Multi-attribute Negotiation Research Articles

An agent-based persuasion model using emotion-driven concession and multi-objective optimization

Multi-attribute negotiation is essentially a multi-objective optimization (MOO) problem, where models of agent-based emotional persuasion (EP) can exhibit characteristics of anthropomorphism. This paper proposes a novel EP model by fusing the strategy of emotion-driven concession with the method of multi-objective optimization (EDC-MOO). Firstly, a comprehensive emotion model is designed to enhance the authenticity of the emotion. A novel concession strategy is then proposed to enable the concession to be dynamically tuned by the emotions of the agents. Finally, a new EP model is constructed by integrating emotion, historical transaction, persuasion behavior, and concession strategy under the framework of MOO. Comprehensive experiments on bilateral negotiation are conducted to illustrate and validate the effectiveness of EDC-MOO. These include an analysis of negotiations under five distinct persuasion styles, a comparison of EDC-MOO with a non-emotion-based MOO negotiation model and classic trade-off strategies, negotiations between emotion-driven and non-emotion-driven agents, and negotiations involving human participants. A detailed analysis of parameter sensitivity is also discussed. Experimental results show that the proposed EDC-MOO model can enhance the diversity of the negotiation process and the anthropomorphism of the bilateral agents, thereby improving the social welfare of both parties.

Can attentional nudges improve efficiency of bilateral multi-attribute negotiations?

This paper reports the results from a lab experiment to simulate negotiation on innovative therapy commercialization. Using a between-subject design, we analyzed the consequences of two light choice interventions: (1) guiding negotiations towards early wins, and (2) inviting negotiators to share information about their priority goals. In both treatments, the total value created exceeded the control value by approximately 9 % of the maximal value that can be created in this experiment. However, it was essentially the buyer who captured the additional value. We found that, conditional on the success of the negotiation, the total value created increased with the time spent negotiating. Negotiator gender had an impact on the negotiation outcome, with women underperforming compared to men.

Multi-attribute negotiation mechanism for manufacturing service allocation in smart manufacturing

Smart manufacturing is undergoing rapid development along with many disruptive technologies, such as Internet of Things, cyber-physical system and cloud computing. A myriad of heterogeneous manufacturing services can be dynamically perceived, connected and interoperated to satisfy various customized demands. In smart manufacturing, the market equilibrium is variable over time due to changes in demand and supply. Thus, efficient manufacturing service allocation (MSA) is critical to implementation of smart manufacturing. This paper considers the MSA problem under market dynamics with maximization of utility of customers and service providers. Many conventional methods generally allocate manufacturing services to the customers by multi-objective optimization without considering the impact of interactions between customers and service providers. This paper presents a multi-attribute negotiation mechanism to address the MSA problem under time constraints relying on autonomous agents. The proposed negotiation mechanism is composed of two models: an atomic manufacturing service negotiation model and a composite manufacturing service coordination. The former model is based on automated negotiation to seek an atomic manufacturing service over multiple attributes for an individual subtask. The latter model incorporates the global distribution and surplus redistribution to coordinate and control multiple atomic manufacturing service negotiations for the whole manufacturing task. Numerical studies are employed to verify the effectiveness of the multi-attribute negotiation mechanism in solving the MSA problem. The results show that the proposed negotiation mechanism can address the MSA problem and surplus redistribution can effectively improve the success rate of negotiations.

Integrative multi-attribute negotiation model to define stakeholders’ responsibilities in the reverse flow channel

An integrative multi-attribute model was developed to define individual solid waste management (SWM) actions that make viable the shared responsibility (SR) implementation foreseen in the National Policy of Solid Waste (NPSW). SR for the product life cycle is a concept that represents a paradigm shift in developing countries, like Brazil, placing SWM as a national priority. This concept extends the responsibility for SWM beyond manufacturers, including other stakeholders such as consumers and public authorities. These multiple agents with different points of view make the definition of responsibilities a complex task. Thus, the model was developed in three phases: Pre-negotiation, Negotiation and Post-negotiation. In the first two phases, a multi-methodology to structure the problem, stimulate collaboration, integrate stakeholder perspectives and preferences into a learning process was proposed. The responsibilities were defined through a multi-attribute evaluation. A Pareto-efficient decision for all stakeholders, simultaneously, was achieved. Finally, a strong engagement between stakeholders, and an effective commitment to the viability of SR was achieved.

