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We present the first game characterization of contrasimilarity, the weakest form of bisimilarity. The game is finite for finite-state processes and can thus be used for contrasimulation equivalence checking, of which no tool has been capable to date. A machine-checked Isabelle/HOL formalization backs our work and enables further use of contrasimilarity in verification contexts.
We revisit the crucial issue of natural game equivalences, and semantics of game logics based on these. We present reasons for investigating finer concepts of game equivalence than equality of standard powers, though staying short of modal bisimulation. Concretely, we propose a more finegrained notion of equality of basic powers which record what players can force plus what they leave to others to do, a crucial feature of interaction. This notion is closer to game-theoretic strategic form, as we explain in detail, while remaining amenable to logical analysis. We determine the properties of basic powers via a new representation theorem, find a matching instantial neighborhood game logic, and show how our analysis can be extended to a new game algebra and dynamic game logic.
A traditional assumption in game theory is that players are opaque to one another -- if a player changes strategies, then this change in strategies does not affect the choice of other players strategies. In many situations this is an unrealistic assumption. We develop a framework for reasoning about games where the players may be translucent to one another; in particular, a player may believe that if she were to change strategies, then the other player would also change strategies. Translucent players may achieve significantly more efficient outcomes than opaque ones. Our main result is a characterization of strategies consistent with appropriate analogues of common belief of rationality. Common Counterfactual Belief of Rationality (CCBR) holds if (1) everyone is rational, (2) everyone counterfactually believes that everyone else is rational (i.e., all players i believe that everyone else would still be rational even if i were to switch strategies), (3) everyone counterfactually believes that everyone else is rational, and counterfactually believes that everyone else is rational, and so on. CCBR characterizes the set of strategies surviving iterated removal of minimax dominated strategies: a strategy $sigma_i$ is minimax dominated for i if there exists a strategy $sigma_i$ for i such that $min_{mu_{-i}} u_i(sigma_i, mu_{-i}) > max_{mu_{-i}} u_i(sigma_i, mu_{-i})$.
We present the design and analysis of a multi-level game-theoretic model of hierarchical policy-making, inspired by policy responses to the COVID-19 pandemic. Our model captures the potentially mismatched priorities among a hierarchy of policy-makers (e.g., federal, state, and local governments) with respect to two main cost components that have opposite dependence on the policy strength, such as post-intervention infection rates and the cost of policy implementation. Our model further includes a crucial third factor in decisions: a cost of non-compliance with the policy-maker immediately above in the hierarchy, such as non-compliance of state with federal policies. Our first contribution is a closed-form approximation of a recently published agent-based model to compute the number of infections for any implemented policy. Second, we present a novel equilibrium selection criterion that addresses common issues with equilibrium multiplicity in our setting. Third, we propose a hierarchical algorithm based on best response dynamics for computing an approximate equilibrium of the hierarchical policy-making game consistent with our solution concept. Finally, we present an empirical investigation of equilibrium policy strategies in this game in terms of the extent of free riding as well as fairness in the distribution of costs depending on game parameters such as the degree of centralization and disagreements about policy priorities among the agents.
Is there a joint distribution of $n$ random variables over the natural numbers, such that they always form an increasing sequence and whenever you take two subsets of the set of random variables of the same cardinality, their distribution is almost the same? We show that the answer is yes, but that the random variables will have to take values as large as $2^{2^{dots ^{2^{Thetaleft(frac{1}{epsilon}right)}}}}$, where $epsilonleq epsilon_n$ measures how different the two distributions can be, the tower contains $n-2$ $2$s and the constants in the $Theta$ notation are allowed to depend on $n$. This result has an important consequence in game theory: It shows that even though you can define extensive form games that cannot be implemented on players who can tell the time, you can have implementations that approximate the game arbitrarily well.
We want to introduce another smoothing approach by treating each geometric element as a player in a game: a quest for the best element quality. In other words, each player has the goal of becoming as regular as possible. The set of strategies for each element is given by all translations of its vertices. Ideally, he would like to quantify this regularity using a quality measure which corresponds to the utility function in game theory. Each player is aware of the other players utility functions as well as their set of strategies, which is analogous to his own utility function and strategies. In the simplest case, the utility functions only depend on the regularity. In more complicated cases this utility function depends on the element size, the curvature, or even the solution to a differential equation. This article is a sketch of a possible game-theoretical approach to mesh smoothing and still on-going research.