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Due to the unpredictable growth of data in various fields, rapid clustering of big data is seriously needed in order to identify the hidden structure of data and discover the relationships between objects. Among clustering methods, density-based clustering methods have an acceptable processing speed for dealing with big data with high dimensions. However, some methods have fixed parameters that are certainly not optimized for all sections. In addition, the complexity of these clustering methods strongly depends on the number of objects. In this paper, a clustering method is presented in order to increase clustering performance and parameter sensitivity according to game-theory and using the concept of Nash equilibrium and dense games, the optimal parameter for clustering is selected and between noise and points clusters make a difference. This method includes (1) searching the grid with several spaces in which there is no cluster, (2) identifying the player through high density data points in order to determine the parameters and (3) combining the clusters to make the game and (4) merging the nearby clusters. The performance of the proposed method was evaluated in four big synthetic datasets, eight real datasets labeled and unlabeled. The obtained results indicate the superiority of the proposed method over SOM, K-means, DBSCAN, SCGPSC methods in terms of accuracy and purity in processing time. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is revolutionizing biomedical data science at an unprecedented pace, transforming various aspects of the field with remarkable speed and depth. However, a critical issue remains unclear: how reproducible are the AI models and systems employed in biomedical data science? In this study, we examine the challenges of AI reproducibility by analyzing the factors influenced by data, model, and learning complexities, as well as through a game-theoretical perspective. While adherence to reproducibility standards is essential for the long-term advancement of AI, the conflict between following these standards and aligning with researchers' personal goals remains a significant hurdle in achieving AI reproducibility. [ABSTRACT FROM AUTHOR]

Purpose – The aim of this study was to investigate the role of immersive gaming on brand coolness and brand equity, with particular emphasis on fashion gaming collaborations. We used game theory as a theoretical framework to analyse immersive branding strategies and gain a deeper understanding of fashion consumers’ decision-making process in gaming environments. Gender, as a significant factor affecting gamers, has been studied as a moderator that impacts the overall proposed framework. Design/methodology/approach – A quantitative method was used to assess the significance of relationships within the proposed model empirically. The partial least squares structural equation modelling technique was implemented to assess the relationships of the framework with a sample size of 160 active Malaysian gamers. Findings – The findings indicate that brand equity is positively associated with perceived brand coolness. Furthermore, of the three core values of online games, perceived enjoyment is most strongly associated with perceived brand coolness, ahead of the values of self-expression and perceived emotional challenge. The results of the multigroup analysis further suggest that in the fashion industry, building brand equity through online games is strongly related to perceived brand coolness among female respondents, the role of perceived brand coolness being weaker among male respondents. Originality/value – The study contributes to the existing literature by providing a deeper understanding of the impact of immersive gaming branding practices on the overall equity of the fashion brand. The results provide insight for fashion brand managers into the significant effect on consumer behaviour outcomes of fashion-gaming collaborations. 研究目的 – 本研究擬探討身歷其境的遊戲體驗在品牌酷感和品牌資產上所扮演的角色; 研究特別強調裝扮遊戲的合作。研究人員以博弈論作為研究的理論框架,來分析沉浸品牌策略、和對時裝消費者在遊戲的環境中如何作出決策取得更深入的瞭解。研究人員探討了性別作為影響遊戲參與者的重要因素,這因素被視為為整個被提出的框架帶來調節的影響和作用。 研究設計/方法/理念 – 研究人員採用定量方法進行研究,目的為於被提出的模型內之各種關聯的意義進行以經驗為依據的評估。研究人員採用了基於偏最小平方法的結構方程模型研究法,來對一個包含160名活躍的馬來西亞遊戲參與者的樣本進行框架的各個聯繫的評估研究。 研究結果 – 研究結果顯示,品牌資產與品牌酷感成正相關; 而且,在網絡遊戲的三個核心價值中,感知享受與品牌酷感之間的聯繫最為強烈和密切,超過自我表現和感知情感挑戰兩者的價值; 多群組的分析結果更暗示了在時裝產業裡,透過網絡遊戲去建立品牌資產在女性回應者中是與品牌酷感有強烈密切的關係; 而在男性回應者中,品牌酷感所扮演的角色則較弱。 研究的原創性/價值 – 本研究使我們能更深入認識沉浸遊戲品牌的慣常做法如何影響時裝品牌的整體資產。研究結果為時裝品牌經理提供了啟示,使他們明瞭時裝遊戲的合作會給消費者行為產生重要的影響。

The problem of managing the composition and structure of a bearing-only measurement system (BOMS) in a game-theoretic formulation is considered. An approach for a cooperative search for the placement of BOMS points and a method for estimating the work-time indicator of the system are proposed. The search for the placement of BOMS points uses the toolkit of multiagent potential games. The criteria for selecting the placement of points and the type of potential function are determined. The management of the composition and structure of the BOMS is based on the results of the cluster-variation method (CVM). A structural and functional description of the simulation model is presented. The presented results of simulation modeling confirm the practical effectiveness of the proposed approaches. [ABSTRACT FROM AUTHOR]

