Predicting Human Operator’s Decisions Based on Prospect Theory

• Published in: Interacting with computers Vol. 32; no. 3; pp. 221 - 232
• Main Authors: de Souza, Paulo E U, Chanel, Caroline P C, Mailliez, Melody, Dehais, Frédéric
• Format: Journal Article
• Language: English
• Published: Oxford University Press 01.05.2020; Oxford University Press (OUP)
• Subjects: Cognitive science; Neuroscience; prospect theory; model-based predictor design; framing effect; human–computer interaction (HCI); cognitive bias; human–robot(s) interaction (HRI); Prospect theory; Human-computer interaction (HCI)- Human-robot(s) interaction (HRI); Model-based predictor design; Framing effect; Cognitive bias
• ISSN: 0953-5438; 1873-7951
• DOI: 10.1093/iwcomp/iwaa016

Abstract The aim of this work is to predict human operator’s (HO) decisions in a specific operational context, such as a cooperative human-robot mission, by approximating his/her utility function based on prospect theory (PT). To this aim, a within-subject experiment was designed in which the HO has to decide with limited time and incomplete information. This experiment also involved a framing effect paradigm, a typical cognitive bias causing people to react differently depending on the context. Such an experiment allowed to acquire data concerning the HO’s decisions in two different mission scenarios: search and rescue and Mars rock sampling. The framing was manipulated (e.g. positive vs. negative) and the probability of the outcomes causing people to react differently depending on the context. Statistical results observed for this experiment supported the hypothesis that the way the problem was presented (positively or negatively framed) and the emotional commitment affected the HO’s decisions. Thus, based on the collected data, the present work is willed to propose: (i) a formal approximation of the HO’s utility function founded on the prospect theory and (ii) a model used to predict the HO’s decisions based on the economics approach of multi-dimensional consumption bundle and PT. The obtained results, in terms of utility function fit and prediction accuracy, are promising and show that similar modeling and prediction method should be taken into account when an intelligent cybernetic system drives human–robot interaction. The advantage of predicting the HO’s decision, in this operational context, is to anticipate his/her decision, given the way a question is framed to the HO. Such a predictor lays the foundation for the development of a decision-making system capable of choosing how to present the information to the operator while expecting to align his/her decision with the given operational guideline.