Models and Algorithms for Higher Order Network Inference

Abstract

Major Goals: The work under this YIP was focused on leveraging recent work by the PI that connects higher-order choice modeling -- approaches that go beyond independence assumptions between the alternatives on offer, sometimes called context effects -- and network science. The goal has been to develop new modeling tools that enrich both camps. The main applications are in modeling competition networks (match-ups) as well as social network formation, with further applications to complex decision-making domains where individuals report set-wise or ranked preferences (ranked choice voting, school choice).

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Document Details

Document Type
Technical Report
Publication Date
Mar 31, 2022
Accession Number
AD1192797

Entities

People

  • Johan Ugander

Organizations

  • Stanford University

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Computations
  • Data Mining
  • Data Science
  • Information Processing
  • Information Science
  • Information Systems
  • Machine Learning
  • Media
  • Network Science
  • Networks
  • Social Media
  • Social Networks
  • Social Sciences
  • Statistics
  • Students
  • World Wide Web

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Psychometric Testing or Psychological Assessment.
  • Systems Analysis and Design

Technology Areas

  • AI & ML