Behavioral factors play a crucial role in the emergence, spread, and containment of human diseases, significantly influencing the effectiveness of intervention measures. However, the integration of such factors into epidemic models is still limited, hindering the possibility of understanding how to optimally design interventions to mitigate epidemic outbreaks in real life. This paper aims to fill in this gap. In particular, we propose a parsimonious model that couples an epidemic compartmental model with a population game that captures the behavioral response, obtaining a nonlinear system of ordinary differential equations. Grounded on prevalence-elastic behavior—the empirically proven assumption that the disease prevalence affects the adherence to self-protective behavior—we consider a nontrivial negative feedback between contagions and adoption of self-protective behavior. We characterize the asymptotic behavior of the system, establishing conditions under which the disease is quickly eradicated or a global convergence to an endemic equilibrium is attained. In addition, we elucidate how the behavioral response affects the endemic equilibrium. Then, we formulate and solve an optimal control problem to plan cost-effective interventions for the model, accounting for their healthcare and social-economical implications. Numerical simulations on a case study calibrated on sexually transmitted diseases demonstrate and validate our findings.

This article studies a game theory-based pricing competition among multiple non-cooperative electric vehicle charging stations (EVCS) that seek to maximize expected profit. In contrast, electric vehicle (EV) drivers aim to minimize charging costs. In this regard, we introduce a modified game setup that alleviates the limitations of a conventional Hotelling’s game (CHG) application to achieve the price equilibrium among multiple non-cooperative players in a discrete dynamic market. In a CHG, it is assumed that each player has a positive market share. However, a CHG provides positive utilities to all players at Nash equilibrium (NE) only if they choose prices that satisfy the market sharing condition (MSC). The NE price depends on the players’ locations. Therefore, positive utilities can be achieved only if the players’ locations are confined such that the resultant NE price satisfies the MSC. The proposed game is called the St-Hot (Stackelberg-Hotelling), which can be applied to achieve the price equilibrium for those locations of players where the CHG fails to satisfy the MSC. The proposed game is applied in an EV charging environment, wherein multiple competitive EVCSs are placed at strategic locations in a linear space. The equilibrium condition is analyzed using closed-form solutions, and the results are compared with the existing CHG model. The analysis reveals that every EVCS remains profitable in a competitive market, irrespective of their location. This demonstrates that the proposed game model ensures profitability for each EVCS, showcasing its superiority over CHG.

In generating adversarial examples, a trade-off problem is always present, as the attacker aims to deceive the classifier in the most subtle way possible in order not to be uncovered. Trade-offs among different objectives can be tackled in many ways, among the most popular ones is to consider several objectives and further select among optimal ones based on some criteria. In this paper, a game theoretic approach to setting the magnitude of adversarial examples for a fast gradient sign model for image classification is proposed. The model controls the size of the pixel change and the number of randomly chosen pixels to be modified. A solution of the game indicates optimal trade-offs between the values. Numerical examples are used to illustrate the approach. [ABSTRACT FROM AUTHOR]

The integration of distributed energy resources and the transition to smart cities are shifting the urban energy sector to a decentralized operating system. Blockchain-based microgrids, where small-scale operators trade electricity among each others, have gained remarkable attention recently. However, most of the proposed schemes study smart grids in prosperous cities. In this study, the performance of a solar-based power trading scheme is investigated in a shortage-prone context, Beirut City. Thus, we resort to a game-theoretic approach to model power trading as a repeated game between buildings at the urban scale. Results show that solar energy can cover up to 25% of the city electricity needs, depending on the rooftops area coverage. On the other hand, we found that deploying a peer-to-peer trading scheme has marginal impact since the energy demand in the city exceeds the supply and most buildings would prioritize self-consumption. [ABSTRACT FROM AUTHOR]

The risk evaluation of tailing ponds is characterized by randomness, fuzziness and numerous uncertainties in the evaluation process. In order to effectively guarantee the operational safety of tailing ponds, a tailing pond risk evaluation method based on a combined assignment-cloud model is proposed. From a system analysis perspective, this paper applies the physical-substance-human-reason (WSR) system theory to explore the factors affecting the safe operation of tailings ponds from three aspects; subject factors, object factors and organisational forms, and constructs a WSR-based tailings pond risk evaluation index system. Secondly, the improved G1 method and CRITIC method are used to assign subjective and objective weights to the evaluation indicators respectively, and the game theory algorithm calculates the combination coefficients with the best weights to derive the combination weights. Finally, the cloud model theory is combined to calculate the relative affiliation and comprehensive characteristic value of each indicator, and the risk level is classified based on the principle of maximum affiliation, and the visualisation effect is presented in the form of a cloud chart. The evaluation method is validated using a tailings pond in Yunnan Province as an example, and the results are consistent with the actual situation, and compared with unknown measures and other methods. [ABSTRACT FROM AUTHOR]

