Use Of Game Theory Research Articles

Day-Ahead Price Forecasting for the Spanish Electricity Market

During the last years, electrical systems around the world and in particular the Spanish electric sector have undergone great changes with the focus of turning them into more liberalized and competitive markets. For this reason, in many countries like Spain have appeared electric markets where producers sell and electricity retailers buy the power we consume. All agents involved in this market need predictions of generation, demand and especially prices to be able to participate in them in a more efficient way, obtaining a greater profit. The present work is focused on the context of development of a tool that allows to predict the price of electricity for the next day in the most precise way possible. For such target, this document analyzes the electric market to understand how prices are calculated and who are the agents that can make prices vary. Traditional proposals in the literature range from the use of Game Theory to the use of Machine Learning, Time Series Analysis or Simulation Models. In this work we analyze a normalization of the target variable due to a strong seasonal component in an hourly and daily way to later benchmark several models of Machine Learning: Ridge Regression, K-Nearest Neighbors, Support Vector Machines, Neural Networks and Random Forest. After observing that the best model is Random Forest, a discussion has been carried out on the appropriateness of the normalization for this algorithm. From this analysis it is obtained that the model that gives the best results has been Random Forest without applying the normalization function. This is due to the loss of the close relationship between the objective variable and the electric demand, obtaining an Average Absolute Error of 3.92€ for the whole period of 2016.

Zonal-Based GrEEn Algorithm for Augmenting the Battery Life in Spectrum Shared Networks via D2D Communication

This paper proposes a zone-based approach referred to as zone-based GrEEn (ZBGrEEn) algorithm, for energy-efficient spectrum sharing (SS) networks. The SS network is considered to encompass device-to-device (D2D) transmissions between a single primary transmitter (PT) and multiple primary receivers (PRs), placed in distinctive zones around the PT. Power allocation is performed optimally so that the target rate of each application demanded by the PRs are adequately met. When the PT fails to meet the target rates over ${\rm{PT}} \to {\rm{PR}}$ D2D links, it transmits cooperatively through an appropriate secondary transmitter (ST) in the SS network, which acts as an amplify-and-forward (AF) relay. The ST's basis for selection is the ${\rm{PT}} \to {\rm{ST}}$ channel state and power level at the ST. Use of game theory for solving the energy efficiency (EE) maximization problem has also been quantified. ZBGrEEn introduces zone factor for determining the transmission time for each zone in the SS network. This time determines the energy consumption at the user terminals, thereby impacting its battery lifetime. The efficiency of the proposed scheme has been validated through simulation results, with the enhancement in battery lifetime of up to 46.28% achievable for the PT. Game theory is also observed to be rewarding in battery lifetime augmentation, especially for ultradense networks (UDNs). Performance of other user terminals in the network can be similarly evaluated.

On network analysis using non-additive integrals: extending the game-theoretic network centrality

There are large amounts of information that can be represented in terms of graphs. This includes social networks and internet. We can represent people and their interactions by means of graphs. Similarly, we can represent web pages (and sites) as well as links between pages by means of graphs. In order to study the properties of graphs, several indices have been defined. They include degree centrality, betweenness, and closeness. In this paper, we propose the use of Choquet and Sugeno integrals with respect to non-additive measures for network analysis. This is a natural extension of the use of game theory for network analysis. Recall that monotonic games in game theory are non-additive measures. We discuss the expected force, a centrality measure, in the light of non-additive integral network analysis. We also show that some results by Godo et al. can be used to compute network indices when the information associated with a graph is qualitative.

A Survey on the Combined Use of Optimization Methods and Game Theory

Game theory is a field of applied mathematics that studies strategic behavior of rational factors. In other words, game theory is a collection of analytical tools that can be used to make optimal choices in interactional and decision making problems. Optimization in mathematics and computer science is the choice of the best member of an existing collection for a specific purpose. Several optimization methods have been used in many problems to minimize costs or maximize profits. From a particular point of view, it can be said that the game theory is in fact a kind of optimization. In this paper, a combined use of game theory and optimization algorithms has been reviewed and a new categorization is presented for researches which have been conducted in this area. In some of these combinations, game theory has been used to improve the performance of optimization algorithms, and in some others, optimizations methods help to solve game theory problems. Game theory and optimization algorithms are also used together to solve some other problems.

