Second-Guessing in Group Decision Making

Group decision-making, in which a collective decision needs to be derived from individual agents’ preferences has been an active area of research. In group decisionmaking, autonomous agents need some procedures or mechanisms that can collect preference information from them and make a decision; or some protocols that enable them to be involved in the decision process, to interact with each other and to jointly make a collective decision. Examples include preference aggregation mechanisms, negotiation protocols, voting procedures, and auctions. This thesis studies the problem of agent-based group decision-making in various problem settings, proposing several efficient approaches to support multiple agents in reaching efficient and fair collective decisions overmultiple issues. The research work in this thesis can be divided into two parts, addressing the agent-based group decision-making problem in circumstances in which there is, respectively, complete and incomplete information available. The first part of this thesis focuses on preference aggregation in combinatorial domains, provided all agents’ preferences. Structured preferences Conditional Preference Network (CP-nets) and its variant Trade-off enhanced CP-nets (TCP-nets) are used as formal models for representing the agents’ preferences in group decision-making. Before going into the problem of making a group decision from a collection of CP-nets or TCP-nets, the problem of individual preference reasoning is studied and a computationally efficient heuristic algorithm for dominance testing in CP-nets, which forms the basis for many group decision-making mechanisms, is introduced. Subsequently, we consider the group decision-making problem with multiple agents’ CP-nets and TCP-nets. As the space of possible outcomes in combinatorial domains has a size exponential in the number of variables, computational complexity in group decision-making will be one of the issues that need to be addressed. Two computationally efficient approaches are proposed in order to support agent-based group decisionmaking in combinatorial domains with different decision criteria: (i) an approach that converts the qualitative preferences derived from CP-nets or TCP-nets into numerical penalty scoring functions, and then searches for an optimal collective decision based on the individual penalty scores; and (ii) an approach that focuses on combinatorial vote and computes the winners (i.e., winning alternatives) from a collection of CP-nets according to Majority voting rule. The second part of this thesis is concerned with the disadvantage of disclosing preference information in group decision-making and considers the incomplete information setting. Two negotiation approaches are presented in order to support agent-based group decision-making in, respectively, combinatorial domains and in domains with multiple continuous issues. Firstly, a negotiation protocol in combinatorial domains is proposed, which enables agents to distributively make decisions and leads them to Pareto-optimal agreements under incomplete information setting. Then for the domains with multiple continuous issues, a cooperative mediated negotiation approach is proposed to support the agents searching for joint utility gains and reaching an efficient and fair agreement. For every approach introduced in this thesis, experiments are carried out and the results are presented and analysed in order to provide insights into the performance of these approaches.

When the group denies individual nutritional wisdom

This research reviews the Heuristic-Systematic Model (HSM). It investigates how communication signals might influence people's opinions Shelly Chaiken (1987) developed this model, which is a dual-processing model with two information processing modalities, the first of which is: Heuristic processing, which includes features that are part of The message's source, template, length, and case name are all instances of "Heuristic Cues" that are given in the message to allow for a quick evaluation of the message's validity. The second kind is in-depth processing (Systematic processing), which searches for the substance of the message. To arrive at an accurate assessment of the message. In social psychology, binary information-processing models give empirical evidence for distinguishing the many ways individuals think about politics. Over the last two decades, binary information-processing models have grown in prominence in social psychology. Individual and collective decision-making, preconceptions and biases, personal perception and social categorization, individual and group decision-making, and even fundamental memory mechanisms are all explained by these theories. Keywords: Heuristic Systematic Model; Mass Communication; Public Opinion

The core question explored in this study was whether social value theory, which can explain the domain specificity of self-other differences, still was applicable when choice recipients change. A pre-experiment and three formal experiments were conducted to explore the differences in self-other risk decision-making in different domains and different recipients from the perspective of social value theory. Experiment 1 involved 152 participants who were asked to make decisions for themselves and a single other in three risk domains. In Experiment 2, 178 participants were recruited, with money loss domain added and the "others" divided into "intimate others" to explore the effect of social distance on self-others risk decision-making in four domains. In Experiment 3, 233 participants were involved, and the number of "others" was expanded to explore the differences between "individual decision-making" and "group decision-making". In the relationship domain, individuals were more risk-averse when making decisions for themselves, while in the money gain domain and personal safety domain, individuals' risk-taking tendencies when making decisions for themselves were significantly higher than that for new friends. In the money loss domain, no significant difference was found among the three decision-maker roles. When making decisions for a group (including the decision-maker), individuals exhibit a "compromise effect" in the non-monetary domain, where their risk appetite falls between making decisions for themselves and for the group. The domain differences in self-other risk decision-making can be explained by the social value theory.

