Individuals make decisions under uncertainty every day based on incomplete information concerning the potential outcome of the choice or chance levels. The choices individuals make often deviate from the rational or mathematically objective solution. Accordingly, the dynamics of human decision making are difficult to capture using conventional, linear mathematical models. Here, we present data from a two choice task with variable risk between sure loss and risky lossto illustrate how a simple nonlinear dynamical system can be employed to capture the dynamics of human decision-making under uncertainty (i.e., multi-stability, bifurcations). We test the feasibility of this model quantitatively and demonstrate how the model can account for up to 86% of the observed choice behavior. The implications of using dynamical models for explaining the nonlinear complexities of human decision making are discussed, as well as the degree to which nonlinear dynamical systems theory might offer an alternative framework for understanding human decision-making processes.
|Title of host publication||Cognitive Science Society|
|Number of pages||6|
|Publication status||Published - 2013|
- complex systems
- dynamical systems modeling
- risky choice
- phase transitions