Multi-issue Negotiation Research Articles

Traders’ Subjective Appraisals: Comparison of Negotiations and Auctions

ABSTRACTThe assessments of business trade often involve economic and relational concerns. They may become more challenging when our understanding of the impacts of trading mechanisms is still limited. The current experimental study compares two generic trading mechanisms, namely, multibilateral multi-issue negotiations and multicriteria auctions. By examining economic measures and subjective appraisals in controlled exchange episodes, the study shows some subtle relationships between mechanism use, substantive outcomes, and subjective appraisals. While use of negotiations versus auctions did not reveal significant differences on economic measures, traders are strongly influenced by the gain-or-loss contingency. When they win a contract, their subjective appraisals are heavily influenced by their achieved substantive outcomes. When they do not win a contract, they feel auctions are better than negotiations. The results confirm the assessments of business trade that rely solely on substantive measures are not sufficient.

Bayesian-based preference prediction in bilateral multi-issue negotiation between intelligent agents

Agent negotiation is a form of decision making where two or more agents jointly search for a mutually agreed solution to a certain problem. In multi-issue negotiation, with information available about the agents’ preferences, a negotiation may result in a mutually beneficial agreement. In a competitive negotiation environment, however, self-interested agents may not be willing to reveal their preferences, and this can increase the difficulty of negotiating a mutually beneficial agreement. In order to solve this problem, this paper proposes a Bayesian-based approach which can help an agent to predict its opponent’s preference in bilateral multi-issue negotiation. The proposed approach employs Bayesian theory to analyse the opponent’s historical offers and to approximately predict the opponent’s preference over negotiation issues. A counter-offer proposition algorithm is also integrated into the prediction approach to help agents to propose mutually beneficial offers based on the prediction results. Experimental results indicate good performance of the proposed approach in terms of utility gain and negotiation efficiency.

A hybrid model for cloud providers and consumers to agree on QoS of cloud services

This paper describes a new agenda based approach which facilitates multi-issue negotiation process between service consumer and service provider over the quality of service (QoS) requirements in cloud services. We assume that the order of agenda (issues) has impact on the negotiation outcome, but a group of service consumers and providers may have conflicting preferences over the importance of issues. In a multiple-issue negotiation process it is difficult to reach an optimal outcome when their preferences and the relative importance of their associated issues are not known to each other. The proposed agenda based preference ordering approach helps consumers and providers to reach a consensus over the issues and to construct a common preference sequence to improve the efficiency of the issue-by-issue negotiation outcome. Consequently, a co-evolutionary negotiation model based on the result of preference ordering approach is introduced for the agents to negotiate and reach an agreement, if there is any. The contribution of the research is a new mechanism that is able to formulate issue sequence along with a co-evolutionary negotiation approach that can effectively facilitate negotiation over QoS issues in cloud computing. Finally, a case study is provided to illustrate the proposed approach.

An approach to complex agent-based negotiations via effectively modeling unknown opponents

Negotiation among computational autonomous agents has gained rapidly growing interest in previous years, mainly due to its broad application potential in many areas such as e-commerce and e-business. This work deals with automated bilateral multi-issue negotiation in complex environments. Although tremendous progress has been made, available algorithms and techniques typically are limited in their applicability for more complex situations, in that most of them are based on simplifying assumptions about the negotiation complexity such as simple or partially known opponent behaviors and availability of negotiation history. We propose a negotiation approach called OMAC★ that aims at tackling these problems. OMAC★ enables an agent to efficiently model opponents in real-time through discrete wavelet transformation and non-linear regression with Gaussian processes. Based on the approximated model the decision-making component of OMAC★ adaptively adjusts its utility expectations and negotiation moves. Extensive experimental results are provided that demonstrate the negotiation qualities of OMAC★, both from the standard mean-score performance perspective and the perspective of empirical game theory. The results show that OMAC★ outperforms the top agents from the 2012, 2011 and 2010 International Automated Negotiating Agents Competition (ANAC) in a broad range of negotiation scenarios.

