Statistical Challenges in Agent-Based Modeling

Abstract

Agent-based models (ABMs) are popular in many research communities, but few statisticians have contributed to their theoretical development. They are models like any other models we study, but in general, we are still learning how to fit ABMs to data and how to make quantified statements of uncertainty about the outputs of an ABM. ABM validation is also an underdeveloped area that is ripe for new statistical developments. In what follows, we lay out the research space and encourage statisticians to address the many research issues in the ABM ambit.

Keywords:

• Emulator
• Individual-based model
• Mechanistic model

Acknowledgments

The authors thank the Statistics and Applied Mathematics Sciences Institute (SAMSI) program on Model Uncertainty: Mathematical and Statistical (MUMS). Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Additional information

Funding

This research was funded by NSF DEB 1927177 and NSF DMS 1614392.