Generating Strong Diversity of Opinions: Agent Models of Continuous Opinion Dynamics

Abstract

Opinion dynamics is the study of how opinions in a group of individuals change over time. A goal of opinion dynamics modelers has long been tofind a social science-based model that generates strong diversity - smooth, stable, possibly multi-modal distributions of opinions. This research lays the foundations for and develops such a model. First, a taxonomy is developed to precisely describe agent schedules in an opinion dynamics model. The importance of scheduling is shown with applications to generalized forms of two models. Next, the meta-contrast influencefield (MIF) model is defined. It is rooted in self-categorization theory and improves on the existing meta-contrast model by providing a properly scaled, continuous influence basis. Finally, the MIF-Local Repulsion (MIF-LR) model is developed and presented. This augments the MIF model with a formulation of uniqueness theory. The MIF-LR model generates strong diversity. An application of the model shows that partisan polarization can be explained by increased non-local social ties enabled by communications technology.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Sep 13, 2018
Accession Number
AD1063478

Entities

People

  • Christopher W. Weimer

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Agent-Based Simulations
  • Air Force
  • Algorithms
  • Cognitive Science
  • Computational Science
  • Geography
  • Information Operations
  • Mathematical Models
  • Multiagent Systems
  • Network Science
  • Operations Research
  • Political Science
  • Psychology
  • Social Media
  • Social Networks
  • Social Psychology
  • Test And Evaluation

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Operations Research
  • Theoretical Analysis.