In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

NUDGE: IMPROVING DECISIONS ABOUT HEALTH, WEALTH, AND HAPPINESS by Richard H. Thaler and Cass R. Sunstein Penguin Books, 2009, 312 pp, ISBN 978-0-14-311526-7This book is best described formally as a general explanation of and advocacy for libertarian paternalism, a term coined by the authors in earlier publications. Informally, it is about how leaders, systems, organizations, and governments can nudge people to do the things the nudgers want and need done for the betterment of the nudgees, or of society. It is paternalism in the sense that "it is legitimate for choice architects to try to influence people's behavior in order to make their lives longer, healthier, and better", (p. 5) It is libertarian in that "people should be free to do what they like - and to opt out of undesirable arrangements if they want to do so", (p. 5) The built-in possibility of opting out or making a different choice preserves freedom of choice even though people's behavior has been influenced by the nature of the presentation of the information or by the structure of the decisionmaking system. I had never heard of libertarian paternalism before reading this book, and I now find it fascinating.Written for a general audience, this book contains mostly social and behavioral science theory and models, but there is considerable discussion of structure and process that has roots in mathematical and quantitative modeling. One of the main applications of this social system is economic choice in investing, selecting and purchasing products and services, systems of taxes, banking (mortgages, borrowing, savings), and retirement systems. Other quantitative social choice systems discussed include environmental effects, health care plans, gambling, and organ donations. Softer issues that are also subject to a nudge-based approach are marriage, education, eating, drinking, smoking, influence, spread of information, and politics. There is something in this book for everyone.The basis for this libertarian paternalism concept is in the social theory called "science of choice", the study of the design and implementation of influence systems on various kinds of people. The terms Econs and Humans, are used to refer to people with either considerable or little rational decision-making talent, respectively. The various libertarian paternalism concepts and systems presented are tested and compared in light of these two types of people. Two foundational issues that this book has in common with another book, Network of Echoes: Imitation, Innovation and Invisible Leaders, that was also reviewed for this issue of the Journal are that 1 ) there are two modes of thinking (or components of the brain) - an automatic (intuitive) process and a reflective (rational) process and 2) the need for conformity and the desire for imitation are powerful forces in human behavior. …

Nudge: Improving Decisions about Health, Wealth, and Happiness

Mechanical Turk (MTurk), an online labor market created by Amazon, has recently become popular among social scientists as a source of survey and experimental data. The workers who populate this market have been assessed on dimensions that are universally relevant to understanding whether, why, and when they should be recruited as research participants. We discuss the characteristics of MTurk as a participant pool for psychology and other social sciences, highlighting the traits of the MTurk samples, why people become MTurk workers and research participants, and how data quality on MTurk compares to that from other pools and depends on controllable and uncontrollable factors.

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Inside the Turk Understanding Mechanical Turk as a Participant Pool

Mechanical Turk (MTurk), an online labor system run by Amazon.com, provides quick, easy, and inexpensive access to online research participants. As use of MTurk has grown, so have questions from behavioral researchers about its participants, reliability, and low compensation. In this article, we review recent research about MTurk and compare MTurk participants with community and student samples on a set of personality dimensions and classic decision-making biases. Across two studies, we find many similarities between MTurk participants and traditional samples, but we also find important differences. For instance, MTurk participants are less likely to pay attention to experimental materials, reducing statistical power. They are more likely to use the Internet to find answers, even with no incentive for correct responses. MTurk participants have attitudes about money that are different from a community sample’s attitudes but similar to students’ attitudes. Finally, MTurk participants are less extraverted and have lower self-esteem than other participants, presenting challenges for some research domains. Despite these differences, MTurk participants produce reliable results consistent with standard decision-making biases: they are present biased, risk-averse for gains, risk-seeking for losses, show delay/expedite asymmetries, and show the certainty effect—with almost no significant differences in effect sizes from other samples. We conclude that MTurk offers a highly valuable opportunity for data collection and recommend that researchers using MTurk (1) include screening questions that gauge attention and language comprehension; (2) avoid questions with factual answers; and (3) consider how individual differences in financial and social domains may influence results. Copyright © 2012 John Wiley & Sons, Ltd.

