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The business activity is inseparable from the support of documental information flow. In the past, the commercial documents needed a lot of time to get to the point-to-point, which could lead to problems, such as insecurity or the relatively long time. With the emergence of new technologies, it is possible to change this information transmission method to enable fast and accurate dissemination. For considering the possibility of technology adoption, this study investigates the role of blockchain service in term of market size and file circulation time. Specifically, we construct a game-theoretic model to find the equilibrium strategy from a manufacturer and two competing retailers. From the perspective of equilibrium, we investigate the results for market effects, economic effects, service effects, and service efficiency, respectively. Moreover, we also analyze the service strategy of retailers providing service by manufacturer, time sensitivity, and competitive intensity, respectively. Based on the above analysis, this study has obtained an equilibrium to support the commercial practice. In summary, this study has two contributions. On the one hand, we analyze the impact of the technological investment in blockchain service on asymmetric market scale competition. This result extends the application scope of blockchain service in the market. On the other hand, this study discusses the impact of time value on technology investment in blockchain service. Therefore, through the discussion of our study, the results show the value of blockchain service in information exchanges. [ABSTRACT FROM AUTHOR]

Patent pricing is the core issue in patent industrialisation. This paper uses game theory to study the effects of auction behaviours when the members of the patent portfolio are auctioned either together or separately. A comparison of patent product yields the results (1) the outputs of a patented product are lower in a packaged auction than in a separate auction, and the outputs of the two auction modes decrease as the correlation between patent members increases; (2) the profits of package portfolios are higher under packaged auctions than under separate auctions, and an enterprise's profit is negatively related to the correlation between the patent members; (3) when a patent portfolio is subjected to separate auction, the product outputs under Stackelberg equilibrium in a game are higher than that under static equilibrium, and the product outputs in the Stackelberg equilibrium decrease with the correlation between patent members; and (4) when the members of a patent portfolio are auctioned separately, the profits in the Stackelberg equilibrium and static equilibrium are related to the correlation between patent portfolio members; the profits in the Stackelberg equilibrium will first decrease and then increase as the correlation between patent portfolio members increases. [ABSTRACT FROM AUTHOR]

We study combinatorial auctions with bidders that exhibit an endowment effect. In most of the previous work on cognitive biases in algorithmic game theory the focus was on analyzing the implications and mitigating their negative consequences. In contrast, in this paper we show how in some cases cognitive biases can be harnessed to obtain better outcomes. Specifically, we study Walrasian equilibria in combinatorial markets. It is well known that Walrasian equilibria exist only in limited settings, e.g., when all valuations are gross substitutes, but fail to exist in more general settings, e.g., when the valuations are submodular. Our main result shows that when the valuations are submodular, even a mild degree of endowment effect is sufficient to guarantee the existence of Walrasian equilibria. [ABSTRACT FROM AUTHOR]

Linear complementarity problems (LCPs) are a powerful tool for modeling many practically relevant situations such as market equilibria. They also connect many subareas of mathematics like game theory, optimization, and matrix theory. Despite their close relation to optimization, the protection of LCPs against uncertainties-especially in the sense of robust optimization-is still in its infancy. During the last years, robust LCPs have only been studied using the notions of strict and Γ-robustness. Unfortunately, both concepts lead to the problem that the existence of robust solutions cannot be guaranteed. In this paper, we consider affinely adjustable robust LCPs. In the latter, a part of the LCP solution is allowed to adjust via a function that is affine in the uncertainty. We show that this notion of robustness allows us to establish strong characterizations of solutions for the cases of uncertain matrix and vector, separately, from which existence results can be derived. Our main results are valid for the case of an uncertain LCP vector. Here, we additionally provide sufficient conditions on the LCP matrix for the uniqueness of a solution. Moreover, based on characterizations of the affinely adjustable robust solutions, we derive a mixed-integer programming formulation that allows us to solve the corresponding robust counterpart. If, in addition, the certain LCP matrix is positive semidefinite, we prove polynomial-time solvability and uniqueness of robust solutions. If the LCP matrix is uncertain, characterizations of solutions are developed for every nominal matrix; i.e., these characterizations are, in particular, independent of the definiteness of the nominal matrix. Robust solutions are also shown to be unique for a positive definite LCP matrix, but both uniqueness and mixed-integer programming formulations still remain open problems if the nominal LCP matrix is not positive definite. [ABSTRACT FROM AUTHOR]

Energy storage can provide a range of revenue streams for investors in electricity markets. However, as their deployments continue to rise, storage will no longer be a player on the sidelines and remain a price-taker, rather, these assets will start to impact prices. In this study, accounting for energy storage as a price-maker and using data from CAISO, we investigate strategic market behavior among competing investors using a non-cooperative game. We establish a centralized optimization problem to compute the market equilibrium. In the game, each investor initially decides on how much to invest and then how to operate their asset over a time horizon. Investors are allowed to deploy different energy storage technologies. Analytically, we show that an increasing number of investors will increase the market competition thereby reducing profits while increasing the total capacity of storage deployed. Numerically, our key findings include: (a) the difference in optimal investments under price-taker and price-maker assumptions, (b) as wind and solar assets expand under different resource-mix scenarios, there is a corresponding non-monotonic variation in arbitrage potential for energy storage, (c) when competition is intense and investors are allowed to invest in different technologies, a slight increase in storage technology performance can significantly improve an investor's profit share, (d) when the arbitrage opportunity increases with renewables, longer duration storage technologies may gain an advantage, (e) Li-ion batteries dominate the market under low-moderate renewable energy penetration or with steep energy-capacity cost reductions in the future. • We develop a game-theoretical framework for strategic investments in energy storage. • The framework derives a centralized optimization problem to compute the Nash equilibrium. • The impact of energy storage on market price is characterized using CAISO data. • We present insights on arbitrage potential and market competition for energy storage investors. • We quantify the effects of the number of investors, renewable energy levels, and technology heterogeneity. [ABSTRACT FROM AUTHOR]

