Decision Science News

SOME EMPIRICAL BASES FOR CHOOSING CERTAIN RISK REPRESENTATIONS OVER OTHERS

An example of an information grid

This week DSN posts some thoughts (largely inspired by the work of former colleagues Stephanie Kurzenhäuser, Ralph Hertwig, Ulrich Hoffrage, and Gerd Gigerenzer) about communicating risks to the general public, providing references and delicious downloads where possible.

Representations to use less often

Single-event probabilities as decimals or percentages

Statements of the form “The probability that X will happen is Y”, such as “The probability that it will rain on January 1^st is 30%” are single-event probability statements. They are problematic not only for philosophical reasons (some “frequentists” (as opposed to “Bayesians”) say that such statements are meaningless), but also because they are ambiguous: they do not specify if we’re saying this about January First based on other January Firsts, or if we’re saying it based on all January Firsts at a particular weather station (or an average across many weather stations), or if we’re not even considering the date but basing our prediction on today’s weather, a mathematical model, an average of other people’s forecasts, our intuition, or what.

What may seem unambiguous is actually interpreted by different people in different ways. A survey of people in 5 international cities found no agreement on what a 30% chance of rain means. Some thought it means rain on 30% of the the day’s minutes, others thought rain in 30% of the land area, and so on [1]. A further problem with the statement is that it gives no information about what it means to rain. Does one drop of rain count as rain? Does a heavy mist? Does one minute of rain count?

In addition, when risks are described as probabilities, people tend to overweight small probabilities and underweight large probabilities. This observation shows up in the “probability weighting function” of Tversky & Kahneman’s Prospect Theory, the dominant behavioral model of gamble evaluations. A representation that leads to misperceptions of underlying probabilities is undesirable.

Conditional probabilities as decimals or percentages

Doctors given problems of the type:

The probability of colorectal cancer in a certain population is 0.3% [base rate]. If a person has colorectal cancer, the probability that the haemoccult test is positive is 50% [sensitivity]. If a person does not have colorectal cancer, the probability that he still tests positive is 3% [false-positive rate]. What is the probability that a person from the population who tests positive actually has colorectal cancer?

give mostly incorrect answers that span the range of possible probabilities. Typical answers include 50% (the “sensitivity”) or 47% (the sensitivity minus the “false positive rate”). The correct answer is 5%. [2]

It seems as if people given conditional probabilities, such as the sensitivity or the false-positive rate, confuse them with the posterior probability they are being asked for. This likely happens because each numerical representation lends itself to computations that are easy or difficult for that representation. The thing to do with the conditional probabilities listed above is to plug them into Bayes Theorem, which most people do not know. Even if they know the theorem, they have little intuition for it and cannot make good mental estimates.

Fortunately, there are other ways to represent information than conditional probabilities that allow even those who do not know Bayes’ theorem to arrive at the correct answer, as we shall see.

Relative risks

Relative risk statements speak of risk increasing or decreasing by a percentage, for instance, that mammography in women over 40 reduces the risk of breast cancer by 25%. But all percentages erase the frequencies from which they were derived. We cannot tell from the relative risk reduction what is the absolute risk reduction: by how much does the risk of breast cancer actually decrease between those who get mammographies and those who do not: the answer is .1%

Relative risk information does not give information on how many people need to undergo a treatment before a certain benefit is obtained. In particular, based on the relative risk information, can one say how many women must be screened before a single life is saved? If your intuition tells you 4, you are again far off, as 1000 women must be screened to save the one life. In this way, relative risk information can cause people to misjudge the effectiveness of treatments [3].

Representations to use more often

Natural frequencies instead of probabilities

Consider the colorectal cancer example given previously. Only 1 in 24 doctors tested could give the correct answer. The following, mathematically-equivalent, representation of the problem was given to doctors (also from [3]):

Out of every 10,000 people, 30 have colorectal cancer. Of these 30, 15 will have a positive haemoccult test. Out of the remaining 9,970 people without colorectal cancer, 300 will still test positive. How many of those who test positive actually have colorectal cancer?

Without any training whatsoever, 16 out 24 physicians obtained the correct answer to this version. That is quite a jump from 1 in 24.

