Wisdom Of Crowds Research Articles

Assessing Spatial Synergy Between Integrated Urban Rail Transit System and Urban Form: A BULI-Based MCLSGA Model With the Wisdom of Crowds

Spatially synergizing the urban rail transit (URT) network integrated with its feeder transit system and the urban form plays an important role in improving the effectiveness of URT in mitigating traffic congestion, reducing air pollution, optimizing urban spatial structure, etc. This article mainly focuses on assessing the spatial synergy between the two parts by specifying the assessment criteria that can effectively characterize the spatial synergic mechanism between the two parts and developing a novel multicriteria large-scale group assessment (MCLSGA) model, in which basic uncertain linguistic information (BULI), as an extended form of fuzzy linguistic approach, is used to model and process the subjective assessment information (Assess-Inf) elicited by experts. In order to alleviate the computing complexity, an agglomerative hierarchical clustering algorithm is introduced to cluster the Assess-Inf given by a huge number of experts as per the organizers’ expected discrimination level on reliability-related Assess-Inf. A method for weighting the clusters is then proposed to control the roles played by the items of the Assess-Inf, representing each cluster and having different reliability levels in their fusing process to tap into wisdom of crowds (WOC) while accommodating the organizers’ trust level in the reliability-related Assess-Inf given by experts. Afterward, the best–worst method is extended to the BULI-based large-scale group assessment context with the aim of accurately weighting the criteria by drawing on WOC. Finally, a case study on assessing the spatial synergy between Chongqing’s integrated URT system and urban form is conducted to validate the validity of the proposed MCLSGA model.

Crowd Wisdom vs in-House Expertise: A Comprehensive Analysis of Quality Assurance Approaches

In the dynamic landscape of software quality assurance, this research paper conducts an insightful comparative analysis of the contrasting methodologies: crowd-sourced testing and in-house testing. By closely examining their individual merits, limitations, and adaptability across diverse project contexts, the study explores crucial metrics encompassing cost-effectiveness, comprehensive test coverage, and adept defect detection. Integrating quantitative data alongside real-world instances, the paper not only elucidates the intricate factors pivotal in making informed decisions between these methodologies but also provides practical, well-grounded recommendations for their efficacious implementation. The study serves as a compass, empowering software practitioners and decision-makers with a discerning outlook to deftly navigate and choose the testing strategy that impeccably aligns with their project requisites and overarching organizational goals.

Realtime user ratings as a strategy for combatting misinformation: an experimental study

Because fact-checking takes time, verdicts are usually reached after a message has gone viral and interventions can have only limited effect. A new approach recently proposed in scholarship and piloted on online platforms is to harness the wisdom of the crowd by enabling recipients of an online message to attach veracity assessments to it. The intention is to allow poor initial crowd reception to temper belief in and further spread of misinformation. We study this approach by letting 4000 subjects in 80 experimental bipartisan communities sequentially rate the veracity of informational messages. We find that in well-mixed communities, the public display of earlier veracity ratings indeed enhances the correct classification of true and false messages by subsequent users. However, crowd intelligence backfires when false information is sequentially rated in ideologically segregated communities. This happens because early raters’ ideological bias, which is aligned with a message, influences later raters’ assessments away from the truth. These results suggest that network segregation poses an important problem for community misinformation detection systems that must be accounted for in the design of such systems.

Wise groups and humble persons: the best of both worlds?

This paper is about a problem that can arise when we try to harness the “wisdom of the crowd” from groups comprised of individuals who exhibit a certain kind of epistemic humility in the way they respond to testimonial evidence. I begin by setting out the problem and then make some initial steps toward solving it. The solution I develop is tentative and may not apply in all circumstances, but it promises to alleviate what seems to me to be a problem of both theoretical interest and practical importance.

