568 research outputs found
Payoff levels, loss avoidance, and equilibrium selection in the Stag Hunt: an experimental study
Game theorists typically assume that changing a gameâs payoff levelsâby adding the same constant to, or subtracting it from, all payoffsâshould not affect behavior. While this invariance is an implication of the theory when payoffs mirror expected utilities, it is an empirical question when the âpayoffsâ are actually money amounts. In particular, if individuals treat monetary gains and losses differently, then payoffâlevel changes may matter when they result in positive payoffs becoming negative, or vice versa. We report the results of a humanâsubjects experiment designed to test for two types of loss avoidance: certainâloss avoidance (avoiding a strategy leading to a sure loss, in favor of an alternative that might lead to a gain) and possibleâloss avoidance (avoiding a strategy leading to a possible loss, in favor of an alternative that leads to a sure gain). Subjects in the experiment play three versions of Stag Hunt, which are identical up to the level of payoffs, under a variety of treatments. We find differences in behavior across the three versions of Stag Hunt; these differences are hard to detect in the first round of play, but grow over time. When significant, the differences we find are in the direction predicted by certainâ and possibleâloss avoidance. Our results carry implications for games with multiple equilibria, and for theories that attempt to select among equilibria in such games
Repeated Multimarket Contact with Private Monitoring: A Belief-Free Approach
This paper studies repeated games where two players play multiple duopolistic
games simultaneously (multimarket contact). A key assumption is that each
player receives a noisy and private signal about the other's actions (private
monitoring or observation errors). There has been no game-theoretic support
that multimarket contact facilitates collusion or not, in the sense that more
collusive equilibria in terms of per-market profits exist than those under a
benchmark case of one market. An equilibrium candidate under the benchmark case
is belief-free strategies. We are the first to construct a non-trivial class of
strategies that exhibits the effect of multimarket contact from the
perspectives of simplicity and mild punishment. Strategies must be simple
because firms in a cartel must coordinate each other with no communication.
Punishment must be mild to an extent that it does not hurt even the minimum
required profits in the cartel. We thus focus on two-state automaton strategies
such that the players are cooperative in at least one market even when he or
she punishes a traitor. Furthermore, we identify an additional condition
(partial indifference), under which the collusive equilibrium yields the
optimal payoff.Comment: Accepted for the 9th Intl. Symp. on Algorithmic Game Theory; An
extended version was accepted at the Thirty-Fourth AAAI Conference on
Artificial Intelligence (AAAI-20
Coalition structure generation in cooperative games with compact representations
This paper presents a new way of formalizing the coalition structure generation problem (CSG) so that we can apply constraint optimization techniques to it. Forming effective coalitions is a major research challenge in AI and multi-agent systems. CSG involves partitioning a set of agents into coalitions to maximize social surplus. Traditionally, the input of the CSG problem is a black-box function called a characteristic function, which takes a coalition as input and returns the value of the coalition. As a result, applying constraint optimization techniques to this problem has been infeasible. However, characteristic functions that appear in practice often can be represented concisely by a set of rules, rather than treating the function as a black box. Then we can solve the CSG problem more efficiently by directly applying constraint optimization techniques to this compact representation. We present new formalizations of the CSG problem by utilizing recently developed compact representation schemes for characteristic functions. We first characterize the complexity of CSG under these representation schemes. In this context, the complexity is driven more by the number of rules than by the number of agents. As an initial step toward developing efficient constraint optimization algorithms for solving the CSG problem, we also develop mixed integer programming formulations and show that an off-the-shelf optimization package can perform reasonably well
Strategyproof matching with regional minimum and maximum quotas
This paper considers matching problems with individual/regional minimum/maximum quotas. Although such quotas are relevant in many real-world settings, there is a lack of strategyproof mechanisms that take such quotas into account. We first show that without any restrictions on the regional structure, checking the existence of a feasible matching that satisfies all quotas is NP-complete. Then, assuming that regions have a hierarchical structure (i.e., a tree), we show that checking the existence of a feasible matching can be done in time linear in the number of regions. We develop two strategyproof matching mechanisms based on the Deferred Acceptance mechanism (DA), which we call Priority List based Deferred Acceptance with Regional minimum and maximum Quotas (PLDA-RQ) and Round-robin Selection Deferred Acceptance with Regional minimum and maximum Quotas (RSDA-RQ). When regional quotas are imposed, a stable matching may no longer exist since fairness and nonwastefulness, which compose stability, are incompatible. We show that both mechanisms are fair. As a result, they are inevitably wasteful. We show that the two mechanisms satisfy different versions of nonwastefulness respectively; each is weaker than the original nonwastefulness. Moreover, we compare our mechanisms with an artificial cap mechanism via simulation experiments, which illustrate that they have a clear advantage in terms of nonwastefulness and student welfare
- âŚ