DOCSLIB.ORG

The Devil Is in the Defaults Ian Kerr*

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
The Devil Is in the Defaults Ian Kerr* The Devil Is in the Defaults Ian Kerr* Abstract This review essay explores the notion of “shifting defaults” as discussed by Mireille Hil- debrandt in her book Smart Technologies and the End(s) of Law. Although readers might mistakenly see defaults as a fleeting topic within Hildebrandt’s text, this review essay demonstrates their central significance, identifying and elaborating on fundamental char- acteristics of four categories of shifting defaults: (i) natural defaults; (ii) technological defaults; (iii) legal defaults; and (iv) normative defaults. The essay concludes by reframing Hildebrandt’s central question, considering whether the Rule of Law is itself merely our current legal default, and whether it could be legitimately displaced by smart technologies. I. Introduction Although it will surely cause discomfort to some, very few members of an increasingly tech-savvy legal profession would be surprised by claims that advances in robotics and artificial intelligence will revolutionize the delivery of legal services and transform the practice of law.1 But here is a truly disquieting thought: what if “modern law is dependent on and shares the affordances of the printing press . and, ultimately . under the next dominant information and communications infrastructure, law as we know it might be gone”?2 In her perspicacious new book, Smart Technologies and the End(s) of Law, Mireille Hildebrandt, philosopher, lawyer, and internationally renowned expert in intellectual mac- ramé, examines this and other core questions at the nexus of philosophy of technology and philosophy of law. In a stunning display of rigor and discipline, Hildebrandt weaves together a dazzling array of key sources from law, philosophy, computer science, psychol- ogy, sociology, anthropology, science and technology studies, and other fields, exercising some of the very best lateral thinking I have seen in the past decade. * Canada Research Chair in Ethics, Law and Technology, University of Ottawa, Faculty of Law. Contact: [email protected]. I would like to thank Dustin Moores for his tireless enthusiasm, his ability to find anything under the sun and for the high quality research assistance that he so regularly and reliably provides. Thanks also to Ryan Calo, Woodrow Hartzog and Jason Millar for insightful feedback on an earlier draft. Finally, thank you to Mireille Hildebrandt for her superb and inspiring scholarship, her many intellectual contributions to our community, and for the honor of including me to participate in this book forum with such eminent scholars in the field. 1 Richard Susskind & Daniel Susskind, The Future of the Professions: How Technology Will Transform the Work of Human Experts (2015); Julie Sobowale, How Artificial Intelligence is Transforming the Legal Profession, ABA J. (Apr. 1, 2016), http://www.abajournal.com/magazine/article/how_artificial_intelligence_is_ transforming_the_legal_profession. 2 Mireille Hildebrandt, Smart Technologies and the End(s) of Law 176 (2015). ISSN 2291-9732 92 Critical Analysis of Law 4:1 (2017) Commencing with a compelling day-in-the-life narrative, she illustrates how smart technologies will catalyze the coming shift from life online to an “onlife world”—“a trans- formative life world, situated beyond the increasingly artificial distinction between online and offline . thriv[ing] on artificial life forms, or at least on ‘things’ that seem to develop a life of their own” (8). As Hildebrandt goes on to explain, through autonomic computing and emerging techniques in machine learning, these “data-driven agents” will generate the capability to profile us, and to predict and preempt many of our day-to-day activities. Without our help (often, without our awareness), they will anticipate and serve our prefer- ences and, in some instances, reconfigure them. Imagining, as she does, a world in which we have delegated many of our daily decisions to machines, devices that we come to rely upon to anticipate our needs and accomplish our goals, Hildebrandt warns that smart technologies have the potential to threaten fundamental rights including: privacy, identity, autonomy, non-discrimination, due process, and the presumption of innocence. In 226 pages of erudite, urbane investigation, Hildebrandt offers an extremely so- bering synthesis that details the character of the threats that smart technologies pose to fundamental rights and to the end(s) of law. One intriguing thing about her book, howev- er, is that despite these threats, she believes that “[t]here is no way back, we can only move forward” (214). With no turning back Hildebrandt instead forewarns that the only thing able to save the Rule of Law as we know it is “legal protection by design.” These legal protections are not meant simply to forbid preemptive computing when it interferes with fundamental rights.3 Rather, Hildebrandt follows a well-entrenched regulate-by- design approach, recommending two mechanisms for legal reform as examples of how the legal protection by design