Simulation-based inference with deep learning shows speed climbers combine innovation and copying to improve performance

In the Olympic sport of speed climbing, athletes compete to reach the top of a 15-meter wall as quickly as possible. Since the standardization of the speed climbing route in 2007, improvement has been driven by a process of cumulative cultural evolution—new route sequences innovated by some are copied and improved upon by others. In this study, we use simulation-based inference to fit an agent-based model of speed climbing to 12 years of competitive speed climbing times (2007-2019). By analyzing the dynamics of the fitted model, we are able to gain some insight into the processes that may be driving cumulative improvement in speed climbing. Innovation and copying are used roughly equally by climbers, with copying having only a slight advantage. Slower agents in the model are more likely to innovate, likely in pursuit of strategies that give them a competitive advantage. Population size negatively predicts innovation, presumably because innovation is not as useful when there are plenty of existing solutions to choose from. Finally, the model suggests that climbers may not be using innovation and copying optimally—continued improvement may require more emphasis on innovation and less reliance on copying.