Objective Different mechanisms such as psychological, neurobiological, autonomic, and genetic factors might be involved in the etiology of lifelong Premature Ejaculation (PE). Albeit cortical activity changes have been reported, the relationship between PE and impulsivity/decision-making is minimally studied to date. The present study aimed to assess impulsivity and risky decision-making in individuals with lifelong PE for the first time. Method 26 lifelong PE patients were diagnosed by the International Society for Sexual Medicine (ISSM-2014) criteria and 26 healthy volunteers were recruited. The Premature Ejaculation Diagnostic Tool (PEDT), International Erectile Function Index (IIEF), Barratt Impulsiveness Scale-11 (BIS-11), Patient Health Questionnaire-9 (PHQ-9), Balloon Analogue Risk Task (BART), and Monetary Choice Questionnaire-27 (MCQ-27) were administered. Results The mean age was 37.28 (18-55). No differences were found between groups in risky decision-making and impulsivity. Conclusion No alterations of impulsivity and risky decision-making were found in lifelong PE patients. Impulsivity may only exist in a subset of individuals with lifelong PE or may only be evident in neural levels or specific subtypes of impulsivity.

In classical group decision-making (GDM) analysis, ratings of potential alternatives and weights of criteria or decision makers (DMs) are known precisely. However, DMs for dealing with uncertain situations can define their opinions in linguistic variables based on fuzzy sets in industrial selection problems. In this respect, an interval-valued hesitant fuzzy set (IVHFS) is the suitable and capable theory that could help the DMs by assigning some interval-valued membership degrees for a candidate or option under a set. This paper introduces a novel interval-valued hesitant fuzzy distance-based group decision (IVHF-DBGD) model by a group of DMs, in which the best potential alternative can be appraised and selected among the conflicting criteria. In the proposed IVHF-DBGD model, the weight of each criterion is determined by extended IVHF-entropy method along with the DMs’ opinions about the criteria’ weights. Also, the weight of each DM is computed by a new IVHF-order preference method with the relative closeness. Moreover, this paper introduces a new IVHF-collective index to discriminate among potential alternatives in the selection process. Finally, the computational results with a robot selection from the literature indicate that the proposed IVHF-DBGD model is the suitable group decision-making tool for industrial selection problems.

Group Decision Making (GDM) refers to the selection of an alternative from a set of feasible alternatives that better satisfies some criteria according to a group of individuals (experts). There exist several different models to simulate GDM processes, but many of those models do not usually take into account some dynamical aspects of real decision processes. For example, those models normally do not allow the experts set to change during the process (adding or removing experts), the alternatives to change (incorporating or discarding alternatives) or even to change the criteria. In this work we present a new model which allows to undertake GDM situations in which a large number of individuals (for example an on-line community) has to choose among different alternatives. To be able to obtain a good solution of consensus, the group of experts will be firstly simplified into a smaller group (using a simple clustering technique and a kind of trust network) which can then discuss about best solution to be selected.

Cross-border e-commerce logistics supply chain alliances face the practical challenge of allocating profits in a way that is both fair and operationally controllable when member contributions and cooperation priorities jointly matter. This study proposes an integrated framework that first evaluates member contributions via a group decision-making (GDM) procedure to derive contribution weights, and then aggregates them to coalition-level importance scores to rank feasible cooperation structures. Building on these inputs, we formulate a Priority-Based Multiobjective Linear Programming (P-MOLP) model that performs tiered (priority) optimization under individual rationality and budget-balance constraints, thereby ensuring implementable allocations. Using alliance data, P-MOLP provides clearer structural differentiation than an ordinary goal-programming model: high-contribution, strategically central members receive larger shares, and low-contribution members receive less (“more-work-more-reward”). Unlike weighted-Shapley, which can violate individual rationality under extreme weights, P-MOLP is individually rational and budget-balanced, aligns better with observed practice, remains applicable when some coalition values are infeasible or missing, and attains ε-core near-stability. Priority weights serve as managerial levers to tune outcomes.

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.

