DOCSLIB.ORG

Addressing Present Bias in Movie Recommender Systems and Beyond

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Addressing Present Bias in Movie Recommender Systems and Beyond Addressing Present Bias in Movie Recommender Systems and Beyond Kai Lukoffa aUniversity of Washington, Seattle, WA, USA Abstract Present bias leads people to choose smaller immediate rewards over larger rewards in the future. Recom- mender systems often reinforce present bias because they rely predominantly upon what people have done in the past to recommend what they should do in the future. How can recommender systems over- come this present bias to recommend items in ways that match with users’ aspirations? Our workshop position paper presents the motivation and design for a user study to address this question in the domain of movies. We plan to ask Netflix users to rate movies that they have watched in the past for thelong- term rewards that these movies provided (e.g., memorable or meaningful experiences). We will then evaluate how well long-term rewards can be predicted using existing data (e.g., movie critic ratings). We hope to receive feedback on this study design from other participants at the HUMANIZE workshop and spark conversations about ways to address present bias in recommender systems. Keywords present bias, cognitive bias, algorithmic bias, recommender systems, digital wellbeing, movies 1. Introduction such as Schindler’s List [2]. Recommender systems (RS), algorithmic People often select smaller immediate re- systems that predict the preference a user wards over larger rewards in the future, a would give to an item, often reinforce present phenomenon that is known as present bias bias. Today, the dominant paradigm of rec- or time discounting. This applies to deci- ommender systems is behaviorism: recom- sions such as what snack to eat [1,2], how mendations are selected based on behavior much to save for retirement [3], or which traces (“what users do”) and they largely ne- movies to watch [2]. For example, when peo- glect to capture explicit preferences (“what ple choose a movie to watch this evening users say”) [4]. Since “what users do” reflects they often choose guilty pleasures like The a present bias, RS that rely upon such actions Fast and The Furious, which are enjoyable in- to train their recommendations will priori- the-moment, but then quickly forgotten. By tize items that offer high short-term rewards contrast, when they choose a movie to watch but low long-term rewards. In this way, rec- next week, they are more likely to choose ommender systems may reinforce what the films that are challenging but meaningful, current self wants rather than helping peo- ple reach their ideal self [5]. Joint Proceedings of the ACM IUI 2021 Workshops, April This position paper for the HUMANIZE 13-17, 2021, College Station, USA workshop proposes a study design to address " [email protected] (K. Lukoff) these topics in the domain of movies. In ~ https://kailukoff.com (K. Lukoff) Study 1, a survey of Netflix users, we in- 0000-0001-5069-6817 (K. Lukoff) © 2020 Copyright for this paper by its authors. Use permit- vestigate: How should a RS make recom- ted under Creative Commons License Attribution 4.0 Inter- national (CC BY 4.0). mendations by asking ordinary users about CEUR Workshop Proceedings CEUR http://ceur-ws.org Workshop ISSN 1613-!!" the rather academic concept of "long-term Proceedings (CEUR-WS.org) rewards"? And can long-term rewards be to transform their behavior [9]. Lukoff et predicted based on existing data (e.g., movie al. previously explored how experience sam- critic ratings)? In Study 2, a participatory de- pling can be used to measure how meaning- sign exercise with a movie RS, we ask: How ful people find their interactions with smart- do users want a RS to balance short-term phone apps immediately after use [10]. How- and long-term rewards? And what controls ever, building such reflection into RSs re- would users like to have over such a RS? mains a major challenge because it is un- We expect that our eventual findings will clear how and when a system should ask a also inform the design of recommender sys- user about the "long-term rewards" of an ex- tems that address present bias in other do- perience. It may be that the common ap- mains such as news, food, and fitness. proach of asking users to rate items on a "5- star" scale reflects a combination of short- term and long-term rewards, and that a dif- 2. Related Work ferent prompt is required to capture evalua- tions of long-term rewards more specifically. The social psychologist Daniel Kahneman It is also an open question how well such describes people as having both an experi- long-term rewards can be inferred from ex- encing self, who prefers short-term rewards isting data. like pleasure, and