Group Decision and Negotiation: A Multidisciplinary Perspective

In many real-world conflict situations, decision-makers (DMs) integrate multiple objectives rather than considering just one objective or dimension. A multi-objective graph model (MOGM) is proposed to balance each DM’s objectives in both two-DM and multi-DM conflicts. To identify Nash stability in MOGMs, a comprehensive preference matrix with weight parameters on objectives is developed for each DM, along with a unilateral move matrix including preference weights (UMP). Then, considering the subjective uncertainty of DMs, interval numbers are used to represent the degree of uncertainty of preference. Subsequently, Nash equilibria and interval Nash equilibria are developed for MOGMs, and the dependence of these equilibria on weights is shown. To illustrate how MOGM can be applied in practice and provide valuable strategic insights, it is used to investigate a US-China trade dispute model. The stability results suggest potential strategic resolutions of bilateral trade disputes, and how DMs can attain them. The case analysis process suggests that a peaceful settlement of the dispute may be achievable.

The purpose of this paper is to develop a four-stage conflict resolution model. In the first stage, a multicriteria model is developed for each of the conflicting parties, taken as decision makers (DMs) facing evaluations of a set of alternatives according to proper criteria. In the second stage, the composition of probabilistic preferences (CPP) methodology is applied to identify the best alternative for each of the conflicting parties. In the third stage, negotiation is carried out to remove alternatives and to focus on the subset of best alternatives for the group of DMs. The fourth stage consists of applying CPP again to choose one among the remaining alternatives. The model is illustrated by means of applying it to a real-world conflict in Brazil, related to implementation of the New Recife Project. The main features of the model are that it allows the DMs (i) to understand differences and proximities among the positions of each of them, (ii) to strategically reduce the initial set of alternatives, (iii) to advance in their positions towards a common goal, and (iv) to construct a unique final solution quickly.

As a result of the global warming, the situation in the Barents Sea leads to several important consequences. Firstly, oil and gas drilling becomes much easier than before. Therefore, it may raise the level of discussions on disputed shelf zones where the deposits are located, especially near to Norway-Russia sea border. Secondly, oil and gas excavation leads to potential threats to fishing by changing natural habitats, which in turn can create serious damage to the economies.

We construct a model, which helps to highlight potential disputed territories and analyze preferences of the countries interested in fossil fuels and fish resources. We also compare different scenarios of resource allocation with allocation by current agreement.

The option prioritization is the most effective preference ranking approach within the framework of the graph model for conflict resolution, in which a set of option statements for each decision maker (DM) involved in a dispute is determined by individual judgments. Inevitably, some option statements may be unnecessary or redundant. To address the redundancy of option statements, a novel option statement reduction method as well as an effective reduction algorithm is developed in this research based on the rough set theory. Furthermore, the Elmira conflict is utilized to show how the proposed option statement reduction method can be employed for efficiently eliminating redundant option statements of DMs.

This study aims to deepen the understanding of the drivers of bargaining power in negotiations and in particular the role of best alternatives (BATNA) and time pressure. Previous experimental negotiation research mainly focused on the power of BATNA and the influence of the context on the negotiation outcome, raising the question as to whether BATNA is indeed the only relevant power lever in negotiations. Especially game theorists have shown that time-related costs have a decisive influence on negotiation outcomes. The study proposes a framework to actually measure and compare the relevance and force of different power levers in a simulated distributive buyer-seller negotiation. The results suggest that time pressure can be as influential as an alternative; however, students and professionals seem to react differently to power manipulations. Whereas the student sample was significantly influenced by time pressure but not by alternatives, the opposite could be observed in the professional group. The findings question the common belief that alternatives are the key driver of power in negotiations.

We consider settings of combinatorial and collective decision-making where a set of agents make choices on a set of issues in sequence based on their preferences over a set of alternatives for each issue. While agents have their initial preferences on issues, they may influence others and be influenced by others, consequently changing their preferences or choices on these issues in the process of decision-making. Though the influence among multiple agents making decisions on one issue and the dependency (influence) among multiple issues decided by one agent have been fully discussed in previous work, the influence from multiple sources across both agents and issues in the context of combinatorial and collective decision-making has been ignored. In this paper, we proposed a preliminary framework to address the influence transcending multiple agents and multiple issues with two rules: weighted influence and one dominant influence.

Many lessons for procedural choice have been provided by axiomatic studies of decision procedures. However, there appears to be a gap between these axiomatic studies and the actual determination of appropriate procedures, as an axiomatic characterization does not directly answer which axiom should be appropriate—particularly when there is no agreement on the relative desirability of criteria. The present study proposes a formal model of procedural choice based on preferences over criteria (PCBPC). Specifically, we focus on the aggregation rule that maps a dichotomous preference profile over criteria for decision procedures to a nonempty set of decision procedures. We prove that the counting rule, which chooses the decision procedures with greatest supports, is the unique aggregation rule that satisfies anonymity (A), neutrality (N), strict monotonicity (SM), and partition consistency (PC), where PC is proposed based on the idea that representations of equivalent criteria in different ways should not affect the results. Two distinct standpoints for PCBPC are highlighted: one is to regard criteria as atomic, i.e., inseparable, objects and the other as composite, i.e., separable, objects. The difference between them is made explicit with two impossibility theorems showing the inconsistency between unanimity in the former standpoint and A (or PC) in the latter standpoint.

