The role of information search and its influence on risk preferences

Abstract

According to the 'Description-Experience gap' (DE gap), when people are provided with the descriptions of risky prospects they make choices as if they overweight the probability of rare events; but when making decisions from experience after exploring the prospects' properties, they behave as if they underweight such probability. This study revisits this discrepancy while focusing on information-search in decisions from experience. We report findings from a lab-experiment with three treatments: a standard version of decisions from description and two versions of decisions from experience: with and without a 'history table' recording previously sampled events. We find that people sample more from lotteries with rarer events. The history table proved influential; in its absence search is more responsive to cues such as a lottery's variance while in its presence the cue that stands out is the table's maximum capacity. Our analysis of risky choices captures a significant DE gap which is mitigated by the presence of the history table. We elicit probability weighting functions at the individual level and report that subjects overweight rare events in experience but less so than in description. Finally, we report a measure that allows us to compare the type of DE gap found in studies using choice patterns with that inferred through valuation and find that the phenomenon is similar but not identical across the two methods.

Experience and rationality under risk: re-examining the impact of sampling experience

“…Moreover, DFE is a rich experimental environment that can give rise to new theoretical approaches that provide alternatives to the Bayesian approach with EU. For example, some vital aspects of the sampling paradigm, such as memory, adaptive learning, and information search have been previously studied in the DFE literature (Ashby and Rakow 2014 ; Hertwig and Pleskac 2010 ; Hills and Hertwig 2010 ; Lejarraga et al 2012 ; Kopsacheilis 2017 ; Ert and Haruvy 2017 ; Golan and Ert 2015 ). However, these aspects of sampling are not usually modeled in the traditional decision theories used in economics.…”

The Future of Decisions from Experience: Connecting Real-World Decision Problems to Cognitive Processes

“…But in typical DfE paradigms, this information has disappeared by the time participants are probed to make a preferential choice. Thus, participants have to draw on information from previous experiences stored in memory (cf., Kopsacheilis, 2018). As a result, DfE offers a rich environment to study and examine how memory processes shape preferential decision-making.…”

Explaining the description-experience gap in risky decision-making: Learning and memory retention during experience as causal mechanisms

“…Relatedly, an important limitation of the current study is that the RL-CPT model does not predict participant's sampling behavior, which means that it is not a full generative model of how people behave during EBDs. Part of the reason for this is that sampling behavior is intensely idiosyncratic, with information search varying according to momentary fluctuations in experienced sample variance, participants working memory capacity, and sampling heuristics that vary widely across participants or even within a session (Kopsacheilis, 2018). Despite this, models including search and/or optional stopping rules show promising results (e.g., Busemeyer, 1985;Markant, Pleskac, Diederich, Pachur, & Hertwig, 2015;Wulff, Markant, Pleskac, & Hertwig, 2019) and may be well-suited to integrate with models such as the RL-CPT.…”