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 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 g versus c: comparing individual and collective intelligence across two meta-analyses

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Luke I. Rowe ◽  
John Hattie ◽  
Robert Hester
Keyword(s):  
Statistical Power ◽  
Group Performance ◽  
Collective Intelligence ◽  
Meta Analysis ◽  
Performance Criterion ◽  
General Factor ◽  
General Mental Ability ◽  
Meta Analyses ◽  
Primary Predictor ◽  
Better Than

AbstractCollective intelligence (CI) is said to manifest in a group’s domain general mental ability. It can be measured across a battery of group IQ tests and statistically reduced to a latent factor called the “c-factor.” Advocates have found the c-factor predicts group performance better than individual IQ. We test this claim by meta-analyzing correlations between the c-factor and nine group performance criterion tasks generated by eight independent samples (N = 857 groups). Results indicated a moderate correlation, r, of .26 (95% CI .10, .40). All but four studies comprising five independent samples (N = 366 groups) failed to control for the intelligence of individual members using individual IQ scores or their statistically reduced equivalent (i.e., the g-factor). A meta-analysis of this subset of studies found the average IQ of the groups’ members had little to no correlation with group performance (r = .06, 95% CI −.08, .20). Around 80% of studies did not have enough statistical power to reliably detect correlations between the primary predictor variables and the criterion tasks. Though some of our findings are consistent with claims that a general factor of group performance may exist and relate positively to group performance, limitations suggest alternative explanations cannot be dismissed. We caution against prematurely embracing notions of the c-factor unless it can be independently and robustly replicated and demonstrated to be incrementally valid beyond the g-factor in group performance contexts.

scholarly journals Algorithmic and human prediction of success in human collaboration from visual features

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Martin Saveski ◽  
Edmond Awad ◽  
Iyad Rahwan ◽  
Manuel Cebrian
Keyword(s):  
Machine Learning ◽  
Visual Cues ◽  
Success Factors ◽  
Group Performance ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Adventure Game ◽  
Group Success ◽  
The Relationship ◽  
Better Than

AbstractAs groups are increasingly taking over individual experts in many tasks, it is ever more important to understand the determinants of group success. In this paper, we study the patterns of group success in Escape The Room, a physical adventure game in which a group is tasked with escaping a maze by collectively solving a series of puzzles. We investigate (1) the characteristics of successful groups, and (2) how accurately humans and machines can spot them from a group photo. The relationship between these two questions is based on the hypothesis that the characteristics of successful groups are encoded by features that can be spotted in their photo. We analyze >43K group photos (one photo per group) taken after groups have completed the game—from which all explicit performance-signaling information has been removed. First, we find that groups that are larger, older and more gender but less age diverse are significantly more likely to escape. Second, we compare humans and off-the-shelf machine learning algorithms at predicting whether a group escaped or not based on the completion photo. We find that individual guesses by humans achieve 58.3% accuracy, better than random, but worse than machines which display 71.6% accuracy. When humans are trained to guess by observing only four labeled photos, their accuracy increases to 64%. However, training humans on more labeled examples (eight or twelve) leads to a slight, but statistically insignificant improvement in accuracy (67.4%). Humans in the best training condition perform on par with two, but worse than three out of the five machine learning algorithms we evaluated. Our work illustrates the potentials and the limitations of machine learning systems in evaluating group performance and identifying success factors based on sparse visual cues.

Deciding Clause Classes by Semantic Clash Resolution

1993 ◽  
Vol 18 (2-4) ◽  
pp. 163-182
Author(s):  
Alexander Leitsch
Keyword(s):  
Decision Procedure ◽  
Decision Procedures ◽  
General Concept ◽  
Complexity Measure ◽  
Syntactical Structure

It is investigated, how semantic clash resolution can be used to decide some classes of clause sets. Because semantic clash resolution is complete, the termination of the resolution procedure on a class Γ gives a decision procedure for Γ. Besides generalizing earlier results we investigate the relation between termination and clause complexity. For this purpose we define the general concept of atom complexity measure and show some general results about termination in terms of such measures. Moreover, rather than using fixed resolution refinements we define an algorithmic generator for decision procedures, which constructs appropriate semantic refinements out of the syntactical structure of the clause sets. This method is applied to the Bernays – Schönfinkel class, where it gives an efficient (resolution) decision procedure.

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.

scholarly journals Remote sensing of aerosols by synergy of caliop and modis

2018 ◽  
Vol 176 ◽  
pp. 08012
Author(s):  
Rei Kudo ◽  
Tomoaki Nishizawa ◽  
Akiko Higurashi ◽  
Eiji Oikawa
Keyword(s):  
Remote Sensing ◽  
Biomass Burning ◽  
Sea Salt ◽  
Water Soluble ◽  
Dust Particles ◽  
Vertical Profiles ◽  
Dust Event ◽  
Carbonaceous Particles ◽  
The Individual ◽  
Better Than

For the monitoring of the global 3-D distribution of aerosol components, we developed the method to retrieve the vertical profiles of water-soluble, light absorbing carbonaceous, dust, and sea salt particles by the synergy of CALIOP and MODIS data. The aerosol product from the synergistic method is expected to be better than the individual products of CALIOP and MODIS. We applied the method to the biomass-burning event in Africa and the dust event in West Asia. The reasonable results were obtained; the much amount of the water-soluble and light absorbing carbonaceous particles were estimated in the biomass-burning event, and the dust particles were estimated in the dust event.