method can be baked directly into the emerging infor- mation and communications infrastructure. These and other such mechanisms are meant to ensure that not only our fundamental rights, but also the very fabric of modern law, remain intact. To whet appetites, I will quickly mention her two primary examples, both of which are too complicated to fully develop in my brief description. After that, I will turn my focus to a recurring theme in the book: the shifting defaults in our technological and legal architectures. First, Hildebrandt recommends the creation of a legal obligation to provide the socio-technical infrastructure necessary to enshrine “counter-profiling” as a fundamental right. Performing an analogous role to “freedom of expression” in our inherited legal in- frastructure (which she argues was constructed as a result of the affordances of the 4 printing press), the right to counter-profile would entail that the objects of profiling be- 3 This is an approach that I had suggested in an earlier volume co-edited by Hildebrandt. See Ian Kerr, Prediction, Preemption, Presumption: The Path of Law After the Computational Turn, in Privacy, Due Process and the Computational Turn (Mireille Hildebrandt & Katja de Vries eds., 2013); see also Ian Kerr & Jessica Earle, Prediction, Preemption, Presumption: How Big Data Threatens Big Picture Privacy, 66 Stan. L. Rev. Online 65 (2013). 4 As Hildebrandt puts it: “In many ways, counter-profiling has the same function as the free press had in the era of books and newspapers. From the perspective of the Rule of Law, the right to freedom of information has horizontal effect and may include a positive obligation on the state to ensure that the right is effective” (223). Kerr — The Devil is in the Defaults 93 come agents of profiling, rendering them capable of conducting data mining operations on the behaviors of those that are in the business of profiling. It is crucial to understand, however, that this prescription is far more than merely sanctioning audits or permitting counter-profiling as an activity. It is a much bigger deal than that. Hildebrandt is recom- mending a legal requirement that we build a technological infrastructure that would allow everyone the continuous ability to watch the watchers. Such a change to our infrastructure would be a serious and contentious commitment to transparency. Second, Hildebrandt advocates the adoption of “morphological computing” as a means of diminishing dependence on central control models of intelligence. “Morphological computing refers to the embedded nature of cognition, which enables granular adaptive re- sponses by body parts based on the morphology of that particular part instead of being initiated by a control mechanism like the brain” (29). Although it is unclear whether this kind of computation would likewise become an actual legal requirement, to the extent that more morphological computation means less data-driven computation, dictating the use of such techniques accords with the prime directive of “big data space”: namely, data minimization.5 One of the things I admire about Hildebrandt’s book is the way in which it moves beyond Lessig’s famous watchword that “code is law.”6 Following on Joel Reidenberg’s classic work on the formulation of information policy rules through technology,7 Lessig was among the first technology lawyers to recognize as dangerous the strategy of using preemptive technologies as a means of regulating conduct.8 As Hildebrandt depicts the strategy, “If the idea is to influence behavior instead of addressing action, and if the means are interchangeable, why not opt for a means that is less visible, less contestable and thus more influential in getting people to behave in alignment with policy objec- tives?” (165). Hildebrandt opposes this strategy and provides several successful counter- examples throughout the book. However, Hildebrandt’s disquisition is not simply about the perils of such techno-regulation or how best to address them. In addition, Hilde- brandt calmly, meticulously, and persuasively offers a philosophically rich account that explains how smart technologies might alter the modality of law’s existence. Her book isn’t just about how to debug legal code in order to hold techno-regulation in check. Alt- hough she does offer proposals in that regard, her central purpose is to provide an ontological account of the nature of law through an examination of its philosophical en- tanglements with technology. It is a tall order and she rises to the occasion. Luddite jurisprudes, take notice: your understanding of the philosophy of law is impoverished and 5 According to Hildebrandt, morphological computing permits many of our transactions with data-driven agents to escape the storage and searchability of human behaviors, enabling smooth and safe interaction with humans based on the physical design of robots, instead of being based on the data-driven computations in their central processing units. 6 Lawrence Lessig, Code 2.0 (2d ed. 2006). 7 Joel R. Reidenberg, Lex Informatica: The Formulation of Information Policy Rules Through Technology, 76 Tex. L. Rev. 553 (1997).
