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It is well known that a player in a non-cooperative game can benefit by publicly restricting his possible moves before play begins. We show that, more generally, a player may benefit by publicly committing to pay an external party an amount that is contingent on the game’s outcome. We explore what happens when external parties – who we call “game miners” – discover this fact and seek to profit from it by entering an outcome-contingent contract with the players. We analyze various structured bargaining games among such miner(s) and players that determine such an outcome-contingent contract before the start of the original game. These bargaining games include playing the players against one another as in the original game, as well as allowing the players to pay the miner(s) for exclusivity and first-mover advantage. We establish restrictions on the strategic settings in which a game miner can profit and bounds on the game miner’s profit. We also find that game miners can lead to both efficient and inefficient equilibria.

Online news websites provide huge amounts of timely news, bringing the challenge of recommending personalized news articles. Generative adversarial networks (GAN) based on collaborative filtering (CFGAN) can achieve effective recommendation quality. However, CFGAN ignores item contents, which contain more latent preference features than just user ratings. It is important to consider both ratings and item contents in making preference predictions. This study aims to improve news recommendation by proposing a GAN-based news recommendation model considering both ratings (implicit feedback) and the latent features of news content.

The collaborative topic modeling (CTM) can improve user preference prediction by combining matrix factorization (MF) with latent topics of item content derived from latent topic modeling. This study proposes a novel hybrid news recommendation model, Hybrid-CFGAN, which modifies the architecture of the CFGAN model with enhanced preference learning from the CTM. The proposed Hybrid-CFGAN model contains parallel neural networks – original rating-based preference learning and CTM-based preference learning, which consider both ratings and news content with user preferences derived from the CTM model. A tunable parameter is used to adjust the weights of the two preference learnings, while concatenating the preference outputs of the two parallel neural networks.

This study uses the dataset collected from an online news website, NiusNews, to conduct an experimental evaluation. The results show that the proposed Hybrid-CFGAN model can achieve better performance than the state-of-the-art GAN-based recommendation methods. The proposed novel Hybrid-CFGAN model can enhance existing GAN-based recommendation and increase the performance of preference predictions on textual content such as news articles.

As the existing CFGAN model does not consider content information and solely relies on history logs, it may not be effective in recommending news articles. Our proposed Hybrid-CFGAN model modified the architecture of the CFGAN generator by adding a parallel neural network to gain the relevant information from news content and user preferences derived from the CTM model. The novel idea of adjusting the preference learning from two parallel neural networks – original rating-based preference learning and CTM-based preference learning – contributes to improve the recommendation quality of the proposed model by considering both ratings and latent preferences derived from item contents. The proposed novel recommendation model can improve news recommendation, thereby increasing the commercial value of news media platforms.

Practical needs in geographical information systems (GIS) have led to the investigation of formal, sound and computational methods for spatial analysis. Since models based on topology of R² have a serious problem of incapability of being applied directly for practical computations, we have noticed that models developed on the raster space can overcome this problem. Because some models based on vector spaces have been effectively used in practical applications, we then introduce the idea of using the raster space as our platform to study spatial entities of vector spaces. In this paper, we use raster spaces to study not only morphological changes of spatial entities of vector spaces, but also equal relations and connectedness of spatial entities of vector spaces. Based on the discovery that all these concepts contain relativity, we then introduce several new concepts, such as observable equivalence, strong connectedness, and weak connectedness. Additionally, we present a possible method of employing raster spaces to study spatial relations of spatial entities of vector spaces. Since the traditional raster spaces could not be used directly, we first construct a new model, called pansystems model, for the concept of raster spaces, then develop a procedure to convert a representation of a spatial entity in vector spaces to that of the spatial entity in a raster space. Such conversions are called approximation mappings.

The objective of this paper is to investigate the impact of the information sharing of the dynamic demand on green technology innovation and profits in supply chain from a long-term perspective.

The authors consider a supply chain consisting of a manufacturer and a retailer. The retailer has access to the information of dynamic demand of the green product, whereas the manufacturer invests in green technology innovation. Differential game theory is adopted to establish three models under three different scenarios, namely (1) decentralized decision without information sharing of dynamic demand (Model N-D), (2) decentralized decision with information sharing of dynamic demand (Model S-D) and (3) centralized decision with information sharing of dynamic demand (Model S-C).

The optimal equilibrium results show that information sharing of dynamic demand can improve the green technology innovation level and increase the green technology stocks only in centralized supply chain. In the long term, the information sharing of dynamic demand can make the retailer more profitable. If the influence of green technology innovation on green technology stocks is great enough or the cost coefficient of green technology innovation is small enough, the manufacturer and decentralized supply chain can benefit from information sharing. In centralized supply chain, the value of demand information sharing is greater than that of decentralized supply chain.