Statements like 15 out of 30 are “natural frequency” statements. They correspond to the, trial-by-trial way we experience information in the world. (For example, we’re more likely to encode that 3 of our last 4 trips to JFK airport were met with heavy rush-hour traffic than encoding p = .75, which removes any trace of the sample size). Natural frequency statements lend themselves to simpler computations than does Bayes’ Theorem, and verbal protocols show that given statements like the above, many people correctly infer that the probability of cancer would be the number testing positive and who have the disease (15) divided by the number who get back positive test results (15 who actually have it + 300 false alarms). 15 divided by 315 is 5%, the correct answer. Bet you didn’t know you were doing a Bayesian computation.

Frequencies relative to a reference class

While compact statements of probability such as a “there is a 30% chance of rain on April first” save words, they do not reveal their underlying reference classes. When information is conveyed with statements like “In New York City, 3 out of every 10 April Firsts have more than a centimeter of rain” there is no ambiguity as to whether the 30% refers to days, area, or time, and it is more clear what “rain” means. It also conveys how you arrived at the forecast (an analysis of historic data, not a prediction based on a model).

Information grids

The Information Grid from the surprisingly popular Decision Science News post Tuesdays’ Child is Full of Probability Puzzles

Since probabilities can be translated to frequencies out of 100, 1000, 10000 and so on, they can easily be represented visually on grids that allow for visual assessment of area and facilitate counting. Research by my former office-mate Peter Sedlmeier [4] used information grids to teach people how to solve Bayesian reasoning problems (like the original colorectal cancer problem) by converting them into natural frequencies and representing them on a grid. Even six months later, experimental participants who received the information grid instruction were able to solve the problems correctly, while those who were instructed with the classic version of Bayes Theorem did not retain what they learned.

Information grids whose squares are embellished with faces showing positive or negative emotions have also proven effective in presenting treatment alternatives to patients [5].

Absolute risk reductions as frequencies

The statement that a certain treatment causes a 25% risk reduction, as mentioned, does not disclose the magnitudes of the risks involved. In the case studied, among women receiving mammographies 3 in 1000 died of cancer, while among women not receiving mammographies 4 in 1000 died of this cause. The absolute risk reduction pops out of this formulation, and we see it to be 1 in 1000. The number needed to treat, which is not computable from the relative risk reduction is now clear: to save one life, 1000 women must be screened. This formulation not only expresses the difference between alternative actions, but relates absolute magnitudes of risk as well.

The (probability) Distribution Builder of Goldstein, Johnson and Sharpe (2008)

Animations

While descriptive numerical probability formats leads to overweighting of small probabilities, recent research shows that when people learn probabilities through experience (actually taking draws from a distribution) it may lead to the opposite tendency: underweighting of large probabilities. An exciting possibility is that when descriptive and experienced probability formats are combined, the effects may cancel each other out. Other research shows that making draws from animated probability distributions led people to arrive at the most accurate estimates of the probability of a loss and of upside return of an investment [6]. Decision aids such as the Distribution Builder of Goldstein, Johnson, & Sharpe [7] allow participants to visually observe the magnitude of probabilities (as information grids do), while animating numerous draws from the distribution to allow people to experience random sampling. We propose to experiment with this format to see if it may lead to calibrated probability weighting.

The simulator of Haisley, Kaufmann and Weber

[1]

Gigerenzer, G. , Hertwig, R., van den Broek, E., Fasolo, B., & Katsikopoulos, K. V. (2005). “A 30% chance of rain tomorrow”: How does the public understand probabilistic weather forecast? Risk Analysis, 25, 623-629. Available online.

[2]

Hoffrage, Ulrich & Gigerenzer G. (1998). Using natural frequencies to improve diagnostic inferences. Academic Medicine, 73, 538-540. Available online.

[3]

Kurzenhauser, Steffi & Ralph Hertwig (2006). Kurzenhäuser, S., & Hertwig, R. (2006). How to foster citizens’ statistical reasoning: Implications for genetic counseling. Community Genetics, 9, 197-203. Available online.

[4]

Sedlmeier, Peter and Gerd Gigerenzer (2001) Teaching Bayesian reasoning in less than two hours. Journal of Experimental Psychology General, 130, 380–400. Available online.