A Bilateral Multi-Attribute Negotiation-Based Approach for a VE Configuration

In this paper, a specific case faced during the process of a Virtual Enterprise (VE) configuration from the Virtual Breeding Environment (VBE), for a specific Business Opportunity (BO), was studied. The considered case deals with the existence of a unique qualified partner in the VBE with a specific competence required by an activity of the BO at hand. This partner may behave opportunistically, taking advantage of being the only competent VBE member to achieve an activity, influencing, then, the effectiveness of the VE configuration. This paper proposed the integration of a bilateral multi-attribute negotiation process in the partner selection mechanism to improve the unique competent partner proposal and the development of a negotiation process-based concession. During negotiation, automated agents, representing the broker and the unique partner, exchange proposals and counter-proposals to reach a mutual agreement. Then, the accepted proposal is integrated into the Multi-Objective Programming model to complete the partner selection and order allocation mechanism. A real case study from a network in textile sector was presented to prove the contribution of the development and the integration of the negotiation process in the partner selection decision-making mechanism. The performance evaluation shows that integrating negotiation in the decision-making mechanism improves the VE objectives and performances.

RETRACTED: A bilateral negotiation model of complex contract based on negotiator’s utility information protection

With the development of artificial intelligence technology and big data technology, the multi-attribute negotiation contract with complex dependencies among the multiple attributes, named “complex ...

A Complex Negotiation Model for Multi-Echelon Supply Chain Networks

This paper focuses on a complex negotiation model for supply chain networks (SCNs), where multi-attribute, multi-item, multi-linked, and multi-echelon are simultaneously involved. An ideal simultaneous multi-attribute multi-item negotiation model for multi-echelon SCNs is proposed, where the decision makings of manufacturer agents (MAs) for retailer agents and the decision makings of MAs for material supplier agents (MSAs) are made simultaneously. Buyers in the proposed model are assumed build-to-order. Both concessions and preferences among attributes and items are considered during decision makings of agents. It is verified that the proposed simultaneous model based negotiation protocol is feasible and effective in solving the complex negotiation problems, and it is a “triple-win” solution for all the participants in SCNs.

A complex contract negotiation model based on hybrid intelligent algorithm

Intelligent negotiation plays an important role in e-commerce. In the research of multi-attribute negotiation, the non-linear relationship between attributes is usually neglected. Multi-attribute multilateral negotiation model with such complex relationship is more practical. In this paper, the building of a multilateral negotiation model of complex contracts with nonlinear dependencies between attributes by applying Multi-Agent System and hybrid intelligent algorithm is studied. And the approximate optimal solution of Nash equilibrium within a certain accuracy range is given. Finally, through simulation examples of multilateral negotiation with two attributes of price and quality, the correctness and validity of the method are verified. The research provides new ideas for intelligent negotiation research.

Smart Business Networks: Their Evolution

In the field of research and international developments in business management, in particular in e-commerce, the most significant achievements outreach intelligents contracts and block-chains. They are called Smart business networks. Indeed, in line with business agility concepts enabled by the generalized deployment of IP networks, they integrate not only business process management, auction based negotiations (direct and reverse auctions), but above all they rely on dynamic linking of partners via a network. Alike exploratory IP packet routing, a smart business network shall: 1) search for partners who, at a given instant, may co-bid to a specific request for proposal, 2) checks the conformity between business process blocks (via their BPM, UML or other specifications), 3) executes a multi-attribute negotiation (price, delays, quality, etc..), 4) synthezises the features of the virtual network which associates winning bidders for the order fulfillment. Another much simplified way of looking at these theories and implementations, is to get inspiration from plug and play or from quick connects & disconnects deals. The paper outlines the main concepts, provides examples of implementations, discusses a concrete case, specifies the required architectural components, and discusses how to extend smart business networks to the case where intellectual property rights too get networked. The conclusion addresses risks and future research avenues.

Automated Multilateral Negotiation on Multiple Issues with Private Information

In this paper, we propose and analyze a distributed negotiation strategy for a multi-agent, multi-attribute negotiation in which the agents have no information about the utility functions of other agents. We analytically prove that, if the zone of agreement is nonempty and the agents concede up to their reservation utilities, agents generating offers using our offer-generation strategy, namely the sequential projection strategy, will converge to an agreement acceptable to all the agents; the convergence property does not depend on the specific concession strategy. In considering agents’ incentive to concede during the negotiation, we propose and analyze a reactive concession strategy. Through computational experiments, we demonstrate that our distributed negotiation strategy yields performance sufficiently close to the Nash bargaining solution and that our algorithms are robust to potential deviation strategies. Methodologically, our paper advances the state of the art of alternating projection algorithms, in that we establish the convergence for the case of multiple, moving sets (as opposed to two static sets in the current literature). Our paper introduces a new analytical foundation for a broad class of computational group decision and negotiation problems.