Four-Node Quadrilateral Element with Continuous Nodal Stress for Geometrical Nonlinear Analysis

2017 ◽  
Vol 15 (02) ◽  
pp. 1850005 ◽  
Author(s):  
Yongtao Yang ◽  
Xuhai Tang ◽  
Hong Zheng ◽  
Quansheng Liu
Keyword(s):  
Nonlinear Analysis ◽  
Meshfree Method ◽  
High Tolerance ◽  
Solid Mechanic ◽  
Quadrilateral Element ◽  
Mesh Distortion ◽  
Geometrical Nonlinear ◽  
Geometrical Nonlinear Analysis ◽  
The Individual ◽  
Better Than

In this paper, the performance of a hybrid ‘FE-Meshfree’ quadrilateral element with continuous nodal stress (Quad4-CNS) is investigated for geometrical nonlinear solid mechanic problems. By combining finite element method (FEM) and meshfree method, this Quad4-CNS synergizes the individual strengths of these two methods, which leads to higher accuracy, better convergence rate, as well as high tolerance to mesh distortion. Therefore, Quad4-CNS is attractive for geometrical nonlinear solid mechanic problems where excessive distorted meshes occur. For geometrical nonlinear analysis, numerical results show that the results of Quad4-CNS element are much better than those of four-node isoparametric quadrilateral element (Quad4), and are comparable to quadratic quadrilateral element (Quad8) and other hybrid ‘FE- Meshfree’ elements.

scholarly journals Individualization of Products and Services with Artificial General Intelligence and General Collective Intelligence

2020 ◽  
Author(s):  
Andy E Williams
Keyword(s):  
Online Education ◽  
News Media ◽  
Intelligent Agents ◽  
Collective Intelligence ◽  
Online News ◽  
General Intelligence ◽  
Entertainment Media ◽  
Individual User ◽  
Artificial General Intelligence ◽  
The Individual

INTRODUCTION: With advances in big data techniques having already led to search results and advertising being customized to the individual user, the concept of an online education designed solely for an individual, or the concept of online news or entertainment media, or any other virtual service being designed uniquely for each individual, no longer seems as far fetched. However, designing services that maximize user outcomes as opposed to services that maximize outcomes for the corporation owning them, requires modeling user processes and the outcomes they target.OBJECTIVES: To explore the use of Human-Centric Functional Modeling (HCFM) to define functional state spaces within which human processes are well-defined paths, and within which products and services solve specific navigation problems, so that by considering all of any given individual’s desired paths through a given state space, it is possible to automate the customization of those products and services for that individual or to groups of individuals.METHODS: An analysis is performed to assess how and whether intelligent agents based on some subset of functionality required for Artificial General Intelligence (AGI) might be used to optimize for the individual user. And an analysis is performed to determine whether and if so how General Collective Intelligence (GCI) might be used to optimize across all users.RESULTS: AGI and GCI create the possibility to individualize products and services, even shared services such as the Internet, or news services so that every individual sees a different version.CONCLUSION: The conceptual example of customizing a news media website for two individual users of opposite political persuasions suggests that while the overhead of customizing such services might potentially result in massively increased storage and processing overhead, within a network of cooperating services in which this customization reliably creates value, this is potentially a significant opportunity.

scholarly journals Play Experience as Individual Ability and a Factorof Individual Resistance to Psychosomatic Disorders

2017 ◽  
Vol 13 (4) ◽  
pp. 118-126
Author(s):  
A.V. Serikov
Keyword(s):  
Statistical Analysis ◽  
Cultural Diversity ◽  
Inverse Correlation ◽  
Psychosomatic Disorders ◽  
Individual Resistance ◽  
Significant Inverse Correlation ◽  
Individual Ability ◽  
The Individual ◽  
Play Experience

The paper analyzes the constructs of ‘play experience’ and ‘play experiencing ability’ from the perspective of cultural-historical psychology. The paper stresses the importance of education, play, art, wealth and cultural diversity in the formation of healthy and independent personality. The role of play experience as a healthful factor that allows an individual to acquire resistance to psychosomatic disorders is supported both theoretically and empirically. It is argued that the individual capable of play experience can transform the meaning of a situation (within his/her play experience) and therefore eliminate its psychotraumatic effect which contributes to the development of psychosomatic disorders. The paper provides outcomes of an empirical research with 73 participants (40 female, 33 male; aged 18—45, with the average age of 25 years). The statistical analysis revealed a significant inverse correlation between the level of the individual’s play experiencing ability and the level of his/her somatization (rs = -0,435; p ≤ 0,01), which confirms the research hypothesis.

scholarly journals Social learning strategies regulate the wisdom and madness of interactive crowds

2018 ◽  
Author(s):  
Wataru Toyokawa ◽  
Andrew Whalen ◽  
Kevin N. Laland
Keyword(s):  
Social Learning ◽  
Learning Strategies ◽  
Group Size ◽  
Collective Intelligence ◽  
Collective Wisdom ◽  
The Social ◽  
Challenging Tasks ◽  
Task Uncertainty ◽  
The Individual ◽  
Social Learning Strategies

AbstractWhy groups of individuals sometimes exhibit collective ‘wisdom’ and other times maladaptive ‘herding’ is an enduring conundrum. Here we show that this apparent conflict is regulated by the social learning strategies deployed. We examined the patterns of human social learning through an interactive online experiment with 699 participants, varying both task uncertainty and group size, then used hierarchical Bayesian model-ftting to identify the individual learning strategies exhibited by participants. Challenging tasks elicit greater conformity amongst individuals, with rates of copying increasing with group size, leading to high probabilities of herding amongst large groups confronted with uncertainty. Conversely, the reduced social learning of small groups, and the greater probability that social information would be accurate for less-challenging tasks, generated ‘wisdom of the crowd’ effects in other circumstances. Our model-based approach provides evidence that the likelihood of collective intelligence versus herding can be predicted, resolving a longstanding puzzle in the literature.

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