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

When the group denies individual nutritional wisdom

Interval type-2 fuzzy sets, each of which is characterized by the footprint of uncertainty, are a very useful means to depict the linguistic information in the process of decision making. In this article, we investigate the group decision making problems in which all the linguistic information provided by the decision makers is expressed as interval type-2 fuzzy decision matrices where each of the elements is characterized by interval type-2 fuzzy set, and the information about attribute weights is completely unknown.We first introduce the average centroid matrix of the interval type-2 fuzzy decision matrix, and then utilize the interval type-2 fuzzy averaging operator to aggregate all individual interval type-2 fuzzy decision matrices into a collective interval type-2 fuzzy decision matrix. Based on the average centroid matrix of the collective interval type-2 fuzzy decision matrix and information theory, we develop an optimization model by which a straightforward formula for deriving attribute weights can be obtained. Furthermore, based on the interval type-2 fuzzy averaging operator, we utilize the average centroid measure to give an approach to ranking the given alternatives and then selecting the most desirable one(s). Finally, we give an illustrative example.

The selection of a waste-to-energy (WTE) plant site is the core issue that determines whether the WTE project can effectively treat municipal solid waste, reduce environmental pollution, and promote the development of a circular economy, and is often determined through group decision-making. The complexity of this group decision problem makes the opinions of decision makers often with uncertainty. The single-valued neutrosophic set (SVNS) can reduce the loss of information that contains uncertainty by quantitatively describing the information through three functions. In addition, existing studies on group decision-making for WTE plant siting suffer from the problem that decision maker weights do not change in concert with those decision makers’ decision information. Therefore, this study proposes a group decision-making method based on SVNSs. First, a group consensus strategy is proposed to improve group consensus by removing the evaluation value of the corresponding solution for decision makers who do not reach consensus and are unwilling to modify their preferences. Second, a decision maker weight determination and adjustment method is proposed to readjust the decision maker weights from the solution level according to their respective consensus degree when the decision makers’ preference information changes. This method enables the decision makers’ preferences and weights to be changed jointly. An illustrative example and a comparative analysis of WTE plant siting decisions demonstrate the feasibility and superiority of the method. The experimental results show that the method is effective in helping decision makers to select the optimal WTE plant site more accurately.

This paper proposes and characterizes a sequential decision aggregation system consisting of agents performing binary sequential hypothesis testing and a fusion center which collects the individual decisions and reaches the global decision according to some threshold rule. Individual decision makers’ behaviors in the system are influenced by other decision makers, through a model for social pressure; our notion of social pressure is proportional to the ratio of individual decision makers who have already made the decisions. For our proposed model, we obtain the following results: First, we derive a recursive expression for the probabilities of making the correct and wrong global decisions as a function of time, system size, and the global decision threshold. The expression is based on the individual decision makers’ decision probabilities and does not rely on the specific individual decision-making policy. Second, we discuss two specific threshold rules: the fastest rule and the majority rule. By means of a mean-field analysis, we relate the asymptotic performance of the fusion center, as the system size tends to infinity, to the individual decision makers’ decision probability sequence. In addition to theoretical analysis, simulation work is conducted to discuss the speed/accuracy tradeoffs for different threshold rules.

Trail network systems among ants have received a lot of scientific attention because of their various applications in problem solving of networks. Recent studies have shown that ants select the fastest available path when facing different velocities on different substrates, rather than the shortest distance. The progress of decision making by these ants is determined by pheromone-based maintenance of paths, which is a collective decision. However, path optimization through individual decision making remains mostly unexplored. Here, we present the first study of time-optimized path selection via individual decision making by scout ants. Megaponera analis scouts search for termite-foraging sites and lead highly organized raid columns to them. The path of the scout determines the path of the column. Through installation of artificial roads around M. analis nests, we were able to influence the pathway choice of the raids. After road installation, 59% of all recorded raids took place completely or partly on the road, instead of the direct, i.e. distance-optimized, path through grass from the nest to the termites. The raid velocity on the road was more than double that on the grass, and the detour thus saved 34.77±23.01% of the travel time compared with a hypothetical direct path. The pathway choice of the ants was similar to a mathematical model of least time, allowing us to hypothesize the underlying mechanisms regulating the behavior. Our results highlight the importance of individual decision making in the foraging behavior of ants and show a new procedure of pathway optimization.

This paper investigates how individual decisions are aggregated to team decisions under risk in field experiment with a sample of Ethiopian farmers and the team decision-making process behind this aggregation. In an experiment structurally similar to the Gneezy-Potters' risk game, subjects make decisions first individually and then jointly with a random anonymous partner. In the aggregate, teams make higher allocation into the risky asset compared to the average of individual decisions. However, teams are neither polarizing nor simply averaging individual preferences. Instead, team decisions are consistent with an outcome of group bargaining and deliberation process based on intensity of individual preferences. But more risk-taking subjects have a stronger influence on team decisions. This influence of more risk-taking members on the group decision is leveraged by their better education levels. Analysis of the team decision-making process reveals several interesting insights. About 54% of the teams do not reach immediate agreement with initial allocations. Both less risk-taking and more risk-taking team members are equally likely to disagree with initial allocations but for different reasons. Teams that disagreed with initial allocations reached final team decisions significantly different from disagreed initial allocations. Less risk-taking subjects are more willing to concede to allocations proposed by more risk-taking subjects to reach at an agreed team decision. Demanding messages in group communications have a stronger effect on outcomes of group decision-making. Finally, teams with greater differences in willingness to take risk among members are more likely to disagree with initial team allocations, take more rounds of deliberations to come to a decision, and make choices further away from the average of individual decisions. Our results permit to better characterize the process of group decision-making beyond differences between individual and group decisions.

