Negotiation Problem Research Articles

On the issues of business negotiation processes and prospects for overcoming them through large language models

This article investigates the potential of large language models in overcoming problems within the business negotiation process. Typical issues encountered in business negotiations at various communication stages, which highlight the necessity for systematic resolution (such as breaches of good faith principles, ineffective communication strategies, lack of clear planning, etc.), are identified. The study focuses on a key negotiation problem related to the increasing transaction costs of businesses due to unmet interaction results. It demonstrates how large language models can optimize the negotiation process at every stage: from preparation (gathering and analysing information, scenario modelling) to conducting negotiations (contextual analysis, proposal formulation) and implementing agreements (execution control, monitoring). The study highlights that large language models can ensure: objectivity and speed in information processing; improved planning and forecasting; enhanced communication efficiency; and reduced transaction costs. The findings of the research conclude that the application of large language models is a promising development direction for optimizing the business negotiation process, contributing to the reduction of transaction costs and strengthening companies’ competitiveness by expanding human capabilities in managing business communications.

Reward-Based Negotiating Agent Strategies

This study proposed a novel reward-based negotiating agent strategy using an issue-based represented deep policy network. We compared the negotiation strategies with reinforcement learning (RL) by the tournaments toward heuristics-based champion agents in multi-issue negotiation. A bilateral multi-issue negotiation in which the two agents exchange offers in turn was considered. Existing RL architectures for a negotiation strategy incorporate rich utility function that provides concrete information even though the rewards of RL are considered as generalized signals in practice. Additionally, in existing reinforcement learning architectures for negotiation strategies, both the issue-based representations of the negotiation problems and the policy network to improve the scalability of negotiation domains are yet to be considered. This study proposed a novel reward-based negotiation strategy through deep RL by considering an issue-based represented deep policy network for multi-issue negotiation. Comparative studies analyzed the significant properties of negotiation strategies with RL. The results revealed that the policy-based learning agents with issue-based representations achieved comparable or higher utility than the state-of-the-art baselines with RL and heuristics, especially in the large-sized domains. Additionally, negotiation strategies with RL based on the policy network can achieve agreements by effectively using each step.

Enterprise negotiation and communication management system under the guidance of the Internet of Things.

The multi-agent system is used to study the negotiation problem of virtual enterprises in the context of the Internet of Things (IoT) to strengthen the decision-making ability of enterprises and improve the negotiation efficiency between different enterprises. Firstly, virtual enterprises and high-tech virtual enterprises are introduced. Secondly, the virtual enterprise negotiation model is implemented using the agent technology in the IoT, including constructing the operation mode of the alliance enterprise agent and the member enterprise agent. Finally, a negotiation algorithm based on improved Bayesian theory is proposed. It is applied to virtual enterprise negotiation, and the effect of the negotiation algorithm is verified by setting an example. The results show that: (1) When one side of the enterprise adopts a risk-taking strategy, the number of negotiation rounds between the two sides increases. (2) High joint utility can be achieved when both parties to the negotiation adopt a conservative strategy. (3) The improved Bayesian algorithm can improve the negotiation efficiency of enterprises by reducing the number of negotiation rounds. This study aims to achieve efficient negotiation between the alliance and the member enterprises to improve the decision-making ability of the alliance owner enterprise.

Analysis of Interest Groups in the Process of Installing Elevators in Shanghai

The interest groups related to the installation of elevators in old residential areas are multiple interest groups with specific purposes and identities. The negotiation dilemma caused by the redistribution of group interests due to the installation of elevators led to the stagnation of the installation of elevators, which should have net income. Using the method of interest group analysis, the method of consultative democracy and the conceptual model, this paper makes a detailed analysis of the attitude of different interest groups under the new situation of installing elevators, and finally puts forward measures to solve the negotiation problems from the calculation of average net income and the government's work in installing elevators.

Collaborative bargaining solution in tandem supply chain through cooperative game theoretical approach

There are many studies about negotiation procedures for contract problems in supply chains. Several recent papers have considered a new negotiation procedure for a repurchase contract problem in a supply chain consisting of a manufacturer and a retailer. There, usually, are some wholesalers between a manufacturer and a retailer. Therefore, a supply chain including some wholesalers in addition to a manufacturer and a retailer should be considered. In this study, we call the supply chain in which three or more members are arranged in series the tandem supply chain. We, firstly, address a negotiation problem for a contract about wholesale and repurchase prices in the tandem supply chain in which three members, that is, a manufacturer, a wholesaler and a retailer are arranged in series. The whole contract in the tandem supply chain is composed of two contracts dependent mutually, i.e., the contract between the manufacturer and wholesaler and the contract between the wholesaler and retailer. The collaborative bargaining solution in the tandem supply chain consisting of three members is discussed. This paper, finally, formulates the tandem supply chain including several wholesalers.

