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This conceptualization describes how positive and negative mood states, information processing, cognitive processing strategies, and group interaction combine to influence group judgment and decision making. The crux of the conceptualization is a dominant cognitive processing strategy. Positive moods inform group members that the situation is benign and reinforce dominant cognitive processing strategies. Negative moods provide feedback that the situation is problematic, leading to inhibition or revision of dominant cognitive processing strategies. Moods achieve these impacts through their influences on the cognitive processes of attention, processing objectives, and cognitive representations. Group interaction also accentuates these cognitive processes and related strategies. This conceptualization is applied to understand mood influences on jury decision making, group social dilemmas, hidden profile tasks, group judgments of opinions, judgments with cognitive biases, and quantitative judgments. The discussion considers mood influences in other group judgment and decision making related phenomena of group brainstorming, intergroup negotiation, stress, and groupthink. [ABSTRACT FROM AUTHOR]

Although the individual has been the focus of most research into judgment and decision-making (JDM), important decisions in the real world are often made collectively rather than individually, a tendency that has increased in recent times with the opportunities for easy information exchange through the Internet. From this perspective, JDM research that factors in this social context has increased generalizability and mundane realism relative to that which ignores it. We delineate a problem-space for research within which we locate protocols that are used to study or support collective JDM, identify a common research question posed by all of these protocols--'What are the factors leading to opinion change for the better ('virtuous opinion change') in individual JDM agents?'--and propose a modeling approach and research paradigm using structured groups (i.e., groups with some constraints on their interaction), for answering this question. This paradigm, based on that used in studies of judge-adviser systems, avoids the need for real interacting groups and their attendant logistical problems, lack of power, and poor experimental control. We report an experiment using our paradigm on the effects of group size and opinion diversity on judgmental forecasting performance to illustrate our approach. The study found a U-shaped effect of group size on the probability of opinion change, but no effect on the amount of virtuous opinion change. Implications of our approach for development of more externally valid empirical studies and theories of JDM, and for the design of structured-group techniques to support collective JDM, are discussed. [ABSTRACT FROM AUTHOR]

Individuals often underutilize the advice they receive from others, a phenomenon known as egocentric advice discounting. Recent research suggests that this tendency may be even stronger in groups (Minson & Mueller, 2012; Schultze, Mojzisch, & Schulz-Hardt, 2019). Using a quantity estimation task, we tested five hypotheses about advice taking by groups and individuals. Chief among these is that groups will discount advice more than individuals do when they have reached consensus on the quantity in question prior to receiving that advice, but will discount it less than individuals do when, in addition to being prevented from reaching consensus beforehand, their members initially have rather different opinions about that quantity. We also tested the hypothesis that advice received from groups will be discounted less than advice received from individuals. Individuals and two-person teams received advice on 15 general knowledge questions that each called for a percentage response. Half of the teams were required to reach an initial consensus judgment before receiving that advice, while the rest were prevented from doing so. Study results support most of our hypotheses, and are discussed in terms of the motivated cognitive closure likely induced by pre-advice consensus seeking in groups. [ABSTRACT FROM AUTHOR]

G-I transfer denotes an increase in individual performance due to group interaction, for example, because of acquiring certain skills or knowledge from the other group members. Whereas such G-I transfer has been successfully shown for problem-solving tasks, evidence for G-I transfer on quantitative estimation tasks is scarce. We address this research gap with a focus on how often a group has to interact in order to fully exploit the benefit of this learning effect. Results from two experiments support the idea that a single group interaction is sufficient to induce a stable G-I transfer, which reduces group members’ metric error. Smaller metric errors indicate that people improved their representation of the correct upper and lower boundaries, or what range of values is plausible. In contrast to nominal groups, both members of continuously interacting groups and members of groups with only one initial interaction exhibited stable G-I transfer, and the size of this transfer did not significantly differ between the latter two conditions. Furthermore, we found evidence for differential weighting of group members’ individual contributions that goes beyond sheer individual capability gains under certain circumstances, namely in tasks with a population bias.

In group decision making, it is inevitable that the individual decision maker’s subjectivity is involved, which causes difficulty in reaching a group decision. One of the difficulties is to aggregate a small set of expert opinions with the individual subjectivity or uncertainty modeled with probability theory. This difficult problem is called probability distribution function aggregation (DFA). This paper presents a simple and efficient approach to the DFA problem. The main idea of the proposed approach is that the DFA problem is modeled as a nonlinear function of a set of probability distribution functions, and then a linear feedback iteration scheme is proposed to solve the nonlinear function, leading to a group judgment or decision. Illustration of this new approach is given by a well-known DFA example which was solved with the Delphi method. The DFA problem is a part of the group decision problem. Therefore, the proposed algorithm is also useful to the decision making problem in general. Another contribution of the this paper is the proposed notation of systematically representing the imprecise group decision problem with the classification of imprecise information into three classes, namely incomplete information, vague information, and uncertain information. The particular DFA problem dealt with in this paper is then characterized with this general notation. [ABSTRACT FROM AUTHOR]