a remembering self, who The third perspective leverages the wis- prefers long-terms rewards like meaningful dom of the crowd, by using the collective experiences [6]. Lyngs et al. describe three elicited preferences of similar users with more different approaches to the thorny question experience to make recommendations. Rec- of how to measure a user’s “true preferences" ommender systems today tend to use the "be- [5]. The first approach aligns with the expe- havior of the crowd" as input into their mod- riencing self, the second with the remember- els, in the form of behavioral data of similar ing self, and the third with the wisdom of the users, but largely neglect elicited preferences crowd. [4]. The first approach follows the experienc- Finally, Ekstrand and Willemsen propose ing self and asserts that what users do is what participatory design as a general corrective they really want, which many in the Silicon to the behaviorist bias of recommender sys- Valley push one step further to what we can tems [4]. Harambam et al. explored using get users to do is what they really want [7]. participatory methods to evaluate a recom- Social media that are financed by advertising mender system for news, suggesting that giv- are "compelled to find ways to keep users en- ing users control might mitigate filter bub- gaged for as long as possible" [8]. To achieve bles in news consumption [11]. Participa- this, social media services often give the ex- tory design is a promising way to investigate periencing self exactly what it wants, know- how users want a RS to balance short-term ing that it will override the preferences of the and long-term rewards and the controls they remembering self and lead the user to stay would like to have. engaged for longer than they had intended. The second approach prioritizes the re- membering self, calling for systems to 3. Proposed Study Design prompt the user to reflect on their ideal self. In this vein, Slovak et al. propose to design In what follows, we propose a study design to to help users to reflect upon how they wish better understand how to measure the long- term rewards of items in the context of movie • For long-term rewards: How reward- recommendations. We hope to receive feed- ing was this movie after you watched back on this study design from other par- it? ticipants at the HUMANIZE workshop and prompt conversations about ways to address Participants will rate all questions on a 1-5 present bias in recommender systems more scale, from "Not at all" to "Very." broadly. We are also interested in understanding what other constructs are correlated with the both short-term and long-term rewards. To 3.1. Study 1: Eliciting user this end, we are also asking about related preferences for long-term constructs, such as: rewards • How enjoyable was this movie while Our first study is a survey of Netflix users you were watching it? that we are currently piloting, which ad- dresses two research questions: • How interesting was this movie while you were watching it? • RQ 1a: What wording should be used to ask users to rate the “short-term re- • How meaningful was this movie after wards” and “long-term rewards” of a you watched it? movie? In other words, what wording • How memorable was this movie after captures the right construct and makes you watched it? sense to users? We are currently piloting all questions us- • RQ 1b: How well can a recommender ing a talk-aloud protocol in which partici- system predict the long-term rewards pants explain their thinking to us as they of a movie for an individual using data complete the survey. We are checking to other than explicit user ratings? make sure that the wording makes sense to participants and to identify constructs that 3.1.1. Study 1 Methods are related to short-term and long-term re- We will ask Netflix users who watch at least wards. The constructs that are most closely one movie per month to download their past related to these rewards in the piloting will viewing history and share it with us. We will be included in the final survey, so that each ask them to rate 30 movies: 10 watched in movie will be rated for a cluster of constructs the past year, 10 watched 1-2 years ago, and all related to short-term and long-term re- 10 watched 2-3 years ago. Participants will wards. For the final survey, we plan to recruit rate each movie for short-term reward, long- about 50 Netflix users to generate a total of term reward, and other constructs that might 1,500 movie ratings. be correlated with these rewards (e.g., mean- ingfulness, memorability). 3.1.2. Study 1 Planned Analysis The current wording of our questions is: To address RQ 1a, we will report the qual- • For short-term rewards: How reward- itative results of our talk-aloud piloting of ing was this movie while you were the survey wording.
