Ambient Intelligence and Crowdsourced Genetics for Understanding Loss Aversion in Decision Making

The big challenge for Artificial Intelligence is a better understanding of human nature. Our fundamental motivation is to understand the minds of modern people by uncovering mechanisms of the brain, genes, and body, and enhancing our health and cognitive talents with Artificial Intelligence technologies. This paper presents how we can quantify cognitive biases in the decision-making process and understand the evolutionary mechanisms using Ambient Intelligence and crowdsourced genetics technologies. We focus on prospect theory (proposed by Daniel Kahneman), which models how people choose between options involving gains or losses. People perceive losses to hurt more than gains feel good. This “loss aversion” is an important cognitive bias in decision-making. However, little is known about individual differences in loss aversion. We launched a citizen science project to test the hypothesis that mutations in genes related to neural processes are related to individual variation in loss aversion. Our preliminary experiment showed that DRD2 gene mutations may be related to individual variation in loss aversion. This crowdsourced genetics research is probably the first trial to report the possibilities of individual genetic differences in loss aversion behaviors. We discuss the future paradigms in Ambient Intelligence for health and cognitive enhancement.

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