[5]

Man-Son-Hing, Malcolm et al (1999) Therapy for Stroke Prevention in Atrial Fibrillation: A Randomized Controlled Trial. Journal of the American Medical Association, 282(8):737-743. Available online.

[6]

Haisley, Emily, Christine Kaufmann and Martin Weber (working paper) The Role of Experience Sampling and Graphical Displays on One’s Investment Risk Appetite. Available online.

[7]

Goldstein, Daniel G., Johnson, Eric J. & Sharpe, William F. (2008). Choosing Outcomes Versus Choosing Products: Consumer-Focused Retirement Investment Advice. Journal of Consumer Research, 35 (October), 440-456. Available online.

COLUMBIA OR DUKE, TAKE YOUR PICK

Thanksgiving is the time to eat with family, to attend homecoming games, and to find postdoctoral positions. (International readers may ignore this paragraph, except for the idea that late November is a fine season to hunt postdocs).

In a Decision Science News first, one (1) Center is offering two (2) postdocs on two (2) campuses. Regardless of which side of the Mason-Dixon line you choose to lay your head, there is a CRED (Center for Research on Environmental Decisions) postdoc for you. What’s more you’ll be working under the supervision of Eric Johnson & Elke Weber (under whose tutelage Decision Science News built is vast media empire) or Rick Larrick (who is competing with his co-author Jack Soll for sweeping the likeable*smart category in the 2012 Olympics).

DESCRIPTION
The Center for Research on Environmental Decisions (CRED) is hiring two postdocs who will work on Decision Architecture projects related to energy consumption and related environmentally relevant decisions over the next two years. One postdoc will be located at Columbia University, working with Eric Johnson and Elke Weber, the other will be located at Duke University, working with Rick Larrick. Qualified applicants are invited to apply for either one or both positions.

LINKS
For more information, click on the links below:

Duke Postdoc
Columbia Postdoc

MICHIGAN SCHOOL OF PUBLIC HEALTH OFFERING TWO JOBS: DEC 1 DEADLINE

University of Michigan School of Public Health
Two Tenure-Track Assistant Professor Positions in Risk Science

The University of Michigan Risk Science Center (http://www.sph.umich.edu/riskcenter) is seeking applicants for two tenure track positions, to be appointed at the Assistant Professor level in the School of Public Health (SPH). The Risk Science Center is a growing cross-disciplinary center dedicated to fostering new thinking, new understanding and new tools to support evidence-informed and socially relevant decisions on both emergent and extant risks to public health. As the center develops, the appointed faculty will be expected to make a significant contribution to its future direction and impact. Candidates are sought from a wide range of backgrounds who have a strong and proven interest in risk science, and who have an ability to work synergistically across disciplines.

Position: Successful candidates will join a growing group of core faculty at the University of Michigan exploring new integrative approaches to addressing risks to human health, and translating research into tools, resources, and policy frameworks that support informed decision-making on risks to human health. Each new hire will hold a primary academic appointment within one of the School of Public Health’s five departments – Biostatistics, Environmental Health Sciences, Epidemiology, Health Behavior and Health Education, or Health Management and Policy. Secondary appointments, either within SPH or with other units at the University, are possible. Appointees will engage fully with the activities of their department but will also contribute substantially to activities associated with the Risk Science Center, including developing and engaging with cross-department and cross-disciplinary research, teaching, and communication/outreach. Candidates will be expected to develop their own areas of research specialization, and to obtain competitive research funding.

Qualifications: Candidates must have a doctorate (e.g., PhD, ScD, MD, DrPH) in a field related to public health and risk science, demonstrate evidence of independent and collaborative research potential, demonstrate interest and ability in teaching at the graduate level, and have a record of peer reviewed publications and presentations commensurate with experience and rank. We are open with regard to disciplinary and methodological specialization; successful candidates could be from the social or natural sciences or professional schools, and conduct qualitative or quantitative research. Given the nature of the Risk Science Center, applications are encouraged from backgrounds that are relevant to new and integrative approaches to identifying, assessing, analyzing, managing, communicating, and/or governing risks to human health. In particular, candidates are sought who have the potential to work synergistically across disciplines and to think innovatively about new challenges, while being grounded in evidence-informed decision-making.

