A computational knowledge-based model for emulating human performance in the Iowa Gambling Task

A new computational knowledge-based model for emulating human performance in decision making tasks is proposed. This model is mainly based on the knowledge acquired through past experience, the knowledge extracted from the environment and the relationships between the concepts that represent these two kinds of knowledge. The proposed model divides the decision making process into two phases. The first phase lies in the estimation of the decision outcomes using a net of concepts. In the second phase, the proposed model uses a value function to score each possible alternative. The design of the model focuses on some psychological and neurophysiological evidence from current research. In order to validate the model, it is compared with other widely used models that implement different theories of decision making under risk and uncertainty. The model comparison is centered on a well defined task, the Iowa Gambling Task, used in several psychological experiments. The comparison applies an evaluation method based on the optimization of each model in order to emulate human performance individually starting both the participant and the model from the same environmentally available information. The results show that the performance of the proposed model is quantitatively better than the other compared models. Besides, using relevant concepts extracted from interviews with the participants increases the performance of the proposed model.