The technique for order preference by similarity to ideal solution (TOPSIS) has become a popular multi-criteria decision making (MCDM) technique, since it has a comprehensible theoretical structure and is able to provide an exact model for decision making. For the use of TOPSIS in group decisions, the common approaches in aggregating individual decision makers’ judgments are the geometric and the arithmetic mean methods, although these are too intuitive and do not consider either preference levels or preference priorities among alternatives for individual decision makers. In this paper, a TOPSIS group decision aggregation model is proposed in which the construction consists of three stages: (1) The weight differences are calculated first as the degrees of preferences among different alternatives for each decision maker; (2) The alternative priorities are then derived, and the highest one can be denoted as the degree to which a decision maker wants his most favorite alternative to be chosen; (3) The group ideal solutions approach in TOPSIS is used for the aggregation of similarities obtained from different decision makers. A comparative analysis is performed, and the proposed aggregation model seems to be more satisfactory than the traditional aggregation model for solving compromise-oriented decision problems.

A vague set fuzzy multi-attribute group decision-making model for selecting onsite renewable energy technologies for institutional owners of constructed facilities

This paper studies the problem of majority-rule-based collective decision-making where the agents' preferences are represented by CP-nets (Conditional Preference Networks). As there are exponentially many alternatives, it is impractical to reason about the individual full rankings over the alternative space and apply majority rule directly. Most existing works either do not consider computational requirements, or depend on a strong assumption that the agents have acyclic CP-nets that are compatible with a common order on the variables. To this end, this paper proposes an efficient SAT-based approach, called MajCP (Majority-rule-based collective decision-making with CP-nets), to compute the majority winning alternatives. Our proposed approach only requires that each agent submit a CP-net; the CP-net can be cyclic, and it does not need to be any common structures among the agents' CP-nets. The experimental results presented in this paper demonstrate that the proposed approach is computationally efficient. It offers several orders of magnitude improvement in performance over a Brute-force algorithm for large numbers of variables.

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.

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.

The amount of land for the current location of housing development has resulted in developers choosing the location of housing development regardless of the condition of the land, infrastructure, socio-economic. To overcome this problem a computer system is needed in the form of a GDSS that can assist in the selection of Housing Development Locations.This study aims to implement a GDSS with ANP and Borda methods to determine the selection of the right and fast housing development location. GDSS is needed because there are 3 Individual Decision Makers, DM-1 assessing based on Land Conditions, DM-2 assessing Infrastructure-based, DM-3 assess the Socio-Economic and Decision Maker based groups to make the final decision. The ANP method is used to weight the criteria from each alternative location, to the alternative ranking of housing construction locations for each individual Decision Maker. The Borda method is used to combine the results of ranking carried out by the Group Decision Maker so that it gets the final ranking as a determinant of the Location of Housing Development.The final result of this research is a decision support system that can help developers to get a priority recommendation according to the needs of the developer.

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

Social interactions have important consequences for individual fitness. Collective actions, however, are notoriously context-dependent and identifying how animals rapidly weigh the actions of others despite environmental uncertainty remains a fundamental challenge in biology. By exposing zebrafish (Danio rerio) to virtual fish silhouettes in a maze we isolated how the relative strength of a visual feature guides individual directional decisions and, subsequently, tunes social influence. We varied the relative speed and coherency with which a portion of silhouettes adopted a direction (leader/distractor ratio) and established that solitary zebrafish display a robust optomotor response to follow leader silhouettes that moved much faster than their distractors, regardless of stimulus coherency. Although recruitment time decreased as a power law of zebrafish group size, individual decision times retained a speed-accuracy trade-off, suggesting a benefit to smaller group sizes in collective decision-making. Directional accuracy improved regardless of group size in the presence of the faster moving leader silhouettes, but without these stimuli zebrafish directional decisions followed a democratic majority rule. Our results show that a large difference in movement speeds can guide directional decisions within groups, thereby providing individuals with a rapid and adaptive means of evaluating social information in the face of uncertainty.

Judgment aggregation problems form a class of collective decision-making problems represented in an abstract way, subsuming some well known problems such as voting. A collective decision can be reached in many ways, but a direct one-step aggregation of individual decisions is arguably most studied. Another way to reach collective decisions is by iterative consensus building – allowing each decision-maker to change their individual decision in response to the choices of the other agents until a consensus is reached. Iterative consensus building has so far only been studied for voting problems. Here we propose an iterative judgment aggregation algorithm, based on movements in an undirected graph, and we study for which instances it terminates with a consensus. We also compare the computational complexity of our itterative procedure with that of related judgment aggregation operators.