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Modeling Users’ Trust in Drought Forecasts

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  • 1 a Ven Te Chow Hydrosystems Laboratory, Department of Civil and Environmental Engineering, University of Illinois at Urbana–Champaign, Urbana, Illinois
  • | 2 b Department of Engineering Systems and Environment, University of Virginia, Charlottesville, Virginia
  • | 3 c Department of Finance, Gies College of Business, University of Illinois at Urbana–Champaign, Champaign, Illinois
  • | 4 d DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana–Champaign, Urbana, Illinois
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Abstract

Forecast valuation studies play a key role in understanding the determinants of the value of weather and climate forecasts. Such understanding provides opportunities to increase the value that users can obtain from forecasts, which can in turn increase the use of forecasts. One of the most important factors that influences how users process forecast information and incorporate forecasts into their decision-making is trust in forecasts. Despite the evidence from empirical and field-based studies, modeling users’ trust in forecasts has not received much attention in the literature and is therefore the focus of our study. We propose a theoretical model of trust in information, built into a forecast valuation framework, to better understand 1) the role of trust in users’ processing of drought forecast information and 2) the dynamic process of users’ trust formation and evolution. Using a numerical experiment, we show that considering the dynamic nature of trust is critical in more realistic assessment of forecast value. We find that users may not perceive a potentially valuable forecast as such until they trust it enough, implying that exposure to even highly accurate forecasts may not immediately translate into forecast use. Ignoring this dynamic aspect could overestimate the economic gains from forecasts. Furthermore, the model offers hypotheses with regard to targeting strategies that can be tested with empirical and field-based studies and used to guide policy interventions.

Significance Statement

A key factor that determines how users respond to forecast information is the extent to which they trust the information. We propose a model of trust in drought forecast information that captures how users’ trust forms and evolves over time and shows how trust influences users’ decisions. We find that even if a user is exposed to relatively accurate forecasts, he or she may not use them immediately because a minimum trust level must be developed before forecasts are perceived to be valuable. We also show that encouraging users to rely on poor forecasts can make them worse off, potentially deterring them from using forecasts in the future. Our findings highlight the importance of credible communication of forecast accuracy.

Shafiee-Jood’s ORCID: 0000-0002-5808-3393.

Deryugina’s ORCID: 0000-0003-0870-8655.

Cai’s ORCID: 0000-0002-7342-4512.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Ximing Cai, xmcai@illinois.edu

Abstract

Forecast valuation studies play a key role in understanding the determinants of the value of weather and climate forecasts. Such understanding provides opportunities to increase the value that users can obtain from forecasts, which can in turn increase the use of forecasts. One of the most important factors that influences how users process forecast information and incorporate forecasts into their decision-making is trust in forecasts. Despite the evidence from empirical and field-based studies, modeling users’ trust in forecasts has not received much attention in the literature and is therefore the focus of our study. We propose a theoretical model of trust in information, built into a forecast valuation framework, to better understand 1) the role of trust in users’ processing of drought forecast information and 2) the dynamic process of users’ trust formation and evolution. Using a numerical experiment, we show that considering the dynamic nature of trust is critical in more realistic assessment of forecast value. We find that users may not perceive a potentially valuable forecast as such until they trust it enough, implying that exposure to even highly accurate forecasts may not immediately translate into forecast use. Ignoring this dynamic aspect could overestimate the economic gains from forecasts. Furthermore, the model offers hypotheses with regard to targeting strategies that can be tested with empirical and field-based studies and used to guide policy interventions.

Significance Statement

A key factor that determines how users respond to forecast information is the extent to which they trust the information. We propose a model of trust in drought forecast information that captures how users’ trust forms and evolves over time and shows how trust influences users’ decisions. We find that even if a user is exposed to relatively accurate forecasts, he or she may not use them immediately because a minimum trust level must be developed before forecasts are perceived to be valuable. We also show that encouraging users to rely on poor forecasts can make them worse off, potentially deterring them from using forecasts in the future. Our findings highlight the importance of credible communication of forecast accuracy.

Shafiee-Jood’s ORCID: 0000-0002-5808-3393.

Deryugina’s ORCID: 0000-0003-0870-8655.

Cai’s ORCID: 0000-0002-7342-4512.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Ximing Cai, xmcai@illinois.edu
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