Load more

Recommended publications
  • When Nudges Fail: Slippery Defaults Lauren E
    When Nudges Fail: Slippery Defaults Lauren E. Willist Inspired by the success of "automaticenrollment" in increasingparticipation in defined contribution retirement savings plans, policymakers have put similar policy defaults in place in a variety of other contexts, from checking account over- draft coverage to home-mortgage escrows. Internetprivacy appearspoised to be the next arena.But how broadly applicable are the results obtained in the retirement- savings context? Evidence from other contexts indicates two problems with this ap- proach. the defaults put in place by the law are not always sticky, and the people who opt out may be those who would benefit the most from the default. Examining the new default for consumer checking account overdraft coverage reveals that firms can systematically undermine each of the mechanisms that might otherwise operate to make defaults sticky. Comparing the retirement-savings default to the overdraft default, four boundary conditions on the use of defaults as a policy tool are apparent: policy defaults will not be sticky when (1) motivated firms oppose them, (2) these firms have access to the consumer, (3) consumers find the decision environment confusing, and (4) consumerpreferences are uncertain.Due to consti- tutional and institutionalconstraints, government regulation of the libertari- an-paternalism variety is unlikely to be capable of overcoming these bounds. Therefore, policy defaults intended to protect individuals when firms have the mo- tivation and means to move consumers out of the default are unlikely to be effective unless accompanied by substantive regulation. Moreover, the same is likely to be true of "nudges" more generally, when motivated firms oppose them.
    [Show full text]
  • Default Effect Versus Active Decision
    Kyoto University, Graduate School of Economics Research Project Center Discussion Paper Series Default Effect Versus Active Decision: Evidence from a Field Experiment in Los Alamos Wenjie Wang and Takanori Ida Discussion Paper No. E-14-010 Research Project Center Graduate School of Economics Kyoto University Yoshida-Hommachi, Sakyo-ku Kyoto City, 606-8501, Japan September, 2014 June, 2017 Revised Default Effect versus Active Decision: Evidence from a Field Experiment in Los Alamos Wenjie Wang∗ and Takanori Ida† June 2017 Abstract We examine how consumers respond to distinct combinations of preset defaults and subsequent economic incentives. A randomised controlled trial is implemented to investigate the demand reduction performance of two electricity pricing programmes: opt-in and opt-out critical peak pricing. Both the intention-to-treat and the treatment-on-the-treated are more pronounced for customers assigned to the opt-in group. This result suggests that the opt-in type active enrolment itself had an impact on customers' subsequent behavior and made them more responsive to the treatment intervention. Moreover, only the opt-in treatment has spillover effects beyond the treatment time window. Our results, therefore, highlight the important difference between an active and a passive decision-making process. JEL classification: C93, D12 Keywords: Field Experiment, Default Effect, Opt-in, Opt-out, Price Elasticity ∗Graduate School of Social Sciences, Hiroshima University, Japan. E-mail address: [email protected]. †Corresponding author. Graduate School of Economics, Kyoto University. Yoshida-Honmachi, Sakyo, Kyoto, 606- 8501, Japan. Tel: +81-75-753-3477. E-mail address: [email protected]. 1 1 Introduction Decisions by default have become an important issue in behavioural economics and public pol- icy (Johnson and Goldstein, 2013).