An Intelligent Agent for Bilateral Negotiation with Unknown Opponents in Continuous-Time Domains

Automated negotiation among self-interested autonomous agents has gained tremendous attention due to the diversity of its broad range of potential real-world applications. This article deals with a prominent type of such negotiations, namely, multiissue negotiation that runs under continuous-time constraints and in which the negotiating agents have no prior knowledge about their opponents’ preferences and strategies. A negotiation strategy called Dragon is described that employs sparse pseudoinput Gaussian processes. Specifically, Dragon enables an agent (1) to precisely model the behavior of its opponents with comparably low computational load and (2) to make decisions effectively and adaptively in very complex negotiation settings. Extensive experimental results, based on a number of negotiation scenarios and state-of-the-art negotiating agents from Automated Negotiating Agents Competitions, are provided. Moreover, the robustness of our strategy is evaluated through both empirical game-theoretic and spatial evolutionary game-theoretic analysis.

Even easier multi-issue negotiation through Modified Even-Swaps considering practically dominated alternatives

“Modified Even-Swaps” is a mechanism recently developed for multi-issue negotiation, which is highly complicated to realize and therefore more dependent to sophisticated information and communication technologies (ICTs) when compared to single-issue (price-only) negotiation. This mechanism is based on a modified version of Even-Swaps method finding differences among alternatives in terms of negotiable issues and then making use of those differences to assess value of multi-issue offers. A fuzzy-inference system supports this mechanism for bargaining on several issues simultaneously as well. Number of trade-offs performed significantly influences routines of the Modified Even-Swaps mechanism. In this study, a novel approach providing practical dominance reinforcement is introduced to accelerate the mechanism through eliminating unnecessary trade-offs. This approach proposes to use of “Simple Additive Weighting” (SAW) method in order to combine issues which have high variation among consequences of alternatives under consideration. As a result, these issues utilize a combined consequence for each alternative. Phase of the mechanism aiming to identify dominated alternatives is executed by using revised decision-matrix including consequences of issues having high variation as combined. This revision allows the mechanism to identify dominated alternatives more practically. Possible improvements are demonstrated and discussed with some cases in this study.

Addressing Complexity in Multi-Issue Negotiation via Utility Hypergraphs

There has been a great deal of interest about negotiations having interdependent issues and nonlinear utility spaces as they arise in many realistic situations. In this case, reaching a consensus among agents becomes more difficult as the search space and the complexity of the problem grow. Nevertheless, none of the proposed approaches tries to quantitatively assess the complexity of the scenarios in hand, or to exploit the topology of the utility space necessary to concretely tackle the complexity and the scaling issues. We address these points by adopting a representation that allows a modular decomposition of the issues and constraints by mapping the utility space into an issue-constraint hypergraph. Exploring the utility space reduces then to a message passing mechanism along the hyperedges by means of utility propagation. Adopting such representation paradigm will allow us to rigorously show how complexity arises in nonlinear scenarios. To this end, we use the concept of information entropy in order to measure the complexity of the hypergraph. Being able to assess complexity allows us to improve the message passing algorithm by adopting a low-complexity propagation scheme. We evaluated our model using parametrized random hyper- graphs, showing that it can optimally handle complex utility spaces while outperforming previous sampling approaches.

A Pareto-efficient and fair mediation approach to multilateral negotiation

In multi-issue negotiations, autonomous agents can act cooperatively to benefit from mutually preferred agreements. However, empirical evidence suggests that autonomous agents often fail to elicit possible joint gains and end up with inefficient results in multi-issue negotiation, especially when it involves multiple parties. In order to address this problem, we propose a mediated negotiation approach to support the agents reaching an efficient and fair agreement in multi-party multi-issue negotiation. The proposed approach improves the agents' utility values from the status quo, by searching for the mutually preferred outcomes that minimizes the difference between the agents' utility gains, leading to fair agreements. We present two case studies to illustrate the capability of the proposed approach in real-world negotiation applications. We also provide an experimental evaluation of the overall performance of our proposed approach. The experimental results demonstrate that out proposed approach does not only guarantee Pareto-efficiency, but also produces outcomes that are close to the fair egalitarian solutions.

Learning from the Metaheuristics: Protocols for Automated Negotiations

Nowadays, enterprises are more and more interconnected such that operational planning has to consider the different interests of the involved organizations. This may be a challenging and complex task as it is subject to strategic interactions and incomplete information. Automated negotiation by software agents is a powerful tool which can handle these issues and facilitate intercompany planning. Nevertheless, sophisticated negotiation protocols that govern the rules of the negotiation are needed. In this study, we present and evaluate two configurable protocols for multi-issue negotiations, which are inspired by general heuristic optimization algorithms for centralized problems, so-called metaheuristics. The protocols consist of several policy building blocks; these are evaluated with regard to their impact on the negotiation outcome. The evaluation shows that both protocols can efficiently achieve beneficial solutions—even for complex, nonlinear contract spaces—given the parameterization and the configuration of building blocks are chosen appropriately. Furthermore, we elaborate on requirements for appropriate protocol design and find that both protocols adequately comply with the requirements.

