Game Theory Concepts Research Articles

Application of Game Theory in Reliability-Centered Maintenance of Electric Power Systems

As the electricity market undergoes continuous evolutions, along with the widely-recognized outdated nature of the grid, system operators would have to be able to more effectively manage the system operation expenses since waves of maintenance costs and equipment investments would be anticipated in a few years to come. Strategic implementation of cost-effective reliability-centered maintenance (RCM) approaches seems to be a key solution. This paper proposes an efficient method to assess the component criticality for system overall reliability and further maintenance focuses. A solution concept of game theory, called shapely value, is proposed that is able to fairly identify the contribution of every single equipment's to the system reliability performance once a high-order contingency occurs under different loading conditions. The identified critical components are then systematically involved in a new optimization framework for effective scheduling of RCM implementation in power systems. The suggested framework helps in realizing where investments and maintenance to be made in the grid to ensure a desirable system reliability performance. Implemented on the IEEE reliability test system, the effectiveness of the suggested framework is confirmed by comparing to conventional techniques under various scenarios.

Distributed generation for economic benefit maximization through coalition formation-based game theory concept

For enhancing the awareness and competitiveness of distributed energy resources (DER) in energy markets, an optimal and implementation and operation of these resources. To address this issue, in this paper, objective, DERs that can collaborate with other DER installed as closer or far topologies, forming coalitions for gaining competitiveness between each other in the energy market, have been selected for study. The profit allocation due to coalition between DERs is being identified as an important issue for ensuring renewable sources installation in smart grids. A methodology with, bi-level, structures is proposed to maximize the profit in a competitive market and allocate profit resulting from a coalition formation between multiple energy districts encompassing DERs. In the primary level analysis, a mechanism based on noncooperative game theory is presented with the aim of creating a competitive market, as well as, to study the supply energy strategies based on pricing mechanisms of multiple energy suppliers. In the second level, various distribution methods, namely, Shapley, Nucleolus, and Merge and Split, are compared with each other for profit allocation analysis. Our study highlights that the disconnection of DERs resulting from pricing decisions allows them to collaborate together with aggregated facilities to achieve higher profits due to excess production and avoid penalties due to shortages in production, which in essence, demonstrates a significant operational increase in their profits, a concept that favors the likelihood for all energy suppliers and producers to form a coalition for economic optimizations.

An integrated approach to evaluating sustainability in supply chains using evolutionary game theory

Sustainability in supply chains is typically studied across one or more dimensions such as environmental, social, economic, culture and governance. Traditionally sustainability in supply chains has focused on environmental dimensions, while a few have attempted to focus on social and economic dimensions without really integrating them. There has been only a small effort to define sustainability by integrating all relevant dimensions (a holistic approach). This paper proposes to fill this gap. We identify sustainability of a supply chain with the equilibrium of the system over a long (but finite) period of time after integrating the various dimensions. Thus it necessitates looking at factors that can cause a shift in the equilibrium. Towards this, we propose to build a strong theoretical framework to integrate, explain, and predict sustainability for supply chains using cross-disciplinary effort. In our theoretical framework, evolutionary game theory serves as the pure conceptual theory-building tool, the metrics are qualitative in nature and the indicators are quantitative statistical measures. The use of evolutionary game theory concepts allows us to understand how sometimes trivial actions by members of the supply chain can trigger cascading effects that can move the system away from equilibrium. One of the salient aspects of our model is its complete scalability in terms of changes to the dimensions and metrics. As an example, we explain and predict social and economic sustainability (in tandem) for a public health insurance supply chain using evolutionary game theory.

Game Theory Basics and Its Application in Cyber Security

The concept of game theory has in recent times has found application extensively in the area of security usually called security games. A game could be normal form or extensive form and are used to model the behaviour of players in a simple or complex contest for resources within a given scenario. Game theory finds application in various areas including finance, economics, politics, auction, sciences and cyber security. This work reviews the application of game theory in cyber security. A brief introduction to the concept of game theory is presented alongside a detailed review of research works carried out using the concept of game theory for cyber security.