In organizations, groups of decision makers often meet to make judgments as a group on issues and tasks such as, hiring a person who best fits an open position. In such tasks called cognitive conflict tasks, where there is no conflict of interest, group members attempting to reach a common solution often differ on their perspectives to the problem. Cognitive conflicts have been studied in the context of Social Judgment Theory, which posits that persons or judges make a set of judgments about a set of events based on observation of a set of cues related to the events. Disagreement arises because the judges fail to understand each other’s judgment making policies. In order to reduce disagreement and move the group towards a group judgment policy that has the consensus of the group members and is applied consistently, a computerized decision aid is proposed that can be built around a Group Support System using cognitive mapping as a method of providing cognitive feedback and the Analytic Hierarchy Process to process the conflicting criteria and help an individual formulate a judgment policy, as well as aggregate the individual policies into a group judgment policy. It is argued that such as decision aid by supporting every decision maker in the group to effectively use information about the task so that they have a good understanding of the judgment policy they form, to communicate their evaluation policies accurately to other members, and by providing an iterative mechanism through which members can arrive at a compromise solution to the task, is expected to improve the quality of group judgments.

Research shows that crowds can provide more accurate estimates of uncertain quantities than individuals (Surowiecki, 2004). But little is known about how to organize crowd members to maximize accuracy. When should crowd members work independently, and when should they work collaboratively? We examined the effects of social influence on estimation accuracy, consensus, and confidence. Participants first made independent estimates of uncertain quantities, such as the percentage of U.S. deaths due to heart attacks or the height of the tallest building. Then, in some conditions, they interacted with others online. After the discussion, they made second independent estimates. Social interaction improved accuracy. Despite well-known problems with groups, such as herding and free riding, discussion resulted in more accurate estimates and greater consensus relative to independent estimates. We offer a simple model that describes the process by which group discussion improves the estimates of uncertain quantities. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Groups are often tasked with making important organizational decisions. For example, CEO compensation judgments are made by groups of decision makers with no specialized compensation training who receive advice from a third party with a potential bias. This study seeks to understand the effects of this challenging context on decision making. We explore whether and how decision makers adjust their judgments, considering the potential for advisor bias. This is an essential issue in CEO compensation that is difficult to assess through traditional archival research based on publicly available data. Our study explores how decision makers, in the context of compensation committees, process and use advice provided by external parties. Our findings illustrate that individuals adjust for known conflicts of interest in the information provided to them. However, we do not find that individuals discount lavish advice to a greater extent than more modest advice, and the group decision-making process fails to correct for this tendency. [ABSTRACT FROM AUTHOR]

The traditional subjective weight method-AHP (eigenvalue method) contains certain disadvantages. To overcome these disadvantages, the rank correlation analysis gives an intuitive, simple and easy-to-calculate subjective weight method. The paper extends the rank correlation analysis with point values as scales. The interval numbers and group judgments are considered, and an interval scales rank correlation analysis group evaluation method is put forward. An application example is given to illustrate the effectiveness of the proposed method. The results show that this method is closer to the actual application. [ABSTRACT FROM AUTHOR]

The work of intelligence analysts is fundamentally cognitive in nature. Intelligence analysis consists largely of identifying problems, generating and evaluating hypotheses, identifying and assessing open source and classified information, recognizing patterns in large sets of data, aggregating information, and providing results in the form of judgments, forecasts, and insights to policymakers. These activities are often conducted by individuals; however, intelligence agencies and experts have called increasingly for the use of teams in intelligence analysis. This article reviews the research literature on group-level phenomena (that is, process losses) that are most relevant to the work of intelligence analysts, including productivity losses in brainstorming, the common knowledge effect, group polarization, confirmation bias, overconfidence, and pressures toward uniformity. We describe how features of intelligence analysis teams' tasks, context, and structure affect these processes, present methods to minimize these process losses and increase process gains, and discuss directions for future research. Although our focus is on intelligence analysis teams, these processes and interventions are relevant to a range of analytical teams that share common characteristics. [ABSTRACT FROM AUTHOR]

Abstract: The importance of product reliability attracts increasing attentions from manufacturers, as it has become a vital factor to success in the current competitive world. Reliability can be achieved throughout lifecycle of product among which design stage has acquired large amounts of research achievements. However, few have been done on the assurance of reliability in product manufacturing stage, except in semiconductor manufacturing industry. The difficulty lies in the identification of the relationship between product reliability indices and its quality characteristics, which is essential for the control of the product reliability in manufacturing stage. In this paper, we present a method of combining a group of experts in help to identify the accurate relationship, according to their experiences. The method, based on Bayesian theory, incorporates experts’ experiences with quantitative identification process. The proposed method can be used in various areas of manufacturing as it is a general methodology to identify the relationship between the product reliability indices and quality characteristics. [Copyright &y& Elsevier]