Reducing Mistrust in Agent-Human Negotiations

Face-to-face negotiations always benefit if the interacting individuals trust each other. But trust is also important in online interactions, even for humans interacting with a computational agent. In this article, the authors describe a behavioral experiment to determine whether, by volunteering information that it need not disclose, a software agent in a multi-issue negotiation can alleviate mistrust in human counterparts who differ in their propensities to mistrust others. Results indicated that when cynical, mistrusting humans negotiated with an agent that proactively communicated its issue priority and invited reciprocation, there were significantly more agreements and better utilities than when the agent didn't volunteer such information. Furthermore, when the agent volunteered its issue priority, the outcomes for mistrusting individuals were as good as those for trusting individuals, for whom the volunteering of issue priority conferred no advantage. These findings provide insights for designing more effective, socially intelligent agents in online negotiation settings.

Interpersonal effects of expressed anger and sorrow in morally charged negotiation

AbstractThe expression of emotion can play a significant role in strategic decision-making. In this study, we hypothesized that emotion expression alters behavior in morally charged negotiation. We investigated the impact of facial displays of discrete emotions, specifically anger and sadness, in a morally charged multi-issue negotiation task. Our results indicate that if a negotiator associated moral significance to the object of the negotiation, displays of anger resulted in reduced concession making whereas displays of sadness increased concession making. Moral significance of the issues fostered an emotional matching mechanism of sorrow, where a sorrow expression from one party elicited a sorrow expression from the other. Taken together, the results indicate that emotional expressions can affect morally charged negotiation in ways that can inhibit as well as promote cooperation.

Tri-Training based Bilateral Multi-Issue Negotiation Framework

Negotiation has been extensively discussed in electronic commerce for decades. Recent growing interest in importing machine learning algorithm in electronic commerce has given increased importance to automated negotiation. A Tri-Training based algorithm was proposed to learn opponent's negotiation preference. The process of negotiation was viewed as a proposal's sequence which can be mapped into bidding trajectory feature space to form sample set. Due to fierce competition, unlabeled training examples are readily available but labeled ones are fairly expensive to obtain. Therefore, Tri-Training, as a semi- supervised method, was imported into negotiation framework to increase the number of samples and improve perdition accuracy of opponent's negotiation preference learning. Based on negotiation preference of both side, an optimization algorithm is conducted to compute win-win counter proposal. The experimental results show that the proposed method can decrease the number of negotiation steps and increase the overall utility of negotiation.

The Influence of Asymmetric Creative Thinking on the Negotiation Process and Outcomes

Negotiators rely on idea generation in integrative negotiations. Although research suggests negotiators’ individual creativity is positively related to joint gains, little is known about how a dyad’s creative thinking may stimulate or stifle negotiation outcomes. We propose that intra-dyad asymmetry, or heterogeneity, in negotiators’ thinking styles positively influences the dyad’s joint outcomes but also enhances negative behaviors, such as dishonesty. To test our hypotheses, we conducted a laboratory study to examine the effect of asymmetric creative thinking on negotiation outcomes and behaviors; participants engaged in a two-party, multi-issue negotiation. As predicted, this asymmetry positively influenced negotiators’ engagement in dishonest behaviors but also increased joint gains.

Transfer for Automated Negotiation

Learning in automated negotiation is a difficult problem because the target function is hidden and the available experience for learning is rather limited. Transfer learning is a branch of machine learning research concerned with the reuse of previously acquired knowledge in new learning tasks, for example, in order to reduce the amount of learning experience required to attain a certain level of performance. This paper proposes a novel strategy based on a variation of TrAdaBoost—a classic instance transfer technique—that can be used in a multi-issue negotiation setting. The experimental results show that the proposed method is effective in a variety of application domains against the state-of-the-art negotiating agents.

Agent-based multi-issue negotiation algorithm and strategy of technology innovation platform

To solve the problem of technology docking negotiation of technology innovation platform, the Agent-based multi-issue negotiation algorithm and strategy were analyzed and designed with reference to the technology of IntelliSense Agent. In the practical application environment of technology docking on technology innovation platform, the data of history technology docking proposals were fully used and the technology docking benefits of both the technology trading sides was fully considered, and then the Agent-based multi-issue negotiation algorithm was designed. Based on this algorithm, the technology docking strategy and the proposed solution in technology docking negotiation were designed and proposed. Thus, the optimality of comprehensive benefits was ensured and the " win-win" benefits situation was reached by the two trading sides in the technology docking negotiation. Through practical examples of technology docking on the technology innovation platform, the applicability, rationality, feasibility and effectiveness of this negotiation algorithm and negotiation strategy in its practical application environment were exemplified.

