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We assess the performance of recently developed hospital merger simulation methods in a Monte Carlo setting. We specify a theoretical model of hospital markets and generate true price eects of mergers by solving a simultaneous Nash bargaining game between the hospitals and MCOs for pre- and post-merger cases. We then take the data generated by the theoretical model that would be available in a real-world prospective merger analysis to the merger simulation methods and compare their predictions to the true price eects. We perform this exercise for multiple parameterizations, including dierent levels of competition in the hospital and insurance markets. We examine two related merger simulation methods and nd that both are preform well, although each exhibits some tendency to under- or over-predict merger price eects. We also nd some sensitivity to variation in the model parameters that govern consumer preferences over hospital networks and the division of the joint surplus in the bargaining game. 1

Power systems are facing the challenges of integrating massive electric vehicles (EVs) and lacking operational flexibility. An optimal and efficient coordination strategy is needed to coordinate the operations among EV charging stations (CSs) and the distribution system operator (DSO) and to make full use of the EVs charging flexibility for improving the operational flexibility of power systems. To this end, this paper proposes a CSs-DSO distributed coordination strategy. The proposed strategy integrates a supply-curve-based quantification method to quantify the EVs flexibility contribution and a Nash bargaining game-theory-based peer-to-peer (P2P) transactions model for maximizing the profits of entities participating in the coordination. To tackle the CSs-DSO coordination problem that has a bi-level-parallel coupling structure, an analytical target cascading (ATC) and alternating direction method of multipliers (ADMM) jointly-based distributed solution approach is proposed. First, the CSs-DSO bi-level model is decoupled by ATC with tie-line power being the only exchanged information; then, the model is further decomposed for each CS by ADMM for protecting the CSs privacy and autonomy. The effect of coordination and the computational efficiency of the proposed strategy is verified by numerical simulations on a modified IEEE 33-bus distribution system connecting multiple CSs.

Device-to-device (D2D) content sharing holds great promise to alleviate the growing strain on cellular networks, as it offloads popular content data onto direct peer-to-peer links. However, it is still unexplored how content sharing could benefit from utilizing multihop rather than the conventional single-hop D2D communications. As a step towards this end, this paper proposes a game theoretic approach to enable D2D content sharing with multihop communication capabilities. Given a subset of participants, a Nash bargaining game is modeled to provide the routing and pricing graphs, where a novel incentive mechanism is adopted to stimulate cooperation. By iteratively evaluating the solution of the Nash bargaining subgame, participants that include content sources and transmission relays are determined, which ensures that all participants make contributions to the content sharing process. An additional procedure termed pricing plan is introduced to make sure that the final pricing graph is practical and feasible in terms of D2D communication. Experimental results are presented to demonstrate that the proposed game theoretic approach could not only jointly deal with the participants selection, routing, and pricing problems in multihop D2D content sharing, but also effectively restrict utilities and transmission resources to only contributive participants.

Problem definition: Inspired by India’s challenges in importing pulses, we study the negotiation of government-to-government food importing contracts, with a focus on ad hoc and forward negotiations with multiple suppliers (henceforth referred to as multiple-sourcing negotiations). Academic/practical relevance: We are the first to comprehensively study ad hoc and forward multiple-sourcing negotiations for food importing. Such problems are widespread, especially in developing nations, and thus the research can be relevant to the wellbeing of large underprivileged populations. Methodology: We develop an analytical negotiation model in the Nash bargaining framework and adopt the Nash-in-Nash framework to analyze multiple-sourcing negotiations. Results: We find that while forward negotiations are not necessarily better than ad hoc negotiations for the buyer, it would be true with sufficiently many suppliers. When facing a supplier pool, we show that it may be optimal to mix forward and ad hoc suppliers. In general, fewer suppliers should be assigned as ad hoc as the pool size increases. We also find that adding a hybrid supplier (engaged in a forward negotiation with an ad hoc negotiation as the fallback option) may be better or worse than adding a forward supplier in the presence of other suppliers. Managerial implications: Our findings inform how a food importer should strategically utilize ad hoc and forward negotiations with its suppliers to improve the outcome. The work may help countries’ food importing policymaking and strategies and may improve the wellbeing of large underprivileged populations.

We call a decision maker risk averse for losses if that decision maker is risk averse with respect to lotteries having alternatives below a given reference alternative in their support. A two-person bargaining solution is called invariant under risk aversion for losses if the assigned outcome does not change after correcting for risk aversion for losses with this outcome as pair of reference levels, provided that the disagreement point only changes proportionally. We present an axiomatic characterization of the Nash bargaining solution based on this condition, and we also provide a decision-theoretic characterization of the concept of risk aversion for losses.

