Decision making under risk and ambiguity in human and non-human primates

Practical information
31 January 2020

ENS, Jaurès building, Ribot room, 24 rue Lhomond, 75005 Paris


Decision making under risk is fundamental in humans and other animals. Biologists generally aim at highlighting particular attitudes towards risk (i.e. risk proneness or risk aversion for example) that would reflect naturally selected adaptations to past environmental conditions for a given species. By contrasts with human studies of choice, those studies often fail to consider the interplay between the mechanisms involved in the decision such as the respective role of loss aversion, risk attitudes and probability distortion. In a first part of this talk, I will present data of four ape and two monkey species tested in a food gambling game to uncover the determinants of their gambling decisions. Data were first analysed using CPT and EUT models in an attempt to quantify parameters known to influence decision making in humans. In parallel, we investigated whether some individuals used decisional heuristics to reduce the cost of evaluating odds at each new trial. Data show that several subjects used the Maximax heuristic, focussing on potential gains, while ignoring potential losses. In a second part of this talk, we investigated the response of these species under ambiguous conditions. Subjects were tested in a test very similar to the gambling game but with some information missing. They had no exact knowledge of the odds associated to the outcome of their gambling. Dealing with unpredictability may be more challenging and require more cognitive flexibility. We expected some species to reject ambiguity and/or use even simpler decision rules than under risk. However, under this ambiguous context, individuals gambled as if they had built expectations about the missing information and more so in orangutans, gorillas and chimpanzees. Using decision trees, we could identify each step of the decisional process. Results show that non-human primates can combine several decision rules to make their decision in an unpredictable environment.  


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