Automated Multilateral Negotiation on Multiple Issues with Private Information

In this paper, we propose and analyze a distributed negotiation strategy for a multi-agent multi-attribute negotiation in which the agents have no information about the utility functions of other agents. We analytically prove that, if the zone of agreement is non-empty and the agents concede up to their reservation utilities, agents generating offers using our offer-generation strategy, namely the sequential projection strategy, will converge to an agreement acceptable to all the agents; the convergence property does not depend on the specific concession strategy. In considering agents’ incentive to concede during the negotiation, we propose and analyze a reactive concession strategy. We demonstrate through computational experiments that our distributed negotiation strategy yields performance sufficiently close to the Nash bargaining solution, and that our algorithms are robust to potential deviation strategies. Methodologically, our paper advances the state of the art of alternating projection algorithms, in that we establish the convergence for the case of multiple, moving sets (as opposed to two, static sets in the current literature). Our paper introduces a new analytical foundation for a broad class of computational group decision and negotiation problems.

A multi-attribute multi-item negotiation mechanism of supply chain networks between buyers and sellers

This article focuses on the multi-attribute multi-item negotiation between multiple manufacturer agents (MAs) and multiple material supplier agents (MSAs). A coalition formation-based negotiation protocol is proposed. The MSAs are allowed to establish coalitions when the orders of the MAs exceed their abilities. Three attributes (price, quantity and lead time) of the items are considered during the negotiation. Both the MAs and the MSAs can give concessions among the attributes in tradeoff relationships according to their own preferences. The non-cooperative game is introduced to find the equilibria of the negotiations. The final allocation scheme is determined to maximise the total profit of the supply chain networks based on the equilibria. Simulations are provided to verify the effectiveness and feasibility of the proposed protocol.

An Application of Automated Mediation to International Climate Treaty Negotiation

A generic framework for automated mediation of multi-attribute negotiation has recently been reported (Lai and Sycara in Group Decis Negot 18:169–187, 2009). This framework seeks to shorten time to agreement, achieve agreements that are closer to Pareto optimality, and remain tractable in situations involving multiple issues, incomplete information, and dynamic reservation utility. These objectives are all relevant to international climate treaty negotiation. Therefore, in this paper, we describe how this mediation framework can be applied to the climate policy setting and articulate a necessary extension to allow for more than two negotiating parties. We then demonstrate application of the framework, employing some simple economic and procedural assumptions. This example shows that automated mediation can add value to the negotiation process without placing an undue mental or computational burden on negotiators. Part of this value comes simply from encouraging negotiators to be explicit about their assumptions and preferences, even if most of this information is not shared with their opponents.

Hierarchical-Game Based Multi-Attribute Negotiation of Supply Chain Network

This paper focuses on hierarchical-game based multi-attribute negotiation between multiple MAs (Manufac- ture Agents) and multiple MSAs (Material Supplier Agents) of SCN (Supply Chain Network). The attributes of the wholesale price of the product, the quantity of the order and the lead time of the order are negotiated simultaneously. A modified hierarchical-game based negotiation protocol is proposed based on the previous work. It is a changeable hierarchical game. It is a two-layer game when all the orders are in abilities of all the MSAs. However, it is a three-layer game when there are some orders out of abilities of some MSAs. The second layer game is not necessary, it is triggered to find coalitions only if the order is out of ability of MSA. The third layer game is used to determine the final quotation of the negotiation. Then, the first layer game is aimed to find the optimal allocation scheme to maximize the total profit of SCN based on the results of the second and the third layer games. Numerical example is provided to illustrate the proposed protocol.

Coalition Formation Based Multi-item Multi-attribute Negotiation of Supply Chain Networks

In this paper, a coalition formation based negotiation protocol, which aims to reduce the workload and time of the manufacturer agent (MA), is proposed for the material supplier agent (MSA) to find partners to establish a coalition when the order is out of its ability. Concessions among the multi-attribute are considered by MA and MSAs to tradeoff the multi-attribute according to their preferences. Stackelberg game is introduced to find the equilibrium of the multi-item multi-attribute negotiation. The feasibility and effectiveness of the proposed protocol are verified by computational experiments.

