Convergence and Asymptotic Agreement in Distributed Decision Problems,

Abstract

We consider a distributed team decision problem in which different agents obtain from the environment different stochastic measurements, possibly at different random times, related to the same uncertain random vector. Each agent has the same objective function and prior probability distribution. We assume that each agent can compute an optimal tentative decision based upon his own observation and that these tentative decisions are communicated and received, possibly at random times, by a subset of other agents. Conditions for asymptotic convergence of each agent's decision sequence and asymptotic agreement of all agents' decisions are derived. (Author)

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1982
Accession Number
ADA113602

Entities

People

  • John N. Tsitsiklis
  • Michael Athans

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Agreements
  • Convergence
  • Dynamic Programming
  • Functional Analysis
  • Guarantees
  • Hilbert Space
  • Inequalities
  • Massachusetts
  • New York
  • Observation
  • Probability
  • Probability Distributions
  • Random Variables
  • Sequences
  • Social Sciences
  • Stochastic Processes

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.