Two-level consensus modeling with utility and cost constraints

There exist many two-level group consensus problems with different psychological behaviors of decision makers. To deal with these group consensus problems and reach a stable consensus, based on the principles and methods of grey system, utility theory and group consensus, we use grey utility function to describe and reflect decision makers' opinion preferences in different subgroups and different levels, and then we construct a two-level group consensus method with a moderator, and exploit it to solve the negotiation problems of the natural gas subsidy.

Intellectual Property and International Clean Technology Diffusion: Pathways and Prospects

AbstractInternational clean technology diffusion is essential to mitigate and adapt to climate change, while fast and optimal diffusion can be prevented by the paywall of patents. This article explores the deficiency in clean technology diffusion caused by the legal fragmentation and rule complex of international environmental law and intellectual property law. It systematically examines three pathways to foster international clean technology diffusion through: restriction of intellectual property, including imposing external restraints in environmental law; striking internal balancing in maximizing TRIPS flexibilities; and keeping the status quo. It argues that treaty pathways may not work, and an operable pathway to promote clean technology diffusion is to maximize and consolidate TRIPS flexibilities in national laws. This option challenges the popular proposal of a “Doha-like” declaration on TRIPS and climate change due to the paralyzed multilateral trade mechanism, asymmetrical negotiation power of developing countries, prolonged negotiation process, and categorization problem in treaty negotiations.

How Can China Resolve the NIMBY Dilemma in a Network Society? Government and Society-Negotiated Decisions Based on Evolutionary Game Analysis

Infrastructure, such as waste incineration infrastructure, is entangled in the “not in my back yard” (NIMBY) dilemma. Consequently, the closed public decision-making model has difficulty adapting to the increasingly strong public-participation needs. Thus, a new mode of negotiated decision making is urgently required. This paper focuses on the negotiation problem, considers the government and society as game players, builds an evolutionary game model of negotiated decisions based on the bounded rationality hypothesis, and simulates the process and results of the evolution of the players’ strategy. The study concludes that the stable state of system evolution depends on the initial state of the dual populations and the comparative relationship between changes in revenue and cost, and the sufficient and unnecessary condition for the government and society to both agree on the given strategy. Taking the waste incineration projects in Yuhang and Chaonan as examples, the negotiated decision-making practice of NIMBY facilities is analysed, and it is found that it takes less time for the system to reach equilibrium in the latter. Finally, optimisation strategies for decision making regarding NIMBY facilities are systematically proposed.

Convention-based Intent Integration in a Haptic Shared Control Paradigm

This paper establishes a platform to study the principles of convention formation in a haptic shared control framework wherein both humans and automation collaboratively control the steering of a semi-automated ground vehicle. We then apply these principles to determine optimal strategies for exchanging the control authority between human drivers and an automation system. In the first step, we introduce a modular structure to separate partner-specific conventions from task-dependent representations. Next, we develop a map that connects different forms of conventions to the outputs of human-automation interaction. Finally, using the convention map, we developed a reinforcement-learning-based model predictive controller to enable the automation system to learn complex policies and adapt its behavior accordingly. We applied the proposed platform to the problem of intent negotiation for resolving a conflict. Specifically, we considered a scenario where both humans and automation detect an obstacle but choose different paths to maneuver around the obstacle. The simulation results demonstrate that the convention-based handover strategies can successfully resolve a conflict and improve the performance of the human-automation teaming.

Negotiation problem

We propose and solve a negotiation model of multiple players facing many alternative solutions. The model can be generalized to many relevant circumstances where stakeholders’ interests partially overlap and partially oppose. We also show that the model can be mapped into the well-known directed percolation and directed polymers problems. Moreover, many statistical mechanics tools, such as the Replica method, can be fruitfully employed. Studying our negotiation model can enlighten the links between social–economic phenomena and traditional statistical mechanics and help to develop new perspectives and tools in the fertile interdisciplinary field.

Hopeful dying? The meanings and practice of hope in palliative care family meetings

Hope can carry considerable allure for people facing imminent mortality and for those who care for them. Yet, how hope is variously and relationally (re)produced within end-of-life care settings, remains under-researched. In this study, we aimed to better understand hope as it circulates within palliative care, drawing on video recorded family meetings and pre- and post-meeting qualitative interviews, within two hospitals in Queensland, Australia. Our findings highlight family meetings as an important site for articulations of hope and hopefulness. The results illustrate how hope is recalibrated within the transition to and through palliative care, the tensions between hope and futility, and the work of hope in discussions of goals and expectations. Through our analysis we argue that hopefulness within family meetings, and in palliative care more broadly, is collectively produced and opens up discourses of hope to the lived experience of terminality. Attending to the nuances of hope, including moving beyond the determinative (hope for more life/hope for a quick death), can elucidate the possibilities and problems of the collective negotiation of hope at the end of life, including how hope can be drawn on to express support and solidarity.

