EAGER: Demonstration of Scaling Impact on Coalition Formation in Agent-based Simulation

Information

  • NSF Award
  • 2333570
Owner
  • Award Id
    2333570
  • Award Effective Date
    8/1/2023 - a year ago
  • Award Expiration Date
    7/31/2025 - 8 months from now
  • Award Amount
    $ 198,534.00
  • Award Instrument
    Standard Grant

EAGER: Demonstration of Scaling Impact on Coalition Formation in Agent-based Simulation

This EArly-concept Grants for Exploratory Research (EAGER) award will advance the field of Modeling and Simulation through a series of computational experiments focused on social simulation. Social simulations are used to understand better the large-scale, complex problems that the nation faces such as the obesity epidemic and the housing foreclosure crisis. The significance of this research lies in its potential to help provide a deeper understanding of these complex issues, enabling government officials to make more informed decisions when formulating new policies to tackle these societal challenges. Specifically, this award will examine how individual behavior changes with the number of decision-making agents active in the simulation. Through a partnership with a local medical school, the grant will help provide insight into the issues relating to the increasing placement of doctors in hospitals. This grant is accompanied by an educational plan to develop a cross-disciplinary course on modeling situations with multiple decision-makers at Old Dominion University, a minority-serving institution in southeastern Virginia.<br/><br/>This grant will challenge the widespread assumption that behavioral models remain invariant regardless of the size of the social community under study by designing and analyzing a series of computational agent-based simulation modeling experiments. Specifically, the research goal is to demonstrate the impact of scale on coalition formation behavior within an agent-based simulation. A game-theoretical framework will be used for the development of the simulation’s conceptual model, the modeling scenario, and the test dataset. The experiments involve developing behavioral sub-models using machine learning at different scales. The computational experiments will be repeated for various scenarios, game types, and fitness functions. This research hypothesizes that the derived behavioral sub-models will differ substantially between scales in an explainable manner.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

  • Program Officer
    Reha Uzsoyruzsoy@nsf.gov7032922681
  • Min Amd Letter Date
    8/16/2023 - a year ago
  • Max Amd Letter Date
    8/16/2023 - a year ago
  • ARRA Amount

Institutions

  • Name
    Old Dominion University Research Foundation
  • City
    NORFOLK
  • State
    VA
  • Country
    United States
  • Address
    4111 MONARCH WAY
  • Postal Code
    235082561
  • Phone Number
    7576834293

Investigators

  • First Name
    Andrew
  • Last Name
    Collins
  • Email Address
    ajcollin@odu.edu
  • Start Date
    8/16/2023 12:00:00 AM

Program Element

  • Text
    OE Operations Engineering

Program Reference

  • Text
    SIMULATION MODELS
  • Text
    EAGER
  • Code
    7916