Load more

Recommended publications
  • A Task-Based Taxonomy of Cognitive Biases for Information Visualization
    A Task-based Taxonomy of Cognitive Biases for Information Visualization Evanthia Dimara, Steven Franconeri, Catherine Plaisant, Anastasia Bezerianos, and Pierre Dragicevic Three kinds of limitations The Computer The Display 2 Three kinds of limitations The Computer The Display The Human 3 Three kinds of limitations: humans • Human vision ️ has limitations • Human reasoning 易 has limitations The Human 4 ️Perceptual bias Magnitude estimation 5 ️Perceptual bias Magnitude estimation Color perception 6 易 Cognitive bias Behaviors when humans consistently behave irrationally Pohl’s criteria distilled: • Are predictable and consistent • People are unaware they’re doing them • Are not misunderstandings 7 Ambiguity effect, Anchoring or focalism, Anthropocentric thinking, Anthropomorphism or personification, Attentional bias, Attribute substitution, Automation bias, Availability heuristic, Availability cascade, Backfire effect, Bandwagon effect, Base rate fallacy or Base rate neglect, Belief bias, Ben Franklin effect, Berkson's paradox, Bias blind spot, Choice-supportive bias, Clustering illusion, Compassion fade, Confirmation bias, Congruence bias, Conjunction fallacy, Conservatism (belief revision), Continued influence effect, Contrast effect, Courtesy bias, Curse of knowledge, Declinism, Decoy effect, Default effect, Denomination effect, Disposition effect, Distinction bias, Dread aversion, Dunning–Kruger effect, Duration neglect, Empathy gap, End-of-history illusion, Endowment effect, Exaggerated expectation, Experimenter's or expectation bias,
    [Show full text]
  • Identifying Present Bias from the Timing of Choices∗
    Identifying Present Bias from the Timing of Choices∗ Paul Heidhues Philipp Strack DICE Yale February 22, 2021 Abstract A (partially naive) quasi-hyperbolic discounter repeatedly chooses whether to com- plete a task. Her net benefits of task completion are drawn independently between periods from a time-invariant distribution. We show that the probability of complet- ing the task conditional on not having done so earlier increases towards the deadline. Conversely, we establish non-identifiability by proving that for any time-preference pa- rameters and any data set with such (weakly increasing) task-completion probabilities, there exists a stationary payoff distribution that rationalizes the agent's behavior if she is either sophisticated or fully naive. Additionally, we provide sharp partial identifica- tion for the case of observable continuation values. ∗We thank seminar and conference audiences, and especially Nageeb Ali, Ned Augenblick, Stefano DellaVigna, Drew Fudenberg, Ori Heffetz, Botond K}oszegi, Muriel Niederle, Philipp Schmidt-Dengler, Charles Sprenger, Dmitry Taubinsky, and Florian Zimmermann for insightful and encouraging comments. Part of the work on this paper was carried out while the authors visited briq, whose hospitality is gratefully acknowledged. Philipp Strack was supported by a Sloan fellowship. Electronic copy available at: https://ssrn.com/abstract=3386017 1 Introduction Intuition and evidence suggests that many individuals are present-biased (e.g. Frederick et al., 2002; Augenblick et al., 2015; Augenblick and Rabin, 2019). Building on work by Laibson (1997) and others, O'Donoghue and Rabin (1999, 2001) illustrate within the quasi-hyperbolic discounting model that present bias, especially in combination with a lack of understanding thereof, leads individuals to procrastinate unpleasant tasks and to precrastinate pleasant experiences.
    [Show full text]
  • Poverty and Decision-Making
    Poverty and decision-making How behavioural science can improve opportunity in the UK Kizzy Gandy, Katy King, Pippa Streeter Hurle, Chloe Bustin and Kate Glazebrook October 2016 1 © Behavioural Insights Ltd Acknowledgements Many people contributed to this report. Particular thanks go to David Halpern, Andy Hollingsworth, Raj Chande, Tiina Likki, Ben Curtis and Susannah Hume from the Behavioural Insights Team who provided expert advice on the literature and policy context. Many people inside government also provided review, and several leading academic were an invaluable source of insights and knowledge, particularly Eldar Shafir, Jonathan Morduch and Evrim Altintas. Finally, we thank the Joseph Rowntree Foundation for providing useful guidance along the way and for their enthusiasm and support for our intellectual ambition. 2 © Behavioural Insights Ltd Contents Glossary of terms ............................................................................................. 6 Executive summary ............................................................................................ 7 Chapter 1 - Introduction: applying behavioural science to the study of poverty .... 11 Section 1.1 Background and principal audiences for the report .................. 11 Section 1.2 Report aims and research questions ....................................... 11 Section 1.3 The psychology of decision-making ........................................ 12 Section 1.4 Structure of the report .......................................................... 14 Chapter 2
    [Show full text]
  • Health Insurance & Behavioral Economics
    Health Insurance & Behavioral Economics Alan C. Monheit, Ph.D. Rutgers University School of Public Health Rutgers Center for State Health Policy National Bureau of Economic Research Standard Model Underlying Consumer Choice • In the standard economic model of consumer choice, individuals are characterized as “. lightning quick calculators of pleasure and pain.” • Built on very strong behavioral assumptions: – Rationality – Full information on attributes of commodities and their substitutes. – Easy to assess the benefits & costs of consumption alternatives. – Individuals are constrained by a budget. – More choice is welfare enhancing. • These assumptions of rationality & full information carry over to the standard or benchmark model of health insurance choice. Center for State Health Policy Benchmark Model of Health Insurance Choice • Key assumptions: – Individuals are risk averse: • Prefer a loss with certainty rather than a gamble with the same expected loss. • Will incur a certain loss to obtain an uncertain flow of benefits. • Are willing to pay a “risk premium” above an actuarially fair insurance premium. – Can accurately assess the probability of future losses. – Can accurately assess income losses from adverse events. – Can accurately assess the benefits & costs of purchasing or not purchasing insurance. Center for State Health Policy Implications from the Standard Model • Value of the standard model is that it provides testable predictions about health insurance choices: – More risk averse individuals will purchase more insurance than those who are less risk averse. – Generally, as the probability of a loss increases, individuals will purchase more insurance. However, • Individuals will not purchase insurance for very large or very small probabilities of income loss. – More insurance is purchased as the expected income loss increases.
