Group Decision and Negotiation最新文献

In the literature, many desirable properties for allocations of indivisible goods have been proposed, including envy-freeness, Pareto optimality, and maximization of either the total welfare of all agents, the welfare of the worst-off agent, or the Nash product of agents’ welfares. In the two-person context, we study relationships among these properties using both analytical models and simulation in a setting where individual preferences are given by additive cardinal utilities. We provide several new theorems linking these criteria and use simulation to study how their values are related to problem characteristics, assuming that utilities are assigned randomly. We draw some conclusions concerning the relation of problem characteristics to the availabilty of allocations with particular properties.

Consensus-reaching among decision-makers (DMs) is an important prerequisite for effective group decision-making. Determining a collective value function that is recognized by major DMs is new in consensus research. We are approaching this problem by adopting the preference disaggregation analysis (PDA) to construct a novel consensus-reaching process (CRP). More precisely, we define the value function that can restore the preference information of all DMs as the consensus value function, and determine all such value functions by the PDA method. A consensus discriminant model is constructed to determine whether DMs can reach a consensus. Considering the adjustment cost of DMs, the minimum cost consensus model, and the minimum cost inconsistency elimination model, are constructed by introducing estimation errors and 0–1 variables, respectively, thus assisting DMs to reach a consensus. Furthermore, in the process of selecting a representative collective value function from the consensus space for subsequent decision analysis, a lexicographic optimization process is applied to convert the multi-objective programming problem of DMs’ individual requirements for the collective value function into a multi-stage single-objective programming problem. This study provides a new concept of consensus and extends the classic minimum cost consensus model to the case of value functions. Finally, an illustrative example showing the proposed CRP in action is presented, while conducting sensitivity analysis to explore the impact of parameter changes on the model.

In a decision-making problem where a group must select an action from risky lotteries to receive a payoff that needs to be distributed among the group members, the group’s external risky choice and internal resource allocation emerge as two critical and interrelated subproblems. Individuals unconsciously exhibit social comparison behavior in many group contexts, profoundly impacting their payoff preferences. In this study, we first formulate a novel group resource allocation model and explore how the allocation equality of group resource is influenced by social comparisons. Particularly, we discuss the non-dictatorship condition under social comparison effects, which guarantees an extreme case of group resource allocation, i.e., “winner-take-all,” does not appear. Subsequently, we investigate the group risky choice under the effects of social comparison. The main results show that (1) introducing social comparison effects can increase allocation equality when loss aversion is high but decrease allocation equality when loss aversion is low, and (2) the classical risk sharing rule still holds in the group under social comparison effects, but introducing social comparison effects will lead to the group being more risk averse.

In a ranked voting system, voters select a subset of candidates and rank them from most to least preferred. Data envelopment analysis (DEA)-based voting models, among others, are used to determine the rank-position weights most favorable for each candidate, with the goal of achieving the highest aggregate score. However, concerns have been raised about the weights assigned to each rank position, as well as the potential for rank reversal of some candidates resulting from changes in votes earned by other candidates. To address these issues, some authors have developed two improved models. These models aim to incorporate the constraints of candidates that are not being evaluated into a single restriction, preventing inefficient candidates from influencing the order of efficient candidates. Moreover, these models treat the parameters used to make the distance between successive ranks as variable weights, and calculate average efficiency scores of candidates while considering the entire range of parameters. In this study, we revisit the two improved models and explore an alternative approach based on results from linear algebra and convex analysis, which is more intuitive and easier to understand. Furthermore, we provide closed-form optimal solutions for DEA-based voting models that share the common goal of maximizing the distance between successive ranks while considering both efficiency-related and weight constraints. The analysis of these four models offers a better understanding of their similarities and differences.

This study presents empirically calibrated simulations of three different variants of environmental matching agreements aimed at reducing global greenhouse gas emissions. We determine whether matching agreements can produce larger stable coalitions and increase abatement contributions and payoffs as compared to standard agreements. The matching agreements we analyze feature uniform matching rates by which coalition members match the unconditional contributions of (i) the other coalition members, (ii) all other players, or (iii) only non-members, while non-members do not commit to any matching and maximize their individual payoffs. The simulation considers twelve asymmetric world regions with linear abatement benefits and quadratic costs, calibrated based on the STACO 3 model, and uses emissions data from the shared socioeconomic pathways database. We find that the first variant of the matching game fails to produce any stable coalitions and thus performs worse than the standard agreement that produces a stable two-player coalition. The second variant produces a stable grand coalition and significantly increases the abatement and payoff levels beyond the non-cooperative Nash baseline. Partial coalitions are unstable in this game. The third variant produces a two-player coalition similar to the standard coalition formation game, but with different members and higher abatement and payoff levels due to the matching mechanism.

