Applying a Bayesian Stackelberg game to secure infrastructure system

This paper studies the modeling problem of event- triggered networked hybrid dynamic games (HDGs). By considering the influence of an event-triggering mechanism, the evolution process of dynamic games with hybrid characteristics is analyzed, and we point out the complexity and technical difficulties in the analysis of such a game problem. From the perspective of network science, we give a viewpoint on network-based modeling of event-triggered HDGs. On the basis of the state-space model with established, we first give the normal form of HDGs by a seven-tuple. We then establish the directed dynamic network model of HDGs for the first time, involving a graph-based tree structure form, which can well describe the distinctive features of the continuous-time and discrete-event dynamic game process on both sides, and has great advantages in evolutionary analysis. An example of the evolution of event-triggered HDGs show the innovation of the proposed model.

This collection began as a conference on Modeling Rational and Moral Agents that combined two themes. First is the problematic place of morality within the received theory of rational choice. Decision theory, game theory, and economics are unfriendly to crucial features of morality, such as commitment to promises. But since morally constrained agents seem to do better than rational agents - say by co-operating in social situations like the Prisoner’s Dilemma - it is difficult to dismiss them as simply irrational. The second theme is the use of modeling techniques. We model rational and moral agents because problems of decision and interaction are so complex that there is much to be learned even from idealized models. The two themes come together in the most obvious feature of the papers: the common use of games, like the Prisoner’s Dilemma (PD), to model social interactions that are problematic for morality and rationality.’

Using network science to understand the link between subjects and professions

This study investigates the application of game-based learning in game theory education, presenting a comprehensive framework for implementing gamification strategies through a "Market Gaming Simulation" approach. Traditional game theory instruction often struggles to effectively convey complex theoretical concepts and bridge the gap between abstract mathematics and practical applications. To address these challenges, this research develops and analyzes a strategic simulation game that integrates core game theory concepts—including Nash equilibrium, Prisoner's Dilemma, dynamic game analysis, mixed-strategy equilibrium, and Bayesian games—into an interactive learning environment.The study first examines the current limitations in game theory pedagogy and establishes the theoretical foundation for game-based learning implementation. It then presents a detailed design of the "Market Gaming Simulation," including game objectives, core mechanisms, role design, and operational processes. The simulation creates a competitive marketplace where students, through multiple rounds of decision-making, experience the practical applications of game theory concepts while developing strategic thinking capabilities.A systematic implementation framework is proposed, encompassing pre-course preparation, classroom execution, and post-game analysis. The framework includes comprehensive guidelines for both instructors and students, real-time feedback mechanisms, and evaluation systems. Results indicate that this gamified approach significantly enhances student engagement, improves understanding of theoretical concepts, and develops practical decision-making skills. While acknowledging implementation challenges such as the need for elevated pedagogical expertise and flexible game design, this study provides valuable insights for integrating game-based learning into game theory education, contributing to the advancement of interactive and effective teaching methodologies in higher education.

Enhancing secure routing in Mobile Ad Hoc Networks using a Dynamic Bayesian Signalling Game model

To enhance the resilience and sustainability of urban metro systems under operational uncertainties and external disturbances, critical station identification and vulnerability assessment should be further investigated from the perspective of network science. In this paper, the presented comprehensive clustering algorithm and the Pearson correlation coefficient are adopted to explore the origin-destination (OD) passenger flow characteristics on different date classifications, and the different dates should be reasonably classified into three categories, including working day, weekends, and holiday. Meanwhile, this paper proposes the dynamic DomiRank algorithm and flow DomiGCN model to identify critical stations from network structure and function on different data classifications respectively, and further studies the vulnerability property of metro networks under simulated attacks. The Shanghai metro network is selected as case to prove the feasibility and correctness of the model. The results show that the dynamic DomiRank algorithm is relatively effective to identify critical stations from network structure, and the flow DomiGCN model is also relatively effective to identify critical stations from network function. Moreover, simulated attacks to these critical stations detected by the proposed methods can cause more damages than the other methods. These findings provide some supports for protection of metro infrastructure and contribute to the sustainable operation and development of urban rail transit systems.

A logic Petri net model for dynamic multi-agent game decision-making

Because of the nature of high mobility and dynamic network topology, Intrusion Detection Systems (IDSs) in Vehicular Ad-hoc Networks (VANETs) face the challenge in balancing the accuracy and efficiency of detection. Two crucial problems are remained unsolved in existing studies: 1) how to perceive the environmental change in the perspective of an IDS? 2) how to make the IDS adaptive in different scenarios? In this paper, a self-adaptive scheme is proposed for IDSs in VANETs based on Bayesian game theory and Deep Q-learning Network (DQN). In the scheme, the interactions between an IDS and attackers are formulated as a dynamic intrusion detection game, in which the proposed scheme decides either to just adjust or to completely retrain the IDS. The Nash Equilibria (NE) of the game is derived to reveal how the optimal decision of the IDS depends on the detection performance and road conditions. Moreover, a DQN-Adjustment is proposed to realize the self-adaptation of the IDS in the dynamic game. Simulation results show that the IDS with the proposed self-adaptive scheme has better performance than other existing IDSs with higher detection rate as well as lower detection time and overhead.