In-situ (InS) server systems are typically deployed in special environments to handle in-situ workloads which are generated from environmentally sensitive areas or remote places lacking modern power supply infrastructure. This special operating environment of InS servers urges such systems to be powered by renewable energy. In addition, the InS systems are vulnerable to soft errors due to the harsh environments they deploy. This paper tackles the problem of allocating harvested energy to renewable powered servers and assigning the in-situ workloads to these servers for optimizing throughput of both the overall system and individual servers under energy and reliability constraints. We perform the energy allocation based on system state. In particular, for systems in low energy state, we propose a game theoretic approach that models the energy allocation as a cooperative game among multiple servers and derives a Nash bargaining solution. To meet the reliability constraint, we analyze the reliability optimality of assigning tasks to multiple servers and design a reliability-aware task assignment heuristic based on the analysis. Experimental results show that with a small time overhead, the proposed energy allocation approach achieves a high throughput from perspectives of both the overall system and individual servers, and the proposed task assignment approach ensures an increased system reliability.

In this study, we provide a characterization of the class of proportional bargaining solutions introduced by Kalai. Our result differs from earlier axiomatizations in that we use a property that we label individual disagreement point concavity. This property is a weakening of disagreement point concavity used by Chun and Thomson. An application illustrates the potential usefulness of our new property in a strategic setting.

Problem definition: Few papers have explored the impact of price matching negotiation (PM), in which a channel matches its price with the resulting wholesale price bargained by another channel, on firms’ performances, consumer welfare, and social welfare, with and without supply chain coordination. Academic/practical relevance: Negotiation has been widely seen in determining both uniform and discriminatory wholesale prices, which affect outcomes of competitive supply chain practices. Methodology: To characterize the PM mechanism, we use game theory and Nash bargaining theory to compare PM with simultaneous negotiation (SN) through a common-seller two-buyer differentiated Bertrand competition model. Results: Our analysis reveals that PM can benefit the seller but hurt all buyers, which is at odds with some fair wholesale pricing clauses intending to protect buyers. Under coordination with side payments, however, all firms can conditionally benefit more from PM than from SN. Despite firms’ gains, PM leads to less consumer utility and social welfare compared with SN, unless the second buyer in PM is considerably less powerful than the first buyer. Coordination further worsens PM’s negative impact on consumer utility and social welfare. Moreover, the existence of a spot market can increase the wholesale price in PM, hurting buyers, consumers, and society. Furthermore, the qualitative results about PM remain robust under an alternative disagreement point for PM, multiple buyers, and other extensions. Managerial implications: This paper delivers insights on when price matching in supply chain wholesale price negotiation can benefit a seller, buyers, consumers, and society in a variety of scenarios. It advocates how managers can use PM to their own advantages and provides rationale to decision makers for policy regulations regarding wholesale pricing.

In wireless-powered device-to-device (D2D) networks with backscatter-aided passive relays, the cooperation among D2D pairs is of special necessity. With the cooperation among D2D pairs, the devices can act as passive relays to enhance the signal reception, and share their active transmission time to improve the resource utilization. Therefore, how the D2D pairs form coalitions and how to reallocate the active transmission time to the D2D pairs after forming coalitions should be studied. In this paper, we develop a hierarchical cooperative game framework for the network to investigate how the D2D pairs cooperate with each other. Specifically, in the upper layer of the game, we employ the coalition game to formulate how the D2D pairs form coalitions, and analyse the stability of the coalition structure and the complexity of the proposed strategy. In the lower layer of the game, we employ the bargaining game to investigate how the D2D pairs share the active transmission time, and analytically evaluate the complexity of deriving the Nash bargaining solution. Simulation results demonstrate the effectiveness of our proposed scheme. The results also show that the computational complexity of our proposed scheme is only a small fraction of that of the exclusive search-based scheme. Nevertheless, compared with the exclusive search-based scheme, the total effective amount of the transmitted data in the proposed cooperative scheme reduces no more than 2%, which is very minor.