As user-generated online content has been flourishing with both useful information and misinformation. One of the complexities surrounding such phenomena is its huge amounts of data and its associated difficulties to effectively moderate content, particularly as most initiatives are centralised and fraught with its intrinsic trust issues. One of the few examples using mainly a decentralised (i.e., community-driven) mechanism is Twitter’s Community Notes (once named as Birdwatch) experimental project. This paper thus is about testing the efficiency of such community-based content moderation mechanism and scenarios of interest aiming to better understanding how the users themselves better moderate online content. This is done through an agent-based approach and three conclusions are discussed in detail: (1) to some extent the community is able to fight against misinformation, (2) a Birdwatch-like mechanism can indeed boost the community’s content moderation ability, but there is a nontrivial trade-off between social influence and content timeliness and (3) a simple proposition, in the form of a reminder mechanism to users, cannot fulfil the task of improving the content moderation efficiency, which means a different approach to design is needed.

The increasing frequency of extreme weather (EW) events has created significant vulnerabilities in the normal operations of the prefabricated construction supply chain (PCSC). This study aims to enhance the resilience of the PCSC against EW by utilizing smart contracts. The study proposes a prototype smart contract application to mitigate the risks posed by EW to the PCSC. Additionally, it identifies 28 potential barriers affecting smart contract adoption in the PCSC using the Technology-Organization-Environment framework. Furthermore, the paper presents a multi-objective optimization-based group decision-making method to assess the feasibility of smart contract adoption in the PCSC. An online survey was then conducted among 50 stakeholders from various links of the PCSC to gather insights into smart contract adoption. The results indicate that stakeholder awareness of smart contracts and the current corporate level are the most influential factors in decision-making. This research extends the application of smart contracts to risk management within the PCSC, offering valuable insights for stakeholders to enhance resilience and address the adverse effects of EW proactively.

There are numerous proposals for Group Decision-Making (GDM) inspired by the ELECTRE multiple criteria decision approach. These proposals capitalize on ELECTRE's resemblance to certain voting systems and its ability to navigate veto situations. However, while ELECTRE-based methods have commendable features for establishing the credibility degree of the predicate “x is collectively considered at least as good as y”, they do not address three relevant issues: (1) the reinforced preference in favor of x exhibited by certain members of the group; (2) the strength of the coalition of Decision-Makers (DMs) who favor y over x; and (3) the effects of preference dependence (complementarity, redundancy, antagonism) among different DMs. This paper addresses group ranking problems within scenarios where a group is under the control of a special powerful actor, called a “Supra-Decision Maker”, or when a group adheres to a predetermined system of rules agreed upon by its members. Unlike other ELECTRE-based methods for GDM, this proposal comprehensively addresses the issues (1), (2) and (3) to determine the credibility degree of the collective outranking predicate. This determination can be utilized to derive a collective ranking or another form of recommendation in GDM. This proposal is expected to excel in a collaborative organizational environment where group members express genuine judgments, devoid of malicious intentions to manipulate collective decisions. Moreover, it has relevance in socially oriented decision-making contexts, especially when government agencies seek to reconcile opinions of diverse stakeholder groups with highly contradictory points of view. In such scenarios, where phenomena such as preference dependence, reinforced preference, and intense disagreement manifest, this proposal could offer valuable insights.

Group consensus (GC) is important for generating a group solution satisfactory or acceptable to most decision-makers in a group. Its convergency usually depends on several rounds of iterations and becomes more difficult with unknown parameters because GC is usually associated with parameters. To address the GC with unknown parameters, this paper proposes a Markov chain-based GC method, in which criterion weights and expert weights are considered as parameters. Given the interval-valued assessments of decision-makers, the GC at the alternative and global levels is defined. Based on the Markov chain, a two-hierarchical randomization algorithm is designed with unknown criterion weights to determine the transition probability matrix used to generate the stable GC. To accelerate the stable GC’s convergency, criteria significantly contributing negatives to the stable GC are identified and suggestions on helping renew decision-makers’ assessments on the identified criteria are provided. On the condition that the stable GC is definitely satisfied, a GC-based two-hierarchical randomization algorithm is designed based on the Markov chain to determine the transition probability matrix for generating the stable ranking value distribution of each alternative. The proposed method is employed to analyze a development mode selection problem. It is compared with the stochastic multicriteria acceptability analysis and simple additive weighting methods based on the problem by calculation and principle.

Bargaining with suppliers is a key Purchasing and Supply Management (PSM) activity but there is considerable ambiguity over what bargaining entails and the concept currently lacks a systematic treatment, despite its significant interest to PSM professionals. The literature shows that bargaining can be seen as an adversarial approach to negotiation (in contrast to more integrative/collaborative ones) and also the back-and-forth discussion over price and other variables between buying and supplying organisations to reach an agreement. In addition, many will move between fundamentally distributive and integrative approaches as the discussions play out. A systematic literature review of the Scopus, ProQuest, ScienceDirect, JSTOR and Web of Science databases was undertaken to address this gap, identifying 427 relevant journal papers that were systematically analysed. First, descriptive techniques identified the trajectory of published papers, methods, theories and their industrial context. Second, content analysis identified the key constructs and associated operational measures/variables of bargaining. Third, the constructs have then been ordered temporally and by areas of location (organisational/departmental and individual levels) to generate a model and inform a series of practice-based recommendations at different stages of the bargaining process. The findings will allow future researchers to use the constructs either directly in developing focused hypotheses to test relationships or as a basis for refinement and extension in cumulative theory building and testing. In addition, a series of focused research gaps have been identified, such as addressing the current contradictory findings of the effect of purchasing volume or organisational size on bargaining power.