The School is especially interested in qualified candidates who can contribute, through their research, teaching, and/or service, to the diversity and excellence of the academic community.

Salary/Benefits: The University offers a competitive salary with an attractive benefits package.

Applications: Applications should be submitted electronically as a PDF containing a cover letter, curriculum vitae, a statement outlining research interests, relevance of experience to risk science and teaching philosophy, and a list of at least three persons who can provide letters of recommendation. Applications should be submitted via email to hildiris@umich.edu, Attn: Hilda McDonald, Search Committee Coordinator, University of Michigan Risk Science Center. Review of applications will begin December 1 and continue until suitable candidates are found. A start date on or before September 1, 2011 is anticipated.

The University of Michigan is an equal opportunity/affirmative action employer.

FACEBOOK ALLOWS USERS TO GET (A COPY OF) THEIR DATA BACK

After a few run-ins with the press and public over privacy and user control, and one not-so-positive movie, Facebook now deserves a hat tip for doing what many people thought it never would: allowing users to download a copy of everything they’ve uploaded.

Now, some snarky ones out there might say “but that’s not the same as their deleting your data if you request”, and that’s true. However, it is a Good Thing ™.

Here’s why it’s good business. If Facebook didn’t allow people to take their data with them, they could be accused of strong-arming people into not quitting the service. After all, who would deactivate their account if it meant losing some photos or videos or even losing track of all the contact information you imported into the site. With this move, Facebook is saying “you’re free to leave if you want”, and that’s how it should be. Companies should make you want to stay a customer though what they offer next, not by imposing switching costs.

TWO PROFESSORSHIPS: ECONOMIC, ORGANIZATIONAL OR SOCIAL PSYCHOLOGY

Party! Party!

Recall what DSN recently said about Tilburg:

Decision Science News recently visited the Marcel Zeelenberg, Diederik Stapel, Gideon Keren and the gang (which is a very impressive gang indeed) at Tilburg in the Netherlands. It left with the impression that its other visitors (another very impressive gang) had–that this is a first rate place to work in psychology and behavioral economics, as well as one in which the people there really know how to live. Savoir faire, joie de vivre, they have it all. Why stay where you are with happiness level X when you could apply to Tilburg and, if you make it, achieve happiness level 2X? Interested? Read on:

It’s “good news for you” time because now at the Tilburg School of Social and Behavioral Sciences, they are looking for:

Two Assistant/Associate professors in Organizational-, Economic- or Social Psychology, Tenure Track (full time), UVT-INT-2010-0280

The job
Members of the Department of Social Psychology supervise students and teach a variety of modules at both Bachelors and Masters level and participate in the two-year Research Master, covering a variety of significant topics from Social Psychology, Economic Psychology and Work & Organisational Psychology. The overarching research program of the Department of Social Psychology is Social Decision Making. The Department of Social Psychology participates in the interdisciplinary research institute TIBER, the Tilburg Institute for Behavioral Economics Research, devoted to studying the psychological processes underlying individual choice and economic decision making from an interdisciplinary perspective.

Tasks
The professors will work in the area of organizational psychology, economic psychology or social psychology. The tasks are:
– Conducting empirical research fitting the research program of the Department
– Writing articles in high quality scientific journals
– Teaching courses (in Dutch or English) in Organizational Psychology or Economic Psychology or other courses offered by the department, on the BSc and MSc level
– Supervising individual student projects at BSc
– and MSc -level.

Your profile
The candidate has the following qualifications:
– PhD in Psychology or related areas
– Passionate researcher/teacher
– High quality publications in scientific journals
– Experience and affinity with teaching in the area of Psychology
– Profound knowledge of English
– For non-Dutch candidates: readiness to learn Dutch

Employment terms and conditions
For the tenure track there is a four-year contract, with the possibility of tenure thereafter. The salary for the position of an Assistant Professor on a full-time basis ranges between € 3195 and € 4970,- gross per month (various allowances are not included). For the Associate Professor position the salary on a full-time basis ranges between € 4428,- and € 5920,- gross per month (various allowances are not included).