Innovations in the Internet of Things (IoT) technology have revolutionized several industrial domains for smart decision-modeling. The capacity to perceive data about ubiquitous instances has resulted in numerous innovations in sensitive sectors like national security, and police departments. In this paper, an extensive IoT-based framework is introduced for assessing the integrity of police personnel based on his/her performance. The work introduced in this research is centered around analyzing several activities of police personnel to assess his/her integral behavior. In particular, the Probabilistic Measure of Integrity (PMI) is formalized based on professional data analysis for classification based on Bayesian Model. Moreover, the 2-player game model has been presented to assess the performance of police personnel for efficient decision-making. For validation purposes, the presented framework is deployed over challenging datasets acquired from the online repository of UCI. Based on the comparative analysis with the state-of-the-art decision-making models, the presented approach has registered enhanced performance in terms of Temporal Delay, Classification, Prediction, Reliability, and Stability. [ABSTRACT FROM AUTHOR]

This study investigates power pricing decisions in a resilient supply chain, including the main biomass supplier, biorefinery, the hybrid power plant, and the backup supplier. The power plant's main fuel is considered biofuel derived from Jatropha Curcas L. (JCL). However, due to unavoidable uncertainties in biofuel supply links, the shortage in meeting demand is inevitable. Therefore, for increasing the supply chain's resiliency, the fossil fuel backup supplier is taken into account to make up the shortage with anticipated fossil fuel. Due to environmental concerns, the penalty cost is considered for excessive use of fossil fuel. The model is studied under two cases by considering a dual-channel biorefining. Different game-theoretic models are established for varied power configurations and interactions of supply chain members. It is observed from the numerical study that, depending on the fractional part of the power plant's requirements of biofuel and JCL supplied by the biorefinery (Case 1) and the main supplier (Case 2), respectively the performance of the supply chain increases in the presence of the backup supplier. Additionally, the sensitivity analysis shows that Case 1 is more profitable than Case 2 for chain members. Moreover, in contrast to the fossil fuel replacement coefficient, the greater the convertibility degree of the JCL, the larger the profits for the members. • Studying the power pricing decisions in a resilient supply chain. • Considering backup supplier for a hybrid biofuel-fossil fuel power plant. • Applying game-theoretic model for choosing the best supply chain structure. • Conducting numerical study and performing sensitivity analysis. [ABSTRACT FROM AUTHOR]

The lack of adequate support in strategic decision-making hampers the widespread of industrial symbiosis (IS). Although various business models for IS are available, existing literature focuses solely on cooperation between waste producers and waste users to replace primary inputs with waste. This paper proposes a strategic analysis for waste producers introducing IS, comparing the traditional cooperative approach with a competitive new product development strategy. Using a game-theoretical model, a third hybrid strategy is defined. The paper examines the impact of market characteristics, investment requirements, IS costs, and primary input purchase costs on the strategic choices of waste producers, providing insights for managers and decision-makers. Results suggest that the characteristics of the market where to enter significantly affect the final strategic behavior. Specifically, the main strategic discriminating factors are the market size, the consumers' willingness to pay, and the investment extent. • The literature focuses on cooperative approaches toward industrial symbiosis. • Waste producers can cooperate with symbiotic partners or compete with them. • The strategic choice of a waste producer is analyzed with a game-theoretical model. • Market characteristics and investment extent are the main strategic drivers. [ABSTRACT FROM AUTHOR]

In the world of online advertising, demand for banner slots by advertisers is matched with the inventory available at different publishers by an intermediary (Ad Network or an Exchange). One important feature of auctions in the online advertising space is that publishers typically have multiple slots and advertisers are not necessarily interested in purchasing one unit but are rather interested in purchasing thousands of impressions. Furthermore, an ad network may have different types of publishers with varying quality of advertising space available. Consequently, bidders may value slots in one set of publishers very differently from slots in a different set. For instance, firms selling financial products and services may value slots at CNN.com's financial section or WSJ.com very differently from slots available at people.com or even the weather section in CNN.com. So, the dilemma confronting an ad network that has inventory from different publishers, facing demand from say, advertisers selling financial products is whether to pool the inventory and conduct a single auction or conduct separate auctions so that the advertisers know that they are bidding for slots on CNN.com's financial section or on WSJ.com and not for slots on the weather section on CNN.com or some other less preferred slots. Given the critical role that ad networks play, in serving the request of advertisers to get their advertising banners displayed in online media we examine the economic incentives of these intermediaries to derive implications for the optimal market design. More specifically, we seek answers to the following questions. Given the variation in the quality of inventory available from different publishers under what market conditions should the intermediary pool the inventory across the different publishers and conduct a single (undisclosed) auction and when would it be more profitable to conduct different (disclosed) auctions? Given a fixed number of bidders, if the intermediary chooses to conduct two auctions how many bidders should be allocated to each auction and how do market parameters such as the number of bidders or the inventory available of each type affect the allocation rule. Finally, if the intermediary chooses to conduct two auctions should they charge the same commission or different commissions in each auction? We find that when the number of advertisers is small then pooling inventory and conducting a single auction is the optimal strategy. Under these conditions when the inventory of the publishers is sufficiently differentiated it may even be optimal for the intermediary to conduct a single auction but ignore the inventory of the publisher that is valued lower. When the number of advertisers is large, we find very interestingly that conducting multiple auctions is not always optimal. Indeed, when the inventory of publishers is sufficiently differentiated conducting a single auction and ignoring the inventory of the publisher that is valued lower can still be optimal. We also identify market conditions when conducting two auctions and charging a single commission in both markets is more profitable than conducting two auctions and charging separate commissions (and vice versa). [ABSTRACT FROM AUTHOR]

