A Personalized Computational Model for Human-Like Automated Decision-Making

We propose a computational model for enabling robots to automatically make decisions under risk in a human-like way. Human decision-making (DM) under risk is influenced by psychological effects, including regret effects, probability weighting effects, and range effects. On the basis of regret theory, we devise a mathematical DM model to encompass these psychological effects. To further quantify the model, we cast the model into a state-space representation and design a fuzzy logic controller to obtain desired preference data from individual decision makers. The data from each individual were used to train a personalized instance of the model. The resulting model is quantitative. It sheds light on the psychological mechanism of risk-attitudes in human DM. The prediction accuracy of the model was statistically tested. On average, the accuracy of our model is 74.7%, which is significantly close to the average accuracy of the subjects when they repeated their own previously made decisions (73.3%). Furthermore, when only the decisions that were repeated consistently by the subjects are examined, the average accuracy of our model is 86.6%.

Automated summary

A computational model is proposed to enable robots to make decisions under risk in a human-like way, incorporating psychological effects such as regret theory. The model is further quantified, trained with individual preference data, and shown to have high prediction accuracy compared to human decision-making.