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Linear Biases and Pandemic Communications

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  • Daniel Villanova

    (Department of Marketing, Sam M. Walton College of Business, University of Arkansas, Fayetteville, AR, USA (DV))

Abstract

Background Previous research has demonstrated a tendency for individuals to mentally linearize nonlinear trends, leading to forecast errors. The present research notes that prior conceptualizations of these linear biases do not make identical predictions and examines how linear biases affect forecasts and risk perceptions of an unfolding epidemic. Methods This research uses an online experiment and a preregistered direct replication in a different online participant pool (total N = 608) to assess the trajectories of forecasts and risk perceptions over time in an unfolding epidemic. Results Framing the progress of the epidemic using total cases (v. the rate of new cases) leads to higher forecasts. This research also finds that the effect of frame varies over different time points in the epidemic and differs for forecasts versus risk perceptions. Finally, the effect of frame for forecasted totals is weaker among more numerate individuals. Limitations The studies use repeated measures that occur in 1 session rather than over the course of several months and involve a smooth epidemic curve rather than a noisy one with jagged case counts. Conclusions This research compares prior conceptualizations of linear biases and yields data with implications both for theory on linear biases and for communicators involved in disseminating information about epidemics. Highlights Framing the progress of the epidemic using total cases (v. the rate of new cases) leads to higher forecasts. The effect of frame varies over different time points in the epidemic and differs for forecasts v. risk perceptions. The effect of frame for forecasted totals is weaker among more numerate individuals.

Suggested Citation

  • Daniel Villanova, 2022. "Linear Biases and Pandemic Communications," Medical Decision Making, , vol. 42(6), pages 765-775, August.
    • Handle: RePEc:sae:medema:v:42:y:2022:i:6:p:765-775
    DOI: 10.1177/0272989X221107907
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    References listed on IDEAS

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    1. Ritwik Banerjee & Priyama Majumdar, 2023. "Exponential growth bias in the prediction of COVID‐19 spread and economic expectation," Economica, London School of Economics and Political Science, vol. 90(358), pages 653-689, April.
    2. Angela Fagerlin & Brian J. Zikmund-Fisher & Peter A. Ubel & Aleksandra Jankovic & Holly A. Derry & Dylan M. Smith, 2007. "Measuring Numeracy without a Math Test: Development of the Subjective Numeracy Scale," Medical Decision Making, , vol. 27(5), pages 672-680, September.
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    Cited by:

    1. Villanova, Daniel & Pandelaere, Mario, 2024. "A Numeracy-Task interaction model of perceived differences," Organizational Behavior and Human Decision Processes, Elsevier, vol. 185(C).

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