Boosting the Wisdom of Crowds Within a Single Judgment Problem: Weighted Averaging Based on Peer Predictions

A combination of point estimates from multiple judges often provides a more accurate aggregate estimate than a point estimate from a single judge, a phenomenon called “the wisdom of crowds.” However, if the judges use shared information when forming their estimates, the simple average will end up overemphasizing this common component at the expense of the judges’ private information. A decision maker could in theory obtain a more accurate estimate by appropriately combining all information behind the judges’ opinions. Although this information underlies the judges’ individual estimates, it is typically unobservable and thus cannot be directly aggregated by a decision maker. In this article, we propose a weighting of judges’ individual estimates that appropriately combines their collective information within a single estimation problem. Judges are asked to provide both a point estimate of the quantity of interest and a prediction of the average estimate that will be given by all other judges. Predictions of others are then used as part of a criterion to determine weights that are applied to each judge’s estimate to form an aggregate estimate. Our weighting procedure is robust to noise in the judges’ responses and can be expressed in closed form. We use both simulation and data from a collection of experimental studies to illustrate that the weighting procedure outperforms existing methods. An R package called metaggR implements our method and is available on the Comprehensive R Archive Network. This paper was accepted by Manel Baucells, behavioral economics and decision analysis. Funding: This work was supported by the Indiana University Kelley School of Business and INSEAD. Supplemental Material: The data files and e-companion are available at https://doi.org/10.1287/mnsc.2022.4648 .

Wisdom of crowds and commodity pricing

AbstractWe extract commodity‐level sentiment from the Twittersphere in 2009–2020. A long–short strategy based on sentiment shifts more than doubles the Sharpe ratio of extant commodity factors. Commodities with lower (higher) sentiment shifts tend to be overvalued (undervalued) when the aggregate market is in backwardation (contango). The sentiment premium is more pronounced during periods of macro contraction and deteriorating funding liquidity. While the premium concentrates in commodities with higher tweet intensity, sentiment extracted from influential tweets (i.e., high number of retweets/likes) does not exhibit stronger predictive ability than low‐attention tweets. Consistent with the overreaction hypothesis, the sentiment premium fully reverses 3 years postformation.

Crowd Decision Making: Sparse Representation Guided by Sentiment Analysis for Leveraging the Wisdom of the Crowd

The “wisdom of the crowd” theory states that a nonexpert crowd makes smarter decisions than a reduced set of experts. Social network platforms are a source of evaluations in the natural language of any topic, which may be considered as the evaluations of a nonexpert crowd. Decision-making (DM) models are constrained by their inability of processing large amounts of evaluations in natural language, as those ones from social networks. We claim that evaluations from social networks can enhance the quality of multiperson multicriteria DM models. Accordingly, we propose a crowd DM model guided by sentiment analysis (SA), which solves decision situations leveraging the wisdom of the crowd available in social networks. The model uses several deep-learning SA classification models through opinion triplets to incorporate all the evaluation shades in the DM model. Likewise, the likely lack of information stemmed from the consideration of a large set of users is tackled with a sparse representation of the evaluations. We annotate and release the TripR-2020Large dataset, and we use it to evaluate the model in the use case of restaurant choice. The results show that the integration of the wisdom of the crowd and the different shades of the evaluations enhances the quality of the decision.

ANN crowds in early-stage design: an investigation of influence of small training sets on prediction

The objective of this paper is to present and evaluate an approach to early-stage design product performance predicting that employs the “wisdom of the crowd” of artificial neural networks (ANN). The wisdom of the crowd has been used to gather insights, ideas, and problems by using responses from large numbers of people. In this work, 189 different ANN architectures will be used instead of human agents, thus creating an ANN Crowd. This research explores the question “should every agent in the crowd has the same voice?” To answer this, this research aims to broadly understand the sensitivity of influencing factors (training set selection, nodes, layers, and architecture) in the performance of the ANN Crowd. This will then support refinement to achieve generalizability for the ANN Crowds. The specific focus of this paper is to explore the effect of the training set selection on prediction accuracy. To do this, four different scenarios are explored (2 inputs x 2 outputs). ANN Crowds are created for these scenarios with different product training configurations to test how the training set influences the predictions (k-fold = 10). Results show that training set selection has a statistical and practical influence on prediction accuracy. Analyzing the presence of edge cases in training sets and the effects of training targets suggests further influence of training set selection. Future work includes a deeper exploration of the influence of edge cases within the training sets. Building on this foundation, the members of the ANN Crowd (different architectures) will be studied for their influence on prediction performance.