The authors used game theory to investigate demand information sharing and the green technology innovation in a supply chain. Specially, the demand information is dynamic, which is a variable that changes over time. Moreover, our research is based on a long-term perspective. Thus, differential game is adopted in this paper.

Because of the multiple design elements and complicated relationship among design elements of complex products design, it is tough for designers to systematically and dynamically express and manage the complex products design process.

To solve these problems, a supernetwork model of complex products design is constructed and analyzed in this paper. First, the design elements (customer demands, design agents, product structures, design tasks and design resources) are identified and analyzed, then the sub-network of design elements are built. Based on this, a supernetwork model of complex products design is constructed with the analysis of the relationship among sub-networks. Second, some typical and physical characteristics (robustness, vulnerability, degree and betweenness) of the supernetwork were calculated to analyze the performance of supernetwork and the features of complex product design process.

The design process of a wind turbine is studied as a case to illustrate the approach in this paper. The supernetwork can provide more information about collaborative design process of wind turbine than traditional models. Moreover, it can help managers and designers to manage the collaborative design process and improve collaborative design efficiency of wind turbine.

The authors find a new method (complex network or supernetwork) to describe and analyze complex mechanical product design.

The purpose of the article is to construct a new class of higher-order iterative techniques for solving scalar nonlinear problems.

The scheme is generalized by using the power-mean notion. By applying Neville's interpolating technique, the methods are formulated into the derivative-free approaches. Further, to enhance the computational efficiency, the developed iterative methods have been extended to the methods with memory, with the aid of the self-accelerating parameter.

It is found that the presented family is optimal in terms of Kung and Traub conjecture as it evaluates only five functions in each iteration and attains convergence order sixteen. The proposed family is examined on some practical problems by modeling into nonlinear equations, such as chemical equilibrium problems, beam positioning problems, eigenvalue problems and fractional conversion in a chemical reactor. The obtained results confirm that the developed scheme works more adequately as compared to the existing methods from the literature. Furthermore, the basins of attraction of the different methods have been included to check the convergence in the complex plane.

The presented experiments show that the developed schemes are of great benefit to implement on real-life problems.

As agent‐based systems are increasingly used to model real‐life applications such as the internet, electronic markets or disaster management scenarios, it is important to study the computational complexity of such usually combinatorial systems with respect to some desirable properties. The purpose of this paper is to consider two computational models: graphical games encoding the interactions between rational and selfish agents; and weighted directed acyclic graphs (DAG) for evaluating derivatives of numerical functions. The author studies the complexity of a certain number of search problems in both models.

The author's approach is essentially theoretical, studying the problem of verifying game‐theoretic properties for graphical games representing interactions between self‐motivated and rational agents, as well as the problem of searching for an optimal elimination ordering in a weighted DAG for evaluating derivatives of functions represented by computer programs.

A certain class of games has been identified for which Nash or Bayesian Nash equilibria can be verified in polynomial time; then, it has been shown that verifying a dominant strategy equilibrium is non‐deterministic polynomial (NP)‐complete even for normal form games. Finally, it has been shown that the optimal vertex elimination ordering for weighted DAGs is NP‐complete.

This paper presents a general framework for graphical games. The presented results are novel and illustrate how modeling real‐life scenarios involving intelligent agents can lead to computationally hard problems while showing interesting cases that are tractable.

The purpose of this paper is to examine the impact of carbon permit allocation rules (grandfathering mechanism and benchmarking mechanism) on incentive contracts provided by the retailer to encourage the manufacturer to invest more in reducing carbon emissions.

The authors consider a two-echelon supply chain in which the retailer offers three contracts (wholesale price contract, cost-sharing contract and revenue-sharing contract) to the manufacturer. Based on the two carbon permit allocation rules, i.e. grandfathering mechanism and benchmarking mechanism, six scenarios are examined. The optimal price and carbon emission reduction decisions and members’ equilibrium profits under six scenarios are analyzed and compared.

The results suggest that the revenue-sharing contract can more effectively stimulate the manufacturer to reduce carbon emissions compared to the cost-sharing contract. The cost-sharing contract can help to achieve the highest environmental performance, whereas the implementation of revenue-sharing contract can attain the highest social welfare. The benchmarking mechanism is more effective for the government to prompt the manufacturer to produce low-carbon products than the grandfathering mechanism. Although a loose carbon policy can expand the total emissions, it can improve the social welfare.

These results can provide operational insights for the retailer in how to use incentive contract to encourage the manufacturer to curb carbon emissions and offer managerial insights for the government to make policy decisions on carbon permit allocation rules.

This paper contributes to the literature regarding to firm’s carbon emissions reduction decisions under cap-and-trade policy and highlights the importance of carbon permit allocation methods in curbing carbon emissions.