Most leading computational theories of decision-making under risk do not have mechanisms to account for the incorporation of cultural factors. Therefore, they are of limited utility to scholars and practitioners who wish to model, and predict, how culture influences decision outcomes. Fuzzy Trace Theory (FTT) posits that people encode risk information at multiple levels of representation – namely, gist, which captures the culturally contingent meaning, or interpretation, of a stimulus, and verbatim, which is a detailed symbolic representation of the stimulus. Decision-makers prefer to rely on gist representations, although conflicts between gist and verbatim can attenuate this reliance. In this paper, we present a computational model of Fuzzy Trace Theory, which is able to successfully predict 14 experimental effects using a small number of assumptions. This technique may ultimately form the basis for an agent-based model, whose rule sets incorporate cultural and other psychosocial factors.