Cost allocation is a way to promote enterprises to actively implement green supply chain management, and the behavior preference of decision makers will affect the process. This paper considers the situation that manufacturer has fairness preference and retailer is fair neutral, and constructs a secondary supply chain composed of a single manufacturer and a single retailer. We explore two models of cost allocation—based on the Stackelberg model, the impact of retailers’ nonparticipation/participation in cost allocation on supply chain is discussed, while through the Nash bargaining model, the range and optimal solution of the environmental cost allocation ratio are determined. And we show how product wholesale prices, retail prices, green innovation investment, and the optimal ratio of environmental cost allocation are influenced by fairness preference. The results show that retailers’ participation in environmental cost allocation will increase the level of product greenness and make the entire supply chain Pareto optimal. The strong fairness preference of manufacturer will weaken the positive effect of retailers participating in cost allocation on the supply chain. Therefore, the government and enterprises should adopt incentive measures to actively promote the collaboration between channel partners in order to share the environmental cost, and manufacturers should pay less attention to fairness.

To meet the needs of many IoT applications with low-latency requirement, fog computing has been proposed for next-generation mobile networks to migrate the computing from the cloud to the edge of the network. In this paper, we study the fog computing service deployment problem, where the operator allocates and deploys the required computing and network resources on the edge of the network to accommodate the requests of various applications operated by the application service providers (ASPs). The operator negotiates with the ASPs to determine serving QoS of applications and how much to pay. A queuing-based latency performance model with bulk arrival is proposed for the problem to estimate the resources needed for the fog network to achieve the QoS requirements of applications. We then model and analyze the interactions between the operator and multiple ASPs as sequential one-to-many bargaining using Nash bargaining. Next, to find the optimal bargaining sequence, we propose an improved optimal algorithm, along with fast heuristic algorithms, to find the optimal sequence with low complexity. Through extensive simulations, we show that the fog service can benefit all parties, and the proposed optimal and heuristic algorithms can improve the OP's payoff by averages of 21.24% and 14.16% respectively.

The two-settlement electricity markets are widely accepted. In current practice, the markets are cleared based on locational marginal prices. However, this market clearing scheme leaves arbitrage opportunities between day-ahead and real-time markets for strategic distribution system operators (DSOs). They can submit biased bids from their actual load forecasts in the day-ahead market for additional benefits. Worse still, due to the high penetration of distributed energy resources, the impact of such strategic behaviors becomes increasingly severe as the uncertainties in DSOs’ load forecasts increase. In this paper, we first identify the profitable arbitrage opportunities of DSOs and the impact of uncertainties by a simplified analytical model. We then propose a Nash bargaining solution based market clearing scheme for real-time coordination between the transmission system operator and the DSOs. This scheme guarantees the fairness of net-benefit allocation among all the operators, prevents the strategic behaviors of DSOs, and helps the consistency between day-ahead commitment and real-time operation. Finally, we validate the effectiveness of the proposed scheme on both a simplified system and a practical power system testbed.

We model a two-level supply chain where Nash bargaining occurs upstream, while firms compete in a differentiated products logit setting downstream. The parameters of this model can be calibrated with a discrete set of data on prices, margins, and market shares. Using a series of numerical experiments, we illustrate how the model can simulate the outcome of both horizontal and vertical mergers. In addition, we extend the framework to allow for downstream competition via a second score auction.

The antitrust enforcement Agencies’ 2020 Vertical Merger Guidelines (VMGs) introduce a nontechnical application of bargaining theory into the competitive assessment of vertical acquisitions. This bargaining theory has much in common with the theory of unilateral effects that is applied to horizontal mergers. The VMGs focus on post-merger price increases requires consideration of a vertical merger’s role in eliminating double marginalization (EDM). The problem occurs when two bargaining firms both have market power but are unable to coordinate their output. Assessing EDM bundles two themes that Ronald Coase developed in his two most well-known articles: “The Nature of the Firm” and “The Problem of Social Cost”. The first argued that the boundaries of a firm are determined by the firm’s continuous search to minimize costs. The second argued that two traders in a well-functioning market will achieve the joint-maximizing solution. Anti-interventionists rely heavily on Coasean arguments that unless high transaction costs get in the way firms will bargain to joint maximizing results. If that is true, then double marginalization will rarely provide a defense to a vertical merger. The law of vertical mergers deals largely with firms that transact with one another routinely, in legally enforceable buy-sell relationships. In a well-functioning vertical market durable double marginalization should be rare.

The fundamental surplus isolates parameters that determine how sensitively unemployment responds to productivity shocks in the matching models of Christiano et al. ( 2016 and this issue) under either Nash bargaining or alternating-offer bargaining. Those models thus join a collection of models in which diverse forces are intermediated through the fundamental surplus.