Smart grids are the latest technology to generate and dispatch an optimal amount of power. Thus, there is a need for stability analysis in smart grid systems. If the smart grid is incorporated into the power system, then the phasor measurement unit (PMU) is used to measure the voltage, current, and frequency. Additionally, the central control unit monitors and controls the power. However, there is a possibility of inserting wrong data into the smart grid as the PMUs are transmitting the data through the Internet and other wireless protocols. There is a need to find solutions to this threat to make the power flow safe and secure in the future. In this paper, two-area load frequency control (LFC) is used for testing the game-theory based security treatment and improving the system's stability by using an artificial neural network. The two-area system and five-area system are used to test the stability of the power system. [ABSTRACT FROM AUTHOR]

Medication adherence is a challenge for patients, drugmakers, and payers. To promote adherence, some payers adopt a form of pay-for-success value-based risk-sharing agreements. Drugmakers reduce prices for meaningful improvement in adherence and share patient information and resources with payers; as a return, payers run patient support programs and put drugs on a tier with lower copays. We use a game-theoretic approach to investigate the optimal program effect and the optimal prices with and without improvement in adherence, measured by Proportion of Days Covered, under such an agreement. Since negotiation power impacts how prices are determined, we consider several pricing settings: the payer or the drugmaker sets both prices or sets one price simultaneously or sequentially. Although a discount for improved adherence tends to promote adherence, it may not always be achievable nor guarantees better adherence. The drugmaker with strong negotiation power can align its interest with social welfare but the payer may not. The payer with strong negotiation power can improve more adherence than the drugmaker. Balanced negotiation power contributes either the most or the least to adherence depending on contract form and decision sequence. Although cost-sharing by the drugmaker expects to increase program efforts, it may not be true. We find that the policymaker prefers different levels of cost-sharing under different pricing settings. The payer may have a first-mover advantage when setting the price without improved adherence; the drugmaker, however, does not have such an advantage. [ABSTRACT FROM AUTHOR]

Identifying those nodes that play a critical role within a network is of great importance. Many applications such as gossip spreading, disease spreading, news dispersion, identifying prominent individuals in a social network, etc. may take advantage of this knowledge in a complex network. The basic concept is generally to identify the nodes with the highest criticality in a network. As a result, the centrality principle has been studied extensively and in great detail, focusing on creating a consistent and accurate location of nodes within a network in terms of their importance. Both single centrality measures and group centrality measures, although, have their certain drawbacks. Other solutions to this problem include the game-theoretic Shapley Value (SV) calculations measuring the effect of a collection of nodes in complex networks via dynamic network data propagation process. Our novel proposed algorithm aims to find the most significant communities in a graph with community structure and then employs the SV-based games to find the most influential node from each community. A Susceptible-Infected-Recovered (SIR) model has been employed to distinctly determine each powerful node's capacity to spread. The results of the SIR simulation have also been used to show the contrast between the spreading capacity of nodes found through our proposed algorithm and that of nodes found using SV-algorithm and centrality measures alone. [ABSTRACT FROM AUTHOR]

