The challenge of information overload often leads decision-makers to favor simpler, more intuitive options over complex ones, even when the latter offer potentially greater rewards. Previous research has primarily focused on the behavioral phenomenon of complexity aversion, attributing it to the avoidance of cognitive effort.

In this study, we employ a drift diffusion modeling (DDM) approach to explore how complexity shapes decision-making across different experimental contexts. Our experiments manipulate lottery complexity along three dimensions: probabilities (Study 1), outcomes (Study 2), and number of outcomes (Study 3). Beyond comparing decisions involving complex versus simple options, we also examine scenarios where both options are either uniformly complex or simple. This comprehensive design allows us to uncover the cognitive mechanisms underpinning complexity aversion across diverse contexts.

Contrary to prior research, our findings reveal that complexity aversion does not consistently emerge across all three studies. Through DDM, we disentangle the cognitive processes driving these effects. In cases where complexity aversion occurs, our analysis reveals it is primarily driven by pre-valuation bias rather than a discounting effect during decision-making. These results offer a more nuanced perspective on the relationship between complexity and cognitive effort.

When both complex and simple options are presented, complexity influences several facets of decision-making, including response times, choice consistency, risk-taking behavior, and the weighting of rare outcomes.

Our experimental results and computational modeling contribute to a deeper understanding of how complexity shapes risk-taking behavior, offering new insights into the cognitive processes involved in decision-making under complexity.