Egocentrism drives misunderstanding in conflict and negotiation

A key barrier to conflict resolution is that parties exaggerate the degree to which the other side's interests oppose their own side's interests. Here we examine egocentrism as a fundamental source of such biased conflict perceptions. We propose that parties rely on their own interests and priorities when estimating those of the other side, and ignore the other side's true interests and priorities. Three experiments involving multi-issue negotiations provide strong evidence of such egocentric misperception. Participants judged their own important issues to be more important to their negotiation opponent, regardless of their opponent's actual interests (Experiment 1). Furthermore, accuracy increased when attention was experimentally focused on the opponent's interests rather than their own (Experiment 2), and perceptions of opponent's interests were more closely related to own interests than to the opponent's actual interests (Experiment 3). In the discussion, we highlight the broader implications of the egocentrism account for other areas of conflict.

A bilateral and multi-issue negotiation framework to support a supply chain of construction industry

ABSTRACT. Any interaction involving individuals, whose objectives are conflicting with each other,may establish a negotiation process. In a negotiation, each party should develop his/her own strategy and,normally, a win-lose vision is frequently adopted. The main consequence of this behavior is a result, inwhich both parties lose, especially when the negotiation involves more than one aspect, such as negotia-tions resulting from purchases of material for construction i ndustry, where aspects like price, quality andlead-time should be considered. Most of the negotiation involving construction industry adopts a win-losevision; and, commonly, only the issue price is considered.The goal of this paper is to propose a frameworkto support negotiations between two parties (buyer and seller) in the supply chain of construction industry.The combination of a win-win strategy with a multicriteria analysis pr oduces a best compromise solutionfor both parties. A simulation of negotiation using realistic data is presented.

The Remarkable Robustness of the First-Offer Effect

The first-offer effect demonstrates that negotiators achieve better outcomes when making the first offer than when receiving it. The evidence, however, primarily derives from studies of Westerners without systematic power differences negotiating over one issue-contexts that may amplify the first-offer effect. Thus, the present research explored the effect across cultures, among negotiators varying in power, and in negotiations involving single and multiple issues. The first two studies showed that the first-offer effect remains remarkably robust across cultures and multi-issue negotiations. The final two studies demonstrated that low-power negotiators benefit from making the first offer across single- and multi-issue negotiations. The second and fourth studies used multi-issue negotiations with distributive, integrative, and compatible issues, allowing us to show that first offers operate through the distributive, not the integrative or compatible issues. Overall, these results reveal that moving first can benefit negotiators across many organizational and personal situations.

Interdependent Multi-Issue Negotiation for Energy Exchange in Remote Communities

We present a novel negotiation protocol to facilitate energy exchange between off-grid homes that are equipped with renewable energy generation and electricity storage. Our protocol imposes restrictions over negotiation such that it reduces the complex interdependent multi-issue negotiation to one where agents have a strategy profile in subgame perfect Nash equilibrium. We show that our negotiation protocol is tractable, concurrent, scalable and leads to Pareto-optimal outcomes in a decentralised manner. We empirically evaluate our protocol and show that, in this instance, a society of agents can (i) improve the overall utilities by 14% and (ii) reduce their overall use of the batteries by 37%.

A Mediation Mechanism for Automated Negotiating Agents Whose Utility Changes over Time

Multi-issue negotiation protocols are an important field of study because real-world negotiation problems are often complex and involve multiple issues. Although much previous work has only addressed linear utility function, that is, simple negotiations involving independent issues, recently, non-linear utility functions for complex negotiations involing interdependent issues have gained attention. Most studies, however, do not focus on the changes in utility space over time. In economic theory, it is often assumed that the utility function changes dynamically over time. It is important to seek the Pareto front, which refers to the set of Pareto optimal points, in negotiation problems. Therefore, in this paper we propose a complex utility space that changes over time and a negotiation mechanism in which the mediator takes the lead in negotiation based on the genetic algorithm (GA). The experimental results show that our approach is suitable for utility that dynamically changes over time, and finds and follows the Pareto front effectively.