• A peer-to-peer energy trading market mechanism with shared energy storage units is proposed. • A non-cooperative game among peer-to-peer market participants is presented. • The generalized Nash equilibrium problem among shared energy storage users is depicted. • A game equilibrium solution based on KKT condition is proposed. • Proposed mechanism improves the efficiency and ensures fairness. The high penetration of distributed energy resources with local renewable energy consumption facilitates the emergence of peer-to-peer (P2P) energy trading, where a peer can share excessive energy with local peers. P2P energy trading is expected to be a promising business model in the future transactive energy market. Influenced by the sharing economy principle, shared energy storage(SES) is investigated to reduce the costly initial investment and improve the utilization rate of storage devices. Given the context, this paper proposes an equilibrium model of a P2P energy trading market, considering the deployment of shared energy storage in the residential consumer-side. The market equilibrium problem consists of two interwoven games. Considering the benefit conflicts between energy sellers and buyers in P2P energy trading, the P2P transaction between peers can be described as a non-cooperative game. Considering the limited SES capacity resources, the competition among SES users can be viewed as a generalized Nash equilibrium problem. These two games in the P2P market interact with each other and can be summarized as a holistic equilibrium problem, in which the P2P transaction price is endogenously determined and storage capacity is allocated. The market equilibrium problem can be solved based on Karush-Kuhn-Tucker (KKT) optimality conditions and linearization techniques, finally cast as a mixed-integer linear program. Numerical simulation demonstrates that P2P energy trading benefits all participants and the introduction of shared energy storage can further reduce energy costs. [ABSTRACT FROM AUTHOR]

The global share of renewable energy sources (RES) in total generation capacity reached 34.7% in 2019 and has been continuously increasing. Power system flexibility addressing the uncertainty and variability of RES has become a major concern in energy transition. This paper proposes to apply mobile energy storage (MES) from independent MES owners as a flexibility-enhancement ancillary service in the day-ahead electricity market. First, we have proposed a market-based methodology to incent MES owners to provide flexibility service in power systems by simultaneously releasing Locational Marginal price (LMP) and Uncertainty Marginal Price (UMP). The LMP and UMP were obtained by solving robust unit commitment and security-constrained economic dispatch, representing the need for flexibility resources. Second, the optimal operational strategy of MESs was established to respond to LMP and UMP and maximize their profits by providing energy and reserve flexibility subjects to the reserve and traffic constraints. Third, a game-theoretic model was developed to determine market equilibrium by minimizing the operational costs of the power system and maximizing the profits of MES owners. To avoid homogenous behaviors of MESs in solving this model, a sequential dispatching algorithm was developed such that each MES of one MES owner has a unique price signal. An objective function update strategy was accordingly developed to take the interaction among MESs into consideration and ensure a maximum total profit for MES owners. Finally, case studies with IEEE 6-bus system, IEEE 118-bus system, and MATPOWER 300-bus system were provided to validate the proposed methods, demonstrating that MESs increase power system flexibility and their own profits while decreasing the energy and reserve payment of consumers. • Mobile energy storage has temporal, spatial, and power/energy flexibility. • We provide a market-based methodology to incent mobile energy storage to provide flexibility. • Efficient algorithm is developed to obtain the market equilibrium. • Flexibility provided by mobile energy storages reduces the operational cost of power systems. • Mobile energy storage owners obtain revenue by providing flexibility service. [ABSTRACT FROM AUTHOR]

• Propose an OD competitive network equilibrium model for ride-sourcing market. • Adopt finite-dimensional variational inequality based on non-cooperative game theory. • Incorporate heavy-asset and light-asset transportation network companies (TNCs). • Derive analytical monotonicity of equilibrium optima and sensitivity analysis. • Gain managerial insights for both the government and TNCs. The ride-sourcing market (RSM) consists of three parts: ride-sourcing platforms or transportation network companies (TNCs) that set the price for passengers and wage of drivers, passengers requesting on-demand ride services, and drivers offering the services. This paper proposes an origin–destination (OD) competitive network equilibrium model for RSM with heterogeneous TNCs. A finite-dimensional variational inequality is adopted to formulate the RSM competitive network equilibrium model based on the non-cooperative game theory. It assumes that each TNC and individual drivers aim to maximize profits or earnings, while passengers of demand regions seek utility maximization. The monotonicity of equilibrium optima is analytically derived, and the sensitivity analysis of parameters is theoretically conducted and numerically illustrated. The managerial insights are gained both for the government and TNCs. The analytical findings include: (a) market equilibrium price increases and market equilibrium travel quantity decreases in either the transaction cost, driver cost, or TNC cost; (b) market equilibrium price decreases in the number of light-asset TNCs, while the market equilibrium travel quantity increases in it; and (c) raising RSM entry requirements improves the market equilibrium price and reduces travel demand on RSM. The numerical results illustrate the plausibility and flexibility of the analytical model. The proposed model is general and capable of analyzing the network equilibrium with multi-type drivers, demand regions, and heterogeneous ride-sourcing platforms. [ABSTRACT FROM AUTHOR]