The Wisdom of the Crowd: Reliable Deep Reinforcement Learning Through Ensembles of Q-Functions.

Reinforcement learning (RL) agents learn by exploring the environment and then exploiting what they have learned. This frees the human trainers from having to know the preferred action or intrinsic value of each encountered state. The cost of this freedom is that RL is slower and more unstable than supervised learning. We explore the possibility that ensemble methods can remedy these shortcomings by investigating a novel technique which harnesses the wisdom of crowds by combining Q-function approximator estimates utilizing a simple combination scheme similar to the supervised learning approach known as bagging. Bagging approaches have not yet found widespread adoption in the RL literature nor has a comprehensive look at its performance been performed. Our results show that the proposed approach improves all three tasks and RL approaches attempted. The primary contribution of this work is a demonstration that the improvement is a direct result of the increased stability of the action portion of the state-action-value function. Subsequent experimentation demonstrates that the stability in learning allows an actor-critic method to find more efficient solutions. Finally we show that this approach can be used to decrease the amount of time necessary to solve problems which require a deep Q-learning (DQN) approach.

The Wisdom of the Crowd: Uninformed Voting and the Efficiency of Democracy

The Wisdom of the Crowd: Uninformed Voting and the Efficiency of Democracy

On the efficacy of the wisdom of crowds to forecast economic indicators

The interest in the wisdom of crowds stems mainly from the possibility of combining independent forecasts from experts in the hope that many expert minds are better than a few. Hence the relevant subject of study nowadays is the Vox Expertorum rather than Galton’s original Vox Populi. Here we use the Federal Reserve Bank of Philadelphia’s Survey of Professional Forecasters to analyze 15455 forecasting contests to predict a variety of economic indicators. We find that the median has advantages over the mean as a method to combine the experts’ estimates: the odds that the crowd beats all participants of a forecasting contest is 0.015 when the aggregation is given by the mean and 0.026 when it is given by the median. Both aggregation methods yield a 20% error on the average, which must be contrasted with the expected error of a randomly selected forecaster, which is about 22%. We conclude that selective attention is the most likely explanation for the mysterious high accuracy of the crowd reported in the literature. This conclusion is strengthened by the rebuff of the claim that this high accuracy results from the cancellation of the participants’ errors: we find no meaningful correlation between the asymmetry of the distribution of the individuals’ estimates and the collective error.

Economic Linkages from the Wisdom of Crowds

Economic Linkages from the Wisdom of Crowds

A framework for fake news detection based on the wisdom of crowds and the ensemble learning model

Nowadays, the rapid development of social networks has led to the proliferation of social news. However, the spreading of fake news is a critical issue. Fake news is news written to intentionally misinform or deceive readers. News on social networks is short and lacks context. This makes it difficult for detecting fake news based on shared content. In this paper, we propose an ensemble classification model to detect fake news based on exploiting the wisdom of crowds. The social interactions and the user?s credibility are mined to automatically detect fake news on Twitter without considering news content. The proposed method extracts the features from a Twitter dataset and then a voting ensemble classifier comprising three classifiers namely, Support Vector Machine (SVM), Naive Bayes, and Softmax is used to classify news into two categories which are fake and real news. The experiments on real datasets achieved the highest F1 score of 78.8% which was better than the baseline by 6.8%. The proposed method significantly improved the accuracy of fake news detection in comparison to other methods.

Wisdom of Crowds along the Supply Chain: Causal Evidence from Trade Credit

Wisdom of Crowds along the Supply Chain: Causal Evidence from Trade Credit

The Wisdom of the Small Crowd: Myside Bias and Group Discussion

The Wisdom of the Small Crowd: Myside Bias and Group Discussion

'Be the Buyer' — Leveraging the Wisdom of the Crowd in E-Commerce Operations

'Be the Buyer' — Leveraging the Wisdom of the Crowd in E-Commerce Operations

The wisdom of crowd, real option and game theory decisions: can they be used by clubs to improve their investment in football players?