A New Approximation Method for the Shapley Value Applied to the WTC 9/11 Attack

The Shapley value (Shapley (1953)) is one of the most prominent one-point solution concepts in cooperative game theory that divides revenues (or cost, power) that can be obtained by cooperation of players in the game. The Shapley value is mathematically characterized by properties that have appealing real-world interpretations and hence its use in practical settings is easily justified. The down part is that its computational complexity increases exponentially with the number of players in the game. Therefore, in practical problems that consist of more that 25 players the calculation of the Shapley value is usually too time expensive. Among others the Shapley value is applied in the analysis of terrorist networks (cf. Lindelauf et al. (2013)) which generally extend beyond the size of 25 players. In this paper we therefore present a new method to approximate the Shapley value by refining the random sampling method introduced by Castro et al. (2009). We show that our method outperforms the random sampling method, reducing the average error in the Shapley value approximation by almost 30%. Moreover, our new method enables us to analyze the extended WTC 9/11 network of Krebs (2002) that consists of 69 members. This in contrast to the restricted WTC 9/11 network considered in Lindelauf et al. (2013), that only considered the operational cells consisting of the 19 hijackers that conducted the attack.

Energy-aware relay selection in cooperative wireless networks: An assignment game approach

In the future connected societies, everyone and everything will be inter-connected, under the umbrella of the Internet of Things, where tens to hundreds of devices will be serving every single citizen. However, connecting this massive number of energy-constrained devices pose a serious challenge on the wireless networking paradigm; energy efficiency still represents a major challenge within the design of the future generation of wireless networking (5G). In this paper, we address reducing energy consumption of energy-constrained wireless devices, through energy-aware cooperative relaying in future heterogeneous networks (HetNets). Using game theory concepts, we formulate the problem as an assignment game. In the first stage of the game, the optimal relay selection is formulated as a linear programming problem, whose solution assigns suitable relays to their perspective sources. In the final stage, the core solution is derived, which guarantees fair distribution of the payoff among players, to keep them satisfied and discourage them from quitting the coalition. Our solution also proposes a credit based system to reward cooperative players; hence possibly excluding selfish users from cooperative coalitions. The proposed relay selection algorithm is evaluated using extensive NS2 simulations. The simulation results show significant energy savings using cooperative relaying, reaching up to 22%, with extensive increase in battery lifetime.

Judicial review and the power of the executive and legislative branches

The legal system can affect what policies a government can implement. In particular, when there is separation of powers, the strength of the judiciary to review and overturn actions of the executive and legislative branches can affect such things as how much redistribution these policy-making branches can do. Surprisingly, having judicial review helps the policy-making branches—the stronger is the judiciary, the more redistribution they are able to do. This occurs because the policy-making branches must make promises on and off the equilibrium path to individuals in order to make redistribution possible. However, in many circumstances, the government wants to renege on these promises, either to do more redistribution than promised or to not carry out severe threats against any individuals who lied. Judicial review can prevent reneging on these promises, thus making them credible.We develop this in the context of an optimal income tax model with a finite number of individuals where the government knows the exact distribution of types but not which individual is of which type. In this finite model, the government can detect misrevelation by even a single individual so that an individual׳s taxes can depend not just on one׳s own actions but also on others’ actions. Piketty (JET, 1993) showed that the government could implement any full-information Pareto optimal allocation if the government could commit to its announcements, even to infeasible allocations in circumstances after some individuals misreveal. We derive the sequential equilibrium allocations when individuals reveal their types by simple announcements when feasibility on and off the equilibrium path is imposed. Increasing the degree of judicial review expands the set of achievable allocations on the full-information utility possibility frontier. We also relate the different possible legal rules to different solution concepts in game theory.

