scholarly journals Prediction Diversity and Selective Attention in the Wisdom of Crowds

2020 ◽  
Vol 29 (4) ◽  
pp. 861-875
Author(s):  
Davi A. Nobre ◽  
◽  
José F. Fontanari ◽  
Keyword(s):  
Selective Attention ◽  
Current Understanding ◽  
Paper Strip ◽  
Wisdom Of Crowds ◽  
The Poor ◽  
Individual Estimate ◽  
The Individual ◽  
Average Individual ◽  
Better Than ◽  
The Wisdom Of Crowds

The wisdom of crowds is the idea that the combination of independent estimates of the magnitude of some quantity yields a remarkably accurate prediction, which is always more accurate than the average individual estimate. In addition, it is largely believed that the accuracy of the crowd can be improved by increasing the diversity of the estimates. Here we report the results of three experiments to probe the current understanding of the wisdom of crowds, namely, the estimates of the number of candies in a jar, the length of a paper strip and the number of pages of a book. We find that the collective estimate is better than the majority of the individual estimates in all three experiments. In disagreement with the prediction diversity theorem, we find no significant correlation between the prediction diversity and the collective error. The poor accuracy of the crowd on some experiments leads us to conjecture that its alleged accuracy is most likely an artifact of selective attention.

scholarly journals EPISTEMIC DEMOCRACY WITH DEFENSIBLE PREMISES

2013 ◽  
Vol 29 (1) ◽  
pp. 87-120 ◽  
Author(s):  
Franz Dietrich ◽  
Kai Spiekermann
Keyword(s):  
Small Groups ◽  
Wisdom Of Crowds ◽  
Contemporary Theory ◽  
Epistemic Democracy ◽  
Current Form ◽  
Condorcet Jury Theorem ◽  
Jury Theorem ◽  
Misleading Conclusion ◽  
Better Than ◽  
The Wisdom Of Crowds

The contemporary theory of epistemic democracy often draws on the Condorcet Jury Theorem to formally justify the ‘wisdom of crowds’. But this theorem is inapplicable in its current form, since one of its premises – voter independence – is notoriously violated. This premise carries responsibility for the theorem's misleading conclusion that ‘large crowds are infallible’. We prove a more useful jury theorem: under defensible premises, ‘large crowds are fallible but better than small groups’. This theorem rehabilitates the importance of deliberation and education, which appear inessential in the classical jury framework. Our theorem is related to Ladha's (1993) seminal jury theorem for interchangeable (‘indistinguishable’) voters based on de Finetti's Theorem. We also prove a more general and simpler such jury theorem.

scholarly journals DEMOCRACY ISN'T THAT SMART (BUT WE CAN MAKE IT SMARTER): ON LANDEMORE'S DEMOCRATIC REASON

Episteme ◽  
2016 ◽  
Vol 14 (2) ◽  
pp. 161-175 ◽  
Author(s):  
Aaron Ancell
Keyword(s):  
Decision Procedure ◽  
Group Performance ◽  
Collective Intelligence ◽  
Decision Procedures ◽  
Recent Book ◽  
Cognitive Diversity ◽  
Individual Ability ◽  
The Individual ◽  
Average Individual ◽  
Better Than

AbstractIn her recent book, Democratic Reason, Hélène Landemore argues that, when evaluated epistemically, “a democratic decision procedure is likely to be a better decision procedure than any non-democratic decision procedures, such as a council of experts or a benevolent dictator” (p. 3). Landemore's argument rests heavily on studies of collective intelligence done by Lu Hong and Scott Page. These studies purport to show that cognitive diversity – differences in how people solve problems – is actually more important to overall group performance than average individual ability – how smart the individual members are. Landemore's argument aims to extrapolate from these results to the conclusion that democracy is epistemically better than any non-democratic rival. I argue here that Hong and Page's results actually undermine, rather than support, this conclusion. More specifically, I argue that the results do not show that democracy is better than any non-democratic alternative, and that in fact, they suggest the opposite – that at least some non-democratic alternatives are likely to epistemically outperform democracy.

scholarly journals “They don’t know better than I do”: People are reluctant to rely on the wisdom of crowds in individual decision making

2020 ◽  
Author(s):  
Merav Yonah ◽  
Yoav Kessler
Keyword(s):  
Personal Information ◽  
Decision Makers ◽  
Substantial Part ◽  
Wisdom Of Crowds ◽  
Collective Wisdom ◽  
Decision Bias ◽  
Decision Outcomes ◽  
Dot Motion ◽  
Better Than ◽  
The Wisdom Of Crowds

