Predicting Networked Strategic Behavior via Machine Learning and Game Theory

Abstract

The funding for this project was used to develop basic models, methodology and algorithms for the application of machine learning and related tools to settings in which strategic behavior is central. Among the topics studied was the development of simple behavioral models explaining and predicting human subject behavior in networked strategic experiments from prior work. These included experiments in biased voting and networked trading, among others.

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Document Details

Document Type
Technical Report
Publication Date
Jan 13, 2015
Accession Number
ADA621834

Entities

People

  • Michael Kearns

Organizations

  • University of Pennsylvania

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Abstracts
  • Agreements
  • Department Of Defense
  • Education
  • Electronic Commerce
  • Engineering
  • Game Theory
  • Hidden Markov Models
  • Information Operations
  • Learning
  • Machine Learning
  • Markov Models
  • Mathematics
  • Military Research
  • Models
  • Students
  • Technology Transfer

Fields of Study

  • Computer science

Readers

  • Defense Acquisition Program Management
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • AI & ML - Neural Networks