Bargaining Game Research Articles

Independent operation or coordinated integration? Enhancing the system resilience of ports in dealing with congestion based on a bilateral bargaining game

Fierce competition between ports may trigger substantial issues including port congestion, further wreaking havoc on the system resilience of ports. The coordinated integration of ports becomes a feasible and indispensable strategy for mitigating competitive pressure and enhancing the system resilience. In a two-tier supply chain within trade activities between two adjacent ports, we discuss the competitive and cooperative strategy selections between ports by a bilateral bargaining game to investigate how to improve the system resilience of ports in coping with congestion. The results reveal that port cooperation has the advantage of improving throughput and the system resilience of ports by alleviating port congestion, but becomes more challenging as the advantageous port's bargaining power and competitive intensity increase. Although under the pressure of high freight rates, shipping companies are still willing to actively participate in and promote the coordinated integration of ports for a premium service provided by ports. From the overall shipping supply chain, port cooperation optimizes the entire operation of port system and promotes profits from the high system resilience, demonstrating a better environmental performance by dealing with congestion. In light of the conclusions, it is essential to vigorously establish a port-port alliance through coordinated contracts to tackle congestion and enhance resilience, thereby boosting competitiveness and ensuring profitability in the whole system of ports.

Transactive energy management for CIES considering tri-level hybrid game-theoretic approaches with multiple weight allocation factors

In city integrated energy systems, to reduce the costs of energy production and usage, it is extremely important for different entities to design proper energy management approaches since they have diverse behaviours and benefits. The hierarchy of the system is naturally divided into three layers, and hybrid game theories are employed on the basis of the vertical master–slave and horizontal equal cooperation characteristics among energy providers, aggregators, and users. First, to reflect the interactions between different levels' entities, Stackelberg game theory is adopted vertically, and the model complexity is lessened by the KKT conditions. Then, for energy providers, the asymmetric bargaining game is improved by combining multiple weight allocation factors, which can help reduce renewable energy fluctuations and encourage the consumption of clean energy to reduce the carbon emissions of the system. Also, the results show that the proposed method can enhance the utility of renewable energy generation units by approximately 64 % to 69 %. Finally, the optimality and effectiveness of the proposed game theoretic approaches are verified in case studies and in strict mathematical proofs. In addition, the impact mechanisms on the demand response from the perspectives of the user load and price sensitivity are analysed. To explore pricing methods that are more suitable for user aggregators, two pricing strategies are designed for the user aggregator and compared in terms of computation time and revenue. The results show that a unified pricing strategy can increase the aggregator's revenue by 1.24 % and reduce the computation time by 24.47 %.

Consensus methods with Nash and Kalai-Smorodinsky bargaining game for large-scale group decision-making

With the significant advancements in communication technology, group decision-making (GDM) can now be implemented online, allowing a large number of decision-makers (DMs) to participate concurrently. However, current methods for large-scale group decision-making (LSGDM) are primarily suitable for 20 to 50 DMs, and their effectiveness in scenarios involving thousands or even tens of thousands of participants has yet to be fully validated. Furthermore, as the number of participants increases, the evaluation information becomes increasingly diverse and complex. At the same time, the social networks associated with the DMs typically become sparse, making information sharing and consensus building more challenging. In light of these challenges, we develop two new methods based on cooperative games to effectively address the challenges in super LSGDM. First, we propose a two-stage semi-supervised fuzzy C-means (FCM) clustering method with trust constraints, which aims to address the issue of sparsity in relationships within large-scale social networks. This method utilizes trust relationships as reliable resources and prior knowledge to guide and supervise the clustering process. On this basis, we discuss three scenarios from the perspective of cooperative games: (i) subgroup optimal consensus adjustments in non-cooperative situations, (ii) group optimal consensus adjustments in cooperative situations, and (iii) subgroup optimal consensus adjustments in cooperative situations. Subsequently, we view the consensus adjustment allocation as a cost cooperative game problem and propose two new LSGDM consensus methods based on Nash Bargaining (NB) and Kalai-Smorodinsky Bargaining (KSB). Finally, experiments on real datasets demonstrate the superiority and reliability of our proposed LSGDM methods.

