Decentralized decision-making in a large team with local information

https://doi.org/10.1016/S0899-8256(03)00006-XGet rights and content

Abstract

We study a problem involving a team of agents each associated with a node in a chain. Each agent makes a decision that influences only his own cost and those of adjacent agents. Prior to making his decision, each agent observes only the cost structure associated with nodes that can be reached by traversing no more than r arcs. Decisions are selected without any coordination, with the common objective of minimizing average cost among agents. We consider such decisions decentralized since agents act based on different information. Cost incurred by an optimal centralized strategy, in which a single decision-maker has access to all information and dictates all decisions, is employed as a performance benchmark. We show that, to maintain a certain level of performance relative to optimal centralized strategies, decentralized deterministic strategies require r to be proportional to the number of agents. This means that the amount of information accessible to any agent should be proportional to the total number of agents. Stochastic strategies, on the other hand, decentralize more gracefully—the amount of information required by each agent is independent of the total number of agents.

References (15)

  • B. von Stengel et al.

    Minmax equilibria in team games

    Games Econ. Behav.

    (1997)
  • T. Başar

    An equilibrium theory for multiperson decision making with multiple probabilistic models

    IEEE Trans. Automat. Control

    (1985)
  • U. Bertelé et al.

    Nonserial Dynamic Programming

    (1972)
  • D.P. Bertsekas

    Dynamic Programming and Optimal Control

    (1995)
  • E. Billard et al.

    Adaptive coordination in distributed systems with delayed information

    IEEE Trans. Systems Man Cybern.

    (1995)
  • F. Granot et al.

    Substitutes, complements and ripples in network flows

    Math. Oper. Res.

    (1985)
  • F.V. Jensen

    An Introduction to Bayesian Networks

    (1996)
There are more references available in the full text version of this article.
View full text