Predictive rebound & technologies of engagement: science, technology, and communities in wildfire management

Abstract

Technologies of anticipation–such as predictive analytics, forecasting, and modelling–offer appealing promises to those governing risk. While previous work has challenged notions that such technologies are value neutral, we must attend to specific–and subtle–ways that values are embedded and manipulated within these systems. Using the case of wildfire management, I propose the concept of predictive rebound to highlight two challenges: (1) that increasingly accurate predictive models do not always translate into the initially intended real-world gains, but rather can end up being applied to alternative ends and (2) that perceived accuracy of predictive models can be misunderstood as reducing the need for explicit debate about values within decision-making. Further analysis of predictive rebound in real-world contexts will help to inform more effective engagement with stakeholders about values, priorities, and risks; revealing situations where technologies of anticipation obfuscate value-laden decisions and facilitate unintentional drift in management priorities.