On computationally efficient approaches to agent-based group decision-making

Computational social choice is a new interdisciplinary research area at the intersection of social choice theory and computer science that studies computational aspects of social choice mechanisms as well as applications of social choice ideas in the design and analysis of computing systems. Research on computational social choice now encompasses a variety of topics, including the algorithmic aspects of voting rules, computational barriers to strategic behaviour, collective decision-making in multi-agent systems, preference elicitation and communication issues in voting, fair division, computational aspects of weighted voting games, collective decision-making in combinatorial domains, logic-based formalisms for social choice problems, belief and judgement aggregation, and social software. The aim of this special issue is to build on the spectacular success of two workshops on computational social choice (COMSOC’06, Amsterdam and COMSOC’08, Liverpool), and provide a forum for reseachers in the area to present their ideas on a variety of topics. This special issue is composed of nine research articles and a short note that span a fairly wide range of issues, including voting, mechanism design, judgment aggregation, and fairness, and a similarly wide range of techniques, such as logic, experiments, and computational complexity. The paper by Agotnes, van der Hoek and Wooldridge defines a new modal logic for representing and reasoning about preference and judgment aggregation. The authors show that their logic can be used to express a variety of concepts and results from the social choice domain, including majority voting, the discursive paradox, Arrow’s theorem and Condorcet’s paradox. Baharad, Goldberger, Koppel and Nitzan propose a maximum likelihood estimation-based approach for aggregating voters’ opinions over several issues. They argue that their method is vastly superior to simple majority voting, and use both analytical and empirical evaluation to support their argument. Balan, Richards and Luke deal with the problem of selecting a sequence of actions when the actions have multiple beneficiaries. Their goal is to select the actions according to a lexicographic fairness criterion. The explore the complexity of this problem for both finite and infinite sequences of actions and present both algorithms and intractability results for this problem. The paper by Caminada and Pigozzi applies argumentation theory to judgment aggregation, and proposes aggregation operators satisfying not only the usual property of collective rationality, but another highly desirable property,

A group decision-making (GDM) process is a social cognition process, which is a sub-topic of cognitive computation. The normalization of attribute values plays an important role in multi-attribute decision-making (MADM) and GDM problems. However, this research finds that the existing normalization methods are not always reasonable for GDM problems. To solve the problem of attribute normalization in GDM systems, some new normalization models are developed in this paper. An integrative study contributes to cognitive MADM and GDM systems. In existing normalization models, there are some bounds, such as $\text {Max}(u_{j}), \text {Min}(u_{j}),\sum (u_{j}),\text {and} \sqrt {\sum (u_{j})^{2}}$ . They are limited to a single attribute vector uj. The bound of new normalization method proposed in this work is related to one or more attribute vectors, in which the attribute values are graded in the same measure system. These related attribute vectors may be distributed to all decision matrices graded by this decision system. That is, the new bound in developed normalization model is an uniform bound, which is related to a decision system. For example, this uniform bound can be written as one of $\text {Max}(.), \text {Min}(.), \sum (.),\sqrt {\sum (.)^{2}}$ . Some illustrative examples are provided. A practical application to the evaluation of software reliability is introduced in order to illustrate the feasibility and practicability of methods introduced in this paper. Some experimental and computational comparisons are provided. The results show that new normalization methods are feasibility and practicability, and they are superior to the classical normalization methods. This work has provided some new normalization models. These new methods can adapt to all decision problems, including MADM and GDM problems. Some important limitations and future research are introduced.

Collective decision making is the process of mapping the individual views of several individual agents into a joint decision. The need for collective decision making mechanisms is abundant, not just in the realm of politics, but also for a wide range of scientific and technological applications. These include, for instance, logistics, grid computing, and recommender systems. The alternatives to be decided upon often have a combinatorial structure: an alternative is characterised by a tuple of variables, each ranging over a finite domain. Classical approaches to collective decision making, developed in social choice theory, do not take the computational limitations induced by the combinatorial nature of the problem into account. For instance, if we are asked to elect a committee consisting of k representatives, choosing from a pool of n candidates, then we are in fact faced with a social choice problem with $(^n_k)$ alternatives. Asking each voter for their full preferences over these $(^n_k)$ alternatives may not be feasible in practice. Similarly, if we have to choose between accepting or rejecting each of n different propositions, then we are dealing with a social choice problem with 2 n possible outcomes. In this talk I will report on a number of recent developments in the area of collective decision making in combinatorial domains. This includes the study of languages for the compact representation of preferences [2,4], the design of voting rules for multi-issue elections [1], and the analysis of the computational complexity of decision problems arising in judgment aggregation [3].

In the problem of group decision-making, the information about the decision makers' preferences is not complete due to factors of the quality of human resource and the influence of environment. The information gained by decision making can be loss or hazy. When it is hazy the values of the objects to be appraised are interval grey numbers. The group decision making problems including the grey number are called the grey group decision-making problems. Combining the thoughts and methods of the grey system with the classic group decision-making theory, the grey group decision making problems are discussed. In this paper, we develop a GREY DEMATEL method for group decision-making to gather group ideas and analyze the cause-effect relationship of complex problems in grey environments. Procedures of the GREY DEMATEL method are then proposed. Using the GREY DEMATEL procedures, the involved criteria of a system (or subsystem) are separated into the cause and effect groups for helping decision-makers focus on those criteria that provide great influence. Finally, a practical example is used to show feasibility and effectiveness of the proposed approach.