    [Show full text]
  • Options in Economic Games: Experiments Demonstrating a ‘Default Pull’ Effect and Arbitrarily Constructed Preferences
    Default Options in Economic Games: experiments demonstrating a ‘default pull’ effect and arbitrarily constructed preferences Nikhil Dhingra, Zach Gorn, Andrew Kenera Introduction By now, a large literature has used simple economic games, like the standard one- shot dictator game (see Forsythe et al., 1994), to analyze so-called social preferences. In this game, a “dictator” chooses how to divide an experimental endowment of money between herself and an anonymous, passive “receiver”. If players are selfish, then rational choice predicts they should keep the entire endowment for themselves. Numerous experiments, however, have shown that dictators give away on average more than 20% (Camerer, 2003), even when efforts are made to ensure recipient anonymity (thereby eliminating any concern for reputation, future interaction, or punishment). Various attempts have been made to reconcile this behavior with rational choice by assuming people have social preferences that in some way incorporate concern for others’ payoffs (see, e.g., Fehr and Schmidt, 1999). Because these games use real-money endowments, typically without time pressures or complicated contexts, it would seem that a player’s actions reflect a clear and reasoned preference. In the present paper, we challenge this notion by presenting evidence of a strong “default pull” effect: Choices are affected by the presence of a subtle default option, even when the default itself is not chosen. We demonstrate this default pull effect in a standard dictator game with seemingly innocuous default options that appear to be a feature of the software presenting the game; i.e., the first in an apparently randomized list of allocation options is checked off by default.
    [Show full text]
  • Tilburg University Charitable Giving, Emotions, and the Default Effect
    Tilburg University Charitable Giving, Emotions, and the Default Effect Noussair, Charles; Habetinova, Lenka Publication date: 2015 Document Version Early version, also known as pre-print Link to publication in Tilburg University Research Portal Citation for published version (APA): Noussair, C., & Habetinova, L. (2015). Charitable Giving, Emotions, and the Default Effect. (CentER Discussion Paper; Vol. 2015-043). CentER, Center for Economic Research. General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Download date: 30. sep. 2021 No. 2015-043 CHARITABLE GIVING, EMOTIONS, AND THE DEFAULT EFFECT By Lenka Habětínová, Charles Noussair 24 August 2015 ISSN 0924-7815 ISSN 2213-9532 Charitable Giving, Emotions, and the Default Effect Lenka Habˇet´ınov´aand Charles Noussair Tilburg University August 18, 2015 Abstract We report an experiment to study the effect of defaults on charitable giving. In three different treatments, participants face varying default levels of donation.
    [Show full text]
  • Experiencing Default Nudges: Autonomy, Manipulation, and Choice-Satisfaction As Judged by People Themselves
    Experiencing Default Nudges: Autonomy, Manipulation, and Choice-Satisfaction as Judged by People Themselves Patrik Michaelsen*, Lars-Olof Johansson & Martin Hedesström. Department of Psychology, University of Gothenburg. Critiques of nudging suggest that nudges infringe on decision makers’ autonomy. Yet, little empirical research has explored whether people who are subjected to nudges agree. In three online between-group experiments (N = 2083), we subject participants to different choice architectures and measure their experiences of autonomy, choice-satisfaction, perceived threat to freedom of choice, and objection to the choice architecture. Participants who received an opt-out nudge made more prosocial choices but did not report more negative choice experiences compared to participants in opt-in default or active choice conditions. This was predominantly the case even when the presence of the nudge was made transparent to participants, and when choice stakes were increased. Our results suggest that defaults are less manipulative and autonomy-infringing than sometimes feared. Policy-makers should include measures of choice experiences when testing out new nudges. Keywords: choice architecture; nudge; default; autonomy; manipulation; transparency *Corresponding author. [email protected]. Twitter: @ p_michaelsen Unethical behavior change interventions should not be used for policy. However, the class of policy interventions known as nudges, interventions influencing behavior by changing cues in choice environments, have been accused of just this: unethicality (for book-length discussions, see Conly, 2012; Rebonato, 2012; Sunstein, 2014, 2015, 2016; White, 2013). The stakes of the accusation are high, as nudges or similar behaviorally informed interventions are reported to already be in use by over 200 government units all over the world (OECD, 2019).
