Effectuation under risk and uncertainty: A simulation model

Effectuation was first proposed as an expert entrepreneur's decision-making framework under uncertainty, but the applications of effectuation beyond the condition of uncertainty have seen less attention. Using an agent-based simulation model, this paper investigates the effectiveness of effectuation relative to causation in uncertain and risky contexts. The simulation overcomes the shortcomings of think aloud protocols typically used in effectuation research. The results suggest that effectuation outperforms causation in both risky and uncertain contexts until the entrepreneur can predict the future correctly > 75% of the time. This suggests expanding the boundary of effectuation from uncertainty to whenever predicting the future is challenging.