Modeling cooperation and powered-two wheelers short-term strategic decisions during overtaking in urban arterials

Abstract A difference between Powered Two Wheelers (PTW) drivers’ behavior and other drivers’ behavior in urban arterials is the frequency of overtaking. The present paper focuses on PTW overtaking and models the specific behavior using concepts of Game Theory. Both the PTW driver and the lead vehicle’s driver are assumed rational decision-makers that develop strategies, trying to maximize their payoffs. These strategies may be cooperative or not with respect to the distances and safety gaps and other behavioral aspects. The payoff function is formulated based on a novel latent statistically determined driving indicator, which quantifies both the driving risk and comfort. The proposed model is evaluated using trajectory data from video recordings on an urban arterial. Results show that both drivers have maximized gains by following a cooperative strategy. Findings also reveal that the successful overtaking rate is higher, when the PTW driver is non-cooperative, whereas lower overtaking rates occur, when the driver of the lead vehicle is non-cooperative. Finally, the concepts of Dominant Strategies, bounded rationality and the construction of the optimum payoff function are further discussed.

Emergence of cooperation in congested road networks using ICT and future and emerging technologies: A game-based review

Information and communications technologies (ICT) and future and emerging technologies (FET) are expected to revolutionize transportation in the next generation. Travelers’ behavioral adaptation is a key to their success. We discuss the notion of managing traffic congestion by enhancing cooperation in road networks enabled with ICT and FET. Cooperation is an emergent social state related to the dynamics and complexity of road traffic and reinforced learning. Game theory and research in behavioral economics show that cooperation can be leveraged to efficiently solve social dilemmas similar to traffic congestion. We review the applicability of behavioral economics and game theory concepts to route, mode and departure time choice problems. Beyond advancing theory, research on cooperation in the context of transportation is still in its infancy. We discuss state-of-the-art methodologies and their weaknesses and review the unexplored opportunities inherent in game-based methodologies. A behavioral-technological research agenda for FET is also discussed.

A review of the possibility in applying game theory to libraries

This study aims to review literature on classical game theory applications in libraries and enable librarians to identify which games may be played in response to various decision-making situations especially in uncertain settings. Also this review paper provides approaches that are helpful for library cooperation. This study is mainly based on works of Hayes (2003), of various game theoretical concepts and applications of those in the library setting and explores the value of game theory concepts in helping libraries deal with management issues and library cooperation which is not widely practiced among libraries.

Identification of critical generating units for maintenance: a game theory approach

With the rapid evolution of electricity markets and the need for higher economic efficiency of power industry, attempts for lowering down the maintenance costs as a large portion of system operation expenses seem imperative. Recognised as a crucial step in practical implementation of modern maintenance paradigms, e.g. reliability-centred maintenance, this study proposes an efficient method to quantify the generating units’ criticality on the bulk power system reliability performance. The proposed approach utilises a solution concept of game theory and evaluates the contribution of each generating unit on the overall system reliability when a higher-order contingency occurs. The suggested framework would lead to a fair identification of critical units in restructured power systems for more focused maintenance activities and also helps in recognising where investments should be made to improve the system reliability. The effectiveness of the proposed approach is confirmed through implementation on the IEEE 24-bus reliability test system.

Fluid Intelligence and Cognitive Reflection in a Strategic Environment: Evidence from Dominance-Solvable Games.

Dominance solvability is one of the most straightforward solution concepts in game theory. It is based on two principles: dominance (according to which players always use their dominant strategy) and iterated dominance (according to which players always act as if others apply the principle of dominance). However, existing experimental evidence questions the empirical accuracy of dominance solvability. In this study, we study the relationships between the key facets of dominance solvability and two cognitive skills, cognitive reflection, and fluid intelligence. We provide evidence that the behaviors in accordance with dominance and one-step iterated dominance are both predicted by one's fluid intelligence rather than cognitive reflection. Individual cognitive skills, however, only explain a small fraction of the observed failure of dominance solvability. The accuracy of theoretical predictions on strategic decision making thus not only depends on individual cognitive characteristics, but also, perhaps more importantly, on the decision making environment itself.