We consider settings of group decision and negotiation where agents’ preferences (such as intentions, beliefs and opinions) are influenced by each other and thus their behaviors are possibly changed. We build a multi-agent system (MAS_IITIB) in the context of social networks to model the mutual influence among agents’ intentions and the transformation from agents’ intentions to their behaviors in groups. On the micro level of individual agents, we construct the self-evolution rule of agents’ intentions and the generation, constraint and termination rule of agents’ behaviors; on the macro level of networked structure, we describe the mutual influence on intentions among agents, which can be diversified in both strength and polarity. We detail this multi-agent system and run experiments and simulations using the interaction of fertility intentions and the generation of fertility behaviors in families as example. Two experimental programs are designed: one is to adjust the initial fertility intentions of prospective parents, and the other is to adjust the range of weight of influence among family members, to investigate the effects on the childbearing behavior and the number of newborn children in the long-term. This study intends to provide modeling bases for the dynamics of preferences and behaviors due to mutual influence among agents in groups, particularly for the study of fertility intention and behavior in families, and more broadly, the forecast of population growth and effects of fertility policy. It is an innovative try of distributed artificial intelligence (multi-agent system) in the field of demography and public policy, and provides with a new bottom-to-up perspective and unconventional agent-based method.

For the two-dimensional Downsian model the degree of manipulability of 16 known aggregation procedures, based on the majority relation, is evaluated using the Nitzan-Kelly index. Extended preferences for multi-valued choices are used to evaluate the fact of manipulation. Individual manipulability of agents is considered, when manipulating agent moves its ideal point over the plane. The range of possible manipulating positions of the agents is restricted to some rectangle on the two-dimensional coordinate space, within the feasible area of positions of alternatives and agents. The preferences of agents are assumed to be linear orders, constructed by the proximity of the alternatives to the agents, ordered according to Euclidean distance. The computer calculations, using Monte-Carlo simulations has been performed for 3, 4, and 5 alternatives and for even number of agents from 4 to 20. 100 thousands profiles were generated for each number of alternatives – number of agents case. The results of the simulations show that there are groups of procedures with relatively low degree of manipulability for all of the considered multiple-choice extensions.

Negotiation is an important component of the interaction process among humans. With increasing automation, autonomous agents are expected to take over a lot of this interaction process. Much of automated negotiation literature focuses on agents having a static and known reservation value. In situations involving dynamic environments e.g., an agent negotiating on behalf of a human regarding a meeting, agents can have a reservation value (RV) that is a function of time. This leads to a different set of challenges that may need additional reasoning about the concession behavior. In this paper, we build upon Negotiation algorithms such as ONAC (Optimal Non-Adaptive Concession) and Time-Dependent Techniques such as Boulware which work on settings where the reservation value of the agent is fixed and known. Although these algorithms can encode dynamic RV, their concession behavior and hence the properties they were expected to display would be different from when the RV is static, even though the underlying negotiation algorithm remains the same. We, therefore, propose to use one of Counter, Bayesian Learning with Regression Analysis or LSTM model on top of each algorithm to develop the PredictRV strategy and show that PredictRV indeed performs better on two different metrics tested on two different domains on a variety of parameter settings.

Automated negotiation between computational agents or between agents and humans has been a subject of active research with a focus on obtaining better quality solutions within reasonable time frames. The critical issue negotiators face during automated negotiation is that a negotiator may not always know the personality type of the opponent. Studies show that having information about the opponent improves the outcome of negotiation in general. However, unless there is prior knowledge, learning the opponent type in the limited amount of time or number of rounds in a negotiation is a difficult task. In this paper, we use a Partially Observable Markov Decision Process (POMDP) based modeling to perform better modeling of the opponent personality type. In particular, we focus on modeling the opponent into four different types to showcase that a better understanding of personality type can improve the outcome of automated negotiation. Our experiments performed using data sets generated from the IAGO software showcase that we indeed obtain better negotiation outcomes with a higher classification accuracy of the opponent personality type.

The Dominance-based Rough Set Approach (DRSA) is an innovative preference learning approach. It takes as input a set of objects (learning set) described with respect to a collection of condition and decision attributes. It generates a set of if-then decision rules. Initial versions of dominance based rough set approximation methods assume a single decision maker. Furthermore, the proposed extensions to group decision making mainly use an input oriented aggregation strategy, which requires a high level of agreement between the decision makers. In this paper, we propose an output oriented aggregation strategy to coherently combine different sets of decision rules obtained from different decision makers. The proposed aggregation algorithm is illustrated by using real-world data relative to a business school admission where two decision makers are involved. Results show that aggregation algorithm is able to reproduce the individual assignments of students with a very limited preferential information loss.

This article focuses on an ontology construction for collaborative decision making. To do this, a state of the art on collaborative decision-making, on ontology engineering and on collaboration engineering has been done. An eight-step ontology development methodology was adopted and implemented to build the ontology. A corpus made up of more than seventy-seven (77) documents was the starting point for the extraction of terms from the ontology and the UML (Unified Modeling Language) language served as a description language of our ontology. This ontology is intended to be the starting point for a facilitation support system in a Collaborative Decision Making process. The aim of the work is to produce a new system according the “Facilitator in the box” paradigm.

In this work, we add to the rich history of decision-support system research by implementing and evaluating a pilot implementation of a novel system, which we call Decidio. Our tool was integrated into a pre-existing decision-making process regularly conducted by 9 teams of undergraduate students. We find an overall positive response to Decidio based on the results of a tool evaluation survey that we conducted after our experiment. Furthermore, we conduct a Big-Five Factor personality survey of participants and associate personalities with interactions recorded by our tool. We find that the students who demonstrate leadership behaviors through their interactions score higher in extraversion and lower in conscientiousness than other students. Our analysis also reveals that agreeableness is strongly correlated with dissimilarity between group ranking outcomes and initially indicated individual preferences.