We investigate the manipulation of power indices in TU-cooperative games by stimulating (subject to a budget constraint) changes in the propensity of other players to participate to the game. We display several algorithms that show that the problem is often tractable for so-called network centrality games and influence attribution games, as well as an example when optimal manipulation is intractable, even though computing power indices is feasible.
A lot of work has been devoted recently to measuring and characterizing in multi-agent settings @cite_29 @cite_22 @cite_19 . Synergies between players in cooperative games are obviously relevant to the theme of this paper: synergic agents' participation to coalitions increases the Shapley value of the given agent. The nature of some of our results (Theorems , and ), that target nodes in a fixed order, provide a concrete way for ranking synergies between these nodes and the attacked one.
@cite_19 @cite_29 @cite_22
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We investigate synergy, or lack thereof, between agents in co-operative games, building on the popular notion of Shapley value. We think of a pair of agents as synergistic (resp., antagonistic) if the Shapley value of one agent when the other agent participates in a joint effort is higher (resp. lower) than when the other agent does not participate. Our main theoretical result is that any graph specifying synergistic and antagonistic pairs can arise even from a restricted class of cooperative games. We also study the computational complexity of determining whether a given pair of agents is synergistic. Finally, we use the concepts developed in the paper to uncover the structure of synergies in two real-world organizations, the European Union and the International Monetary Fund. Coalition formation is the process of bringing together two or more agents so as to achieve goals that individuals on their own cannot, or to achieve them more efficiently. Typically, in such situations, the agents have conflicting preferences over the set of possible joint goals. Thus, before the agents realize the benefits of cooperation, they must find a way of resolving these conflicts and reaching a consensus. In this context, cooperative game theory offers the voting game as a mechanism for agents to reach a consensus. It also offers the Shapley value as a way of measuring the influence or power a player has in determining the outcome of a voting game. Given this, the designer of a voting game wants to construct a game such that a player's Shapley value is equal to some desired value. This is called the inverse Shapley value problem. Solving this problem is necessary, for instance, to ensure fairness in the players' voting powers. However, from a computational perspective, finding a player's Shapley value for a given game is #P-complete. Consequently, the problem of verifying that a voting game does indeed yield the required powers to the agents is also #P-complete. Therefore, in order to overcome this problem we present a computationally efficient approximation algorithm for solving the inverse problem. This method is based on the technique of 'successive approximations'; it starts with some initial approximate solution and iteratively updates it such that after each iteration, the approximate gets closer to the required solution. This is an anytime algorithm and has time complexity polynomial in the number of players. We also analyze the performance of this method in terms of its approximation error and the rate of convergence of an initial solution to the required one. Specifically, we show that the former decreases after each iteration, and that the latter increases with the number of players and also with the initial approximation error. Coalitions and cooperation are key topics in multi-agent systems (mas). They enable agents to achieve goals that they may not have been able to achieve independently. A range of previous studies have found that many problems in coalitional games tend to be computationally intractable - that is, the computational complexity grows rapidly as a function of the number of participating agents. However, these hardness results generally require that each agent is of a different type. Here, we observe that in many mas settings, while the number of agents may grow, the number of different types of agents remains small. We formally define the notion of agent types in cooperative games. We then re-examine the computational complexity of the different coalition formation problems when assuming that the number of agent types is fixed. We show that most of the previously hard problems become polynomial when the number of agent types is fixed. We consider multiple different game formulations and representations (characteristic function with subadditive utilities, crg, and graphical representations) and several different computational problems (including stability, core-emptiness, and Shapley value). Procuring multiple agents with different ability levels to independently solve the same task is common in labor markets, crowdsourcing environments and research and development projects due to two reasons: some agents may fail to provide a satisfactory solution, and the redundancy increases the quality of the best solution found. However, incentivizing large number of agents to compete for one task is difficult; agents need fair ex-ante guaranteed payoffs that consider their ability levels and failure rates to exert efforts. We model such domains as a cooperative game called the Max-Game, where each agent has a weight representing its ability level, and the value of an agent coalition is the maximal weight of the agents in the coalition. When agents may fail, we redefine the value of a coalition as the expected maximal weight of its surviving members. We analyze the core, the Shapley value, and the Banzhaf index as methods of payoff division. Surprisingly, the latter two, which are usually computationally hard, can be computed in polynomial time. Finally, we initiate the study of a new form of sabotage where agents may be incentivized to influence the failure probabilities of their peers, and show that no such incentive is present in a restricted case of Max-Games.