Optimization of hydrogen based hybrid renewable energy system using HOMER, BB-BC and GAMBIT

Non-conventional energy sources contribute important role in the current environment world policy, assuring a competent way to reduce GHG emission causing a global warming. This work is based on simulation and optimization of non-conventional energy system of an auditorium of Police station Sagar at central India. For this hybrid system the meteorological data of solar insolation, hourly wind speeds are taken at the site considered. The pattern of load consumption is studied and suitably modeled for optimization of the hybrid energy system using HOMER software. The results are compared with that of BIG BANG CRUNCH (BBC) algorithm and GAME Theory based concept. Based on optimization result, it has been found that replacing conventional energy sources by solar-wind-Fuel Cell-Hydrogen tank energy system will be a feasible solution for distribution of electric power for standalone application. The paper employs GAMBIT software to optimize the system which is based on the GAME theory concept. At the last regression analysis to be to find out value of R-square, Durwin-Watson statistics.

Generalized Nash equilibrium with stable belief hierarchies in static games with unawareness

In game theory, recently models and solution concepts of games with unawareness have been developed. Focusing on static games with unawareness, this paper discusses generalized Nash equilibrium, an existing equilibrium concept. Some generalized Nash equilibria can be unstable in the sense that, once an equilibrium is played, some agent’s belief is falsified at some level of someone’s perception hierarchy. Based on the observation, we characterize a particular class of generalized Nash equilibrium that expresses stable belief hierarchies so that it can avoid such a problem. This class of equilibrium can be motivated as a stable convention of the game. We also study how unawareness can affect the agents’ behaviors in a stationary state.

ReIDD: reliability-aware intelligent data dissemination protocol for broadcast storm problem in vehicular ad hoc networks

Vehicular ad hoc networks (VANETs) have emerged as fast growing networks with aims to provide safety, and comfort to the onboard passengers. But, in this environment reliable data dissemination to the destination nodes is one of the biggest challenges as there may be a congestion in the network due to blind flooding of messages to their final destination, called as broadcast storm which may lead to the performance degradation with respect to the metric such as-message delivery, reliability and response time. To address this issue, in this paper, we propose a reliability-aware intelligent data dissemination protocol for broadcast storm problem in VANETs. We have solved the above specified problem using game theory concepts in which players, strategy space and decisions with respect to the current state of the system. To reduce the message overhead with respect to the communication cost among the players of the game, in the proposed scheme, messages are routed to the next destination by selecting the most reliable path in an intelligent manner. A coalition game is formulated among the vehicles by considering vehicles as the players in the game. Each player in the game has an initial payoff value based upon the parameters such as-communication range, storage requirements, and computation power. Based upon the payoff value of the players in the game, different coalitions/clusters are formulated among the players in the proposed scheme. Then, the players send the messages to the other vehicles/players within the same coalition which increases the reliability of transmission. In the proposed scheme, messages are unicasted to their final destination in an intelligent manner. Different algorithms are designed for coalition formation, maintenance, and reliability-aware data dissemination. The performance of the proposed scheme is evaluated using various evaluation metrics such as-service time, packet delivery ration, and throughput. The results obtained prove the effectiveness of the proposed scheme as compared to the other state-of-the-art existing schemes.

