# Gaming the unknown: learning to differentiate and respond to uncertainty through a serious game

## TL;DR

A serious game helped students better understand and respond to different types of uncertainty in climate adaptation planning.

## Contribution

The study shows how serious games can shift strategic preferences by improving understanding of diverse uncertainty dimensions.

## Key findings

- Participants shifted from viewing uncertainty as environmental or institutional to recognizing political and implementation challenges.
- Players preferred flexible strategies over scenario optimization, especially reacting more to political disruptions than environmental shocks.
- Cognitive learning about uncertainty led to normative changes in strategy preferences and reduced confidence in controlling outcomes.

## Abstract

Effective climate adaptation planning requires understanding various sources and types of uncertainty. We investigated how a serious game can improve participants’ understanding of uncertainty in adaptation decision-making. Using mixed-methods with pre-post surveys and debriefing sessions, we engaged 55 university students in an adaptation pathways game simulating regional planning challenges over five decades with disruptive events. Results showed participants shifted from appreciating uncertainty as either environmental or institutional, to incorporating political shifts, institutional dynamics, and implementation challenges. Participants preferred flexible strategies over scenario optimization, with political disruptions generating stronger reactions than environmental shocks. Cognitive learning about uncertainty types facilitated normative learning effects, shifting strategic preferences. Participants recognizing diverse uncertainty dimensions became less confident in controlling outcomes, favouring more flexible and resilient strategies. Our findings, with contextual limitations, indicate serious games could help stakeholders develop an appreciation for adaptation approaches that maintain multiple options in parallel over single-pathway sequential solutions under uncertainty.