    [Show full text]
  • Recommendations Implicit in Policy Defaults Craig R.M
    PSYCHOLOGICAL SCIENCE Research Article Recommendations Implicit in Policy Defaults Craig R.M. McKenzie, Michael J. Liersch, and Stacey R. Finkelstein University of California, San Diego ABSTRACT—Should people be considered organ donors after few donors. Default effects have also been found in decisions their death unless they request not to be, or should they not about pension savings (Madrian & Shea, 2001), insurance (John- be considered donors unless they request to be? Because son, Hershey, Meszaros, & Kunreuther, 1993), and Internet privacy people tend to stay with the default in a variety of domains, policies (Bellman, Johnson, & Lohse, 2001; Johnson, Bellman, policymakers’ choice of default has large and often impor- & Lohse, 2002). tant effects. In the United States, where the organ-donation Clearly, the default selected by policymakers has important policy default is ‘‘not a donor,’’ about 5,000 people die every implications. The question we address in this article is why the year because there are too few donors. Four experiments default matters so much. Three possible answers (which are not examined two domains—being an organ donor and saving mutually exclusive) were suggested by Johnson and Goldstein for retirement—where default effects occur and have (2003). One is that people are loss averse (Tversky & Kahneman, important implications. The results indicate that default 1991). They might view the default as the status quo, and giving effects occur in part because policymakers’ attitudes can be this up might be especially aversive (Johnson et al., 1993). A revealed through their choice of default, and people second possibility is that accepting the default requires no effort, perceive the default as indicating the recommended course whereas changing the default does (e.g., filling out paperwork).
    [Show full text]
  • Multichoice Defaults in Charitable Giving
    Multi­Choice Defaults in Charitable Giving (Or: How to Manipulate People for Social Good) Lucia Chen Yale University Department of Cognitive Science April 22, 2016 1 ABSTRACT Default effects can explain why people end up with a large choice set when they are asked to delete options they do not want, and a smaller choice set when they are asked to add options they do want. This discrepancy may be driven by what Yaniv and Schul (2000) term the “middling options” ­ options that are chosen when they are defaulted, but eliminated when they are not defaulted. The present study examines whether defaulting the middling options in a charitable giving scenario results in more options chosen, with implications for increasing donations to less popular charitable organizations and causes. We do not find evidence to support our hypothesis in our first study, but after strengthening our manipulation in the second study, we find that defaulting the middling options results in a larger final choice set size. 2 INTRODUCTION Have you been subscribed to the same magazines for the past decade? Have you ever unwittingly paid for insurance on a car rental, reservations on a flight, or the wrong shipping option on Amazon? Have you installed extraneous software on the computer because you couldn’t be bothered to uncheck the little boxes on the set­up menu? This is the power of defaults at work. A default is an option that becomes a choice when a decision­maker does not choose otherwise; defaulting an option often increases the likelihood that it will be chosen (Brown & Krishna, 2004; Thaler & Sunstein, 2008; Goswami & Urminsky, 2015).