An efficient intrusion detection in resource-constrained mobile ad-hoc networks

We propose an efficient intrusion detection scheme for Mobile Ad-hoc Networks (MANETs). The scheme allows the reinforcing of network security while preserving the resources of the network nodes (e.g. energy). More specifically, the proposed solution provides MANETs with a distributed intrusion detection system in which activation is made according to the estimated threat. Our approach is characterized by the use of game theory for modeling interactions between a defense coalition and a potentially malicious sender node. We study these interactions in Bayesian games by considering the uncertainty about the sender node's maliciousness. Both one-shot and repeated situations are studied in this work. Particularly, in the repeated Bayesian game formulation, the defense coalition is allowed to adapt its defense strategy by exploiting the revealed information from the observations of the sender node's past actions. Furthermore, we perform a thorough analysis of equilibria emerging from game models and their conditions of existence. Finally, a series of simulations are conducted to show the effectiveness of the proposed approach.

Energy Allocation in Future Heterogeneous Networks Based on Game Theory and Auction Theory

Energy allocation has become one of the most significant issues in the future dense heterogeneous networks, such as the 5G system. In this paper, we surveyed the literatures about how to make use of game theory and microeconomic theory (e.g., action theory) based approaches to resolve the issue of resource allocation. Several key points have been addressed in the discussion section, from system modelling and the studies in C-RAN and HetNets.

A game theoretical approach to the effects of port objective orientation and service differentiation on port authorities' willingness to cooperate

This paper analyzes the effects of the objective orientation of port authorities and the level of service differentiation on the capacity, service price, profits and welfare among competing or cooperating ports. We also examine feasible combinations of these two factors (private objective level and service differentiation) to promote port co-operation. We apply the model starting from a mixed duopoly where a landlord port (a port authority with mixed public and private objectives) competes with a profit-seeking port (a port authority with a fully private objective) with differentiated services. The results show that both the private objective level of the port authority and the service differentiation level have a significant influence on various port competition and co-operation settings. Certain combinations of these factors prove to be useful in view of co-operation among port authorities which previously competed. The paper not only contributes to existing literature on port competition/co-operation and the use of game theory in a port setting. It also provides valuable inputs to port devolution and co-operation discussions at the policy level.

Game Theory for Computer Games Design

Designing and developing computer games can be a complex activity that may involve professionals from a variety of disciplines. In this article, we examine the use of game theory for supporting the design of gameplay within the different sections of a computer game and demonstrate its application in practice via adapted high-level decision trees for modeling the flow in gameplay and payoff matrices for modeling skill or challenge levels.

On the use of game theory to study the planning and profitability of industrial microgrids connected to the distribution network

This study deals with the long-term planning of industrial microgrids (MGs) in order to decrease the electricity bill (compared with the current situation) for participating companies. In such industrial MGs, the distribution system operator, the industrial estate operator and prosumers' objectives, which can be conflicting, need to be taken into account at the same time. The planning problem is formulated as a multi-agent and multi-objective problem which is solved by computing a Nash equilibrium of an extensive game from game theory. Different time horizons of the decision process are taken into account. In this study, the planning tool principle is presented and some applications are provided.

Pricing of perishable products with a speculator and strategic customers

ABSTRACTThis study develops a two-stage dynamic pricing strategy for the retailer based on the consideration of the number of myopic consumers, the reservation prices of bargain-hunting consumers, the deferral purchases of strategic customers, and the arbitrage behavior of the speculator with an aim to maximize the revenue from selling such a perishable product. Likewise, upon learning the retailer’s price, the speculator and strategic consumers would develop their own purchasing strategies accordingly. A backward induction procedure is employed to analyze the related decisions regarding the retailer and the speculator. This study deals with the competitive condition by the use of game theory to all players. According to the results of the numerical analyses, there are two major findings. First, more strategic consumers would result in more deferral purchases, and more revenue is anticipated by the speculator on condition that the retailer had sold out his inventory on hand in the primary selling stage. Second, the speculator acts as a dynamic pricing mechanism in the market, and is able to reduce the retailer’s risk under specific conditions, and thus the existence of a speculator seems harmless and profitable for the retailer.

Negotiating land for flood risk management : upstream‐downstream in the light of economic game theory

This paper discusses the use of game theory as a method to achieve land and water governance for flood retention and resilience on a catchment scale. Therefore, it addresses flood retention in river catchments by using pay‐off matrices of game theory. How do pay‐off matrices between upstream and downstream change when certain property rights are adjusted or institutional conditions are changed? What if liability issues, responsibilities, and externalities of flood protection measures are reframed? Who should pay and who profit from retention measures? Individual scenarios correspond to some basic games from the game theory. The aim of these thought experiments is to develop rules for upstream‐downstream agreements on retention and resilience within a river basin area.