/pdf/data-collection-in-a-flat-world-the-strengths-and-weaknesses-18eu8fjf8f.pdf

Data collection in a flat world: the strengths and weaknesses of mechanical turk samples

Mechanical Turk (MTurk), an online labor system run by Amazon.com, provides quick, easy, and inexpensive access to online research participants. As use of MTurk has grown, so have questions from behavioral researchers about its participants, reliability, and low compensation. In this paper we review recent research about MTurk and compare MTurk participants to community and student samples on a set of personality dimensions and classic decision-making biases. Across two studies, we find many similarities between MTurk participants and traditional samples, but we also find important differences. For instance, MTurk participants are less likely to pay attention to experimental materials, reducing statistical power. They are more likely to use the Internet to find answers, even with no incentive for correct responses. MTurk participants have attitudes about money that are different from a community sample’s attitudes, but similar to students’ attitudes. Finally, MTurk participants are less extraverted and have lower self-esteem than other participants, presenting challenges for some research domains. Despite these differences, MTurk participants produce reliable results consistent with standard decision-making biases: They are present biased, risk-averse for gains, risk-seeking for losses, show delay/expedite asymmetries, and show the certainty effect — with almost no significant differences in effect sizes from other samples. We conclude that MTurk offers a highly valuable opportunity for data collection, and recommend that researchers using MTurk 1) include screening questions that gauge attention and language comprehension, 2) avoid questions with factual answers, and 3) consider how individual differences in financial and social domains may influence results.

/pdf/data-collection-in-a-flat-world-strengths-and-weaknesses-of-1y6f89xxwa.pdf

Data Collection in a Flat World: Strengths and Weaknesses of Mechanical Turk Samples

Consumers often behave differently than they would ideally like to behave. We propose that an anticipatory pain of paying drives tightwads to spend less than they would ideally like to spend. Spendthrifts, by contrast, experience too little pain of paying and typically spend more than they would ideally like to spend. This article introduces and validates the Tightwad-Spendthrift scale, a measure of individual differences in the pain of paying. Spending differences between tightwads and spendthrifts are greatest in situations that amplify the pain of paying and smallest in situations that diminish the pain of paying.

Tightwads and Spendthrifts

Consumers often behave differently than they would ideally like to behave. We propose that an anticipatory pain of paying drives “tightwads” to spend less than they would ideally like to spend. “Spendthrifts,” by contrast, experience too little pain of paying and typically spend more than they would ideally like to spend. This article introduces and validates the “spendthrift-tightwad” scale, a measure of individual differences in the pain of paying. Spending differences between tightwads and spendthrifts are greatest in situations that amplify the pain of paying and smallest in situations that diminish the pain of paying.

/pdf/tightwads-and-spendthrifts-cj1cnkgoc6.pdf

Misery is not miserly: Sadness increases the amount of money that decision makers give up to acquire a commodity. The present research investigated when and why the misery-is-not-miserly effect occurs. Drawing on William James's concept of the material self, we tested a model specifying relationships among sadness, self-focus, and the amount of money that decision makers spend. Consistent with our Jamesian hypothesis, results demonstrated that the misery-is-not-miserly effect occurs only when self-focus is high. That is, self-focus moderates the effect of sadness on spending. Moreover, mediational analyses revealed that, at sufficiently high levels, self-focus mediates (explains) the relationship between sadness and spending. Because the study used real commodities and real money, the results hold implications for everyday decisions, as well as implications for the development of theory. For example, economic theories of spending may benefit from incorporating psychological theories -- specifically, theories of emotion and the self -- into their models.

Cynthia Cryder

Papers

Data collection in a flat world: the strengths and weaknesses of mechanical turk samples

Data Collection in a Flat World: Strengths and Weaknesses of Mechanical Turk Samples

Tightwads and Spendthrifts

Tightwads and Spendthrifts

Misery Is Not Miserly Sad and Self-Focused Individuals Spend More