Thirdly, the findings suggest that a biomimicry approach to designing mass collaborative systems may be fruitful. While swarm intelligence is currently used in areas like social learning  and navigation of robots , relatively little research has been done to extend this concept further into human decisionmaking. For example, future research may investigate how swarming systems might enable human-AI collaboration , whereby human and non-human agents collaborate together to converge upon optional decisions. Additionally, swarming may help to break through functional limits of how large a decision- makinggroup can be. Research shows that after about 12 group members, coordination costs prevent groups from functioning effectively . By contrast, there are few, if any, limitations to how many people can participate in a swarm—swarm.ai has enabled over 40 participants to collaborate in real-time. Human swarming systems may help to achieve what Malone calls a ‘supermind,’ whereby dozens, potentially thousands, of people can simultaneously and successfully collaborate . The design of such collaborative platforms and business intelligence systems might be informed from the swarm intelligence of natural systems.
FIGURE 11. Analysis on the question 2 on co-owners’ questionnaire
FIGURE 12. Analysis on the question 3 on co-owners’ questionnaire
and preserving co-owners privacy, we propose a consensus- reached groupdecisionmaking structure. In the proposed work, when a user wants to post a co-owned data, the user first asks co-owner’s groupdecision in the sharing process, and then makes the final decision by either respecting the group’s decision or dis-respecting co-owners’ group deci- sion. We use users trust values for weighting co-owners’ opinions in the sharing process where users trust values are dynamic values. If co-owners are worried about the co- owned data security features and do not want the data to be shared but the owner shares the data, then the proposed work decreases the co-owner’s trust in the owner otherwise it is increased. We also create an online social network platform in order to show the applicability of the proposed work. With the created social network, we ask users’ opinions about the proposed work by conducting questionnaires. The result shows that the consensus-reached groupdecision is found very useful by the respondents.
A single ant, bee, fish or bird isn’t clever, but their colonies or troop conjointly are of preeminent significance. As a group they can respond quickly and effectively solve problems that are unthinkable to the individual, like finding the shortest path to the food source, assigning different tasks to the members, protecting themselves from their enemies. This type of brilliance is known as SWARM INTELLIGENCE (SI). A Swarm is a loosely structured collection of interacting agents or individuals that belong to a group and Swarm intelligence is an evolutionary approach to hard problem solving that takes motivation from the communicative behaviors of insects and other animals. The term Swarm intelligence was introduced by Gerardo Beni and Jing Wang in 1989.The main features of SI are decentralized and trustworthy way of working and self-organization among the members. Biological Examples are ant colonies, bird flocking, animal herding, bacterial growth, termites and fish schooling. SI based algorithms produce low cost, fast, and potent solutions to several complicated problems.
ensure the best decision is made on behalf of the organization and they have minimized personal biases.
Principles and Practices Central to Effective Decision-Making. Leaders tend to believe that their problem solving and rational decision-making skills are important to the performance of their jobs. However, Stryker (1965) states, “…their actual practices has shown that even veteran managers are likely to be very unsystematic when dealing with problems and decisions” (p.73). The author continues to state that the very nature of the unsystematic way that these leaders use to solve problems can often lead to the wrong decision. Stryker proposes using a systematic method for analyzing problems such as the Kepner-Tregoe model, which develops a systematic approach to help leaders solve problems and make decisions more effectively. This model was developed by Kepner-Tregoe and Associaties in 1960. The model includes using the following steps 1) defining the problem, 2) outlining the specification, 3) spotting the distinction, 4) seeking the cause, and 5) respecifying the problem for resolution.
Facilitation is a dynamic process which implies the management of human relations , tasks and technology, as well as the structuring of the tasks to effective achievement of the decisionmaking objectives. Facilitation is defined as 1 : “the activities carried out before, during and after a decision meeting to support the group of decision makers to achieve their goals during the decisionmaking process”. Activities of group facilitation can be grouped into two kinds: 1) Content facilitation implies interventions directly on the problem to be solved, and focuses on the contents of decisionmaking, data analysis and the communication of the relevant results. It refers to the matter of the decision meeting, the issues, objectives, and organizational outcomes; 2) Process refers to the techniques, such as brainstorming, converging, summarizing, and voting, used to achieve outcomes, as well as to the organization of those techniques. It guarantees an equal participation and a suitable management of time.
