Model docking using knowledge-level analysis

  • Authors:
  • Ethan Trewhitt;Elizabeth Whitaker;Erica Briscoe;Lora Weiss

  • Affiliations:
  • Georgia Tech Research Institute, Atlanta, GA;Georgia Tech Research Institute, Atlanta, GA;Georgia Tech Research Institute, Atlanta, GA;Georgia Tech Research Institute, Atlanta, GA

  • Venue:
  • SBP'11 Proceedings of the 4th international conference on Social computing, behavioral-cultural modeling and prediction
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper presents an initial approach for exploring the docking of social models at the knowledge level. We have prototyped a simple blackboard environment allowing for model docking experimentation. There are research challenges in identifying which models are appropriate to dock and the concepts that they should exchange to build a richer multi-scale view of the world. Our early approach includes docking of societal system dynamics models with individual and organizational behaviors represented in agent-based models. Case-based models allow exploration of historical knowledge by other models. Our research presents initial efforts to attain opportunistic, asynchronous interactions among multi-scale models through investigation and experimentation of knowledge-level model docking. A docked system can supply a multi-scale modeling capability to support a user's what-if analysis through combinations of case-based modeling, system dynamics approaches and agent-based models working together. An example is provided for the domain of terrorist recruiting.