Toward generative narrative models of the course and resolution of conflict

Steven R. Corman, Scott W. Ruston, Hanghang Tong

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Over the last 15 years, interest in narrative as a concept for supporting national defense has grown considerably. But efforts to win the battle of the narrative have yielded limited results because of a reliance on a definition of narrative rooted in the spoken or written word. In this chapter, we adopt a contemporary view of narrative as a cognitive process of comprehension and argue that narratives can be modeled as complex systems, providing insight into how actors understand consequences of events in the real world. To accomplish this, we propose a graph-based generative modeling framework and illustrate its application to well-known fairy tale. We demonstrate that standard network measures can discern structural details about the narrative that would normally require reading the text. We also show that various graph modeling methods can potentially identify information that may be missing in the graph, a necessary feature for applying the technique in situations of less-than-complete information. We conclude by identifying the challenges the technique faces in terms of analysis, scale, and sensitivity.

Original languageEnglish (US)
Title of host publicationSocial-Behavioral Modeling for Complex Systems
PublisherWiley
Pages121-144
Number of pages24
ISBN (Electronic)9781119485001
ISBN (Print)9781119484967
DOIs
StatePublished - Mar 29 2019
Externally publishedYes

Keywords

  • Adversary courses
  • Conflict events
  • Generative narrative models
  • Research applications
  • Resolution nodes
  • Social science modeling

ASJC Scopus subject areas

  • General Engineering

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