Developing a straightforward approach for group decision making based on determining weights of decision makers

The individual decision-making is unstable and risky, comparing with group decision-making. The latter can reduce the risks arising from the decision-making through bringing together all aspects of information. This article introduces a method of group decision-making based on fuzzy evaluation information, and improves the feasibility and exactness of current evaluation models. It takes job value as the target and evaluates the relative value of four jobs. The results show that the model is simple and practical except of its limited availability.

12 - An observational experiment in group decision-making: Can people detect bad group decisions?

Leveraging social network trust relationships among experts to reach consensus has become a popular topic in linguistic group decision making (GDM). However, in linguistic contexts, it is commonly accepted that words mean different things for different people, which indicates the necessity of modeling experts’ personalized individual semantics (PISs). Moreover, experts sometimes may show non-cooperative behaviors during the consensus reaching process (CRP) due to their own interests. As a result, this paper focuses on developing a consensus reaching algorithm with non-cooperative behavior management for PIS-based social network GDM problems. First, linguistic preference relations are transformed into fuzzy preference relations by the PIS model, and then social network analysis techniques are used to obtain experts’ weight vector. Afterwards, we propose a feedback adjustment mechanism to improve experts’ adjustment willingness in CPRs, in which the trust relationships and the PISs of experts are utilized to generate adjustment advice for experts. Furthermore, a non-cooperative behavior management mechanism which dynamically adjusts the trust degrees in social network is devised. Followed by this, a numerical example is provided to demonstrate the proposed algorithm. Finally, detailed simulation results are presented to analyze the influence of different parameters on CRPs and illustrate the validity of the proposed algorithm.

Purpose Drawing on mechanism design theory (MDT) and the structure-conduct-performance (SCP) paradigm, this study developed a cross-level model to examine the double-edged sword effect of supply chain decentralization (SCDece) on supply chain efficiency (SCEffi) and reveal the underlying mechanism. Design/methodology/approach This study collected 78 sets of supply-chain-level data and 372 sets of enterprise-level data through a newly designed multi-time point and multi-source survey. Multi-level structural equation modelling was adopted to verify the hypotheses. Findings The results demonstrated that SCDece had dual effects on SCEffi. On the one hand, it encouraged supply chain enterprises to adopt consistent group decision-making, thereby enhancing overall efficiency. On the other hand, it prompted solipsistic individual decision-making, which diminished SCEffi. Supply chain maturity (SCMatu) moderated these opposing mediation paths: for high SCMatu, the positive mediation effect of group decision-making of enterprises in supply chain was strengthened, while the negative effect of individual decision-making of enterprises in the supply chain (IDESC) was attenuated. Practical implications To optimize SCEffi, managers should strategically align SCDece with SCMatu. Additionally, enterprise supervisors should adopt the principle of “extensive consultation, joint contribution and shared benefits” to strengthen collaborative decision-making and mitigate individual decision-making. Originality/value From a cross-level perspective, this study examined the double-edged sword effect of SCDece on SCEffi and uncovered its underlying mechanisms through differential decision-making behaviours among supply chain enterprises. These findings extend the literature on decentralization and integration development of the MDT and SCP paradigms. It also provides new insights for a supply chain to reduce the negative effects of IDESC.

Mineshaft development mode design and decision-making is a complex system theory problem with significant implications for the mine’s investment cost, operational safety, and production efficiency. Because of the many factors that influence mine shaft development mode decision-making, various decision-makers have different worries and inclinations, resulting in greater subjectivity and uncertainty in the decision-making process. The concept of a specialty chain was born out of a belief in group decision-making. By merging and assessing the decision-making information of different groups in the same specialty chain, a systematic decision-making index database of the mine shaft development model was created. To elucidate the correlation model and hierarchical link between the decision-making indexes, the Interpretative Structure Model (ISM) was applied. The multilevel decision-making index system of mine shaft development mode was established. The decision-making group structure was optimized. The relative importance of the Analytical Hierarchy Process (AHP) was modified to determine the scale. A collaborative weight determination method of multiple decision-making groups was established to reduce the influence of individual subjective consciousness on decision-making results. The ISM-GAHP-FCA decision-making model of mine shaft development mode was built in conjunction with Fuzzy Comprehensive Analysis (FCA) to increase fuzzy decision-making information’s integration and analysis ability. The decision-making outcomes from the analysis of 10 typical mine shaft types in China are adaptable to the actual situation. The model can effectively express the hierarchy, significance, and fuzziness of mine shaft development mode decision-making indexes and limit the interference of decision-maker subjectivity on decision-making results.