PurposeThe aim of this work is to explain the final negotiated prices for some of the most famous transfers of football players over the last twelve years (2007–2018).Design/methodology/approachThe article analyses different values for forwards taken from the sports website Transfermarkt, developing a statistical model based on personal, performance, risk, environmental and popularity variables. From those values, the article finds an explanation for the final prices paid for 20 superstar players based on a combination of real option valuations, incorporating the players' life cycles and game theory.FindingsThe authors find that in a large percentage (70%) of the analysed cases, the price paid was higher than the intrinsic market value resulting from Transfermarkt, implying the existence of monopolistic rents, paid as “growth options” on prices from different negotiating conditions. On occasions, the final prices also exceed the value of the growth option, calculated under neutral bargaining conditions, highlighting the lack of economic viability of important transfers, leading to financial difficulties for the clubs involved.Originality/valueThe algorithm provides more flexibility and realism than previous proposals, based on the life cycle of football players, introducing the uncertainty and volatility of projections through Monte Carlo simulation, the capacity of clubs to bargain a price at any point of the contract and finally, the buyer's ability to transfer the player if his subsequent performance is not as expected.

Wisdom of the crowd - What's the most important food crop

Wisdom of the crowd - What's the most important food crop

Requirement-driven sustainable supplier selection: Creating an integrated perspective with stakeholders' interests and the wisdom of expert crowds

Driven by the pressure from various stakeholders to embrace sustainability and from the market to enhance competitiveness, more and more companies have been committed to conducting sustainable supplier selection (SSS). SSS is a requirement-driven multi-criteria decision-making (MCDM) problem and the existing literatures have attempted to integrate quality function deployment (QFD) with various MCDM methods to model the problem. However, these models have not conducted SSS based on stakeholders’ satisfaction from alternatives, and used the weights of criteria obtained from stakeholders’ preferences for requirements, which is used to model the compensation among satisfaction levels of different requirements, to guide the compensation among alternatives’ performances on different criteria. In order to address these deficiencies, this paper develops a novel generalized (QFD)-based MCDM structure for SSS, based on which we further design a QFD-based multi-stakeholders and multi-experts MCDM model considering stakeholders’ consensus of interest and wisdom of expert crowds. Firstly, multiple stakeholder groups with multiple participants in each one are asked to output their expectations and preferences for their own requirements; within each group, the algorithm for consensus reaching process (CRP) is designed to formulate the acceptable collective expectations, and the best worst method (BWM) method integrated with an algorithm for consistency improving process (CIP) and an algorithm for CRP is developed to weight requirements. Secondly, multiple experts use basic uncertain linguistic information (BULI) to characterize performances of alternatives on criteria and relationship between requirements and criteria, and a method of weighting individual judgements with the capability of drawing on wisdom of crowds (WOC) while accommodating organizers’ trust level in reliability given by experts is used to facilitate formulating collective judgements. Thirdly, for each alternative and each requirement of each stakeholder group, we can quantify the comprehensive performance of the alternative on the requirement with the help of QFD, based on which and the group’s expectation we can use the value function of prospect theory to quantify the group’s satisfaction of the requirement from the alternative; aggregating the weighted satisfaction levels of requirements gives rise to the comprehensive satisfaction level of each group from each alternative; and then the Maximin decision rule is introduced to rank alternatives because of its ability of accommodating the non-compensation among different stakeholder groups. Finally, a case study is conducted to investigate the validity and effectiveness of the proposed model.

Evaluation of editors’ abilities to predict the citation potential of research manuscripts submitted to The BMJ: a cohort study

ObjectiveTo evaluate the ability of The BMJ editors to predict the number of times submitted research manuscripts will be cited.DesignCohort study.SettingManuscripts submitted to The BMJ, reviewed, and subsequently scheduled for...