Data-Driven Decision-Making Under Uncertainty: An Empirical Study of U.S. Wildfire Management

Abstract

Wildfire management in the United States faces prediction accuracy, cost efficiency, and fiscal sustainability issues. This dissertation integrates three interrelated research topics to develop integrated decision models applicable to each stage of wildfire management. The first study evaluates the role of social media analytics (SMA) and Web 3.0 technologies towards improving wildfire prediction, real-time tracking, and response decisions. The study reviewed current social media analytics tools for crisis response, showing how they support crisis tracking, response timing, and crisis communication. The same functionality can presumably be applied to wildfire management. The second study introduces a temporal gravity model that links population- and location-weighted social media activity to wildfire response costs per acre. The model captures behavioral visibility prior to operational deployment and demonstrates stronger informational value than tweet volume alone. The third study investigates how federal budget changes relate to the accuracy of state preparedness decisions. Higher funding is associated with improved accuracy in the short term, but this association weakens in later budget cycles. The analysis treats federal budgets as exogenous inputs and uses panel methods with robustness checks to evaluate decision dynamics under fixed fiscal constraints. Across all three essays, the dissertation highlights the importance of integrating behavioral data and fiscal signals to better inform wildfire planning. It provides empirical evidence that public attention, budget expectations, and institutional coordination jointly influence the quality of response decisions. These findings suggest that effective wildfire management requires models that account for informational uncertainty, fragmented authority, and the timing structure of operational and fiscal systems.

Keywords: Wildfire Management, Decision Science, Behavioral Operations Management, Crisis Informatics, Public Finance, Panel Data Analysis, Gravity Model, Time Series Analysis.