Multi-agent zero-sum differential graphical games for disturbance rejection in distributed control

This paper addresses distributed optimal tracking control of multi-agent linear systems subject to external disturbances. The concept of differential game theory is utilized to formulate this distributed control problem into a multi-player zero-sum differential graphical game, which provides a new perspective on distributed tracking of multiple agents influenced by disturbances. In the presented differential graphical game, the dynamics and performance indices for each node depend on local neighbor information and disturbances. It is shown that the solution to the multi-agent differential graphical games in the presence of disturbances requires the solution to coupled Hamilton–Jacobi–Isaacs (HJI) equations. Multi-agent learning policy iteration (PI) algorithm is provided to find the solution to these coupled HJI equations and its convergence is proven. It is also shown that L2-bounded synchronization errors can be guaranteed using this technique. An online PI algorithm is given to solve the zero-sum game in real time. A simulation example is provided to show the effectiveness of the online approach.

Using Correlated Strategies for Computing Stackelberg Equilibria in Extensive-Form Games

Strong Stackelberg Equilibrium (SSE) is a fundamental solution concept in game theory in which one player commits to a strategy, while the other player observes this commitment and plays a best response. We present a new algorithm for computing SSE for two-player extensive-form general-sum games with imperfect information (EFGs) where computing SSE is an NP-hard problem. Our algorithm is based on a correlated version of SSE, known as Stackelberg Extensive-Form Correlated Equilibrium (SEFCE). Our contribution is therefore twofold: (1) we give the first linear program for computing SEFCE in EFGs without chance, (2) we repeatedly solve and modify this linear program in a systematic search until we arrive to SSE. Our new algorithm outperforms the best previous algorithms by several orders of magnitude.

A bi-level approach for optimal contract pricing of independent dispatchable DG units in distribution networks

Distributed Generation (DG) units are increasingly installed in the power systems. Distribution Companies (DisCo) can opt to purchase the electricity from DG in an energy purchase contract to supply the customer demand and reduce energy loss. This paper proposes a framework for optimal contract pricing of independent dispatchable DG units considering competition among them. While DG units tend to increase their profit from the energy purchase contract, DisCo minimizes the demand supply cost. Multi-leader follower game theory concept is used to analyze the situation in which competing DG units offer the energy price to DisCo and DisCo determines the DG generation. A bi-level approach is used to formulate the competition in which each DG problem is the upper-level problem and the DisCo problem is considered as the lower-level one. Combining the optimality conditions ofall upper-level problems with the lower level problem results in a multi-DG equilibrium problem formulated as an equilibrium problem with equilibrium constraints (EPEC). Using a nonlinear approach, the EPEC problem is reformulated as a single nonlinear optimization model which is simultaneously solved for all independent DG units. The proposed framework was applied to the Modified IEEE 34-Bus Distribution Test System. Performance and robustness of the proposed framework in determining econo-technically fare DG contract price has been demonstrated through a series of analyses.

Evidential Equilibria: Heuristics and Biases in Static Games of Complete Information

Standard equilibrium concepts in game theory find it difficult to explain the empirical evidence from a large number of static games, including the prisoners’ dilemma game, the hawk-dove game, voting games, public goods games and oligopoly games. Under uncertainty about what others will do in one-shot games, evidence suggests that people often use evidential reasoning (ER), i.e., they assign diagnostic significance to their own actions in forming beliefs about the actions of other like-minded players. This is best viewed as a heuristic or bias relative to the standard approach. We provide a formal theoretical framework that incorporates ER into static games by proposing evidential games and the relevant solution concept: evidential equilibrium (EE). We derive the relation between a Nash equilibrium and an EE. We illustrate these concepts in the context of the prisoners’ dilemma game.

Robust Multiplicity with a Grain of Naivete

Rationalizability is a central concept in game theory. Since there may be many rationalizable strategies, applications commonly use refinements to obtain sharp predictions. In an important paper, Weinstein and Yildiz (2007) show that none of these refinements is robust to perturbations of high-order beliefs. We show that robust refinements do exist if we relax the assumption that all players are unlimited in their reasoning ability. In particular, for a class of models, every strict Bayesian-Nash equilibrium is robust. A researcher interested in making sharp predictions can thus use intuitive principles or models of players' reasoning to select among the strict equilibria of the game, and these predictions will be robust.