Optimally manage the energy between electric vehicle charging stations and electricity distribution system: A hybrid technique

AbstractIn this manuscript, the energy trading model is proposed for investigating the cooperative benefits between several electric vehicle charging stations (EVCSs) and integrated energy systems (IES) based on hybrid system. The proposed hybrid system is combination of Radial‐Basis Function Neural Network (RBFNN) and artificial transgender longicorn algorithm (ATLA), hence it is called RBFNN‐ATLA technique. Initially, the RBFNN approach manages the energy between IES and EVCSs. After that, the original issue breaks down into the main energy trade and payment negotiation issue. The energy trading issue and payment negotiation problem can be solved using ATLA approach. This proposed structure may not only diminish the IES cost, however also enlarge the EVCS profit. The uncertainties in electricity and renewable energy prices are modeled using a robust optimization technique. Additionally, the integrated demand response is modeled for maximizing operational performance. The distributed algorithm depends on the proposed technique is evolved for solving the issue of energy trading, ensuring the privacy of players. The proposed algorithm may obtain the global optimal solutions devoid of adjusting the parameter compared with existing algorithm. The proposed system is performed by the matrix laboratory (MATLAB)/Simulink and the performance is evaluated with other existing methods. The RBFNN‐ATLA method reduces the cost up to 3.76%, 7.793%, 8.210% and 9.01% for independent and cooperative mode. The experimental outcomes demonstrate that the proposed system can accurately detect that optimal global solution.

Fuzzy Representation of Principal's Preferences in Inspire Negotiation Support System.

We consider the negotiation problem, in which an agent negotiates on behalf of a principal. Our considerations are focused on the Inspire negotiation support system in which the principal’s preferences are visualised by circles. In this way, the principal describes the importance of each negotiation issue and the relative utility of each considered option. The paper proposes how this preference information may be implemented by the agent for determining a scoring function used to support decisions throughout the negotiation process. The starting point of our considerations is a discussion regarding the visualisation of the principal’s preferences. We assume here that the importance of each issue and the utility of each option increases with the size of the circle representing them. The imprecise meaning of the notion of “circle size” implies that in a considered case, the utility of an option should be evaluated by a fuzzy number. The proposed utility fuzzification is justified by a simple analysis of results obtained from the empirical prenegotiation experiment. A novel method is proposed to determine trapezoidal fuzzy numbers, which evaluates an option’s utility using a series of answers given by the participants of the experiment. The utilities obtained this way are applied to determine the fuzzy scoring function for an agent. By determining such a common generalised fuzzy scoring system, our approach helps agents handle the differences in human cognitive processes associated with understanding the principal’s preferences. This work is the first approach to fuzzification of the preferences in the Inspire negotiation support system.

Criminals at play: Oedipus, Rope, and Telltale’s The Walking Dead

ABSTRACT This article investigates three storytelling arts as spaces of narrative play: theatre, film and narrative-based gaming. It traces the lineage from Oedipus Rex and early tragic theatre to Alfred Hitchcock’s 1948 film Rope, followed by The Walking Dead Telltale Games series, relating how each text presents protagonists who are marked as criminal from the opening of the narrative. Rope and The Walking Dead both work from the prophetic prototype developed in the Oedipus myth and use reflexive engagement with their own storytelling practices to ask open questions of stigma, sexuality, ethnicity and problems in the ongoing negotiation of play as both a coping strategy for social and legal marginalisation, and a safe space for interrogating our precognitive moral intuitions and biases. All play is fragile, and serious consequence always threatens the boundaries of Huizinga’s ‘magic circle’ of play; this means that play statuses must be consistently negotiated and updated by those participating, a concept I refer to as ‘the invitation to play’. This article explores how storytellers navigate such distinctions in asking participants to reflexively consider the boundaries of consequence in the narrative arts and in their lives.

Objectives of an enterprise. Bi-criteria analysis and negotiation problems

Abstract A decision-making process is considered for a firm, in which two coexisting groups of interests pursue different goals. An original model based on a non-neoclassical production function is proposed. The function satisfies the conditions formulated by R. Frisch, which makes it possible to investigate firms operating in the environment far from the perfect competition and pursuing goals other than profit maximization. A two-criteria optimization problem is formulated with the two criteria representing the goals of the groups: maximization of profit and maximization of income generated by the firm with respect to capital and labor. The problem is considered in two variants of the product market, namely the perfect and the imperfect competition. Solutions of the problem are analyzed including the derived Pareto sets. The importance of knowledge about the Pareto set in negotiations between the groups of interests in the firm is illustrated and discussed.