Use of Game Theory to model patient engagement after surgery: a qualitative analysis

BackgroundPatient engagement is challenging to define and operationalize. Qualitative analysis allows us to explore patient perspectives on this topic and establish themes. A game theoretic signaling model also provides a framework through which to further explore engagement. MethodsOver a 6-mo period, thirty-eight interviews were conducted within 6 wk of discharge in patients undergoing thyroid, parathyroid, or colorectal surgery. Interviews were transcribed, anonymized, and analyzed using the NVivo 11 platform. A signaling model was then developed depicting the doctor-patient interaction surrounding the patient's choice to reach out to their physician with postoperative concerns based upon the patient's perspective of the doctor's availability. This was defined as “engagement”. We applied the model to the qualitative data to determine possible causations for a patient's engagement or lack thereof. A private hospital's and a safety net hospital's populations were contrasted. ResultsThe private patient population was more likely to engage than their safety-net counterparts. Using our model in conjunction with patient data, we determined possible etiologies for this engagement to be due to the private patient's perceived probability of dealing with an available doctor and apparent signals from the doctor indicating so. For the safety-net population, decreased access to care caused them to be less willing to engage with a doctor perceived as possibly unavailable. ConclusionsA physician who understands these Game Theory concepts may be able to alter their interactions with their patients, tailoring responses and demeanor to fit the patient's circumstances and possible barriers to engagement.

Diseño experimental para la entrada de una firma en un mercado monopolístico con efectos de red: caso de estudio con estudiantes de pregrado en un rol empresarial

This article examines the importance of experiments in the undergraduate learning process, as well as the use of game theory in experimental designs oriented towards competitive environments. For this purpose, an experimental design based on the theoretical model presented by Karlinger and Motta (2012) is proposed. The experiment was conducted with 24 Economics undergraduate from a polytechnic university (Escuela Superior Politécnica del Litoral). The students were organised to play randomly in two different scenarios. A linear probability model (LPM) was used to test whether there was a statistically significant effect for the experimental scenario on individual purchase decisions, as well as the subsequent entry of the company into the market. It was also found that other idiosyncratic characteristics, such as gender and knowledge of game theory, were relevant factors.

A New Game for Future Wi-Fi Spectrum Sharing

This article offers a vision for moving on from the paradigm of “constrained politeness” that has long governed Wi-Fi access to a shared spectrum – as embodied by the CSMA/CA mechanism and restricted EIRP limits. Instead, it is proposed to embrace a paradigm of a “cooperative game” where quasi-cognitive devices establish by themselves a mutually collaborative spectrum coexistence community, based on a unified set of simple rules, which operates on the basis of balancing individual transmits powers as a function of overall noise environment. It is shown that the proposed new mode of spectrum access, results in a significantly increased overall spectrum use efficiency, defined in terms of aggregate throughput carried in given bandwidth by a populace of devices over unit area. Most importantly, the new spectrum access would be still implicitly suitable for the Wi-Fi ethos of self-management and uncoordinated deployment. The proposed method, supported by both theoretical and practical simulations, represents one of the first practical implementations that prove feasibility and usefulness of Game Theory to the practice of wireless communications.DOI: http://dx.doi.org/10.5755/j01.eie.23.3.18339

Optimal Channel Selection Based on Online Decision and Offline Learning in Multichannel Wireless Sensor Networks

We propose a channel selection strategy with hybrid architecture, which combines the centralized method and the distributed method to alleviate the overhead of access point and at the same time provide more flexibility in network deployment. By this architecture, we make use of game theory and reinforcement learning to fulfill the optimal channel selection under different communication scenarios. Particularly, when the network can satisfy the requirements of energy and computational costs, the online decision algorithm based on noncooperative game can help each individual sensor node immediately select the optimal channel. Alternatively, when the network cannot satisfy the requirements of energy and computational costs, the offline learning algorithm based on reinforcement learning can help each individual sensor node to learn from its experience and iteratively adjust its behavior toward the expected target. Extensive simulation results validate the effectiveness of our proposal and also prove that higher system throughput can be achieved by our channel selection strategy over the conventional off-policy channel selection approaches.