Other
The Department of Social Psychology is an intellectually exciting and productive group, advancing fundamental understanding in the areas of social, economic and work & organisational psychology, whilst also contributing to effective practice in organizations and society. The basic and applied research of the department is highly recognized both nationally and internationally.

Information and Application
Additional information about Tilburg University and the Department of Social Psychology can be retrieved from: http://www.uvt.nl

Specific information about the vacancy can be obtained from Marcel Zeelenberg, professor of Economic Psychology, Tilburg University, P.O.Box 90153, 5000 LE Tilburg, The Netherlands, telephone +31134668276, e-mail M.Zeelenberg@uvt.nl
Applications, including a curriculum vitae, a letter of motivation, and two recent (forthcoming) publications) should be send, before December 1, 2010 to Hans Dieteren, Managing Director Faculty of Social and Behavioral Sciences. This is only possible by using the link below.

APPLY VIA: http://erec.uvt.nl/vacancy?inc=fa1en0001_6

JOURNAL OF INTERACTIVE MARKETING SPECIAL ISSUE ON SOCIAL MEDIA

CALL FOR PAPERS

Journal of Interactive Marketing Special Issue
Social Media: Issues and Challenges

Submission deadline: March 15, 2011

Special Issue Co-Editors

Donna L. Hoffman (donna.hoffman@ucr.edu) and Thomas P. Novak (tom.novak@ucr.edu)
University of California, Riverside

The Journal of Interactive Marketing announces a call for papers on topics related to the marketing issues and challenges presented by the explosive popularity of social media. We seek conceptual, analytical, empirical, and managerial papers, covering the newest and most innovative approaches to the study of this theme. The biggest buzz in the online world in the last few years has been about social media. Consumer usage of applications like Facebook and Twitter has skyrocketed, and marketing managers, increasingly desperate to reach coveted demographics that rarely read newspapers or watch television, are now seriously looking to social Web applications as an important vehicle for reaching their customers.

In keeping with its position as a thought leader and catalyst for shaping ideas and issues associated with electronic, interactive and direct marketing environments, the Journal of Interactive Marketing seeks to publish the most leading-edge academically rigorous ideas, methodologies and insights related to the marketing implications of social media.

Special Issue Topics

The special issue is seeking papers that define and address the social media challenges facing Internet marketers that include but are not limited to the following topics:
* User-generated content represents a wealth of behavioral data. What are the best ways to model and analyze these data for generalizable marketing insight?

* What is the ROI of social media?

* How do social media affect online and offline information seeking and shopping behavior?

* Why does some user-generated content “go viral?”

* Is brand-related user-generated content from different platforms (e.g. Twitter, Facebook, YouTube, and so on) consistent? Do some platforms lend themselves to systematic biases in how consumers discuss brands?

* How can managers integrate the content across multiple social media platforms to develop a consistent view of “consumer chatter” about their brands?

* Can we infer consumer characteristics such as personality, other chronic dispositions, purchase likelihood or other consumer response measures from user-generated content?

* What are the optimal strategies for responding to customer complaints in social media?

* Do social coupon applications – and more generally crowdsourcing applications- have a sustainable business model?

Interested authors should feel free to discuss the fit of their potential topic with the Special Issue editors, Donna Hoffman (donna.hoffman@ucr.edu) and Tom Novak (tom.novak@ucr.edu).

Timeline and Review Details

The deadline for submission of manuscripts is March 15, 2011. This deadline is firm. All manuscripts for the special issue will be reviewed according to the guidelines for the Journal of Interactive Marketing. The special issue will follow the same reviewing process as regular Journal of Interactive Marketing submissions. The special issue editors especially encourage the submission of shorter papers (25-30 pages inclusive).

Authors can expect decisions by June 30, 2011. Revised manuscripts will be due by August 31, 2011; the special issue is slated to appear as the first issue of 2012.

Submission Details

Manuscripts should be submitted electronically via the Journal of Interactive Marketing web site no later than March 15, 2011 according to the submission guidelines. Please indicate that your submission is for the Special Issue on Social Media.

Manuscript guidelines and paper submission: http://ees.elsevier.com/intmar

Please contact Barbara Hruska, Managing Editor, bhruska@directworks.org for submission questions.