    [Show full text]
  • Trends and Issues
    TRENDS AND ISSUES DECEMBER 2015 THE ROLE OF TIME PREFERENCES AND EXPONENTIAL- GROWTH BIAS IN RETIREMENT SAVINGS Gopi Shah Goda Matthew R. Levy Colleen Flaherty Aaron J. Sojourner Joshua Tasoff Stanford University London School of Manchester University of Minnesota Claremont Graduate and NBER Economics University of Minnesota and IZA University EXECUTIVE SUMMARY Understanding the drivers of retirement-wealth accumulation is critical given that changes in employer-provided retirement plans have shifted responsibility for retirement savings increasingly to individuals. This paper empirically investigates two factors – one cognitive and one motivational – that may lead to low levels of retirement wealth accumulation. Cognitively, individuals may underestimate the future value of saving today by neglecting compound interest when assessing returns to saving, which is known as exponential-growth bias (EG bias). The motivational factor is present bias, which is the tendency for individuals to act patient in the long term, but impatient in the near term such that they continually put off saving today. Using an online survey of a broad sample of US households, we measure individuals’ tendency for EG bias and present bias and relate them to individuals’ retirement savings. We find that both biases are prevalent and are negatively related to levels of retirement savings. In particular, 78% of the sample exhibits EG bias and 55% is present biased. However, having one bias does not increase the likelihood of having the other. As for retirement savings, individuals who have accurate understanding of exponential growth have $27,300 more in retirement savings relative to those who neglect exponential growth, while those who value time consistently have $25,200 more in savings relative to individuals who have present bias.
    [Show full text]
  • Behavioral Economics of Education: IZA DP No
    IZA DP No. 8853 Behavioral Economics of Education: Progress and Possibilities Adam M. Lavecchia Heidi Liu Philip Oreopoulos February 2015 DISCUSSION PAPER SERIES Forschungsinstitut zur Zukunft der Arbeit Institute for the Study of Labor Behavioral Economics of Education: Progress and Possibilities Adam M. Lavecchia University of Toronto Heidi Liu Harvard University Philip Oreopoulos University of Toronto, NBER, CIFAR and IZA Discussion Paper No. 8853 February 2015 IZA P.O. Box 7240 53072 Bonn Germany Phone: +49-228-3894-0 Fax: +49-228-3894-180 E-mail: [email protected] Any opinions expressed here are those of the author(s) and not those of IZA. Research published in this series may include views on policy, but the institute itself takes no institutional policy positions. The IZA research network is committed to the IZA Guiding Principles of Research Integrity. The Institute for the Study of Labor (IZA) in Bonn is a local and virtual international research center and a place of communication between science, politics and business. IZA is an independent nonprofit organization supported by Deutsche Post Foundation. The center is associated with the University of Bonn and offers a stimulating research environment through its international network, workshops and conferences, data service, project support, research visits and doctoral program. IZA engages in (i) original and internationally competitive research in all fields of labor economics, (ii) development of policy concepts, and (iii) dissemination of research results and concepts to the interested public. IZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character.