For cloud data centers, a performant yet energy-efficient operation is critical for service quality and experience. The growing demand for cloud-based services has led to the development of large-scale hierarchical data center structures, characterized by horizontal expansion and vertical hierarchy, leading to challenges in managing Virtual Machines (VM) at a granular level. The hierarchical arrangement can increase the risk of deployment failures, often stemming from inadequate computational resources on physical hosts, even when the cluster-level resources seem sufficient. While substantial work has gone into managing VMs at the physical host level, there remains a dearth of research under hierarchical data center configurations. To fill the research gap, we address the hierarchy in cloud data centers with a novel two-stage approach named VMM-HHGT, aiming at suppressing VM deployment failures, while balancing the energy consumption and computation resource contention. VMM-HHGT comprises a Hyper-Heuristic-assisted broker (VMM-HH), which can learn the workload patterns and hardware configurations to generate cluster-selection heuristics. An offline training process is incorporated for continuous heuristic evolution with zero overhead on decision-making. Besides, a Game-Theory-assisted hypervisor (GT) is designed for inter-host live VM migration for fine-grained balancing of energy consumption and resource contention. Extensive experiments with traces from real-world VMware data centers show that VMM-HHGT achieves a higher deployment success rate compared to the state-of-the-art approaches, with a well-situated performance in energy consumption and resource contention. • Handling hierarchy in cloud data centers to suppress VM deployment failures. • A two-stage solution for initial VM placement and live migration in the long term. • A Hyper-Heuristic-assisted broker for initial cluster-level VM placement. • A Game-Theory-assisted hypervisor for host-level VM placement and long-term migration. • Effectively suppressed deployment failures with satisfying performance and energy efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we examine how different governance types impact prosocial behaviors in a heterogenous society. We construct a general theoretical framework to examine a game-theoretic model to assess the ease of achieving a cooperative outcome. We then build a dynamic agent-based model to examine three distinct governance types in a heterogenous population: monitoring one's neighbors, despotic leadership, and influencing one's neighbors to adapt strategies that lead to better fitness. In our research, we find that while despotic leadership may lead towards high prosociality and high returns it does not exceed the effects of a local individual who can exert positive influence in the community. This may suggest that greater individual gains can be had by cooperating and that global hierarchical leadership may not be essential as long as influential individuals exert their influence for public good and not for public ill., (© The Author(s) 2024. Published by Oxford University Press on behalf of National Academy of Sciences.)

This paper studies contractual graphs, where the formation of edges between nodes result in dyadic exchanges. Each dyadic exchange is analyzed as a contractual agreement that is implemented upon fulfilment of underlying conditions. As these dyadic exchanges proliferate, the resulting population of these exchanges creates a contractual graph. A contractual framework for graphs is especially useful in applications where AI-enabled software is employed to create or automate smart contracts between nodes. While some smart contracts may be easily created and executed, others may contain a higher level of ambiguity which may prevent their efficient implementation. Ambiguity in contractual elements is especially difficult to implement, since nodes have to efficiently sense the ambiguity and allocate appropriate amounts of computational resources to the ambiguous contractual task. This paper develops a two-node contractual model of graphs, with varying levels of ambiguity in the contracts and examines its consequences for a market where tasks of differing ambiguity are available to be completed by nodes. The central theme of this paper is that as ambiguity increases, it is difficult for nodes to efficiently commit to the contract since there is an uncertainty in the amount of resources that they have to allocate for completion of the tasks specified in the contract. Thus, while linguistic ambiguity or situational ambiguity might not be cognitively burdensome for humans, it might become expensive for nodes involved in the smart contract. The paper also shows that timing matters—the order in which nodes enter the contract is important as they proceed to sense the ambiguity in a task and then allocate appropriate resources. We propose a game-theoretic formulation to scrutinize how nodes that move first to complete a task are differently impacted than those that move second. We discuss the applications of such a contractual framework for graphs and obtain conditions under which two-node contracts can achieve a successful coalition.

Microbial community is ubiquitous in nature, which has a great impact on the living environment and human health. All these effects of microbial communities on the environment and their hosts are often referred to as the functions of these communities, which depend largely on the composition of the communities. The study of microbial higher-order module can help us understand the dynamic development and evolution process of microbial community and explore community function. Considering that traditional clustering methods depend on the number of clusters or the influence of data that does not belong to any cluster, this paper proposes a hypergraph clustering algorithm based on game theory to mine the microbial high-order interaction module (HCGI), and the hypergraph clustering problem naturally turns into a clustering game problem, the partition of network modules is transformed into finding the critical point of evolutionary stability strategy (ESS). The experimental results show HCGI does not depend on the number of classes, and can get more conservative and better quality microbial clustering module, which provides reference for researchers and saves time and cost. The source code of HCGI in this paper can be downloaded from https://github.com/ylm0505/HCGI. [ABSTRACT FROM AUTHOR]

Social networks have become a critical part of our lives as they enable us to interact with a lot of people. These networks have become the main sources for creating, sharing and also extracting information regarding various subjects. But all this information may not be true and may contain a lot of unverified rumours that have the potential of spreading incorrect information to the masses, which may even lead to situations of widespread panic. Thus, it is of great importance to identify those nodes and edges that play a crucial role in a network in order to find the most influential sources of rumour spreading. Generally, the basic idea is to classify the nodes and edges in a network with the highest criticality. Most of the existing work regarding the same focuses on using simple centrality measures which focus on the individual contribution of a node in a network. Game-theoretic approaches such as Shapley Value (SV) algorithms suggest that individual marginal contribution should be measured for a given player as the weighted average marginal increase in the yield of any coalition that this player might join. For our experiment, we have played five SV-based games to find the top 10 most influential nodes on three network datasets (Enron, USAir97 and Les Misérables). We have compared our results to the ones obtained by using primitive centrality measures. Our results show that SVbased approach is better at understanding the marginal contribution, and therefore the actual influence, of each node to the entire network. [ABSTRACT FROM AUTHOR]