Real world adversarial dynamics such as those encountered in Computer and Network security require modelswhich allow for both imperfect and incomplete information. Recently game theoretic models and speci callysignaling games have been at the forefront of interest for modeling these scenarios. We propose a modi cationof signaling games, a type of Bayesian game, which we believe can serve as a model for these scenarios. Byincorporating real world data into the model, these games could allow interested parties to learn the true natureof the game that they are already playing - though without the rulebook.Keywords: Bayesian games, imperfect information, incomplete information, signaling games, game theory 1. INTRODUCTION Inverse game theory involves the learning of the nature of a game. In certain scenarios the payo s, identities,and/or actions available to the players may be unavailable. Our previous work on the same topic involved theuse of a multiplayer betting game to discover the utility functions of opponents through play, and exploiting thatknowledge to improve the player's winnings.

We consider dynamic cooperative games, where the worth of coalitions varies over time according to the history of allocations. When defining the core of a dynamic game, we allow the possibility for coalitions to deviate at any time and thereby to give rise to a new environment. A coalition that considers a deviation needs to take the consequences into account because from the deviation point on, the game is no longer played with the original set of players. The deviating coalition becomes the new grand coalition which, in turn, induces a new dynamic game. The stage games of the new dynamical game depend on all previous allocation including those that have materialized from the deviating time on. We define three types of core solutions: fair core, stable core and credible core. We characterize the first two in case where the instantaneous game depends on the last allocation (rather than on the whole history of allocations) and the third in the general case. The analysis and the results resembles to a great extent the theory of non-cooperative dynamic games.

Dynamical systems game theory II: A new approach to the problem of the social dilemma

The mining and exploitation of graph structural information have been the focal points in the study of complex networks. Traditional structural measures in Network Science focus on the analysis and modelling of complex networks from the perspective of network structure, such as the centrality measures, the clustering coefficient, and motifs and graphlets, and they have become basic tools for studying and understanding graphs. In comparison, graph neural networks, especially graph convolutional networks (GCNs), are particularly effective at integrating node features into graph structures via neighbourhood aggregation and message passing, and have been shown to significantly improve the performances in a variety of learning tasks. These two classes of methods are, however, typically treated separately with limited references to each other. In this work, aiming to establish relationships between them, we provide a network science perspective of GCNs. Our novel taxonomy classifies GCNs from three structural information angles, i.e., the layer-wise message aggregation scope, the message content, and the overall learning scope. Moreover, as a prerequisite for reviewing GCNs via a network science perspective, we also summarise traditional structural measures and propose a new taxonomy for them. Finally and most importantly, we draw connections between traditional structural approaches and graph convolutional networks, and discuss potential directions for future research.

The Brazilian Antitrust Law established the Administrative Council for Economic Defence (CADE) as the body responsible for investigating and imposing sanctions for acts that violate the economic order, as well as for supervising acts of economic concentration. The field of game theory is concerned with the actions of decision-makers who are aware that their decisions affect one another. The objective of the proposed article is to analyse the legal framework of the Leniency Programme implemented by Brazil's Administrative Council for Economic Defence (CADE) using the tools provided by game theory, in particular sequential games with perfect information. The methodology employed is classified as theoretical research, utilising the deductive method with the objective of enhancing the proposed theme. In order to achieve this, two game models will be developed. Initially, it was observed that the benefits offered by the current Brazilian leniency programme are insufficient to persuade a potential cartel member to propose a leniency agreement. Conversely, in a second step, the introduction of immunity for applicants from civil damages caused by the cartel resulted in the conclusion that the Brazilian antitrust leniency programme becomes an important tool for deterring and combating cartels. KEYWORDS: Antitrust Law. Cartel. Game theory. Leniency. Sequential games.

Stackelberg game in critical infrastructures from a network science perspective

Finite-dimensional, time invariant, linear quadratic dynamic games are perhaps the best understood and researched class of dynamic games. This is particularly true for continuous-time linear quadratic differential games. In this paper, the application of the theory of dynamic games to signal processing is considered. We are interested in digital signal processing and therefore we confine our attention to discrete-time linear-quadratic dynamic games (LQDG). In discrete-time the cost function contains product terms between the decision variables which complicates the analysis compared to its continuous-time analogue. With a view to facilitate the application of the theory of dynamic games to digital signal processing, and in particular, disturbance rejection, the complete solution of the discrete-time LQDG is worked out and explicit results are obtained. Thus,discrete-time LQDGs have the distinct advantage of being amenable to analysis, closed-form solutions are possible, and one is in tune with modern digital signal processing techniques. In this paper, minimal necessary and sufficient conditions for the existence of a solution to the discrete-time LQDG are provided and its explicit, closed-form, solution is worked out. This opens the way to designing novel digital signal processing algorithms for disturbance rejection. Information plays a critical role in game theory and in particular in dynamic games. Using our explicit solution of the deterministic LQDG, a hierarchy of three zero-sum stochastic LQDGs characterized by a sequence of information patterns which increase in complexity is analyzed.