    [Show full text]
  • Cognitive Bias & Choice Architecture
    Cognitive Bias & Choice Architecture Jamie Neville, University Housing Services The Inspiration Goals • To learn the basics of how people make decisions • To learn how we impact those decisions • To recognize the importance of being both ethical and intentional in how we present choices to our students Learning Outcomes • Develop a basic understanding of heuristics, cognitive bias, and choice architecture • Identify ways cognitive bias impact daily decision making • Utilize principles of choice architecture to enhance the decisions made by students • Understand the ethical issues associated with choice architecture APPROXIMATELY 6 ½ MILLION XBOX ONE CONSOLES WERE SOLD IN THE LAST HOLIDAY SEASON. HOW MANY PLAYSTATION 4 CONSOLES WERE SOLD IN THE SAME TIME FRAME? DID MAHATMA GHANDI DIE BEFORE OR AFTER AGE 100? Jargon • Heuristic – A shortcut or rule of thumb used to make a decision • Cognitive Bias – a flaw in judgment which is caused by memory, social attribution, emotional response, and statistical errors Anchoring • A heuristic that involves using a number or value as a starting point, and then adjusting one's answer away from this anchor • Did we fall victim to the anchoring heuristic? JCPenney IN 2012… IN 2015 How We Think Automatic Reflective • Uncontrolled • Controlled • Effortless • Deductive • Associative • Slow • Fast • Resource Demanding • Unconscious • Follows Rules COMMON HEURISTICS & COGNITIVE BIASES Representativeness • Placing something in a category because it has similarities to members of that category • Essentially, stereotyping
    [Show full text]
  • Behavioural Insights Key Concepts, Applications and Regulatory Considerations
    BEHAVIOURAL INSIGHTS KEY CONCEPTS, APPLICATIONS AND REGULATORY CONSIDERATIONS March 29, 2017 INVESTOR OFFICE OSC Staff Notice 11-778 2 OSC Investor Office Behavioural Insights Report OSC Staff Notice 11-778 For questions or comments related to this report, please contact: Tyler Fleming Director, Investor Office [email protected] 416-593-8092 James (Jason) Stewart Senior Advisor, Investor Office [email protected] 416-593-2387 Myha Truong-Regan Senior Economist, Research and Analysis Group [email protected] 416-593-8157 ACKNOWLEDGEMENTS This report was produced by staff of the Ontario Securities Commission, including the primary author James (Jason) Stewart, Tyler Fleming, Myha Truong-Regan, Kevan Hannah, Jason Durham, Denise Morris, Beth Demissie, Domini Canales and Michelle La Fleche. Reviews and other assistances for chapters 1-3 were provided by BEWorks’ Michelle Hilscher, Kelly Peters and Wardah Malik, as well as the University of Toronto’s Dr. Dilip Soman for chapter 1. Insights, feedback and other research were provided by: Paul Adams; Dr. Dan Ariely; Kelly Bidwell; Ravi Dutta; Sarah Edmondson; Dr. Rory Gallagher; Salvatore Gnoni; Stefan Hunt; Dr. Derek Koehler; Elizabeth Hardy; Dr. Julian House; Dr. Jeremy Ko; Dr. Pete Lunn; Clare Marlin; Faisal Naru; Kelly Peters; Matteo Rava; Dr. Odette van de Riet; Dr. Alain Samson; Dr. Dilip Soman; Christian Stenta; Hilary Viney; Geraldine Walsh; Cindy Ward; Damian Yip; Dr. David Yokum. OSC Staff Notice 11-778 OSC Investor Office Behavioural Insights Report 3 TABLE OF 6 Executive Summary 10 Introduction and Report Overview CONTENTS 16 Chapter 1: Setting the Foundations: The core concepts, principles and successes of behavioural insights 17 Why behavioural approaches gained momentum in the public sector 19 Section A: Foundations, milestones and the public sector adoption of behavioural insights 19 I.
    [Show full text]
  • Nudging the Poor and the Rich – a Field Study on the Distributional Effects of Green Electricity Defaults
    Nudging the Poor and the Rich { A Field Study on the Distributional Effects of Green Electricity Defaults Claus Ghesla Manuel Grieder Renate Schubert∗ April 9, 2018 Abstract Choice defaults are an increasingly popular public policy tool. Yet there is little knowledge of the distributional consequences of such nudges for different groups in society. We report results from a field study in the residential electricity market in which we contrast consumers' contract choices under an existing default regime with active choices without any default. We find that the default is successful at curbing greenhouse gas emissions, but it leads poorer households to pay more for their electricity consumption than they would want to, while leaving a significant willingness to pay for green electricity by richer households untapped. Keywords: choice defaults, welfare, green electricity, public policy, nudging JEL Classification: D12, D31, D61, D63, H23, M38, Q48 ∗Swiss Federal Institute of Technology, ETH Zurich, Department of Humanities, Social and Political Sciences, Chair of Economics, Clausiusstrasse 37, 8092 Zurich. Corresponding author: [email protected]; Phone: +41 44 632 50 04. We thank Paula Cacault, Simon H¨anni, Peter Martinsson, Jan Schmitz, Christian Zehnder and partic- ipants of research seminars in Zurich and Lausanne for their helpful and valuable suggestions. We also thank the team at the partnering utility for their enduring support, fruitful discussions, and the permis- sion to explore our research questions with their customers. We further appreciate the help of the ETH Zurich postal service for administering the survey. We thank Alexander G¨otz,Mario Kohler, Julia M¨uller and Felix Schl¨uterfor research assistance.
