Business Administration - Research Publications

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End date: 2019 × Type: Conference Paper × Start date: 2016 × Author: Berbeglia, Gerardo ×

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    Interdependent scheduling games
    Abeliuk, A ; Aziz, H ; Berbeglia, G ; Gaspers, S ; Kalina, P ; Mattei, N ; Peters, D ; Stursberg, P ; Van Hentenryck, P ; Walsh, T (AAAI Press / International Joint Conferences on Artificial Intelligence, 2016-01-01)
    We propose a model of interdependent scheduling games in which each player controls a set of services that they schedule independently. A player is free to schedule his own services at any time; however, each of these services only begins to accrue reward for the player when all predecessor services, which may or may not be controlled by the same player, have been activated. This model, where players have interdependent services, is motivated by the problems faced in planning and coordinating large-scale infrastructures, e.g., restoring electricity and gas to residents after a natural disaster or providing medical care in a crisis when different agencies are responsible for the delivery of staff, equipment, and medicine. We undertake a game-theoretic analysis of this setting and in particular consider the issues of welfare maximization, computing best responses, Nash dynamics, and existence and computation of Nash equilibria.
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    Asymptotic Optimality of Myopic Optimization in Trial-Offer Markets with Social Influence
    BERBEGLIA, G ; Abeliuk, A ; Maldonado, F ; Van Hentenryck, P (AAAI Press / International Joint Conferences on Artificial Intelligence, 2016)
    We study dynamic trial-offer markets, in which participants first try a product and later decide whether to purchase it or not. In these markets, social influence and position biases have a greater effect on the decisions taken in the sampling stage than those in the buying stage. We consider a myopic policy that maximizes the market efficiency for each incoming participant, taking into account the inherent quality of products, position biases, and social influence. We prove that this myopic policy is optimal and predictable asymptotically.