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Register a new userStrategic Inference with a Single Private Sample
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Added by
Erik Miehling
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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Motivated by applications in cyber security, we develop a simple game model for describing how a learning agents private information influences an observing agents inference process. The model describes a situation in which one of the agents (attacker) is deciding which of two targets to attack, one with a known reward and another with uncertain reward. The attacker receives a single private sample from the uncertain targets distribution and updates its belief of the target quality. The other agent (defender) knows the true rewards, but does not see the sample that the attacker has received. This leads to agents possessing asymmetric information: the attacker is uncertain over the parameter of the distribution, whereas the defender is uncertain about the observed sample. After the attacker updates its belief, both the attacker and the defender play a simultaneous move game based on their respective beliefs. We offer a characterization of the pure strategy equilibria of the game and explain how the players decisions are influenced by their prior knowledge and the payoffs/costs.
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Supply chains are the backbone of the global economy. Disruptions to them can be costly. Centrally managed supply chains invest in ensuring their resilience. Decentralized supply chains, however, must rely upon the self-interest of their individual components to maintain the resilience of the entire chain. We examine the incentives that independent self-interested agents have in forming a resilient supply chain network in the face of production disruptions and competition. In our model, competing suppliers are subject to yield uncertainty (they deliver less than ordered) and congestion (lead time uncertainty or, soft supply caps). Competing retailers must decide which suppliers to link to based on both price and reliability. In the presence of yield uncertainty only, the resulting supply chain networks are sparse. Retailers concentrate their links on a single supplier, counter to the idea that they should mitigate yield uncertainty by diversifying their supply base. This happens because retailers benefit from supply variance. It suggests that competition will amplify output uncertainty. When congestion is included as well, the resulting networks are denser and resemble the bipartite expander graphs that have been proposed in the supply chain literature, thereby, providing the first example of endogenous formation of resilient supply chain networks, without resilience being explicitly encoded in payoffs. Finally, we show that a suppliers investments in improved yield can make it worse off. This happens because high production output saturates the market, which, in turn lowers prices and profits for participants.
The Braess paradox is a counter-intuitive phenomenon whereby adding roads to a network results in higher travel time at equilibrium. In this paper we present an algorithm to detect the occurrence of this paradox in real-world networks with the help of an improved graph representation accounting for queues. The addition of queues to the network representation enables a closer match with real data. Moreover, we search for routes causing this phenomenon (Braess routes) rather than links, and advocate removing such routes virtually from navigation systems so that the associated links can continue to serve other routes. Our algorithm relies on a convex optimization problem utilizing Beckmann potentials for road links as well as queues, and results in a route reconfiguration with reduced delay. We assume the availability of historical data to build the optimization model. We also assume the existence of a centralized navigation system to manage the routing options and remove the Braess routes. The theoretical solution demonstrates up to 12% delay reduction in a network from Montgomery County, Maryland. We validate the improvement with simulations.
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A hybrid simulation-based framework involving system dynamics and agent-based simulation is proposed to address duopoly game considering multiple strategic decision variables and rich payoff, which cannot be addressed by traditional approaches involving closed-form equations. While system dynamics models are used to represent integrated production, logistics, and pricing determination activities of duopoly companies, agent-based simulation is used to mimic enhanced consumer purchasing behavior considering advertisement, promotion effect, and acquaintance recommendation in the consumer social network. The payoff function of the duopoly companies is assumed to be the net profit based on the total revenue and various cost items such as raw material, production, transportation, inventory and backorder. A unique procedure is proposed to solve and analyze the proposed simulation-based game, where the procedural components include strategy refinement, data sampling, gaming solving, and performance evaluation. First, design of experiment and estimated conformational value of information techniques are employed for strategy refinement and data sampling, respectively. Game solving then focuses on pure strategy equilibriums, and performance evaluation addresses game stability, equilibrium strictness, and robustness. A hypothetical case scenario involving soft-drink duopoly on Coke and Pepsi is considered to illustrate and demonstrate the proposed approach. Final results include P-values of statistical tests, confidence intervals, and simulation steady state analysis for different pure equilibriums.
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