    [Show full text]
  • Partitioning Default Effects: Why People Choose Not to Choose
    THIS ARTICLE HAS BEEN CORRECTED. SEE LAST PAGE Journal of Experimental Psychology: Applied © 2011 American Psychological Association 2011, Vol. 17, No. 4, 332–341 1076-898X/11/$12.00 DOI: 10.1037/a0024354 Partitioning Default Effects: Why People Choose Not to Choose Isaac Dinner Eric J. Johnson IE Business School, Madrid, Spain Columbia University Daniel G. Goldstein Kaiya Liu Yahoo! Research, New York, NY University of South Dakota Default options exert an influence in areas as varied as retirement program design, organ donation policy, and consumer choice. Past research has offered potential reasons why no-action defaults matter: (a) effort, (b) implied endorsement, and (c) reference dependence. The first two of these explanations have been experimentally demonstrated, but the latter has received far less attention. In three experiments we produce default effects and demonstrate that reference dependence can play a major role in their effectiveness. We find that the queries formulated by defaults can produce differences in constructed preferences and further that manipulating queries can also mitigate default effects. The experimental context involves two environmentally consequential alternatives: cheap, inefficient incandescent light bulbs, and expensive, efficient compact fluorescent bulbs. Within this context we also measure the impact of each potential rationale for a default effect. keywords: defaults, query theory, decision making, preference construction Many active decisions have a default option that is chosen more endorsement: decision-makers may infer a default has been pre- often than expected if it were not labeled the default. In activities as selected due to its merit or the desires of those presenting the diverse as voting, getting a flu shot or heading to the gym, “no-action choice.
    [Show full text]
  • Behavioural Insights Applied to Policy: European Report 2016
    EUR 27726 EN This publication is a Science for Policy report by the Joint Research Centre, the European Commission’s in-house science service. It aims to provide evidence-based scientific support to the European policy-making process. The scientific output expressed does not imply a policy position of the European Commission. Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use which might be made of this publication. JRC Science Hub https://ec.europa.eu/jrc JRC 100146 EUR 27726 EN PDF ISBN 978-92-79-54725-6 ISSN 1831-9424 doi:10.2760/903938 KJ-NA-27726-EN-N Print ISBN 978-92-79-54724-9 ISSN 1018-5593 doi:10.2760/707591 KJ-NA-27726-EN-C © European Union, 2016 Reproduction is authorised provided the source is acknowledged. Joana Sousa Lourenço, Emanuele Ciriolo, Sara Rafael Almeida, and Xavier Troussard; Behavioural insights applied to policy: European Report 2016. EUR 27726 EN; doi:10.2760/903938 All images © European Union 2016, except: front page, Seed Pods, Some weird seed pods - dried out, 2010. Source: brewbooks page 15, Dr David Demer, A small tabular iceberg with an ice cave. Antarctic Peninsula, 2010. Source: NOAA Photo Library Abstract Behavioural Insights Applied to Policy: Overview across 32 European countries The report covers a wealth of policy applications either implicitly or explicitly informed by behavioural insights (BIs). It reviews institutional developments and puts forward a comparative framework (PRECIS) describing behavioural insight teams with six key features. The report reaches four main conclusions: 1. in terms of capacity-building, there is significant dynamism and growing appetite to apply BIs to policy-making; 2.
    [Show full text]