In this paper a game-theoretical model with self-interest pursuing consumers is introduced in order to assess how to design a least-cost distribution tariff under two constraints that regulators typically face. The first constraint is related to difficulties regarding the implementation of cost-reflective tariffs. In practice, so-called cost-reflective tariffs are only a proxy for the actual cost driver(s) in distribution grids. The second constraint has to do with fairness. There is a fear that active consumers investing in distributed energy resources (DER) might benefit at the expense of passive consumers. We find that both constraints have a significant impact on the least-cost network tariff design, and the results depend on the state of the grid. If most of the grid investments still have to be made, passive and active consumers can both benefit from cost-reflective tariffs, while this is not the case for passive consumers if the costs are mostly sunk. [ABSTRACT FROM AUTHOR]

Recommending educational resources to groups of students is a common task in collaborative learning contexts. However, differences in within-group motivational factors might lead to conflicts in students’ intention to use the resources. Previous methods fail to achieve high goodness of recommendation for the majority of students in heterogeneous groups. This study demonstrates a game-theoretic solution for recommending educational resources to homogeneous and heterogeneous groups. The group members are the players, the resources comprise the set of possible actions, and selecting those items that will maximize all students’ motivation in the collaborative activity is a problem of finding the Nash Equilibrium (NE). In case the NE is Pareto efficient, none of the players can get more payoff (motivation) without decreasing the payoff of any other player, indicating an optimal benefit for the group as a whole. The suggested approach was empirically evaluated in a controlled experiment with a real dataset. The relevance of each delivered item to its corresponding students was explored both from the perspective of the group and its the individual students. The accuracy of the predicted group/individual motivation, the goodness of the ranked list of recommendations, and the problem-solving performance for the treatment group were significantly higher compared to the control groups. Limitations of the approach, as well as future work plans conclude the paper. [ABSTRACT FROM AUTHOR]

This article sheds light on the role of information systems in product recovery management. We first develop analytical models and then provide a numerical example to determine under what conditions investments in Information-intensive Product Recovery Systems (IPRS) are economically justifiable for manufacturers and when policy-makers need to consider facilitating their implementation. The results of the analytical models indicate that the recovery network (collection) structure and product characteristics determine if precision improvements or increased sorting speed associated with IS investments will lead to higher profit gains. Manufacturers should carefully assess conflicting impacts of current manufacturing and recycling technology trends on the value of IPRS. Implementing IPRS might end up reducing manufacturers' profits under a highly time efficient decentralised collection structure. We show that negotiations with competitors about IPRS implementation may lead to a win–win situation and allow consumers to enjoy the lowest product price if the binding force of the agreement is strong. Otherwise, some manufacturers follow free-rider strategies. This article has immediate application to manufacturers' IS strategy and to government policy-makers considering investing in and/or structuring product recovery closed-loop supply chain processes within their jurisdictions. It also opens a potential stream of research concerning the role of IPRS to automate, informate and transform closed-loop supply chains for eco-efficiency. [ABSTRACT FROM PUBLISHER]

The digital publishing output increased significantly year by year. It has been the most vigorous point of economic growth and has been more important to press and publication industry. Its distribution channel has been diversified, which is different from the traditional industry. A deep research has been done in digital publishing industry, for making clear of the constitution of the industry chain and establishing the model of industry chain. The cooperative and competitive relationship between different distribution channels have been analyzed basing on a game-theory. By comparing the distribution quantity and the market size between the static distribution strategy and dynamic distribution strategy, we get the theory evidence about how to choose the distribution strategy to get the optimal benefit.

Reclamation is the phenomenon of an oppressed group repurposing language to its own ends. A case study is reclamation of slur words. Popa-Wyatt and Wyatt (2018) argued that a slurring utterance is a speech act which performs a discourse role assignment. It assigns a subordinate role to the target, while the speaker assumes a dominant role. This pair of role assignments is used to oppress the target. Here the author focuses on how reclamation works and under what conditions its benefits can stabilise. She starts by reviewing the data and describing preconditions and motivations for reclamation. Can reclamation be explained in the same basic framework as regular slurring utterances? She argues that it can. The author also identifies some features that must be a prediction of any theory of reclamation. She concludes that reclamation is an instance of a much broader class of acts we do with words to change the distribution of power: it begets power, but it also requires it. [ABSTRACT FROM AUTHOR]

