ISSN 2398-2020 THE BEHAVIORAL ECONOMICS GUIDE 2016 Edited by Alain Samson Introduction by Gerd Gigerenzer behavioraleconomics.com Edited by Alain Samson Introduction by Gerd Gigerenzer The Behavioral Economics Guide 2016 ISSN 2398-2020 Author information: Alain Samson (Editor) Gerd Gigerenzer (Introduction) Rachel Abbott, Liz Barker, Dan Bennett, Zoë Chance, Richard Chataway, Sarah Davies, Sian Davies, Ravi Dhar, Pete Dyson, Gerhard Fehr, Varun Gauri, Vishal George, Tim Gohmann, Christian Goy, Eleanor Heather, Crawford Hollingworth, Moritz Jäger, Alain Kamm, Roger Miles, Ronald S. Mundy, Henry Stott, Richard Thaler, Emma Williams (Contributing authors) Cover and logo design by Jozsef Bagota, Maiol Baraut and Alain Samson. Published on www.behavioraleconomics.com by Behavioral Science Solutions Ltd Copyright © by the authors All rights reserved Requests for permission to reproduce materials from this work should be sent to [email protected] or directly to contributing authors. Suggested citation: Samson, A. (Ed.)(2016). The Behavioral Economics Guide 2016 (with an introduction by Gerd Gigerenzer). Retrieved from http://www.behavioraleconomics.com. Suggested citation for individual sections/authors: [Author(s)] (2016). [Chapter/Section Title]. In A. Samson (Ed.), The Behavioral Economics Guide 2016 (with an introduction by Gerd Gigerenzer)(pp. [nn]-[nn]). Retrieved from http://www.behavioraleconomics.com. With Contributions By: With Further Support By: Table of Contents I N T R O D U C T I O N TAKING HEURISTICS SERIOUSLY (GERD GIGERENZER) ....................................................................... V P A R T 1 - E D I T O R I A L BEHAVIORAL ECONOMICS IN PERSPECTIVE (ALAIN SAMSON) ..........................................................1 BEHAVIORAL ECONOMICS IN 2016 ...................................................................................................... 1 GENERALIZABILITY AND REPLICABILITY IN PSYCHOLOGY AND ECONOMICS ..................................................... 1 AFTER THE NUDGE ........................................................................................................................... 4 BEHAVIORAL SCIENCE AND PUBLIC POLICY ............................................................................................ 8 DEBIASING ..................................................................................................................................... 9 ‘FAST & FRUGAL’ VS. ‘HEURISTICS & BIASES’ ........................................................................................ 11 BEHAVIORAL AND DATA SCIENCE ...................................................................................................... 13 CONSUMER BEHAVIOR AND NEUROSCIENCE ........................................................................................ 14 NUDGING FOR GOOD AND BAD ........................................................................................................ 15 FINANCE, REGULATION AND THE LIMITS OF NUDGING ............................................................................ 16 PRACTITIONER CONTRIBUTIONS IN THIS EDITION .................................................................................. 18 Q&A WITH RICHARD THALER ...................................................................................................................23 Q&A WITH VARUN GAURI .........................................................................................................................25 P A R T 2 - A P P L I C A T I O N S BEHAVIORAL SCIENCE IN PRACTICE .......................................................................................................29 HOW TO APPLY BEHAVIORAL SCIENCE WITH SUCCESS: LEARNING FROM APPLICATION AROUND THE WORLD ...... 30 CASE STUDY: NUDGING AND STEERING SWIMMING IN ENGLAND, 2015 ..................................................... 37 CASE STUDY: OPTIMIZING CUSTOMER COMMUNICATIONS THROUGH BEHAVIORAL ECONOMICS ....................... 41 WHY ONLY BEHAVIORAL ECONOMICS CAN EXPLAIN PREFERENCE ............................................................. 44 CASE STUDY: TURNING A 16 BILLION DOLLAR PROBLEM INTO DOING GOOD .............................................. 51 RISK SCIENCE OR REALPOLITIK: WHAT’S DRIVING THE WORLDWIDE BOOM IN CONDUCT REGULATION? ............. 55 THE DEVIL YOU KNOW: THE CONSUMER PSYCHOLOGY OF BRAND TRUST ................................................... 67 THE BEHAVIORAL CHANGE MATRIX: A TOOL FOR EVIDENCE-BASED POLICY MAKING ..................................... 75 BEHAVIOR CHANGE: WHAT TO WATCH OUT FOR ................................................................................... 82 MAKING THE BEST CHOICE THE EASY CHOICE: APPLYING THE 4PS FRAMEWORK FOR BEHAVIOR CHANGE ........... 