Network games have been commonly used as a formalism to study the provisioning of public goods, and mechanism design as a tool to induce socially desirable effort from agents. When the type of public goods is non-excludable, i.e., an agent can opt out of the mechanism and yet continue to benefit from actions of those who remain in the mechanism, existing literature has shown the difficulty of designing transfer mechanisms that simultaneously achieve social optimality, weak budget balance, and voluntary participation. This challenge stems from the fact that the participation of some agents creates excessive positive externality, thereby reducing the incentive for others to voluntarily opt in. To get around this difficulty, we consider instead a sequential model whereby agents are invited in some order σ to commit to the mechanism, with each agent’s commitment contingent on the decisions of those preceding it and in anticipation of those following it. This gives rise to the notion of sequential participation referred to as σ -voluntary participation ( σ -VP), which is a full-participation outcome under the given order. We show that satisfying the conventional voluntary participation condition is equivalent to satisfying σ -VP for all orderings σ . Furthermore, we demonstrate via examples that it is possible to simultaneously achieve social optimality, weak budget balance, and full participation in the sense of σ -VP, especially in large systems.
Blockchain networks are exposed to several security attacks, including selfish mining attacks, which occur when a miner or a group of miners withhold their newly mined blocks and keep them in their private chain rather than immediately broadcasting them to the rest of the network. This strategy forces honest miners to waste their resources and degrades the efficiency of the network. This paper introduces a novel game-theoretic approach leveraging a war of attrition framework to mitigate selfish mining in PoW blockchains. We design reward and punishment mechanisms to incentivize miners to opt for honest strategies and discourage them from choosing selfish tactics. Additionally, a reputation function is utilized to ensure that miners are not indifferent to the presence of selfish miners in the network. Moreover, we examine whether the honest strategy remains stable within the system using the evolutionary game theory. Our findings indicate that the honest strategy is indeed stable under the proposed model. Our solution will help mitigate the threat of selfish mining in blockchain systems.
In real society, interactions between individuals are accompanied by emotional changes, which in turn affect their decisions. This paper proposes an emotion-strategy persistence mechanism that represents individuals’ value orientations through changes in emotions. Intelligent agents are classified into selfish, competitive individuals and mutually beneficial, non-competitive individuals. A positive-negative emotion threshold is introduced to define the emotional states of intelligent agents. A positive emotional state indicates that the intelligent agent is relatively satisfied with the current strategy, leading to persistence in maintaining it. Conversely, the agent is more inclined to change its current strategy. In addition, this paper introduces the concept of emotional sensitivity to measure the amplitude of emotional fluctuations in intelligent agents and introduces strategy duration sensitivity to measure the willingness of intelligent agents to change their current strategy. Extensive simulation experiments show that non-competitive individuals play a decisive role in promoting and emerging cooperative behavior within the group. When the positive-negative emotion threshold is higher and the emotional sensitivity is greater, the level of group cooperation is higher. However, the impact of strategy duration sensitivity on the group cooperation rate exhibits a non-monotonic trend, with an optimal strategy duration sensitivity that maximizes the level of group cooperation.
Various modeling approaches have been proposed in the literature to forecast the evolution of wildland fires over time and space and to control them. Here, a methodology based on cooperative game theory is proposed to optimize the use of limited resources to control a fire, such as firefighters, Canadair, drones, etc. In particular, the Shapley value, i.e., a solution concept of cooperative games, is exploited. It provides a measure of the value of each player in a so-called transferable-utility game, and recently it has been used to evaluate the importance of edges or nodes in a network. In the proposed approach, the wildland area is modeled as a network, in which nodes represent areas of particular interest. Some of them may become fire outbreaks, from which the fire can reach other nodes. The edges represent possible directions of fire evolution and can be weighted by taking into account several features, including elevation of the terrain, kind of vegetation, and estimated rate of spread. A temperature diffusion process is considered, where the global rate of diffusion is given by the second smallest eigenvalue of the weighted Laplacian matrix of the network. Then, a transferable-utility game is defined, where players form a subset of the edge set, and the utility function is related to the decrease of the global rate of diffusion of the fire when one or more edges are removed. This corresponds to control diffusion paths from a fire outbreak. The Shapley values are exploited to decide the order in which the edges should be removed, that is, how one should act with fire-extinguishing resources, in such a way as to effectively reduce the global fire diffusion rate.
In this paper, we develop a Cournot competition model to analyze a competitive wireless communications market where service providers (SPs) utilize both proprietary bandwidth—exclusively owned and consistently available—and intermittent bandwidth, which becomes available sporadically. Previous studies have considered such a setting where SPs offer a non-intermittent service, meaning that users must be served at all times. Here, we instead consider SPs that offer an intermittent service, where users can tolerate delays in service but at a cost. Our model captures how the availability of intermittent bandwidth affects the service latency experienced by users, the strategies adopted by SPs, and their resulting revenues. Through theoretical analysis, we find that intermittent bandwidth can significantly impact market dynamics, lowering barriers to entry and enabling new entrants to compete effectively against established incumbents. This increased competition can lead to favorable outcomes for consumers, such as lower prices and improved service quality. We support our findings with simulations that illustrate how intermittent bandwidth can be used and its effects on the market. Our study highlights the strategic importance of intermittent bandwidth in shaping competitive markets and offers insights into how it can be leveraged to enhance efficiency and innovation in the telecommunications industry.