While solar PV generation is well-established on single-family houses, there is still a lack of installations on apartment buildings. To understand the effect of sharing distributed generation, we developed two energy sharing models: 1) a welfare optimization, and 2) a game theoretical (bi-level) model. We introduced two type players: 1) the owner of distributed generation (e.g., solar PV and energy storage), and 2) the consumers. Furthermore, we included consumer preferences by multiple objectives such as emissions reduction and distributed generation in addition to cost in the model. We applied both models to a numerical example using data from the electricity market in Texas, USA. The results showed that welfare is maximized in both models, but shared differently between the owner of the generation and the consumers. One exception is the bi-level model with uniform price auctions which results in a reduction in system welfare to maximize owner revenues. [ABSTRACT FROM AUTHOR]

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Highlights • We expand the ideal free distribution using matrix equations. • With this expansion, we can represent any trophic web. • From it we can predict distribution of individuals among habitats or patches. • This model summarizes up to 13 other extensions of the ideal free distribution. Abstract Density-dependent habitat selection is a central theme in ecology. Empirical studies collect data with increasing resolution and provide greater opportunities for its testing. However, several different density-dependent habitat selection models exist in the literature incorporating many different scenarios. We attempt to unify some of these models in a single framework, to increase our predictive power, and assist researchers in making predictions from combinations of these models. To achieve this, we created the generalized ideal free distribution, an expansion of the ideal free distribution model. With this model, we synthesize many of the previous theoretical developments in habitat selection to better incorporate temporal dynamics. By using community matrices to represent the interaction between individuals, we demonstrated that thirteen scenarios represented in other studies can be combined into a single model. In addition, for four of these scenarios, our predictions are similar to the original studies that developed these scenarios. Additionally, we derived four novel predictions that take advantage of using community matrices to represent distribution. We discuss how this model creates a connection between community interactions and the distribution of individuals, and its uses in other subjects in ecology. [ABSTRACT FROM AUTHOR]

The fifth-generation (5G) mobile communication system is expecting to support users with diverse data rate requirements by densely deploying small cells. The users attached with small cells make use of the same frequency band as the existing macro cell users, that causes severe co-channel interference and degrades the performance. To overcome this challenge, we propose a game theoretical framework for the optimal uplink power allocation for small cells, i.e., femtocell deployed underlaid macrocell. In this paper, femtocell users play a non-cooperative game to choose the optimal power to maximize the sum-rate of the system. Furthermore, an iterative quality-of-service (QoS)-aware game theory based power control (QoS-GTPC) scheme is proposed to optimize the femtocell user power taking into account macrocell user QoS requirements. Simulation results verify that the proposed QoS-GTPC scheme significantly improves the sum-rate and reduces outage and interference, as compared with conventional power control scheme. [ABSTRACT FROM AUTHOR]

In decentralized energy systems, electricity generated and flexibility offered by households can be organized in the form of community energy systems. Business models, which enable this aggregation at the community level, will impact on the involved actors and the electricity market. For the case of Germany, in this paper different aggregation scenarios are analyzed from the perspective of actors and the market. The main components in these scenarios are the Community Energy Storage (CES) technology, the electricity tariff structure, and the aggregation goal. For this evaluation, a bottom-up community energy system model is presented, in which the households and retailer are the key actors. In our model, we distinguish between the households with inflexible electricity load and the flexible households that own a heat pump or Photovoltaic (PV) storage systems. By using a game-theoretic approach and modeling the interaction between the retailer and households as a Stackelberg game, a community real-time pricing structure is derived. To find the solution of the modeled Stackelberg game, a genetic algorithm is implemented. To analyze the impact of the aggregation scenarios on the electricity market, a “Market Alignment Indicator” is proposed. The results show that under the considered regulatory framework, the deployment of a CES can increase the retailer’s operational profits while improving the alignment of the community energy system with the signals from the electricity market. Depending on the aggregation goal of the retailer, the implementation of community real-time pricing could lead to a similar impact. Moreover, such a tariff structure can lead to financial benefits for flexible households.

Abstract A game-theory based trading analysis between distribution network operator (DNO) and multi-microgrids (multi-MGs) is proposed in this paper. Firstly, the model of DNO and MG are developed respectively. The operational objective of the DNO is trading profit (TP) maximization and the operational objective of the MGs are operation cost (OC) minimization. Secondly, a game composed of DNO and MGs is formulated and the Karush–Kuhn–Tucker (KKT) method is utilized to achieve the Nash Equilibrium (NE) point. Numerical results show that the proposed solution method is able to balance different interest of DNO and MGs simultaneously and the trading behaviors under different market prices are analyzed, which contributes to the optimal operation in aspect of payoff maximization. [ABSTRACT FROM AUTHOR]