Evaluating Negotiators Who Deceptively Communicate Anger or Happiness: On the Importance of Morality, Sociability, and Competence

AbstractResearch has shown that negotiators sometimes misrepresent their emotions, and communicate a different emotion to opponents than they actually experience. Less is known about how people evaluate such negotiation tactics. Building on person perception literature, we investigated in three preregistered studies (N = 853) how participants evaluate negotiators who deceptively (vs. genuinely) communicate anger or happiness, on the dimensions of morality, sociability, and competence. Study 1 employed a buyer/seller setting, Studies 2 and 3 employed an Ultimatum Bargaining Game (UBG). In all studies, participants learned a negotiator’s (the target’s) experienced and communicated emotions (anger or happiness), before evaluating the target. Across studies, targets were evaluated lower on morality if they deceptively (vs. genuinely) communicated anger or happiness. Notably, negotiators deceptively communicating anger were evaluated lower on morality and sociability but higher on competence than those deceptively communicating happiness. Studies 2 and 3 investigated behavioral consequences by examining whether in a future negotiation participants chose the target to be their opponent or representative. Results showed that for opponents, participants preferred targets who genuinely communicated happiness (vs. anger), which was associated with their perceived morality or sociability. For representatives, participants not only preferred targets who had genuinely communicated happiness (vs. anger), but also targets who had deceptively communicated anger (vs. happiness), which was associated with their perceived competence. These findings show that when evaluating deceptive (vs. genuine) communication strategies, people distinguish between morality, sociability, and competence. The importance they attach to these dimensions is also contingent on the behavioral decisions they face.

Optimal Bidding Strategies for Wind-Thermal Power Generation Rights Trading: A Game-Theoretic Approach Integrating Carbon Trading and Green Certificate Trading

In response to the challenges of low wind power consumption and high pollution emissions from thermal power, the implementation of wind-thermal power generation rights trading is a proactive attempt to reduce wind power curtailment and promote its consumption. This study first regards the alternating bidding process between the two parties as a dynamic game, using the Rubinstein bargaining game model to determine the incremental profit allocation and optimal bidding for both parties in power generation rights trading. Secondly, an energy conservation and emission reduction model is constructed to analyze the benefits from the perspectives of standard coal consumption saving and the carbon emission reduction caused by power generation rights trading. Finally, a combined trading revenue model is established to analyze the final profit of both parties involved in the trading. The results show that the combined trading of wind-thermal power generation rights, incorporating carbon trading and green certificate trading, can effectively promote coal consumption savings in thermal power units and reduce the carbon emissions of the power industry. Moreover, it significantly increases the final profit for both parties, stimulating the enthusiasm of generators for participating in power generation rights trading, and ultimately promoting wind power consumption.

A two-sided equilibrium model of Vehicle-to-Vehicle charging platform

A novel mobile charging service utilizing vehicle-to-vehicle (V2V) charging technology has been proposed as a complement to fixed charging infrastructure (CI), enabling electric vehicles (EVs) to exchange electricity. This study develops a two-sided equilibrium model for a V2V charging platform, where the demand-side charging vehicles (CVs) choose between charging piles and the V2V platform, and the supple-side discharging vehicles (DVs) decide whether to provide discharging services. The V2V platform matches CVs with DVs, receiving compensation from CV owners and reimbursing DV owners. To explore the strategic interactions between the V2V platform and fixed CI operators, we construct an analytical bi-level model that examines different operation regimes, including competitive and cooperative scenarios, i.e., non-cooperative game and Nash Bargaining game. The lower-level model is a two-sided equilibrium model that quantifies charging and discharging choices of EV owners, while the upper-level model optimizes the pricing decisions of the V2V platform and fixed CI operator. Numerical results indicate that the introduction of the V2V platform can reduce the overall charging costs, thus encourage the use of public charging services. Furthermore, it is suggested that the V2V platform be managed by independent enterprises, rather than existing fixed CI operators, to promote the EVs at relatively low charging costs.

Equilibrium analysis in majority-based coalitional bargaining games

This paper introduces majority rule into coalitional bargaining games, adapting traditional models that rely on unanimous consent to more accurately mirror decision-making processes in real-world scenarios. We introduce a majority-based coalitional bargaining game (MBCBG), wherein coalitions pass proposals via majority votes. Our analysis of the stationary subgame perfect equilibrium (SSPE) not only establishes the necessary and sufficient conditions for SSPE strategy profiles but also confirms the existence of no-delay SSPEs in MBCBGs. We further delve into symmetric MBCBGs, delineating conditions that ensure equitable outcomes for homogeneous players. Furthermore, we provide a necessary and sufficient condition for the formation of the grand coalition under SSPEs. Additionally, we briefly explore how asymmetries in coalition values, proposal probabilities, and voting weights may influence both the dynamics of coalition formation and the expected equilibrium payoffs.

