Utility Reversals: Memory and Contextual Biases in Risky Preferences

People’s behaviour in the face of risk implies that they judge and weight the probability of risky events in characteristic ways that deviate from Economic Theory (EUT). Nonetheless, both EUT and the leading psychological theory of human choice under risk share a common assumption: people’s risk preferences and decisions under risk and uncertainty are independent of task. In recent research we find evidence that choice is influenced by the accessibility of familiar events in memory. This suggests that people’s experiences “leak” into decisions even when risk information is explicitly provided (Kusev et al., 2009; Kusev et al., in press). Accordingly, this project aims to investigate the influences of context, memory, patterns and computational complexity on risky preferences.

Judgments of Frequencies, Patterns and Randomness

We often attempt to understand and make inductions about temporal sequences of events (e.g., busy and quiet business days, people’s good and bad moods, sunny and rainy days). A long history of research analyzes how people reason about the processes underlying sequences and how they anticipate individual events in a sequence (Kusev, et al., in press). Extensive research has also investigated memory for - and judgment of - the frequency of events encountered in temporal sequence. Sensitivity to the frequency of events is crucial for judgments and decisions concerning uncertain payoffs or threats. However, very little research has explored if and how the ordering of different sorts of item in a sequence affects judgments and choices about those items. The goal of this project is to address this lacuna: searching for evidence that simple strategies effectively exploit sequence properties to compensate for the processing-capacity limitations underlying memory and judgment.

Judging From Experience: Holistic and Analytical processing

When attempting to predict future events, people commonly rely on historical data. Events in a time series can be experienced sequentially (dynamic mode), as in learning about decisions from experience, or, as with learning about decisions from descriptions, they can also be retrospectively viewed holistically (static mode) – not experienced individually in real time. In this project, we study the influence of presentation mode (dynamic and static) on three sorts of judgments: (i) predictions of the next event (forecast), (ii) estimation of the average value of all the events in the presented series (average) and (iii) judged satisfaction (satisfaction). We found that relative to the static mode participants’ responses in dynamic mode were anchored on more recent events for all three types of judgments but with different consequences – hence dynamic presentation improved prediction accuracy, but not estimation (submitted work).