90 P A R T 3 - R E S O U R C E S SELECTED BEHAVIORAL SCIENCE CONCEPTS .................................................................................... 101 POSTGRADUATE PROGRAMS IN BEHAVIORAL ECONOMICS & BEHAVIORAL SCIENCE ........... 132 BEHAVIORAL SCIENCE EVENTS ............................................................................................................. 148 SCHOLARLY JOURNALS WITH BEHAVIORAL ECONOMICS CONTENT .......................................... 154 OTHER RESOURCES .................................................................................................................................. 167 A P P E N D I X AUTHOR PROFILES ................................................................................................................................... 169 CONTRIBUTING ORGANIZATIONS ....................................................................................................... 170 Behavioral Economics Guide 2016 III Acknowledgements The editor would like to thank Cristiano Codagnone, Tim Gohmann, Andreas Haberl, and Roger Miles for their helpful feedback, and Varun Gauri and Richard Thaler for giving their permission to reprint the original English versions of their Q&As. Special thanks go to Gerd Gigerenzer for writing the introduction to this edition. I am grateful for the support received by the Behavioural Architects, Behavioral Science Lab, Berkeley Research Group, Decision Technology, FehrAdvice & Partners, Ogilvy Change, Yale Center for Customer Insights, City University London, the London School of Economics and Political Science and the University of Warwick. Behavioral Economics Guide 2016 IV INTRODUCTION Taking Heuristics Seriously Gerd Gigerenzer A large retailer habitually sends special offers and catalogues to previous customers. Yet unfocused mass mailing is expensive – and annoying for recipients with no further interest in products from the company, who sometimes voice their complaints in online reviews. Thus, the retailer is keen to target offers at “active” customers who are likely to make purchases in the future, as opposed to “inactive” ones. Given a database with tens of thousands of past buyers, how can the marketing department distinguish active from inactive customers? The conventional wisdom is to solve a complex problem by using a complex method. One such method is the Pareto/NBD model featured in marketing research, where NBD stands for “negative binomial distribution.” Readers who are in marketing may be familiar with it; for the others it suffices to say that the model tries to estimate the purchase rates, dropout rates, and other factors from past data, and delivers exactly what companies want – the probability that a customer is still active. Thus, we might expect that every sensible manager applies this or similar analytical models. But that is not the case. Instead, experienced managers typically rely on simple rules. For instance, managers of a global airline use a rule based on the recency of a customer's last purchase (the hiatus rule): If a customer has not made a purchase for nine months or longer, classify him/her as inactive, otherwise as active. Such rules that ignore part of the available information are called heuristics. The hiatus rule pays attention to only one good reason, the recency of purchase, and ignores the rest – how much a customer bought, the time between purchases, and everything else that complex algorithms such as Pareto/NDB carefully scrutinize and digest. No fancy statistical software is necessary. For some behavioral economists, using heuristics seems naïve, even ludicrous. Annoyed by managers who refused to adopt complex models, Markus Wübben and Florian von Wangenheim, two professors of business administration, empirically tested both the hiatus rule and the Pareto/NBD model. Taking an airline, an apparel retailer, and an online CD retailer, they studied how many times the Pareto/NBD model and the hiatus heuristic correctly predicted which previous customers will make purchases in the future. The result was not what they expected (Figure 1). For the airline, the hiatus rule predicted 77% of customers correctly, whereas the complex model got only 74% right. For the apparel retailer, the difference was even larger, 83% versus 75%. Finally, for the online CD retailer, whose managers used a 6-month hiatus, the number of correct predictions tied. More data, more analysis, and more estimation did not lead to better predictions – on average, the simple heuristic that managers used came out first. More recently, a dozen other companies were tested, with the same result. Behavioral Economics Guide 2016 V Figure 1: Less is more. The simple hiatus rule predicts customer behavior on average better than the complex