Strategic selection between technology spillover and green technology cross-licencing under cap-and-trade policy

ABSTRACT This paper examines the co-opetition strategic selection between two rival manufacturers under a cap-and-trade policy. Based on the classical Cournot game and Nash bargaining game theories, we propose a benchmark model with technology spillover effect and green technology cross-licencing models with both fixed-fee and mixed-fee modes. The results show that manufacturers have the highest outputs in the fixed-fee cross-licencing scenario, and manufacturers’ total profits in the green technology cross-licencing scenarios always outperform that in the technology spillover scenario. Moreover, when the unit carbon quota trading price is high, the technology spillover scenario has the lowest emissions. When the unit cost of governing carbon emissions-related pollution is low, social welfare in the fixed-fee cross-licencing scenario is higher than that in the mixed-fee cross-licencing scenario. Interestingly, the social welfare advantage of the mixed-fee cross-licencing scenario becomes more and more prominent as the unit cost in governing carbon emissions related pollution increases.

Efficient demand management for the on-time arrival problem: A convexified multi-objective approach assuming macroscopic traffic dynamics

This work proposes a novel solution approach to address the On-Time Arrival (OTA) problem, considering macroscopic traffic dynamics. In this context, all drivers who intend to use the road infrastructure are required to communicate their origin–destination pair and desired arrival time to a central scheduler prior to starting their trip. In response, the scheduler assigns a departure time and a multi-regional route to each driver to make possible their on-time arrival at the destination. The OTA problem is formulated as a nonconvex, nonlinear, multi-objective optimization problem considering two objective criteria. The first criterion aims at minimizing the travel time of all drivers in the network to prevent congestion, while the second criterion seeks to minimize the discrepancy between the desired and actual arrival time. The proposed formulation is solved efficiently through an approximated convex solution that leverages the Normal Boundary Intersection (NBI) method to efficiently generate a representative sample of the Pareto Front. Additionally, two solution methodologies based on “knee” solution and the Nash Bargaining Game is proposed which can be used to select a unique solution across all the Pareto points, that offers a reasonable trade-off between the two objective criteria. Finally, simulation results demonstrate that the proposed solution can significantly reduce congestion while ensuring that most drivers will arrive at their destination on their desired time.

An Adaptive Core-Nash Bargaining Game Consensus Mechanism for Group Decision Making

An Adaptive Core-Nash Bargaining Game Consensus Mechanism for Group Decision Making

Game-theoretic analytics for privacy preservation in Internet of Things networks: A survey

Privacy preservation of the big data generated, deposited, and communicated by smart IoT (Internet of Things) nodes is the major challenge in IoT networks. Anonymization, encryption, and routing protocol constitute the existing prevalent privacy-preserving approaches, most of which have successfully implemented the privacy preservation of data query, data mining and data aggregation. Nevertheless, there has been a gradual switch in the selection of privacy-preserving technology. Predictive game-theoretic analytics for privacy preservation in IoT networks has received significant attention since it can effectively settle the conflicts between attackers and defenders. In this survey, we explain the basics of various games mainly applied for IoT privacy preservation, such as simultaneous game, stochastic game, bargain game, differential game, mean field game, aggregation game, Stackelberg game, signaling game, repeated game, evolutionary game, and cooperative game. We then explore different applications for game theory-based privacy preservation in IoT networks, followed by discussing the differences among the existing solution of privacy-preserving issues using different games under specific IoT scenarios. Moreover, we consider the challenges and outline future research directions. In conclusion, this survey not only presents existing work on applying game theory to preserve privacy in current IoT networks including smart grids, intelligent transportation systems, crowdsensing, edge-based IoT, integrated energy systems, blockchain IoT, Social IoT and Industrial IoT, but it also encourages researches to further dig deeper into rare areas.

