1,721,035 research outputs found
The "Less-Is-More" effect in group decision making
No full textIf each member of a group makes less accurate decisions than those of another group, can the former actually make more accurate decisions collectively than the latter? Through four simulation studies, the chapter shows conditions under which such "less-ismore" effect may occur. In each study, a group member adopted either the take-thebest or the minimalist heuristic to make an individual decision, and a simple majority rule was then applied to determine the group decision. Although an individual using take-thebest can generally achieve higher decision accuracy than one using the minimalist, results in Study 1 show that the decision accuracy of a group of take-the-best individuals can be lower than that of a group of minimalist individuals in task environments where the distribution of cue validities is relatively flat. Similar less-is-more effects are found in Studies 2 and 3, where a group of less accurate individuals, due to either their usage of erroneous cue information or cue orders differing from cues' validity order, can outperform another group of more accurate individuals. Finally, the chapter compares the decision accuracy of five-member groups with varying compositions of take-the-best and minimalist members, and found that groups with either one or two take-the-best members can achieve the most robust performance across four task environments. Informational diversity and characteristics of task environments are the main factors underlying the observed less-is-more effects. Therefore, the chapter argues that to understand the rationality of group decision making, these two factors, in addition to the competency of group members, must be taken into consideration.</p
The use of recognition in group decision-making
No full textGoldstein and Gigerenzer (2002) [Models of ecological rationality: The recognition heuristic. Psychological Review, 109 (1), 75-90] found evidence for the use of the recognition heuristic. For example, if an individual recognizes only one of two cities, they tend to infer that the recognized city has a larger population. A prediction that follows is that of the less-is-more effect: Recognizing fewer cities leads, under certain conditions, to more accurate inferences than recognizing more cities. We extend the recognition heuristic to group decision making by developing majority and lexicographic models of how recognition information is used by groups. The chapter formally shows when the less-is-more effect is predicted in groups and the chapter presents a study where threemember groups performed the population comparison task. Several aspects of the data indicate that members who can use the recognition heuristic are, not in all but in most cases, more influential in the group decision process than members who cannot use the heuristic. The chapter also states the less-is-more effect and found that models assuming that members who can use the recognition heuristic are more influential better predict when the effect occurs.</p
When does diversity trump ability (and vice versa) in group decision making? A simulation study.
Full text linkIt is often unclear which factor plays a more critical role in determining a group's performance: the diversity among members of the group or their individual abilities. In this study, we addressed this "diversity vs. ability" issue in a decision-making task. We conducted three simulation studies in which we manipulated agents' individual ability (or accuracy, in the context of our investigation) and group diversity by varying (1) the heuristics agents used to search task-relevant information (i.e., cues); (2) the size of their groups; (3) how much they had learned about a good cue search order; and (4) the magnitude of errors in the information they searched. In each study, we found that a manipulation reducing agents' individual accuracy simultaneously increased their group's diversity, leading to a conflict between the two. These conflicts enabled us to identify certain conditions under which diversity trumps individual accuracy, and vice versa. Specifically, we found that individual accuracy is more important in task environments in which cues differ greatly in the quality of their information, and diversity matters more when such differences are relatively small. Changing the size of a group and the amount of learning by an agent had a limited impact on this general effect of task environment. Furthermore, we found that a group achieves its highest accuracy when there is an intermediate amount of errors in the cue information, regardless of the environment and the heuristic used, an effect that we believe has not been previously reported and warrants further investigation
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Are Well-Connected Entrepreneurs More Successful? A Study of Start-up Founder LinkedIn Profiles and Their Role in Investor Decision-Making
No full textSocial capital through connections in networks has been argued to be important for startup enterprises. Founder human capital qualities like education, experience, skills have also been shown to be important predictors of startup success. However, does founder social capital matter for startup success beyond founder human capital? To answer this question, this project draws from the decision-making literature and uses five decision strategies to explore how founder human capital and social capital are associated with investment funds raised by startup companies. Two studies were conducted. The first study investigated if a decision strategy that looks at founder social capital better predicts which company raises more investment funds than a decision strategy that only uses founder human capital. The second study investigated if actual investors and entrepreneurs, of varying expertise levels, integrate founder social capital variables while making investment decisions. Both studies found that number of LinkedIn connections of founders of a company was the best predictor of investment funds raised by the company. The first study showed that decision strategies that use social capital cues are similar in predicting successful companies compared to strategies that use human capital cues. The next study showed that, contrary to our expectations, decision strategies that use social capital cues better predict investor choices than strategies that use only human capital cues. It was expected that models that used human capital cues would be better predictors of investor choice behavior than social capital cues. Therefore, the two studies show that founder social capital is associated with investment funds raised by a startup company and investors do take founder social capital into consideration while deciding which startup company to invest in. In doing so, the studies establish the importance of founder social capital in the entrepreneurial context. </div
Semantic Persuasion: Exploring Message Effects of Attribute Degree Centrality and Attribute Tie Strength on Decision Making
No full textThree studies were conducted to explore how semantic network analysis can be utilized to define the quality of arguments and to study novel persuasion effects. Drawing on semantic network theory, the present research introduces attribute degree centrality and attribute tie strength as two new criteria of argument quality. In Study 1, analyses of semantic network data (i.e. degree centrality and tie strength scores) collected on over-the-ear headphone attributes from both the environment and cognitive representations are reported. Semantic network structure was found in the environment and participants were sensitive to this structure. Study 2 assessed the effects of an attribute’s weighted degree centrality on decision making. Attributes chosen from Study 1 varying in degree centrality were embedded in advertising arguments to test the novel hypothesis that the number and strength of attribute connections in a semantic network affect persuasion and decision making. As expected, the degree-centrality hypothesis was confirmed: Advertising arguments based on highly central attributes were more persuasive, and increased choice confidence, when compared with arguments based on lowly central attributes. Attributes high in centrality were also perceived as better arguments than attributes low in centrality which supports the claim that degree centrality provides a theory-based criterion of argument goodness. Centrality did not systematically affect the latency of choices or the perceived credibility of the message source. Study 3 replicated the findings of Study 2 and explored the main and interaction effects of degree centrality and tie strength by manipulating both attribute dimensions through a semantic network learning task. An interaction effect of degree centrality and tie strength on choice behavior and main effects of degree centrality on perceived argument quality, and perceived source credibility were revealed
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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