Effects of sibling competition on family firm succession: A game theory approach

Strong interconnections between family and business which are innate to family firms can prove to be the source of conflict. Of all conflicts, those between family members and especially between siblings erode the family’s harmony and can risk the firm’s continuity. The passing of the family firm’s control from the founders to the next generation is a critical stage for the family firm and can be a catalyst for conflict. This article extends the use of game theory in family firm succession to explicitly include the noneconomic factors related to the family dimension, focusing on the emotional cost of conflict resulting from sibling competition. The results show that this cost is fundamental in terms of successor selection. The article shows that the collaborative family outcome, which results from family members cooperating and acting as a unit, is better in promoting firm intergenerational succession and ensuring that the founder’s preferred child is appointed successor.

English

Optimum peace maintenance amongst monitored communities is needed for sustainable and social development of the communities and society. The maintenance of peace with the application of correct strategies should be able to deliver maximum degree of trust and strength of relations among the communities. This study presents STES, an expert system that utilizes a biologically inspired novel optimization technique of semiotics that imitates the behavior of predators in marking their territories with their odours and physical marks, known as territorial predator scent marking strategies (TPSMS), to obtain appropriate strategies for maintenance of peace. The current study presents the use of game theory and deterministic finite automata (DFA) theory to model and design a strategic rule set for the Expert System to take decisions for maintenance of optimum peace amongst the communities. A DFA is obtained using various peace states as the set of states, and the alphabet set is constructed by modelling a two-person Prisoner’s Dilemma game over TPSMS. A context free grammar is then obtained by employing top-down parsing, which would suggest the proper rules to be included in the rule set of the Expert System for the maintenance of peace and bringing out sustainable development. A key objective in such decision making therefore would be to select the semiotical peace maintenance strategy in a manner that their applicability configuration ensures a high degree of cooperation within often intensely conflicting communities, over a long term resource use scenario. Key words: Peace, semiotics, prisoner’s dilemma game, deterministic finite automata (DFA), context free grammar, top-down parsing, territorial predator scent marking strategy.

Model based on Hybridized Game Theory to Optimize Logistics: Case of Blood Supply Chain

Several researches have been done to optimize different flows in blood supply chain. However, the use of game theory in this sense is rare. The following work focus on the case of Morocco, consisting of 16 Regional Blood Transfusion Centers (RBTC) centralized around a National Blood Transfusion and Hematology Center (NBTHC). An approach based on hybridized game theory is adopted to form core and strongly stable coalitions and optimize as much as possible the transport cost. Firstly, the optimal cost of each coalition of the 33 possible coalitions; that the Director of NBTHC validated; is computed by using a mixed integer linear Programming model (MILP). Then these costs are introduced as data of two other MILP to define which structure minimizes the total cost allocated to each RTBC while maintaining core stability, in the case of the first MILP, and strong equilibrium in the case of the second. The VRPPDTW is also introduced within each coalition in order to optimize the cost of transport more. General Terms Mixed integer linear programming, logistics, game theory.

Using game theory to assess multi-company strategies in watershed management

The main objective of this work was to evaluate the use of game theory as a strategic tool for watershed management decision-making. An engineering problem case study was used in which three organizations compare various scenarios when deciding where to locate a polluting plant on a watershed. Six games were modeled to provide a variety of conditions that could feasibly be implemented and were simulated using software for finding Nash Equilibria solutions. The results show that game theory can provide key insights, such as the consideration of other players’ strategies, and identify possible pitfalls that may occur when the companies seek only to maximize their individual profitability.

A game-theoretical and cryptographical approach to crypto-cloud computing and its economical and financial aspects

Recently, crypto-cloud computing has become an interesting research area with many technical, security, commercial and financial aspects, goals and consequences. The original intention of the cloud is to link computing stations (services) for collaboration. Sharing and coordination of computational resources is important because of the activities between service providers and service requesters. Considering the cooperative functionality of crypto-cloud computing, the use of game theory in that area has became very beneficial. In the sequel, we mathematically associate that area with game theory, i.e., the bargaining and compromising of interests of various “players”, by using a game-theoretical approach which arises from networks, servers, operating systems, storage devices, etc. Further, we propose a novel efficient encryption system by using XTR (effective and compact subgroup trace representation) which has the property of semantic security. Those interactions has been constructed in the direction of how cryptographic tools can be used to address a natural optimization problem in the fields of game theory and financial economics. It is believed that game theory and its optimization is going to provide a suitable framework for the design of a crypto-cloud computing system that will be perceived as a strong technique and satisfy the needs of many participants and users of the cloud. The paper ends with a conclusion and an outlook to future studies.