Model docking using knowledge-level analysis

Ethan Trewhitt, Elizabeth Whitaker, Erica Briscoe, Lora Weiss

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationSocial Computing, Behavioral-Cultural Modeling and Prediction - 4th International Conference, SBP 2011, Proceedings
Number of pages8
StatePublished - 2011
Event4th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2011 - College Park, MD, United States
Duration: Mar 29 2011Mar 31 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6589 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other4th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2011
Country/TerritoryUnited States
CityCollege Park, MD

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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