Model‐Based Wisdom of the Crowd for Sequential Decision‐Making Tasks

Thomas, Bobby; Coon, Jeff; Westfall, Holly A.; Lee, Michael

doi:10.1111/cogs.13011

Cited by 8 publications

(6 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the present study, we were primarily interested in how individuals correct beliefs and behavioral intentions after receiving feedback about their own risk misestimation, as opposed to population-wide risk estimation biases. Our findings align with prior studies on the “wisdom of the crowd,” which have shown that averaging variable and inaccurate estimates from many individuals can produce remarkably accurate estimates [ 40 – 42 ].…”

Section: Discussionsupporting

confidence: 90%

Communicating COVID-19 exposure risk with an interactive website counteracts risk misestimation

Sinclair,

Taylor,

Brandel-Tanis

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

During the COVID-19 pandemic, individuals depended on risk information to make decisions about everyday behaviors and public policy. Here, we assessed whether an interactive website influenced individuals’ risk tolerance to support public health goals. We collected data from 11,169 unique users who engaged with the online COVID-19 Event Risk Tool (https://covid19risk.biosci.gatech.edu/) between 9/22/21 and 1/22/22. The website featured interactive elements, including a dynamic risk map, survey questions, and a risk quiz with accuracy feedback. After learning about the risk of COVID-19 exposure, participants reported being less willing to participate in events that could spread COVID-19, especially for high-risk large events. We also uncovered a bias in risk estimation: Participants tended to overestimate the risk of small events but underestimate the risk of large events. Importantly, even participants who voluntarily sought information about COVID risks tended to misestimate exposure risk, demonstrating the need for intervention. Participants from liberal-leaning counties were more likely to use the website tools and more responsive to feedback about risk misestimation, indicating that political partisanship influences how individuals seek and engage with COVID-19 information. Lastly, we explored temporal dynamics and found that user engagement and risk estimation fluctuated over the course of the Omicron variant outbreak. Overall, we report an effective large-scale method for communicating viral exposure risk; our findings are relevant to broader research on risk communication, epidemiological modeling, and risky decision-making.

show abstract

Section: Discussionsupporting

confidence: 90%

Communicating COVID-19 exposure risk with an interactive website counteracts risk misestimation

Sinclair,

Taylor,

Brandel-Tanis

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Also, (Derakhshan Beigy, 2019) suggested that sentiment analysis of the English dataset is more effective and efficient than the Persian dataset for predicting the stock market. The most straightforward approach to merging individual conduct is for the multitude to imitate most individuals in every attempt (Thomas et al, 2021). It is imperative to acknowledge that while the foundation of investor conduct is logical reasoning, they, for diverse reasons, cannot assimilate all the information available in the stock market (Noroozi et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

Tehran Stock Exchange, Stocks Price Prediction, Using Wisdom of Crowd

Sohrabi,

Rouhani,

Yazdani

et al. 2023

IJFIFSA

View full text Add to dashboard Cite

Two predominant methods for analyzing financial markets have been technical and fundamental analysis. However, the emergence of the Internet has altered the trading landscape. The availability of Internet and social media access plays a moderating role in information asymmetry, resulting in investors making informed decisions. Social media has turned into a source of information for investors. Through diverse communication channels on social media, investors articulate their perspectives on whether to buy or sell a stock. According to Surowiecki, the collective opinions gathered through social media frequently offer better predictions than individual opinions, a phenomenon referred to as the Wisdom of the Crowd. The wisdom of the crowd stands as an essential measure within social networks, with its potential to reduce errors and lessen information-gathering costs. In this study, we tried to evaluate the wisdom of the crowd's potential to improve stock price prediction accuracy. So, we developed a prediction model by Long Short-Term Memory based on the wisdom of the crowd. Users' opinions in Persian about the Tehran Stock Exchange (TSE) stocks were collected from SAHMETO for eight months. The Support Vector Machine classified them into buy, sell, and neutral classes. During the research period, people mentioned 823 stocks, and 52 stocks with over 100 signals were chosen. The results of the study show that although the model presented has achieved an acceptable level of accuracy, correlations between the actual and predicted values exceeded 90%. The accuracy metrics of the proposed model compared to the base model were not improved.

show abstract

“…Whereas there is ample evidence for the validity of wisdom of crowds in general (Davis-Stober et al, 2014;Larrick & Soll, 2006;Surowiecki, 2004), this assumption may be inadequate under certain conditions. For instance, judgment processes can lead to non-normal distributions (Hueffer et al, 2013;Lorenz et al, 2011;Lorenz, 2021) or extreme judgments (Thomas et al, 2021) in which the mean of the judgments does not longer reflect the correct answer. However, for the present purpose of comparing the accuracy of independent and dependent judgments, this is not a critical assumption given that it is made both for wisdom of crowds and for sequential collaboration (skewed distributions are considered in the empirical study below).…”

Section: Modeling Independent Judgmentsmentioning

confidence: 99%

Modeling Dependent Group Judgments: A Computational Model of Sequential Collaboration

Mayer,

Heck

2024

Preprint

View full text Add to dashboard Cite

Sequential collaboration describes the incremental process of contributing to online collaborative projects such as Wikipedia and OpenStreetMap. After an initial entry is created by a first contributor, subsequent contributors create a sequential chain by deciding whether to adjust or maintain the latest entry which is updated if they decide to make changes. Sequential collaboration has recently been examined as a method for eliciting numerical group judgments. It was shown that in a sequential chain, changes become less frequent and smaller, while judgments become more accurate. Judgments at the end of a sequential chain are similarly accurate and in some cases even more accurate than aggregated independent judgments (wisdom of crowds). This is at least partly due to sequential collaboration allowing contributors to contribute according to their expertise by selectively adjusting judgments. However, there is no formal theory of sequential collaboration. We developed a computational model that formaluzes the cognitive processes underlying sequential collaboration. It allows modeling both sequential collaboration and independent judgments, which are used as a benchmark for the performance of sequential collaboration. The model is based on internal distributions of plausible judgments that contributors use to evaluate the plausibility of presented judgments and to provide new judgments. It incorporates individuals’ expertise and tendency to adjust presented judgments as well as item difficulty and the effects of the presented judgment on subsequent judgment formation. The model is consistent with previous empirical findings on change probability, change magnitude, and judgment accuracy incorporating expertise as a driving factor of these effects. Moreover, new predictions for long sequential chains were confirmed by an empirical study. Above and beyond sequential collaboration the model establishes an initial theoretical framework for further research on dependent judgments.

show abstract

Model‐Based Wisdom of the Crowd for Sequential Decision‐Making Tasks

Cited by 8 publications

References 63 publications

Communicating COVID-19 exposure risk with an interactive website counteracts risk misestimation

Communicating COVID-19 exposure risk with an interactive website counteracts risk misestimation

Tehran Stock Exchange, Stocks Price Prediction, Using Wisdom of Crowd

Modeling Dependent Group Judgments: A Computational Model of Sequential Collaboration

Contact Info

Product

Resources

About