Groupthink is a characteristic of groupdecisionmaking in which bad decisions are likely to be made. Groupthink can be thought of as a mode of thinking that people engage in when they are deeply involved in a well-united group. Groupthink occurs when individuals in a group feel pressure to comply with what seems to be the dominant view in the group. Opposing views of the majority opinion are suppressed, and alternative courses of action are not fully explored. Some decision makers avoid raising their true opinion on the matter at hand for fear of stepping outside the comfort zone of the group. Members of the group may feel inferior to others and fear a loss of respect for speaking the wrong words. Johnson indicates that those group pressures, biases, and other behaviors reduce the quality of the decision. He also states that ”the decision context and available alternatives will determine the effect of groupthink on a situation” . Although groupthink in different situations can be difficult to detect or measure, it offers insight into understanding how various factors and conditions combine to affect decision outcomes. To prevent groupthink and enhance the effectiveness of groupdecisionmaking, the leader of the group may encourage the group members to express objections, or he or she may also remain unbiased to ideas as they are presented.
Abstract: Groupdecision-making shows an significant role when allocating with decisionmaking problems with the fast growth of society. The foremost determination of this paper is to show the reasonableness of some groupdecisionmaking on the laplacian energy of an intuitionistic fuzzy graphs. we present numerical examples, including Alliance partner selection of a software company , Partner selection in supply chain management and the estimation of the outlines of reservoir action to illuminate the presentations of our planned concepts in result making to rank the best one..
significant contribution to operational effectiveness (Boerner, 2005; Kopelman et als., 1990; Koys & De Cotiss, 1991).
Organizational psychological climate describes the organization’s atmosphere in terms of interpersonal functions within the working environment. The dynamic compositions of individuals within a particular environment create specific situations when the individuals interact (Isaksen & Lauer, 1999; Schneider, 1987). Although the concepts of psychological climate are commonly framed at the individual level, it is also presumed that these processes work in an interactive and reciprocal way. Similar individuals are attracted to similar things, socialize in similar ways, produce similar descriptions of their social environment, and share common interpretations of the same environment. Such processes produce a consensus about a particular psychological situation. When this consensus can be shown as a perception at a higher level, such perceptions can be assembled to provide a construct of a group psychological climate or an organizational psychological climate (James, 1982). A number of researches support this perspective (Gavin, 1975; Jones & James, 1979; Kozlowski & Farr, 1988; Kozloski & Hults, 1987), although many of them are focused on picturing technologies and structures as the key factors of the organizational context (Koslowsky & Doherty, 1989).
Design is a creative process that generates a variety of solutions to a problem. 25 In the ideation phases of design thinking a variety of strategies are recommended in literature to produce a wealth of possible solutions; decision-making phases refine the diverse solutions through comparison to the constraints and criteria identified, as well as evaluation of the benefits and tradeoffs of each alternative. The chance to select from this pool of unique ideas is an important part of generating so many possibilities. 2 Generating many ideas is an opportunity to receive input from every team member and hopefully leads to the development and consideration of the best possible alternative. It is important for designers to be able to move past first ideas because in design decisionmaking, the more ideas the better. 5, 26 Detailing the benefits and tradeoffs of each solution can facilitate a group discussion and should lead to a more unified understanding of the decision.
One dysfunction of highly cohesive groups that has received considerable attention is a phenomenon known as groupthink. Essentially, groupthink is the tendency of cohesive groups to reach consensus on issues without offering, seeking, or considering alternative viewpoints. As a result, groupthink has been blamed for decisionmaking fiascoes in politics, the military, as well as in business. In this article, I discuss some famous examples of political and military fiascoes associated with groupthink, some symptoms of groupthink, and ways to avoid groupthink when makinggroup decisions.
Necessity has driven the MADM to shift from the conventional approach to another that is more dynamic, though it may have to deal with some accurate decision-making. A roadmap to mixture the MADM techniques with the GDM techniques is constructed in this research. This hybrid decision- making approach allows a deeper understanding of the issue which needs a solution. This study also enhances the classic multiple attribute groupdecisionmaking, with the proposed practice of a multiple-cluster multiple-attribute groupdecision-making. The groupdecisionmaking will be more creative when experts and/or decision makers from different approaches are involved. This approach also, with considering decision-making panel with lower level of expertise, could solve the limitation of expert availability in decision makings. The multiple cluster of decision panel, with different weights for their inputs/data, in groupdecisionmaking is not only suggested for Multiple attribute decisionmaking, but also It would be an effective approach in any other type of groupdecision makings.
