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Related Experiment Videos

Predicting the unpredictable.

Eric Bonabeau1

  • 1Icosystem, Cambridge, Massachusetts, USA. eric@icosystem.com

Harvard Business Review
|March 16, 2002
PubMed
Summary
This summary is machine-generated.

Emergent phenomena, like market buzz or productivity changes, arise from individual interactions. Agent-based modeling offers a bottom-up approach to analyze and predict these complex group behaviors, improving business strategies.

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Area of Science:

  • Complex Systems Science
  • Computational Social Science

Background:

  • Collective human behavior in crowds, markets, and organizations presents complex challenges.
  • Traditional top-down analytical methods (e.g., spreadsheets, regression) fail to explain emergent phenomena.
  • Emergent phenomena arise from bottom-up, local interactions among individuals.

Purpose of the Study:

  • To introduce and explain agent-based modeling (ABM) as a tool for analyzing emergent phenomena.
  • To demonstrate the predictive power of ABM in business contexts.
  • To discuss the increasing prevalence and future potential of ABM.

Main Methods:

  • Agent-based modeling (ABM) simulates individual interactions to understand collective behavior.
  • Bottom-up analysis focusing on local interactions, rather than top-down global equations.

Related Experiment Videos

  • Case studies of companies utilizing ABM for strategic decision-making.

    • Main Results:

    • ABM enables the analysis and prediction of emergent phenomena that elude traditional methods.
    • Companies like Macy's, Hewlett-Packard, and Société Générale have successfully applied ABM.
    • Applications include store design, corporate culture prediction, and operational risk assessment.

    Conclusions:

    • Agent-based modeling provides a powerful framework for understanding complex systems.
    • ABM is increasingly relevant for businesses seeking to navigate emergent phenomena.
    • The technology holds potential to revolutionize various scientific and business fields.