    [Show full text]
  • A Framework to Address Cognitive Biases of Climate Change Jiaying
    1 [in press at Neuron] A framework to address cognitive biases of climate change Jiaying Zhao1,2 and Yu Luo1 1Department of Psychology, University of British Columbia 2Institute for Resources, Environment and Sustainability, University of British Columbia Please address correspondence to: Jiaying Zhao Department of Psychology Institute for Resources, Environment and Sustainability University of British Columbia Vancouver, B.C., Canada, V6T 1Z4 Email: [email protected] 2 Abstract We propose a framework that outlines several predominant cognitive biases of climate change, identifies potential causes, and proposes debiasing tools, with the ultimate goal of depolarizing climate beliefs and promoting actions to mitigate climate change. Keywords: decision making, cognition, behavior change, polarization, debias 3 Introduction Climate change is an urgent crisis facing humanity. To effectively combat climate change, we need concerted efforts not only from policymakers and industry leaders to institute top-down structural changes (e.g., policies, infrastructure) but also from individuals and communities to instigate bottom-up behavior changes to collectively reduce greenhouse gas emissions. To this end, psychology has offered useful insights on what motivates people to act on climate change, from social psychology investigating the underlying beliefs and attitudes of climate change, to cognitive psychology uncovering the attentional, perceptual, and decision processes of climate actions, and more recently neuroscience pinpointing the neural circuitry on motivated cognition. Insights gained from these fields have started to generate interventions to shift beliefs and promote behavior change to mitigate climate change. In the search for behavioral climate solutions, a stubborn challenge remains. That is, a sizeable portion of the public still hold disbelief on climate change and refuse to take actions, despite the fact that the vast majority of climate scientists have reached a consensus on anthropogenic causes of climate change.
    [Show full text]
  • Debiasing Management Decisions: Overcoming the Practice/Theory Gap Within the Managerial Decision Process
    Debiasing Management Decisions: Overcoming the practice/theory gap within the managerial decision process TAKE CONFERENCE 2021 Practitioner’s stream Christian Muntwiler University St. Gallen Institute for Media and Communication Management Blumenbergplatz 9 CH-9000 Abstract: The impact of cognitive biases on (managerial) decisions has been recognized over the last decades with a recent surge due to the COVID-19 based policy making. This article analyzes existing debiasing techniques to mitigate the influence of cognitive biases on (managerial) decisions and links the theoretical perspective with the practice. As debiasing techniques have a surprisingly little awareness among managers a card-sort experiment was applied to search for a more practice-oriented understanding and structure of debiasing techniques with the goal of developing a framework that helps practitioners to integrate debiasing techniques more easily in their decision processes. The result of the experiment shows 9 clusters of debiasing techniques (checklists, preparation, what if?, group debiasing, reason analogically, in-process debiasing, starting viewpoints, involvement, and calibration) which can be implemented in different phases of a decision process and thus may help to improve (managerial) decisions in times of uncertainty. Keywords: Debiasing, management decisions, framework, card sorting 1. Introduction Managerial decisions are influenced by the psychological context of decision makers (Bateman & Zeithaml, 1989) and are prone to cognitive biases (Barnes, 1984; Das & Teng, 1999; Lovallo & Kahneman, 2003; Powell, Lovallo, & Fox, 2011; Schwenk, 1984; Simon, 1987). The need to reduce the impact of these biases in strategy workshops and episodes has been recognized by strategy scholars (Hodgkinson, Whittington, Johnson, and Schwarz, 2006; Hodgkinson and Clarke, 2007; Jarzabkowski and Kaplan, 2015).
    [Show full text]
  • A User's Guide to Debiasing
    A USER’S GUIDE TO DEBIASING Jack B. Soll Fuqua School of Business Duke University Katherine L. Milkman The Wharton School The University of Pennsylvania John W. Payne Fuqua School of Business Duke University Forthcoming in Wiley-Blackwell Handbook of Judgment and Decision Making Gideon Keren and George Wu (Editors) Improving the human capacity to decide represents one of the great global challenges for the future, along with addressing problems such as climate change, the lack of clean water, and conflict between nations. So says the Millenium Project (Glenn, Gordon, & Florescu, 2012), a joint effort initiated by several esteemed organizations including the United Nations and the Smithsonian Institution. Of course, decision making is not a new challenge—people have been making decisions since, well, the beginning of the species. Why focus greater attention on decision making now? Among other factors such as increased interdependency, the Millenium Project emphasizes the proliferation of choices available to people. Many decisions, ranging from personal finance to health care to starting a business, are more complex than they used to be. Along with more choices comes greater uncertainty and greater demand on cognitive resources. The cost of being ill-equipped to choose, as an individual, is greater now than ever. What can be done to improve the capacity to decide? We believe that judgment and decision making researchers have produced many insights that can help answer this question. Decades of research in our field have yielded an array of debiasing strategies that can improve judgments and decisions across a wide range of settings in fields such as business, medicine, and policy.