Today the judgment of a group of experts is often at the centre of the decision making process when a problem is deemed difficult to quantify. Whenever one or more objects are evaluated by two or more observers (i.e. judges, experts) we must pose the question as to what extent we can rely on that evaluation. There are many sources of reliability of such group judgments, but the final tool of verification should be the degree of agreement among experts. If the evaluated objects are listed in order of their significance, one of the rank correlation methods is often applied to this end. However, rank correlation methods have their limitations, which are discussed in the first part of the presentation. Next, the case is presented where the preferences of observers are expressed by using an ordinal scale, but the objects are not ranked. When we have a set of grades for the object we can apply many measurements to verify the agreement. Nevertheless, what does the agreement between opinions on the same object really mean, and what type of dispersion measurements would be the best reflection of that agreement? We provide the answer, which is based on modules between grades, and which allows us to develop an appropriate coefficient of agreement. We also indicate the basis of calculating the actual distribution of the coefficient in order to test the statistical significance of the measurement. [ABSTRACT FROM AUTHOR]

The current experiment explored the effect of activating a counterfactual mind-set on the discussion of unique information and group judgment accuracy. Evidence suggests that a counterfactual mind-set is characterized by a focused, analytic mental state and, when activated at the group level, improves group judgment accuracy in the murder mystery paradigm (a hidden profile task). We hypothesized that the beneficial effect of the counterfactual mind-set would only help group problem-solving tasks if the mind-set had been activated at the group level, allowing the analytical mind-set to play out in an atmosphere of synergistic coordination. In contrast, if this highly focused mental state is activated at the individual level, it could impair group judgment quality because inwardly focused analytical individuals may fail to coordinate their behavior with other group members. Consistent with our hypothesis, activating a counterfactual mind-set at the individual level had a debilitating effect on the group judgment task, whereas activating a counterfactual mind-set at the group level had a facilitative effect, increasing information sharing, synergistic coordination and judgment accuracy. Copyright © 2004 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

'Crowd wisdom' refers to the surprising accuracy that can be attained by averaging judgments from independent individuals. However, independence is unusual; people often discuss and collaborate in groups. When does group interaction improve vs. degrade judgment accuracy relative to averaging the group's initial, independent answers? Two large laboratory studies explored the effects of 969 face-to-face discussions on the judgment accuracy of 211 teams facing a range of numeric estimation problems from geographic distances to historical dates to stock prices. Although participants nearly always expected discussions to make their answers more accurate, the actual effects of group interaction on judgment accuracy were decidedly mixed. Importantly, a novel, group-level measure of collective confidence calibration robustly predicted when discussion helped or hurt accuracy relative to the group's initial independent estimates. When groups were collectively calibrated prior to discussion, with more accurate members being more confident in their own judgment and less accurate members less confident, subsequent group interactions were likelier to yield increased accuracy. We argue that collective calibration predicts improvement because groups typically listen to their most confident members. When confidence and knowledge are positively associated across group members, the group's most knowledgeable members are more likely to influence the group's answers. [ABSTRACT FROM AUTHOR]

Cognitive conflicts arise within groups because the members of a group view a problem from different perspectives, even when they have similar interests in achieving a goal. Disagreement within a group may occur due to: (a) differing judgment policies among the members, (b) inconsistency by any member in using a judgment policy, (c) group process losses that prevent group members from understanding each other better, or (d) limited processing capability which may prevent group members from processing all information effectively. Disagreement is especially likely when policies, processes, or information are conflicting in nature. A level 2 GDSS to aid judging in cognitive conflict tasks is presented that combines cognitive feedback and Multi-attribute utility (MAU) theory based multicriteria decision-making techniques with the communication structure and activity-structuring capabilities of a level 1 GDSS. Though cognitive feedback and MAU methods have been used separately to help groups resolve cognitive conflicts, never before have the two decision aids been used together in a computer-based collaborative system. The contributory effects of the components of this GDSS design were empirically tested in a laboratory setting. Three treatments: an unaided face-to-face meeting, a level 1 GDSS supported meeting, and a level 2 GDSS supported meeting were compared in a repeated measures experimental design. Results largely supported the proposed research hypotheses. Some specific findings include: (1) the level 2 GDSS reduced disagreement between group members and improved consistency of judgments better than the other meeting environments did; (2) there was no significant difference in the reduction of disagreement between the level 1 GDSS and face-to-face meetings; and (3) while there was no difference in improvement of consistency of individual judgments between the face-to-face and level 1 GDSS supported meetings, group judgments made in face-to-face meetings were more consistent. [ABSTRACT FROM AUTHOR]