Learning in the Presence of Unawareness

Abstract

The standard assumption in game theory and decision theory is that the game/decision problem is, in a sense, completely understood. For example, decision problems are typically described in terms of states and outcomes, where acts are taken to be functions from states to outcomes. It is typically assumed that a decision maker (DM) knows the state space, the outcome space, and the set of feasible acts. But this is far from clear in practice. In a complex decision problem, agents may be unaware of many relevant features, and thus unaware of possible states, outcomes, and feasible acts.

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Document Details

Document Type
Technical Report
Publication Date
Jan 27, 2015
Accession Number
ADA621833

Entities

People

  • Joseph Halpern

Organizations

  • Cornell University

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Abstracts
  • Agreements
  • Computer Science
  • Cybersecurity
  • Decision Theory
  • Department Of Defense
  • Engineering
  • Game Theory
  • Information Theory
  • Language
  • Learning
  • Mathematics
  • Probability
  • Reasoning
  • Social Sciences
  • Standards
  • Students

Fields of Study

  • Economics

Readers

  • Canine Service Warrior Training Program for Wounded Warriors in the Veterinary Industry, Supported by Donors.
  • Mathematical Modeling and Probability Theory.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • Space