Establishing the way people decide to use or avoid information when making a decision is of great theoretical and applied interest. In particular, the “big data revolution” enable decision makers to harness the wisdom of crowds (WoC) toward reaching better decisions. The WoC is a well-documented phenomenon that highlights the potential superiority of collective wisdom over that of an individual. However, individuals may fail to acknowledge the power of collective wisdom as a means for optimizing decision outcomes. Using a random dot motion task, the present study examined situations in which decision makers must choose between relying on their own personal information or relying on the WoC in their decision. Although the latter was always the rational choice, a substantial part of the participants chose to rely on their own observation and also advised others to do so. This choice tendency was associated with higher confidence, but not with better task performance, and hence reflects overconfidence. Acknowledging and understanding this decision bias may help mitigating it in applied settings.

Applying Formal Social Epistemology to the Real World

2012 ◽  
Vol 34 (2) ◽  
Author(s):  
Carlo Martini
Keyword(s):  
Real World ◽  
Social Epistemology ◽  
Wisdom Of Crowds ◽  
Trade Off ◽  
The Real ◽  
The Social ◽  
Better Than ◽  
The Wisdom Of Crowds

AbstractThe claim that diversity and independence have a net positive epistemic effect on the judgments of groups has been recently defended formally by Scott Page, among others, and popularized in Surowiecki's The Wisdom of Crowds. In Meta-Induction and the Wisdom of Crowds Thorn and Schurz take issue with the claim that more diversity and independence in groups leads to better collective judgments. I argue that Thorn and Schurz's arguments are helpful in clarifying a number of over-generalizations about diversity and independence that are often circulated in the social epistemology literature. I also argue that the relevant formal arguments are easily misunderstood when presented 'in a vacuum', that is, without a context of application in mind. I provide a different approach to understanding formal results in social epistemology: With the help of concrete scenarios and the formal literature, I focus on a trade-off between independence and dependence in groups. I show that the approach works well also for another principle in social epistemology; namely, the principle that 'more heads are better than few'.

Making the most of individual differences in joint decisions

2018 ◽  
Author(s):  
Bahador Bahrami
Keyword(s):  
Decision Making ◽  
Direct Evidence ◽  
Cross Cultural ◽  
Collective Decision Making ◽  
Wisdom Of Crowds ◽  
Trade Off ◽  
Adaptive Value ◽  
Joint Decisions ◽  
Cultural Data ◽  
Better Than

Evidence for and against the idea that “two heads are better than one” is abundant. This chapter considers the contextual conditions and social norms that predict madness or wisdom of crowds to identify the adaptive value of collective decision-making beyond increased accuracy. Similarity of competence among members of a collective impacts collective accuracy, but interacting individuals often seem to operate under the assumption that they are equally competent even when direct evidence suggest the opposite and dyadic performance suffers. Cross-cultural data from Iran, China, and Denmark support this assumption of similarity (i.e., equality bias) as a sensible heuristic that works most of the time and simplifies social interaction. Crowds often trade off accuracy for other collective benefits such as diffusion of responsibility and reduction of regret. Consequently, two heads are sometimes better than one, but no-one holds the collective accountable, not even for the most disastrous of outcomes.

Devanagari Text Detection From Natural Scene Images

2020 ◽  
Vol 10 (3) ◽  
pp. 44-59
Author(s):  
Sankirti Sandeep Shiravale ◽  
R. Jayadevan ◽  
Sanjeev S. Sannakki
Keyword(s):  
Edge Detection ◽  
Image Understanding ◽  
Text Detection ◽  
Experimental Results ◽  
Combined Approach ◽  
Natural Scene ◽  
Light Conditions ◽  
The Individual ◽  
Natural Scene Images ◽  
Better Than

Text present in a camera captured scene images is semantically rich and can be used for image understanding. Automatic detection, extraction, and recognition of text are crucial in image understanding applications. Text detection from natural scene images is a tedious task due to complex background, uneven light conditions, multi-coloured and multi-sized font. Two techniques, namely ‘edge detection' and ‘colour-based clustering', are combined in this paper to detect text in scene images. Region properties are used for elimination of falsely generated annotations. A dataset of 1250 images is created and used for experimentation. Experimental results show that the combined approach performs better than the individual approaches.

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