Consistency and consensus reaching process for group decision making based on complete interval distributed preference relations under social network analysis

Compatibility measures and consensus models for group decision making with intuitionistic multiplicative preference relations

Managing flexible linguistic expressions with subjective preferences and objective information in group decision-making: A perspective based on personalized individual semantics

Very often, we have to look into multiple agents' preferences, and compare or aggregate them. In this paper, we consider the well-known model, namely, lexicographic preference trees (LP-trees), for representing agents' preferences in combinatorial domains. We tackle the problem of calculating the dissimilarity/distance between agents' LP-trees. We propose an algorithm LpDis to compute the number of disagreed pairwise preferences between agents by traversing their LP-trees. The proposed algorithm is computationally efficient and allows agents to have different attribute importance structures and preference dependencies.

Extensive research on judicial politics has documented disparities in adjudication and biases in judging. Yet, lacking statistical methods to infer individual preferences from group decisions, existing studies have focused on courts publishing individual judges’ opinions, leaving a gap in understanding collegial courts that report only collective and unanimous (‘per curiam’) panel decisions. We introduce a statistical methodology to identify the most fitting decision-theoretic models for such collective decisions, infer judges’ individual preferences, and quantify the inconsistency in the courts’ decisions. This methodology is applicable in various small group decision-making contexts where group assignments are repeated and exogenous. Applying it to the Swiss appellate court for asylum appeals, where decisions are made in three-judge panels, we find that in 45 per cent of cases, the chair-as-dictator rule applies (rather than majority rule). Although judges’ preferences vary strongly with partisanship, the partially collective decision making of the panel moderates this heterogeneity.

Most group decision making (GDM) problems based on linguistic approaches use symmetrically and uniformly distributed linguistic term sets to express experts' opinions. However, there exist problems whose assessments need to be represented by means of unbalanced linguistic term sets, i.e. using term sets that are not uniformly and symmetrically distributed. The aim of this paper is to present a consensus model for GDM problems with unbalanced fuzzy linguistic information. This consensus model is based on both a fuzzy linguistic methodology to deal with unbalanced linguistic term sets and two consensus criteria, consensus degrees, and proximity measures. To do so, we use a new fuzzy linguistic methodology improving another approach to manage unbalanced fuzzy linguistic information,1 (Int. J. Intell. Syst.22(11) (2007) 1197–1214), which uses the linguistic 2-tuple model as representation base of unbalanced fuzzy linguistic information. In addition, the consensus model presents a feedback mechanism to help experts for reaching the highest degree of consensus possible. There are two main advantages provided by this consensus model. First, its ability to cope with GDM problems with unbalanced fuzzy linguistic information overcoming the problem of finding different discrimination levels in linguistic term sets. Second, it supports the consensus process automatically, avoiding the possible subjectivity that the moderator can introduce in this phase.

Whilst fundamental to human societies, collective decision-making such as voting systems can lead to non-efficient decisions, as past climate policies demonstrate. Current systems are harshly criticised for the way they consider voters needs and knowledge. Collective decision-making is central in human societies but also occurs in animal groups mostly when animals need to choose when and where to move. In these societies, animals balance between the needs of the group members and their own needs and rely on each individuals (partial) knowledge. We argue that non-human animals and humans share similar collective decision processes, among which are agenda-setting, deliberation and voting. Recent works in artificial intelligence have sought to improve decision-making in human groups, sometimes inspired by animals decision-making systems. We discuss here how our societies could benefit from recent advances in ethology and artificial intelligence to improve our collective decision-making system.

Optimal Truncation in Matching Markets

Aggregating diverse evaluations in group decision making: An approach based on wisdom of crowds

While numerous studies have demonstrated the difficulty minority opinion holders face when trying to persuade a majority, the present research investigated the conditions under which minority members might second-guess themselves and become advocates for the majority’s position even when they have the best information. In a laboratory experiment, we examined whether the structure of monetary incentives (fixed amount vs. performance-based) and group decision-making procedure (collective decisions vs. group discussion then individual decisions) might mitigate second-guessing by minority members when they initially favor the objectively best choice. Our results indicated that compared with fixed-amount incentives, performance-based incentives increased overall information sharing in collective decision-making groups but not in individual decision-making groups. Second-guessing by minority members was most likely to occur in groups that received performance-based incentives and made decisions individually. As a result of second-guessing, these groups also made poorer decisions.

This essay examines the opportunities offered by the cognitive turn and its possible effects on political science. It focuses on the problem of group decision-making. After showing the existence of two different approaches to rationality, the bulk of this essay reviews how social psychology has analyzed group decisions: from experiments influenced by behaviorism, which employed concepts derived from economics (preferences, equilibrium...), to works with an increasingly autonomous approach (sections II and IV). A few researches that exemplify this turn away from economic concepts and the experimental logic are examined (section III). After defining the concepts of cognitive and rhetoric turn, the essay concludes underlining the risks and the opportunities of this new paradigm, usually defined as «deliberative democracy» (sections V and VI).