Search results for " Dynamic Programming"

showing 4 items of 4 documents

Quantized Dissensus in Networks of Agents subject to Death and Duplication

2012

Dissensus is a modeling framework for networks of dynamic agents in competition for scarce resources. Originally inspired by biological cells behaviors, it fits also marketing, finance and many other application areas. Competition is often unstable in the sense that strong agents, those having access to large resources, gain more and more resources at the expense of weak agents. Thus, strong agents duplicate when reaching a critical amount of resources, whereas weak agents die when loosing all their resources. To capture all these phenomena we introduce systems with a discrete time gossip and unstable state dynamics interrupted by discrete events affecting the network topology. Invariancy o…

Dynamic ProgrammingConsensus ProtocolsComputer sciencemedia_common.quotation_subjectDistributed computingSubject (philosophy)Dynamical Systems (math.DS)Network topologyConsensus protocolScarcityCompetition (economics)Settore ING-INF/04 - AutomaticaGossipFOS: MathematicsElectrical and Electronic EngineeringMathematics - Dynamical SystemsMathematics - Optimization and Controlmedia_commonConsensus Protocols; Quantized Control; Dynamic Programming; Network based marketing; Dynamic Pie Diagram.Dynamic Pie Diagramquantized controlComputer Science ApplicationsConsensus protocolsConsensus protocols; network based marketing; quantized controlDiscrete time and continuous timeControl and Systems Engineeringnetwork based marketingOptimization and Control (math.OC)90C3993Dxx34K2034a38Settore MAT/09 - Ricerca Operativa
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Consensus in Noncooperative Dynamic Games: a Multi-Retailer Inventory Application

2008

We focus on Nash equilibria and Pareto optimal Nash equilibria for a finite horizon noncooperative dynamic game with a special structure of the stage cost. We study the existence of these solutions by proving that the game is a potential game. For the single-stage version of the game, we characterize the aforementioned solutions and derive a consensus protocol that makes the players converge to the unique Pareto optimal Nash equilibrium. Such an equilibrium guarantees the interests of the players and is also social optimal in the set of Nash equilibria. For the multistage version of the game, we present an algorithm that converges to Nash equilibria, unfortunately, not necessarily Pareto op…

TheoryofComputation_MISCELLANEOUSComputer Science::Computer Science and Game TheoryCorrelated equilibriumSequential gameComputer scienceDynamic programmingSubgame perfect equilibriumsymbols.namesakeCoordination gameElectrical and Electronic EngineeringRisk dominanceFolk theoremPrice of stabilityNon-credible threatGame theoryCentipede gameImplementation theoryNon-cooperative gameInventoryNormal-form gameStochastic gameComputingMilieux_PERSONALCOMPUTINGTheoryofComputation_GENERALComputer Science ApplicationsConsensus protocols; Dynamic programming; Game theory; InventoryConsensus protocolsZero-sum gameControl and Systems EngineeringNash equilibriumEquilibrium selectionBest responsesymbolsRepeated gameEpsilon-equilibriumConsensus protocols; Dynamic programming; Game theory; Inventory;Potential gameSolution conceptMathematical economicsGame theory
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Noncooperative dynamic games for inventory applications: A consensus approach

2008

We focus on a finite horizon noncooperative dynamic game where the stage cost of a single player associated to a decision is a monotonically nonincreasing function of the total number of players making the same decision. For the single-stage version of the game, we characterize Nash equilibria and derive a consensus protocol that makes the players converge to the unique Pareto optimal Nash equilibrium. Such an equilibrium guarantees the interests of the players and is also social optimal in the set of Nash equilibria. For the multi-stage version of the game, we present an algorithm that converges to Nash equilibria, unfortunately not necessarily Pareto optimal. The algorithm returns a seque…

TheoryofComputation_MISCELLANEOUSDynamic gamesComputer Science::Computer Science and Game TheoryMathematical optimizationCorrelated equilibriumSequential gameConsensus ProtocolsComputer scienceA-priori; Consensus protocols; Dynamic games; Finite horizons; Inventory; Inventory systems; Joint decisions; Multi stages; Nash equilibrium; Pareto-optimal; Single stages; Unilateral improvementsSymmetric equilibriumOutcome (game theory)Joint decisionsNash equilibriumFinite horizonsMulti stagessymbols.namesakeBayesian gameSettore ING-INF/04 - AutomaticaPareto-optimalA-prioriCoordination gameFolk theoremPrice of stabilityRisk dominanceNon-credible threatConsensus Protocols Dynamic Programming Game Theory InventoryInventory systemsTraveler's dilemmaNormal-form gameStochastic gameInventoryComputingMilieux_PERSONALCOMPUTINGTheoryofComputation_GENERALMinimaxConsensus protocolsEquilibrium selectionNash equilibriumBest responseSingle stagesRepeated gamesymbolsEpsilon-equilibriumSettore MAT/09 - Ricerca OperativaSolution conceptDynamic Programming Game TheoryUnilateral improvementsMathematical economicsGame theoryConsensus Protocols; Dynamic Programming Game Theory; Inventory
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A methodology and algorithms for an optimal identification of Tourist Local Systems

2007

In last years, despite the emphasis on the importance of tourism as a leading industry in the development of a country’s economy, there is a lack of criteria and methodologies for the identification, the promotion and the governance of Tourism Local Systems (TLS). Moreover, nowadays an important debate is more and more emerging on the sustainable tourism development which involve three interconnected aspects: environmental, socio-cultural and economic. To this end, in this paper, a rigorous mathematical model is proposed for the optimal identification and dimensioning of TLS. The model here presented consists of a two stage methodology: at first, all the factors that characterize a geograph…

Tourist Local Systems Markov Chain Decision Trees Dynamic Programming
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