Optimal, Distributed Decision-Making: The Case of no Communication

We present a combinatorial framework for the study of a natural class of distributed optimization problems that involve decisionmaking by a collection of n distributed agents in the presence of incomplete information; such problems were originally considered in a load balancing setting by Papadimitriou and Yannakakis (Proceedings of the 10th Annual ACM Symposium on Principles of Distributed Computing, pp. 61–64, August 1991). For any given decision protocol and assuming no communication among the agents, our framework allows to obtain a combinatorial inclusion-exclusion expression for the probability that no “overflow” occurs, called the winning probability, in terms of the volume of some simple combinatorial polytope. Within our general framework, we offer a complete resolution to the special cases of oblivious algorithms, for which agents do not “look at” their inputs, and non-oblivious algorithms, for which they do, of the general optimization problem. In either case, we...

• Type: Conference
• Year: 1999
• Where: PODC
• Authors: Marios Mavronicolas, Paul G. Spirakis
• Keywords: Combinatorial, Distributed And Parallel Computing, Oblivious Algorithm, Optimization Problem, PODC 1999