Decision under risk: A tale of two information modes

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Abstract

Information about risky or uncertain situations typically comes in two forms. In some cases, we have access to complete descriptive summaries of risky outcomes and their associated probabilities (e.g., a physician may rely on published literature and success likelihoods to recommend a risky course of treatment). In other situations, we rely on our own experiences to form estimates of outcomes and probabilities (e.g., a physician drawing on their own prior experience with a particular treatment). Although real-life decision-making often involves combining these sources of information, previous research has primarily examined each source in isolation. In this talk, I will present empirical and computational modelling results that explore how different types of information are combined in studies of risky decision-making. I will also discuss ongoing empirical work investigating whether people naturally prefer one type of information over the other (description vs. experience) and the factors that moderate this preference. Finally, I will address the theoretical, practical, and computational implications of these findings.

The Speaker

Dr. Konstantinidis’ (Ph.D.) main research focus is on judgment and decision making, with an emphasis on the computational modelling of the underlying psychological and cognitive processes. Specifically, he is interested in how people make choices under risk and uncertainty in various basic and applied domains, the role of learning and memory processes, and the combination of different sources of information when people make judgments and decisions. To provide insights into the mechanisms of human behaviour, his research utilises a mixture of laboratory experiments, quantitative techniques, and computational cognitive models.