Jove
Visualize
Contact Us

Related Experiment Videos

Agent-based modeling as organizational and public policy simulators.

Robert Lempert1

  • 1RAND, 1700 Main Street, Santa Monica, CA 90407, USA. lempert@rand.org

Proceedings of the National Academy of Sciences of the United States of America
|May 16, 2002
PubMed
Summary

Agent-based models (ABMs) can simulate complex social systems but struggle with policy support in unpredictable futures. New methods using scenario ensembles and adaptive policies enhance ABM policy-making under deep uncertainty.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A systemic risk assessment methodological framework for the global polycrisis.

Nature communications·2025
Same author

Do small outdoor geoengineering experiments require governance?

Science (New York, N.Y.)·2024
Same author

Reopening California: Seeking robust, non-dominated COVID-19 exit strategies.

PloS one·2021
Same author

On Model Pluralism and the Utility of Quantitative Decision Support.

Risk analysis : an official publication of the Society for Risk Analysis·2021
Same author

Reopening California : Seeking Robust, Non-Dominated COVID-19 Exit Strategies.

medRxiv : the preprint server for health sciences·2021
Same author

Climate change: making decisions in the face of deep uncertainty.

Nature·2020
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Area of Science:

  • Social simulation
  • Computational social science
  • Policy analysis

Background:

  • Agent-based models (ABMs) excel at simulating complex social phenomena intractable with traditional methods.
  • However, their application in policy-making is limited, especially in unpredictable future scenarios.
  • Conventional decision-making approaches are less effective under deep uncertainty.

Purpose of the Study:

  • To explore how agent-based models can better support policy-making.
  • To address the challenge of using ABMs in situations characterized by deep uncertainty.
  • To introduce novel analytical approaches for decision-making with ABMs.

Main Methods:

  • Utilizing large ensembles of scenarios generated by agent-based models.
  • Employing adaptive policies designed to perform well across diverse potential futures.

Related Experiment Videos

  • Evaluating policies based on robustness and effectiveness under uncertainty, rather than optimality.
  • Leveraging new analytical approaches for decision-making under deep uncertainty.

    • Main Results:

    • Demonstrated that agent-based models can be effectively utilized for policy support even in unpredictable environments.
    • Showcased the efficacy of evaluating policies for robustness across a wide range of model-generated scenarios.
    • Highlighted the limitations of traditional analytic methods in highly uncertain conditions.

    Conclusions:

    • New analytical frameworks can unlock the full potential of agent-based policy simulators.
    • Robustness criteria offer a viable alternative to optimality for policy evaluation under deep uncertainty.
    • Agent-based models, when combined with advanced analytical techniques, provide powerful tools for navigating complex policy challenges.