In literature, there also have been many groupdecisionmaking approaches which are based on fuzzy set theory, for example [3, 18]. In those studies, two main concepts are applied: linguistic variables and fuzzy numbers. Therefore, the corresponding methods are usually called fuzzy groupdecisionmaking methods. In this paper, a new groupdecisionmaking method is presented which is also based on fuzzy set theory. However the essential of the proposed method is not fuzzy since it still uses crisp numbers to score the performance of each alternative and the decision-makers/experts. That is, the key idea behind the method is to apply fuzzy set operations to solve crisp groupdecisionmaking problems. This method is suitable when there is a need to seek consensus among many decision makers in crisp data situation.
where |W i j | is the cardinality of the set W i j . Aggrega- tion and fusion of information are basic concerns for all kinds of knowledge based systems, from image processing to decisionmaking, from pattern recog- nition to machine learning. From a general point of view we can say that aggregation has for purpose the simultaneous use of different pieces of informa- tion (provided by several sources) in order to come to a conclusion or a decision. Here max aggrega- tion operator is used for optimal attitude while other operators as min and average etc can also be used. The value r ji is estimated by using reciprocity after having the value of r i j as:
To solve the two problems mentioned above, this paper makes improvements in groupdecision- making compatibility test algorithm and builds a collaborative correction algorithm based on Pearson-Compatibility model. The core of this algorithm is to simulate the expert group-decisionmaking process, discover the common opinions of most experts via data analysis, and revise the opinions of each expert to realize the ﬁnal consistent compatibility. Moreover, to solve the prob- lem of premature convergence, this paper adopts a mode of multi-user collaborative correction. In addition to the usual algorithms, when it calculates the value of each user’s impact, this paper uses the common opinion of all users rather than target user‘s opinion as a measurable index. The user whose opinion is more similar to the common opinion would be given a higher weight, which is diﬀerent to traditional method.
The ideal solution would be to develop a web of reputation and trust, either at a local level (individual websites for example) or across the whole web, that would enable users to express and propagate trust on others to the entire network to allow other users to assess the quality of the information or service provided even without a prior interaction with the agent in question. The ultimate goal, here, is to estimate, for each agent, a reputation level or score. In this sense, reputation can be understood as a predictor of future behaviour based on previous interactions, that is, any agent will be considered as highly regarded if it has consistently performed satisfactorily in the past assuming that the service can be trusted to perform as expected in the future as well. Therefore, reputation and trust based management will ultimately lead to minimise poor and dishonest agents as well as to encourage the reliable and trustworthy behaviours [17, 22, 30], which, in its turn, makes reputation to be one key mechanism for the social governance on the Internet .
The team may have di¤erent rankings of the three alternatives on di¤erent occasions of the choice problem. A plethora of characteristics unobservable to an outside observer may contribute to the attractiveness of di¤erent options to the team. In addition to uncertainties surrounding the team’s deliberation process on each occasion, the unobservable character- istics may include market conditions, behavior of competitors, and new innovations that occur over the period of repeated decision-making. For simplicity of interpretation, imagine a scenario where this information pertains to “exogenous” factors, such as long-run market share, image, and reputation, rather than probabilities of di¤erent outcomes and associated net pro…ts. This information may change the ranking of the three options even without a¤ecting the way they are seen by an outside observer (that is, the representations that appear in (1)). Suppose that, on certain occasions of binary choice, the team may possess information that favors spending most of the budget on advertising the tablets. In this case, which is called core preference ABC; the team strictly prefers option A to B to C. On other occasions, the team may have information suggesting that equally sharing the budget is the best option and that spending on phone ads is a strictly better investment than spending on tablet ads. In this case, which is called core preference BCA; the team strictly prefers option B to C to A. Note that both preference rankings ABC and BCA are transitive. Suppose that on each choice occasion the team’s core preference is drawn according to distribution
prevailing wisdom. As demonstrated by the recurrent nature of asset bubbles and the ﬁnancial crises that they can lead to, the decisions of groups that reason myopically and ignore the bigger picture can do great damage to their organisations and countries.
Experiments typically focus on a single function of groups, whether it be information aggregation, specialisation and task distribution, insurance, greater bargaining power, or a source of social identity and support. But groups and teams in the real world often serve multiple purposes. Indeed, popular measures of group performance typically cor- respond to a point-by-point evaluation of how well a group or team performs a subset of these functions. In inﬂuential work in the management literature, Katzenbach & Smith (1992); Katzenbach et al. (1993) deﬁne a taxonomy of (i) teams that recommend things, (ii) teams that make or do things, and (iii) teams that run things, and suggest that each type of team has to respond to diﬀerent challenges. Furthermore, they dis- tinguish between working groups and teams, a theme that has so far not been picked up by the experimental literature. According to their deﬁnition, working groups, like randomly assembled groups in the laboratory, are focused on individual accountabil- ity and results rather than joint responsibility. This means that working groups may make fundamentally diﬀerent decisions than teams, especially when decisions have an element of risk and thus potentially negative consequences. There is a big opportunity for experimental work to identify how team risk-taking is aﬀected by a team’s oﬃcial, unoﬃcial, and perceived functions and objectives.