Forecasts are essential for policy-making in areas like economics, public health, and climate change, but their impact is often limited by how they are communicated. Misinterpretation or underuse of forecasts can lead to inefficient policies, wasted resources, or missed opportunities. One way to address this gap could be to systematically test which communication methods—such as probabilities versus scenarios, graphs versus narratives, or worst-case versus most likely outcomes—are most effective in helping policymakers understand and act on forecasts.

Testing Communication Methods with Policymakers

The idea involves conducting a randomized controlled trial (RCT) where politicians are presented with identical forecasts using different communication strategies. For example, one group might see a forecast framed as a probability, while another sees it as a scenario. The goal would be to measure how these variations affect comprehension, trust, and willingness to take action. To ensure participation, the study could initially focus on politicians who narrowly lost elections, as they may have both policy interest and availability. Incentives like honorariums or personalized reports could encourage involvement.

Potential Impact and Execution

If successful, this approach could benefit policymakers by improving their decision-making, forecasters by increasing the influence of their work, and the public through better policies. A minimal viable product (MVP) might start with a small-scale pilot involving 20-30 participants to refine the methodology before scaling. Later phases could expand to include civil servants or NGO leaders to validate broader applicability. Key metrics for the study could include comprehension quizzes, trust surveys, and hypothetical policy-choice exercises.

Existing research, such as Fischhoff's work on forecast interpretation or Doyle's studies on risk communication, provides a foundation but doesn't specifically test methods for policymakers. By filling this gap, the project could help bridge the divide between data and action in policy-making.