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Modeling Users’ Trust in Drought Forecasts

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.