https://red.mnstate.edu/commencement/1056/thumbnail.jp

Albert B. Kao (610785)

Andrew M. Berdahl (3288183)

Andrew T. Hartnett (5079770)

Christos C. Ioannou (235288)

Iain D. Couzin (166108)

Joseph B. Bak-Coleman (5079767)

Matthew J. Lutz (383599)

Xingli Giam (212157)

Journal of The Royal Society Interface

English

Moorhead State Teachers College

Minnesota State University, Moorhead

Minnesota State University MoorheadRedCommencement Programs University Archives6-5-1939Commencement Program, June (1939)Moorhead State Teachers CollegeFollow this and additional works at: https://red.mnstate.edu/commencementThis Book is brought to you for free and open access by the University Archives at Red. It has been accepted for inclusion in Commencement Programsby an authorized administrator of Red. For more information, please contact kramer@mnstate.edu.Recommended CitationMoorhead State Teachers College, "Commencement Program, June (1939)" (1939). Commencement Programs. 57.https://red.mnstate.edu/commencement/57

Commencement Program, June (1939)

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Data from Counteracting estimation bias and social influence to improve the wisdom of crowds

Aggregating multiple non-expert opinions into a collective estimate can improve accuracy across many contexts. However, two sources of error can diminish collective wisdom: individual estimation biases and information sharing between individuals. Here, we measure individual biases and social influence rules in multiple experiments involving hundreds of individuals performing a classic numerosity estimation task. We first investigate how existing aggregation methods, such as calculating the arithmetic mean or the median, are influenced by these sources of error. We show that the mean tends to overestimate, and the median underestimate, the true value for a wide range of numerosities. Quantifying estimation bias, and mapping individual bias to collective bias, allows us to develop and validate three new aggregation measures that effectively counter sources of collective estimation error. In addition, we present results from a further experiment that quantifies the social influence rules that individuals employ when incorporating personal estimates with social information. We show that the corrected mean is remarkably robust to social influence, retaining high accuracy in the presence or absence of social influence, across numerosities and across different methods for averaging social information. Using knowledge of estimation biases and social influence rules may therefore be an inexpensive and general strategy to improve the wisdom of crowds.publishe

Kao, Albert B.

Berdahl, Andrew M.

Hartnett, Andrew T.

Lutz, Matthew J.

Bak-Coleman, Joseph B.

Ioannou, Christos C.

Giam, Xingli

Couzin, Iain D.

KOPS - The Institutional Repository of the University of Konstanz

Counteracting estimation bias and social influence to improve the wisdom of crowds

AbstractAggregating multiple non-expert opinions into a collective estimate can improve accuracy across many contexts. However, two sources of error can diminish collective wisdom: individual estimation biases and information sharing between individuals. Here we measure individual biases and social influence rules in multiple experiments involving hundreds of individuals performing a classic numerosity estimation task. We first investigate how existing aggregation methods, such as calculating the arithmetic mean or the median, are influenced by these sources of error. We show that the mean tends to overestimate, and the median underestimate, the true value for a wide range of numerosities. Quantifying estimation bias, and mapping individual bias to collective bias, allows us to develop and validate three new aggregation measures that effectively counter sources of collective estimation error. In addition, we present results from a further experiment that quantifies the social influence rules that individuals employ when incorporating personal estimates with social information. We show that the corrected mean is remarkably robust to social influence, retaining high accuracy in the presence or absence of social influence, across numerosities, and across different methods for averaging social information. Utilizing knowledge of estimation biases and social influence rules may therefore be an inexpensive and general strategy to improve the wisdom of crowds.</jats:p

Albert B. Kao

Andrew M. Berdahl

Andrew T. Hartnett

Matthew J. Lutz

Joseph B. Bak-Coleman

Christos C. Ioannou

Xingli Giam

Iain D. Couzin

Crossref

Aggregatingmultiple non-expert opinions into a collective estimate can improve accuracy acrossmany contexts. However, two sources of error can diminish collective wisdom: Individual estimation biases and information sharing between individuals. Here, we measure individual biases and social influence rules in multiple experiments involving hundreds of individuals performing a classic numerosity estimation task. We first investigate how existing aggregation methods, such as calculating the arithmeticmean or the median, are influenced by these sources of error.We showthat themean tends to overestimate, and the median underestimate, the true value for awide range of numerosities. Quantifying estimation bias, and mapping individual bias to collective bias, allows us to develop and validate three newaggregationmeasures that effectively counter sources of collective estimation error. In addition, we present results from a further experiment that quantifies the social influence rules that individuals employ when incorporating personal estimates with social information. We showthat the corrected mean is remarkably robust to social influence, retaining high accuracy in the presence or absence of social influence, across numerosities andacross differentmethods foraveraging social information.Using knowledge of estimation biases and social influence rules may therefore be an inexpensive and general strategy to improve the wisdom of crowds.</p

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Commencement Program, June (1939)

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Minnesota State University, Moorhead

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