Dynamic bargaining game DEA carbon emissions abatement allocation and the Nash equilibrium

Allocating carbon emissions abatement (CEA) is crucial for reducing carbon footprint of organizations (or decision-making units, DMUs) and mitigating global warming. However, discrepancies often arise among DMUs regarding CEA allocation. This study introduces a dynamic bargaining game approach to address the issue under the nonparametric production frontier analysis framework. Our approach employs an iterative process, allowing each DMU to propose its individual preferred CEA allocation proposal in each iteration. Subsequently, dynamic negotiations among the DMUs occur, leading to the eventual consensus on the CEA allocation result. Our theoretical analysis demonstrates that, at the end of the dynamic bargaining game, all DMUs will converge on the same CEA allocation result, termed as the consensus CEA allocation result. This agreement is reached despite each DMU customizing its individual CEA allocation proposal to suit its own interests. Moreover, we prove that this consensus CEA allocation result represents a Nash equilibrium solution, thereby ensuring its stability and acceptance among all DMUs. Finally, we provide a simple numerical example and a case study of CEA allocation across 27 European Union countries to illustrate the usefulness of our approach and compare it with prior CEA allocation approaches.

The involvement of rTPJ in intention attribution during social decision making: A TMS study

The mini-Ultimatum Game (mini-UG) is a bargaining game used to assess the reactions of a responder to unfair offers made by a proposer under different intentionality conditions. Previous studies employing this task showed the activation of responders’ right temporoparietal junction (rTPJ), which could be related to its involvement in judgments of intentionality. To verify this hypothesis, in the present study we applied online transcranial magnetic stimulation (TMS) over the rTPJ in responders during the mini-UG, in which we manipulated intention attribution implicitly. A cover story was employed to induce participants to believe they were interacting with another agent. We expected that interfering with the rTPJ could affect the ability of responders to assume proposers’ perspective, producing higher rates of rejections of unfair offers when offers are perceived as independent from responders’ intentionality to inequality. Twenty-six healthy women voluntarily participated in the study. In the mini-UG, an unfair distribution of the proposer (8/2 offer) was pitted against one of three alternative offers: fair-alternative (5/5), no-alternative (8/2), hyperfair-alternative (2/8). During the task, a train of TMS pulses was delivered at proposers’ offer presentation in blocks of active (rTPJ) or control (Vertex) stimulation according to an ABAB design. As expected, findings showed that rejection of the no-alternative offers was higher under TMS stimulation of the rTPJ compared with the control TMS. This effect was modulated by the degree of trustworthiness in the cover story. These data contribute defining the mechanisms and brain areas underpinning social decision making as assessed by bargaining tasks.

Modeling Dynamic Bargaining and Stability in a Star-Shaped Trans-Shipment Network

The star-shaped trans-shipment network causes the retailer’s bargaining power to be different, which leads to the misalignment of trans-shipment profit. Aimed at this, we take retailers and the trans-shipment paths as the nodes and edges of the trans-shipment network. Based on this, we model the multilateral negotiations between the central retailer and the local retailer and adopt the Generalized Nash Bargaining game to derive the optimal solution of the value function for the incomplete trans-shipment network under the bargaining mechanism. Furthermore, we reveal the convexity of the optimal trans-shipment value function and give the condition that the allocation of the bargaining mechanism is in the core. Based on this, we introduce the concept of pairwise Nash equilibrium and show the star-shaped trans-shipment network is the optimal endogenous formation of the trans-shipment network. In practice, the central retailer should introduce as many local retailers as possible to join this trans-shipment alliance, which will achieve Pareto improvement. Meanwhile, the central retailer should order as many as possible. Finally, it is more appropriate to establish a star-shaped trans-shipment network when one retailer has stronger negotiation power compared to other retailers in a distribution system, which not only ensures the stability of the allocation of trans-shipment profits but also the stability of the trans-shipment network.

Consideration sets and reference points in a dynamic bargaining game

We introduce the notion of limited consideration into a bilateral, infinite-horizon, alternating-offers bargaining game. Both agents have reference-dependent preferences, and their reference points are dynamically updated. An agent's current consideration set (or focus) is influenced by the last offer he rejected such that the most salient point in that consideration set becomes his current reference point. We provide an implicit characterization of stationary subgame perfect Nash equilibria in our model. Without resorting to asymmetric or incomplete information, we show that our bargaining game can produce delay and disagreement in equilibrium. In addition to analytical results on the disagreement conditions and the nature of agreements, we provide computational comparative statics on model parameters and study their impacts on delay.

