6533b7cffe1ef96bd1259083

RESEARCH PRODUCT

Distributed Consensus in Noncooperative Inventory Games

Raffaele PesentiDario BausoLaura Giarre

subject

TheoryofComputation_MISCELLANEOUSComputer Science::Computer Science and Game TheoryInformation Systems and ManagementGeneral Computer ScienceManagement Science and Operations ResearchIndustrial and Manufacturing Engineeringsymbols.namesakeSettore ING-INF/04 - AutomaticaGame theory; Multi-agent systems; Inventory; Consensus protocolsEconomicsRisk dominanceGame theoryMulti-agent systemsStochastic gameInventoryComputingMilieux_PERSONALCOMPUTINGTheoryofComputation_GENERALRationalizabilityConsensus protocols; Game theory; Inventory; Multi-agent systemsConsensus protocolsMulti-agent systemNash equilibriumEquilibrium selectionModeling and SimulationBest responsesymbolsRepeated gameEpsilon-equilibriumSettore MAT/09 - Ricerca OperativaMathematical economics

description

This paper deals with repeated nonsymmetric congestion games in which the players cannot observe their payoffs at each stage. Examples of applications come from sharing facilities by multiple users. We show that these games present a unique Pareto optimal Nash equilibrium that dominates all other Nash equilibria and consequently it is also the social optimum among all equilibria, as it minimizes the sum of all the players’ costs. We assume that the players adopt a best response strategy. At each stage, they construct their belief concerning others probable behavior, and then, simultaneously make a decision by optimizing their payoff based on their beliefs. Within this context, we provide a consensus protocol that allows the convergence of the players’ strategies to the Pareto optimal Nash equilibrium. The protocol allows each player to construct its belief by exchanging only some aggregate but sufficient information with a restricted number of neighbor players. Such a networked information structure has the advantages of being scalable to systems with a large number of players and of reducing each player’s data exposure to the competitors.

https://hdl.handle.net/11380/1123570