The aim of this article is to present a method, which is called the 'iterative collusive strategy (CS) search method', to detect the CS in prisoner's dilemma game in which there is collusive equilibrium. We apply this method to an example of two‐player prisoner's dilemma game and a numerical duopoly example to show its effectiveness. To simulate the electricity market models, we use this method with a local optimization algorithm. Then, we employ a hybrid technique by applying an agent‐based model to the iterative CS search method for improving the results and speeding up their convergence. We simulate an electricity market example with transmission constraint to test the effectiveness of local and hybrid iterative CS search methods. Simulation results show that both local and hybrid iterative CS search methods could successfully identify the CS; however, the hybrid iterative CS search method converges to the final results with less iteration numbers. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

As increasing numbers of access points (APs) are deployed in wireless local area networks (WLANs), they experience severe channel interference. Thus, a well-defined resource management scheme for APs should be devised. Undoubtedly, a centralized and deterministic resource management is not appropriate, owing to the distributed operation and dynamic channel condition of densely deployed WLANs. To address this problem, an adaptive and distributed radio resource allocation (ADRA) algorithm is proposed, in which an optimal mixed strategy for selecting the channel width of APs is derived via a game-theoretic approach. ADRA also fits into the densely deployed WLAN in that it considers partially overlapped channels. Simulation results show that ADRA can provide a comparable throughput and fairness performance to centralized algorithms, even though it functions in a distributed manner. Moreover, it is shown that ADRA works adaptively even when network environments change rapidly. [ABSTRACT FROM PUBLISHER]

Interpersonal relationship require sophisticated competences of cohabitation. However, the availability of training tools to develop conflict management skills is limited and problematic. The prisoner's dilemma game (PDG), the most widely known example of game theory, a nonzero-sum game, has been used, in higher education, to provide students with an opportunity of active learning and for understanding counterintuitive concepts. It creates a condition of emotive, moral and decisional conflict in and between agents. This paper presents a case-study in higher education in which PDG was proposed to enhance organizational competences for conflict management, according to the psychoanalytic approach to organizational studies. The study aims to explore: (1) the significant characteristics of PDG used in an affective-emotional key in higher education; (2) the learning outcomes that PDG enables to activate in the participants in relation to the development of organizational skills for conflict management. Twenty students' reflective journals were analyzed using thematic analysis. Findings indicated that PDG is perceived as a useful device in students' learning experience, which is appreciated in relation to its concreteness, intensity and debriefing phase. Learning outcomes allow new meanings about conflict, by emphasizing its defensive, automatic and interpersonal dimension. This paper contributes to the understanding of PDG as a tool to develop competences in dealing with the challenges of conflict management, since it seems to favor the overcoming of the individualistic stereotype in conflict representation by highlighting the interdependence of social interaction.

Lesion analysis reveals causal contributions of brain regions to mental functions, aiding the understanding of normal brain function as well as rehabilitation of brain-damaged patients. We applied a novel lesion inference technique based on game theory, Multi-perturbation Shapley value Analysis (MSA), to a large clinical lesion dataset. We used MSA to analyze the lesion patterns of 148 acute stroke patients together with their neurological deficits, as assessed by the National Institutes of Health Stroke Scale (NIHSS). The results revealed regional functional contributions to essential behavioral and cognitive functions as reflected in the NIHSS, particularly by subcortical structures. There were also side specific differences of functional contributions between the right and left hemispheric brain regions which may reflect the dominance of the left hemispheric syndrome aphasia in the NIHSS. Comparison of MSA to established lesion inference methods demonstrated the feasibility of the approach for analyzing clinical data and indicated its capability for objectively inferring functional contributions from multiple injured, potentially interacting sites, at the cost of having to predict the outcome of unknown lesion configurations. The analysis of regional functional contributions to neurological symptoms measured by the NIHSS contributes to the interpretation of this widely used standardized stroke scale in clinical practice as well as clinical trials and provides a first approximation of a ‘map of stroke’.

Recently, the growing ubiquity of location-based service (LBS) technology has increased the likelihood of users’ privacy breaches due to the exposure of their real-life information to untrusted third parties. Extensive use of such LBS applications allows untrusted third-party adversarial entities to collect large quantities of information regarding users’ locations over time, along with their identities. Due to the high risk of private information leakage using resource-constrained smart mobile devices, most LBS users may not be adequately encouraged to access all LBS applications. In this paper, we study the use of game theory to protect users against private information leakage in LBSs due to malicious or selfish behavior of third-party observers. In this study, we model a scenario of privacy protection gameplay between a privacy protector and an outside visitor and then derive the situation of the prisoner’s dilemma game to analyze the traditional privacy protection problems. Based on the analysis, we determine the corresponding benefits to both players using a point of view that allows the visitor to access a certain amount of information and denies further access to the user’s private information when exposure of privacy is forthcoming. Our proposed model uses the collection of private information about historical access data and current LBS access scenario to effectively determine the probability that the visitor’s access is an honest one. Moreover, we present the procedures involved in the privacy protection model and framework design, using game theory for decision-making. Finally, by employing a comparison analysis, we perform some experiments to assess the effectiveness and superiority of the proposed game-theoretic model over the traditional solutions.