Research on Energy Management Strategy for Extended‐Range Electric Vehicle Based on Bargaining Game

To improve the fuel economy and battery life of extended‐range electric vehicles (EREVs), an energy management strategy (EMS) based on the bargaining game (BG) is proposed. This strategy optimizes the overall economy by optimizing power allocation. First, a bargaining model for EREVs is developed. The model considers the auxiliary power unit (APU) and the battery as two parties of the game. The power allocation between the APU and the battery optimizes both the fuel consumption and the battery life. And it also improves the overall benefit of the vehicle. Second, the effect of the discount factor on the control results is analyzed to obtain the best optimization results. Finally, compared to the thermostat strategy and the dynamic programming‐based strategy, the proposed BG‐based strategy reduces fuel consumption by 27.6% and 1.4% under the World Light Vehicle Test Procedure and reduces the effective ampere‐hour fluxes by 62.7% and 14.6%. Under the China Light‐duty Vehicle Test Cycle, it reduces fuel consumption by 32.5% and 0.5% and reduces the effective ampere‐hour fluxes by 56.8% and 27.5%. The simulation results manifest the excellent performance of the proposed BG‐based EMS.

Nash Bargaining Game Enhanced Global Malmquist Productivity Index for Cross-Productivity Index

The Global Malmquist Productivity Index (GMPI) stands as an evolution of the Malmquist Productivity Index (MPI), emphasizing global technology to incorporate all-time versions of Decision-Making Units (DMUs). This paper introduces a novel approach, integrating the Nash Bargaining Game model with GMPI to establish a Cross-Productivity Index. Our primary objective is to develop a comprehensive framework utilizing the Nash Bargaining Game model to derive equitable common weights for different time versions of DMUs. These weights serve as a fundamental component for cross-evaluation based on GMPI, facilitating a holistic assessment of DMU performance over varying time periods. The proposed index is designed with essential properties: feasibility, non-arbitrariness concerning the base time period, technological consistency across periods, and weight uniformity for GMPI calculations between two-time versions of a unit. This research amalgamates cross-evaluation and global technology while employing geometric averages to derive a conclusive cross-productivity index. The core motivation behind this methodology is to establish a reliable and fair means of evaluating DMU performance, integrating insights from Nash Bargaining Game principles and GMPI. This paper elucidates the rationale behind merging the Nash Bargaining Game model with GMPI and outlines the objectives to provide a comprehensive Cross-Productivity Index, aiming to enhance the robustness and reliability of productivity assessments across varied time frames.

Stackelberg-Nash asymmetric bargaining-based scheduling optimization and revenue-allocation for multi-operator regional integrated energy system considering competition-cooperation relationship and source-load uncertainties

The integration of source-grid-load-storage greatly improves the energy managing flexibility of regional integrated energy systems (RIES). Considering the conflicting interests of multi-operator, this paper divides RIES into 1) energy supply center (ESC); 2) load aggregator (LA); 3) energy storage aggregator (ESA), and establishes a multi-operator two-layer optimal scheduling model by combining 1) the Stackelbelberg-Nash asymmetric bargaining game based on the genetic algorithm; and 2) the alternating direction method of multipliers algorithm (GA-ADMM). Firstly, considering the absence of historical data, trapezoidal fuzzy parameters are introduced to construct fuzzy chance constraints based on credibility theory to simulate the source-load uncertainties. Secondly, the uncertainty of demand response behavior, real-time carbon credits trading within RIES, and cooperation-competition behavior of followers (LA and ESA) are considered to simulate the asymmetric relationship between followers and leaders (ESC) based on GA. Then, based on the ADMM algorithm, a Nash asymmetric bargaining game considering energy dependency and energy supply-reception status is proposed to realize the revenue allocation between followers. Finally, simulations are implemented to verify the validity and reliability of the proposed model. Results show that: 1) Cooperation improves the energy-managing flexibility of followers, reduces their dependence on ESC, and thus enhances their game ability. 2) The real-time carbon credits trading enables zero-carbon energy interactions and natural gas replenishment within RIES. 3) Increases in the price of various energy and carbon credits can enhance cooperation between followers.

Power Allocation for Cooperative Localization in Decentralized Distributed Radar Networks: A Nash Bargaining Game Approach

Power Allocation for Cooperative Localization in Decentralized Distributed Radar Networks: A Nash Bargaining Game Approach

Social Value of Public Information in Bargaining

Abstract We consider a bargaining game in which both sides are uncertain about their opponent’s commitment, which leads to delay and welfare loss in equilibrium. We address the following question: does ex ante better public information about a player improve expected social welfare? We show that if the information cannot turn the bargaining table (turns the weak bargainer into a strong one and vice versa), more information does not help. More information about a weak bargainer has zero impact, whereas that about the strong bargainer is strictly detrimental. Moreover, by specialising in a binary signal structure, we show that if the information is more accurate in every state, it improves social welfare when it can turn the table.