Solving the order promising impasse using multi-criteria decision analysis and negotiation

In an inter-firm make-to-order production environment, it is not always possible to satisfy the available-to-promise (ATP) and capable-to-promise (CTP) conditions due to untimely and unmatched production capacity and customer demand. Therefore, it is important to quickly explore alternate solutions that would satisfy both the customers and the suppliers. The purpose of this paper is to present a multi-agent-based system for automated multi-attribute negotiation in order promising. Our proposed solution is based on concepts of evolutionary system design that advocates for continued exploration of new solutions until a satisfactory solution is found. Based on a number of real-life ordering situations—changes of delivery date, price adjustments, and addition/modifications of value-added services as part of the order package, we embed multi-attribute multi-utility simulations into a linear program to search for a negotiated solution when a typical ATP/CTP function of a supply chain management system fails to fulfill a customer order.

A TWO‐STAGE WIN–WIN MULTIATTRIBUTE NEGOTIATION MODEL: OPTIMIZATION AND THEN CONCESSION

Many automated negotiation models have been developed to solve the conflict in many distributed computational systems. However, the problem of finding win–win outcome in multiattribute negotiation has not been tackled well. To address this issue, based on an evolutionary method of multiobjective optimization, this paper presents a negotiation model that can find win–win solutions of multiple attributes, but needs not to reveal negotiating agents’ private utility functions to their opponents or a third‐party mediator. Moreover, we also equip our agents with a general type of utility functions of interdependent multiattributes, which captures human intuitions well. In addition, we also develop a novel time‐dependent concession strategy model, which can help both sides find a final agreement among a set of win–win ones. Finally, lots of experiments confirm that our negotiation model outperforms the existing models developed recently. And the experiments also show our model is stable and efficient in finding fair win–win outcomes, which is seldom solved in the existing models.

A Recursive Protocol for Negotiating Contracts Under Non-monotonic Preference Structures

Negotiating contracts with multiple interdependent issues may yield non-monotonic preference spaces for the participating agents. These negotiations are specially challenging because of the complexity and dimension of the search space. Automated negotiation mechanisms designed and proven useful for monotonic utility spaces may fail in these negotiation scenarios. This paper presents a novel solution to the problem of automated multi-issue negotiations in the context of complex utility spaces. We seek to address the challenge of intractably large contract spaces and utility functions with multiple local optima in automated negotiation scenarios. A protocol for automated bilateral multi-attribute negotiation processes is proposed, in which the individual agents’ preferences can be non-monotonic and discontinuous. The protocol is based on a recursive non-mediated bargaining mechanism, which involves two agents who simultaneously exchange proposals defined as regions within the negotiation space. An agreement on a region implies a new bargaining which is restricted to that region. This recursive process is governed by a set of rules which modulate the joint exploration of the negotiation space until an agreement is found or a deadline expires. The protocol is experimentally evaluated under monotonic and non-monotonic preference scenarios, confirming that the protocol is able to produce outcomes close to the Pareto frontier in acceptable negotiation time, outperforming previous approaches.

Strategies for offer generation and relaxation in fuzzy constraint-based negotiation models

Fuzzy constraints have been used in several automated multi-attribute negotiation models. It is recognized that fuzzy constraints represent an efficient way of capturing requirements and preferences, and an useful mechanism for representing trade-offs. Most approaches are mainly focused on using constraints as a framework for describing preferences, and few works focus on using them as an element in the communication process itself in order to elaborate efficient negotiation strategies. This paper proposes and evaluates a set of strategies for offer generation and for the construction of offer relaxation requests in constraint-based negotiation models. To deploy the strategies, a fuzzy constraint based model for non-mediated bilateral automated purchase negotiations has been used. Fuzzy constraints are used both to represent preferences and to express offers. A set of locutions and decision mechanisms which trigger them are fully specified, where each agent may decide its degree of cooperation and its degree of expressiveness. Expressiveness is based on the propagation of constraints and relaxation requests. The paper analyzes which combination of different agents' attitudes allow to improve the negotiation processes. Experimental evaluation confirms the advantage of an expressive approach based on the propagation of constraints. In addition, this paper studies how applying a clustering algorithm to the seller's catalogue of products instead of managing single products may contribute to an improvement in the duration of the negotiation dialogues, and to a significant improvement on the utility of the deals that are achieved. These results support the usefulness of the proposed negotiation strategies.

Multi-Agent Negotiation in B2C E-Commerce Based on Data Mining Methods

The Multi agent system (MAS) model has been extensively used in the different tasks of e-commerce like customer relation management (CRM), negotiation and brokering. For the success of CRM, it is important to target the most profitable customers of a company. This paper presents a multi-attribute negotiation approach for negotiation between buyer and seller agents. The communication model and the algorithms for various actions involved in the negotiation process is described. The paper also proposes a multi-attribute based utility model, based on price, response-time, and quality. In support of this approach, a prototype system providing negotiation between buyer agents and seller agents is presented.