Is it time for studying real-life debiasing? Evaluation of the effectiveness of an analogical intervention technique.

Balazs Aczel,Bence Lukacs,Andrei Foldes,Bence Bago,Aba Szollosi

doi:10.3389/fpsyg.2015.01120

Abstract

The aim of this study was to initiate the exploration of debiasing methods applicable in real-life settings for achieving lasting improvement in decision making competence regarding multiple decision biases. Here, we tested the potentials of the analogical encoding method for decision debiasing. The advantage of this method is that it can foster the transfer from learning abstract principles to improving behavioral performance. For the purpose of the study, we devised an analogical debiasing technique for 10 biases (covariation detection, insensitivity to sample size, base rate neglect, regression to the mean, outcome bias, sunk cost fallacy, framing effect, anchoring bias, overconfidence bias, planning fallacy) and assessed the susceptibility of the participants (N = 154) to these biases before and 4 weeks after the training. We also compared the effect of the analogical training to the effect of ‘awareness training’ and a ‘no-training’ control group. Results suggested improved performance of the analogical training group only on tasks where the violations of statistical principles are measured. The interpretation of these findings require further investigation, yet it is possible that analogical training may be the most effective in the case of learning abstract concepts, such as statistical principles, which are otherwise difficult to master. The study encourages a systematic research of debiasing trainings and the development of intervention assessment methods to measure the endurance of behavior change in decision debiasing.

Highlights

The early observations of a normative-descriptive gap in human judgment and decision making (Tversky and Kahneman, 1974) has given rise to a prolific research field with the central aim of describing how and why human reasoning falls short of logical, economical, or statistical normative ideals
One can be hopeful about debiasing if considering that general aptitude correlates positively with normative responses (Larrick et al, 1993; Stanovich and West, 1998), or that studying statistics or economics makes one less likely to succumb to decision biases (Lehman and Nisbett, 1990; Fennema and Perkins, 2008)
We present a training method that attempts to utilize analogical encoding (Gentner et al, 2004) in order to explore its potentials for lasting decision making debiasing

Summary

Introduction

The early observations of a normative-descriptive gap in human judgment and decision making (Tversky and Kahneman, 1974) has given rise to a prolific research field with the central aim of describing how and why human reasoning falls short of logical, economical, or statistical normative ideals. The interpretation of these results are not without debate (e.g., Gigerenzer and Todd, 1999; Klein, 1999), a persistent assumption is that people either miss the adequate background knowledge for certain decision problems (Perkins et al, 1993), or they prefer to rely on simple heuristics and strategies that require low cognitive effort, but potentially lead to suboptimal decisions (Kahneman, 2011) These biases and fallacies can impact people’s life to a great degree (Parker and Fischhoff, 2005; Lunn, 2013), our understanding of how to improve human decision making is far from advanced. One can be hopeful about debiasing if considering that general aptitude correlates positively with normative responses (Larrick et al, 1993; Stanovich and West, 1998), or that studying statistics or economics makes one less likely to succumb to decision biases (Lehman and Nisbett, 1990; Fennema and Perkins, 2008)

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Results

Discussion

Conclusion