    [Show full text]
  • Present Bias: Definition and Measurement Alexis Direr
    Present bias: Definition and measurement Alexis Direr To cite this version: Alexis Direr. Present bias: Definition and measurement. 2019. hal-02133525v1 HAL Id: hal-02133525 https://hal.archives-ouvertes.fr/hal-02133525v1 Preprint submitted on 18 May 2019 (v1), last revised 24 Jun 2020 (v3) HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Present bias: Definition and measurement Alexis Direr ∗ May 17, 2019 Abstract A novel definition of present bias is proposed which takes preferences as primitives. A present biased individual over-weights immediate costs and benefits relative to those occurring at any point in the future. The definition allows to sort out previous confounds, such as decreasing im- patience, choice reversal or short-term impatience. It intuitively connects to usual utility representations of present bias like the quasi-hyperbolic model of Laibson (1997) or the fixed cost model of Benhabib, Bisin and Schotter (2010). Drawing on the definition, we propose two experimen- tal methods measuring present bias at the individual level which do not require assumptions about utility or discounting, one for daily trade-offs, the other for intra-daily trade-offs. J.E.L.
    [Show full text]
  • Present-Bias and Time-Inconsistency in Intertemporal Choice
    Procrastination Talk, 15 October 2014 Now or Later? Present-Bias and Time-Inconsistency in Intertemporal Choice 1 Procrastination Talk, 15 October 2014 Now or Later? Present-Bias and Time-Inconsistency in Intertemporal Choice or “When the timing gets flexible, the slow turn pro.” --Knute Rockne (planned but never delivered) Vincent P. Crawford University of Oxford and All Souls College (with large debts to David Laibson, Ted O’Donoghue, and Matthew Rabin; material from their slides is used with permission) 2 Behavioral economics explains phenomena like time-inconsistent intertemporal choice and procrastination as the product of “present-biased” preferences and possible naivete about the choices of one’s own future selves. I will start with an overview of those ideas and the supporting evidence. I will then illustrate their uses in thinking about economic and social policy. 3 Behavioral economics explains phenomena like time-inconsistent intertemporal choice and procrastination as the product of “present-biased” preferences and possible naivete about the choices of one’s own future selves. I will start with an overview of those ideas and the supporting evidence. I will then illustrate their uses in thinking about economic and social policy. Present bias and naivete: ● Qualify the standard arguments for consumer sovereignty and laissez faire ● May provide a rationale for “libertarian paternalism” ● May provide a rationale even for some coercive policies 4 Standard economic model of intertemporal choice Present bias, time-inconsistency, and procrastination are biases (only) relative to the standard “fully rational” model: time-separable utility with exponential discounting (Ramsey 1928 Economic Journal, Samuelson 1937 REStud). In the standard model, the individual chooses as if to maximize: The trade-off between receiving “utils” today and receiving them with a given delay is then independent of when that delay occurs: If you prefer 10 utils now to 15 utils next week, you must also prefer 10 utils in 25 weeks to 15 utils in 26 weeks.
    [Show full text]
  • Present Bias and Criminal Law
    University of Chicago Law School Chicago Unbound Journal Articles Faculty Scholarship 2011 Present Bias and Criminal Law Richard H. McAdams Follow this and additional works at: https://chicagounbound.uchicago.edu/journal_articles Part of the Law Commons Recommended Citation Richard H. McAdams, "Present Bias and Criminal Law," 2011 University of Illinois Law Review 1607 (2011). This Article is brought to you for free and open access by the Faculty Scholarship at Chicago Unbound. It has been accepted for inclusion in Journal Articles by an authorized administrator of Chicago Unbound. For more information, please contact [email protected]. PRESENT BIAS AND CRIMINAL LAW Richard H. McAdams* Although "present bias" (or weakness of will, impulsiveness, myopia, or bounded willpower) was flagged as an issue for legal ex- amination by Tom Ulen and Russell Korobkin over a decade ago, the concept has received insufficient attention in the legal field-and most of that attention has focused on its implications for the regulation of credit and savings. But, as demonstrated by this Article, the inconsis- tency of time preferences has wider implications, especially for crimi- nal law. First, present bias may have significant implications for the general deterrence of crime. Individuals with time-inconsistent pref erences may give in to immediate temptations to offend, even though they will not plan to exploit more distant opportunities to offend. To create additional deterrence by exploiting the present bias, one must either make the deferred costs of crime immediate or make the imme- diate benefits of crime deferred. For this reason, present bias height- ens the importance of timing arrests closer to the commission of a crime-which suggests overlooked benefits from undercover opera- tions.
    [Show full text]