A Simplified Model of Choice Behavior under Uncertainty - Lin, Ching-Hung; Lin, Yu-Kai; Song, Tzu-Jiun; Huang, Jong-Tsun; Chiu, Yao-Chu (Frontiers in Psychology, 2016)
The Iowa Gambling Task (IGT) has been standardized as a clinical assessment tool (Bechara, 2007). Nonetheless, numerous research groups have attempted to modify IGT models to optimize parameters for predicting the choice behavior of normal controls and patients. A decade ago, most researchers considered the expected utility (EU) model (Busemeyer and Stout, 2002) to be the optimal model for predicting choice behavior under uncertainty. However, in recent years, studies have demonstrated the prospect utility (PU) models (Ahn et al., 2008) to be more effective than the EU models in the IGT. Nevertheless, after some preliminary tests, we propose that Ahn et al. (2008) PU model is not optimal due to some incompatible results between our behavioral and modeling data. This study aims to modify Ahn et al. (2008) PU model to a simplified model and collected 145 subjects’ IGT performance as the benchmark data for comparison. In our simplified PU model, the best goodness-of-fit was found mostly while α approaching zero. More specifically, we retested the key parameters α, λ , and A in the PU model. Notably, the power of influence of the parameters α, λ, and A has a hierarchical order in terms of manipulating the goodness-of-fit in the PU model. Additionally, we found that the parameters λ and A may be ineffective when the parameter α is close to zero in the PU model. The present simplified model demonstrated that decision makers mostly adopted the strategy of gain-stay-loss-shift rather than foreseeing the long-term outcome. However, there still have other behavioral variables that are not well revealed under these dynamic uncertainty situations. Therefore, the optimal behavioral models may not have been found. In short, the best model for predicting choice behavior under dynamic-uncertainty situations should be further evaluated.