an earlier stage.

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  1. Keith Oliver, Leslie H. Moeller, and Bill Lakenan, “Smart Customization: Profitable Growth Through Tailored Business Streams,” s+b, Spring 2004; Click here.
  2. Michael Schrage, “Here Comes Hyperinnovation,” s+b, First Quarter 2001; Click here.
  3. Eric Bonabeau, “Agent-Based Modeling: Methods and Techniques for Simulating Human Systems,” PNAS, Vol. 99 (May 14, 2002), 7280–7287
  4. Joshua Epstein and Robert Axtell, Growing Artificial Societies: Social Science from the Bottom Up (MIT Press, 1996)
  5. Navot Israeli and Nigel Goldenfeld, “On Computational Irreducibility and the Predictability of Complex Physical Systems,” unpublished; Click here. 
  6. Proceedings of the Agent 2004 Conference on Social Dynamics: Interaction, Reflexivity, and Emergence (University of Chicago Press, forthcoming); Click here.
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