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In this paper, we examine the sensitivity of the results of an earlier paper which presented and analyzed a dynamic game model of a monetary union with coalitions between governments (fiscal policy makers) and a common central bank (monetary policy maker). Here we examine alternative values of the parameters of the underlying model to show how the earlier results depend on the numerical parameter values chosen, which were obtained by calibration instead of econometric estimation. We demonstrate that the main results are qualitatively the same as in the original model for plausible smaller and larger values of the parameters. For the few cases where they differ, we interpret the deviations in economic terms and illustrate the policies and their macroeconomic effects resulting from the change to the parameter under consideration for one of these cases.

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We consider a discrete-time version of the fish war model, where a regulator imposes a moratorium on fishing activities whenever the stock reaches a predetermined critical low value. The moratorium will be in place until the fish stock recovers, that is, attains a desirable value. We obtain conditions on the parameter values such that a moratorium could be avoided, and its optimal duration when its imposition is deemed necessary. When the players cannot avoid a moratorium under both cooperative and noncooperative modes of play, we propose a moratorium-free strategy profile that matches under some conditions the Nash equilibrium in linear-state strategies. We illustrate our results with some numerical examples.

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This review presents and reviews various solved and open problems in developing, analyzing, and mitigating epidemic spreading processes under human decision-making. We provide a review of a range of epidemic models and explain the pros and cons of different epidemic models. We exhibit the art of coupling between epidemic models and decision models in the existing literature. More specifically, we provide answers to fundamental questions in human decision-making amid epidemics, including what interventions to take to combat the disease, who are decision-makers, and when and how to take interventions, and how to make interventions. Among many decision models, game-theoretic models have become increasingly crucial in modeling human responses or behavior amid epidemics in the last decade. In this review, we motivate the game-theoretic approach to human decision-making amid epidemics. This review provides an overview of the existing literature by developing a multi-dimensional taxonomy, which categorizes existing literature based on multiple dimensions, including (1) types of games, such as differential games, stochastic games, evolutionary games, and static games; (2) types of interventions, such as social distancing, vaccination, quarantine, and taking antidotes; (3) the types of decision-makers, such as individuals, adversaries, and central authorities at different hierarchical levels. A fine-grained dynamic game framework is proposed to capture the essence of game-theoretic decision-making amid epidemics. We showcase three representative frameworks with unique ways of integrating game-theoretic decision-making into the epidemic models from a vast body of literature. Each of the three frameworks has their unique way of modeling and analyzing and develops results from different angles. In the end, we identify several main open problems and research gaps left to be addressed and filled.

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A major challenge in ecosystem management is to promote cooperation among the multiple agents that manage the ecosystem. In particular, sharing information among the agents is often essential for reaching a desirable collective treatment. However, it is unclear how the sharing of information affects the incentives of selfish agents to cooperate and contribute to the common environmental project. Here, we consider a harmful species population that migrates across lands and causes damages to multiple agents, each of which aims to minimize her/his own costs due to both treatment and damages over time. We use game-theoretical models and compare the resulting collective treatment in three scenarios that differ in the information that agents have about (1) the true contribution of their neighbors to the treatment and (2) the true damages inflicted on their neighbors by the harmful species. We demonstrate that sharing such social information may incentivize the agents to free ride on their neighbors' contributions, thereby reducing the efficiency of the collective treatment. This implies that monitoring and sharing information may have negative consequences, and the extent to which information should be shared in joint projects necessitates a careful examination.

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The management of harmful species, including invasive species, pests, parasites, and diseases, is a major global challenge. Harmful species cause severe damage to ecosystems, biodiversity, agriculture, and human health. In particular, managing harmful species often requires cooperation among multiple agents, such as landowners, agencies, and countries. Each agent may have incentives to contribute less to the treatment, leaving more work for other agents, which may result in inefficient treatment. A central question is, therefore, how should a policymaker allocate treatment duties among the agents? Specifically, should the agents work together in the same area, or should each agent work only in a smaller area designated just for her/him? We consider a dynamic game-theoretic model, where a Nash equilibrium corresponds to a possible set of contributions that the agents could adopt over time. In turn, the allocation by the policymaker determines which of the Nash equilibria could be adopted, which allows us to compare the outcome of various allocations. Our results show that fewer agents can abate the harmful species population faster, but more agents can better control the population to keep its density lower. We prove this result in a general theorem and demonstrate it numerically for two case studies. Therefore, following an outbreak, the better policy would be to split and assign one or a few agents to treat the species in a given location, but if controlling the harmful species population at some low density is needed, the agents should work together in all of the locations.

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Modern control systems are featured by their hierarchical structure composed of cyber, physical and human layers. The intricate dependencies among multiple layers and units of modern control systems require an integrated framework to address cross-layer design issues related to security and resilience challenges. To this end, game theory provides a bottom-up modeling paradigm to capture the strategic interactions among multiple components of the complex system and enables a holistic view to understand and design cyber-physical-human control systems. In this review, we first provide a multi-layer perspective toward increasingly complex and integrated control systems and then introduce several variants of dynamic games for modeling different layers of control systems. We present game-theoretic methods for understanding the fundamental tradeoffs of robustness, security and resilience and developing a cross-layer approach to enhance the system performance in various adversarial environments. This review also includes three quintessential research problems that represent three research directions where dynamic game approaches can bridge between multiple research areas and make significant contributions to the design of modern control systems. The paper is concluded with a discussion on emerging areas of research that crosscut dynamic games and control systems.

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We study how payoffs and network structure affect reaching the payoff-dominant equilibrium in a [Formula: see text] coordination game that actors play with their neighbors in a network. Using an extensive simulation analysis of over 100,000 networks with 2-25 actors, we show that the importance of network characteristics is restricted to a limited part of the payoff space. In this part, we conclude that the payoff-dominant equilibrium is chosen more often if network density is larger, the network is more centralized, and segmentation of the network is smaller. Moreover, it is more likely that heterogeneity in behavior persists if the network is more segmented and less centralized. Persistence of heterogeneous behavior is not related to network density.