Towards assisted rationality switching in negotiations

Abstract The text attempts to provide a comprehensive outlook on negotiation processes and support procedures, by deriving a mathematical description based on psychological and managerial context. The authors merge psychological and formal descriptions of the principles underlying the perception of rationality in negotiation problems. The framework allows for explaining the violation of perceived rationality as a result of the deprivation of human needs. The argument, supported by pilot experiments, allows for recommending a procedural approach to negotiation, focused on monitoring perceptions of rationality of proposal submission. The paper is concluded with an attempt to foresight further developments of theoretical investigations and expansion of application fields.

Enabling Cloud Applications to Negotiate Multiple Resources in a Cost-Efficient Manner

Cloud applications can achieve similar performance with diverse multi-resource configurations, allowing cloud service providers to benefit from optimal resource allocation for reducing their operation cost. This paper aims to solve the problem of multi-resource negotiation with considerations of both the service-level agreement (SLA) and the cost efficiency so that the performance requirement for cloud services is satisfied and the cost of resource usage is also minimized. The performance and resource demand are usually application-dependent, making the optimization problem complicated, especially when the dimension of multi-resource configurations is large. To this end, we use reinforcement learning to solve the optimal problem of multi-resource configuration with simultaneous optimization of the learning efficiency and performance guarantee. The developed prototype named SmartYARN is an extended Apache YARN equipped with our learning algorithm which can enable cloud applications to negotiate multiple resources cost-effectively. The extensive evaluations with four typical benchmarks show that SmartYARN performs well in reducing the cost of resource usage while maintaining compliance with the SLA constraints of cloud service simultaneously.

Basketball contract: applying the SIPRES method in the process of evaluating the negotiation template

Abstract In a negotiation process, knowing the preferences of the decision-maker and building a negotiation offer scoring system are very challenging tasks. There are many different methods that can be used to develop such a negotiation support tool, including, but not limited to, techniques based on the multiattribute utility theory (MAUT) or the outranking relation, for instance SAW, AHP or PROMETHEE II. In this paper we present the procedure of evaluating the negotiation template using an unconventional and inventive method called SIPRES, which combines the key elements of the revised Simos’ procedure and the ZAPROS method to elicit the negotiator’s preferences over some reference solutions. This transparent and easy to implement technique – thanks to the ZAPROS-like approach applied within it – allows the decision-makers to define their preferences in a simple manner, providing an effective method for analysing the trade-offs between the alternatives using selected reference alternatives only. Simultaneously, the revised Simos’ procedure, applied in the method, allows for determining the cardinal scores of the alternatives. Thus, sophisticated negotiation analyses can be carried out. As an illustrative example the problem of basketball contract negotiations is discussed.

Matching With Contracts-Based Resource Trading and Price Negotiation in Multi-Access Edge Computing

In order to provide high quality services for Internet of Things (IoT) users with ever-changing requirements, a novel multi-access edge computing (MEC) based network paradigm is proposed in this letter. In the proposed framework, the service providers rent resources from resource providers (RPs), and then deploy them into MEC nodes (MNs). In this way, the MNs are labeled as temporal service points, which is flexible and cost-effective. Aiming to address the resource trading and price negotiation problem between the RPs and MNs, a matching with contracts model is leveraged in our proposed paradigm. Next, we further propose a distributed price negotiation algorithm to solve the problem. Finally, the simulations are presented to demonstrate the superiority of our proposed algorithm.

An incentive mechanism-based negotiation model for green supply chain networks

This research aims to prompt agents to improve their strategies initiatively in order to decrease carbon dioxide emissions and enhance green factors during production and consumption processes. An incentive negotiation mechanism is proposed for agents in supply chains to improve their strategies. Multiple items, multiple attributes, and multiple echelons are involved in the proposed model. In addition, this research takes both the commerce and the environmental attributes into account. The environmental attributes were transformed into rewards or penalty by setting reward factors or penalty factors, and were taken into account during the calculation of the profits. The simulation results show that the proposed model was feasible to solve the complex negotiation problems, and had a good performance. The green factors of agents in the green supply chain network are increased when the agents have low initial green factors. Moreover, the proposed model can effectively reduce the carbon dioxide emissions as well. The proposed model can be seen as a “win–win” solution from the perspective of both business and environmental protection. The total profit of the green supply chain network is improved, and the harm to the environment is decreased as well.