About the Journal of Interactive Marketing

The Journal of Interactive Marketing is a peer-reviewed journal that caters to strong academic and practitioner audiences. Its readership includes direct and interactive marketers, advertisers and advertising agencies, market researchers, e-business executives, and researchers in marketing, strategy, customer behavior, managerial economics, statistics, and information technology. JIM is offered in more than 1,500 institutions and libraries in nearly 40 countries around the world and is indexed in the major bibliographic databases including the Social Science Citation Index, Business Source Premier, and ABI/Inform (FirstSearch).

The 2-year ISI Impact Factor for the Journal of Interactive Marketing is 2.60 and the 5-year Impact Factor is 4.02. These numbers offer strong confirmation that JIM is the leading journal in the area of direct and interactive marketing. The commonly used 2-year measure is the 6th highest score among marketing journals.

Photo credit: http://www.flickr.com/photos/webtreatsetc/4091128553/ & http://webtreats.mysitemyway.com

FASTER, CHEAPER, EASIER BEHAVIORAL RESEARCH ONLINE

One thing Decision Science News particularly enjoys about being at Yahoo! Research is the brilliant colleagues. This week, two of them, Winter Mason and Sid Suri, presented us with this manuscript which is a guide to conducting research on Amazon’s Mechanical Turk.

Manuscript? Manuscript from heaven, we say, for here at DSN we are often being asked the ins and outs of this technology and now have a guide to link to. Read it now before it gets published:

A Guide to Conducting Behavioral Research on Amazon’s Mechanical Turk

See Decision Science News’ earlier posts on MT:

• Amazon’s Mechanical Turk meets classic JDM experiments
• How to run experiments on Mechanical Turk

Photo credit: The R language for statistical computing

WHAT MAKES JDM DISTINCT?

A friend of Decision Science News, who is co-organizing a session on JDM (judgment and decision making research) for students, recently emailed a handful of JDM researchers:

Those of us in the JDM session are doing quite different research and couldn’t really see how we were more “JDM” than, say, someone doing “cognition”, which lead us to the question “What is JDM?”

If you have a few minutes in the next couple days to just shoot me a note about what makes JDM distinct, I’d really appreciate your thoughts. My goal is to give students a couple different (anonymous, of course) opinions about what JDM is from people more senior than those of us in the session.

Here is the opinion that Decision Science News gave:

This post from Decision Science News, based on a text analysis of conference programs, gives some insight into how what is currently being done in JDM is distinct from Social Psych

http://www.decisionsciencenews.com/2010/02/15/the-difference-between-spsp-and-sjdm/

Also, the first list does a pretty good job of showing the core topics of JDM: risk, uncertainty, choice, decision, probability, prediction, future, intertemporal choice. Missing from the list would be: heuristics, utility, forecasting, normative models, prescriptive models, and descriptive models.

The Society for Judgment and Decision Making (SJDM) was formally formed in 1986 (from a core who had been meeting less formally before that) and I’ve heard it was basically people interested in the exciting field of research opened up by Tversky & Kahneman. Their 1974 Science article still touches upon much of what is done today.

The oldest President’s letter to be found online, written by Barbara Mellers in 1996, speaks of “almost five decades” of JDM research, which would point to somewhere in the late 1940s. Well after Brunswik, a few years after Von Neumann and Morgenstern’s “Theory Games and Economic Behavior” and a few year’s before Ward Edward’s Psychological Bulletin article “The theory of decision making”, the abstract of which is (emphasis added):

This literature review of decision making (how people make choices among desirable alternatives), culled from the disciplines of psychology, economics, and mathematics, covers the theory of riskless choices, the application of the theory of riskless choices to welfare economics, the theory of risky choices, transitivity of choices, and the theory of games and statistical decision functions. The theories surveyed assume rational behavior: individuals have transitive preferences (“… if A is preferred to B, and B is preferred to C, then A is preferred to C.”), choosing from among alternatives in order to “… maximize utility or expected utility.”

And Meller’s President’s letter (emphasis added) describes what she saw as the big topics (in addition to her opinions about the focus, which we won’t touch upon here):

For almost five decades, researchers in judgment and decision making have explored human errors in judgment and choice. We have documented instances in which people violate fundamental principles and axioms. We have discovered cases in which people disobey the most basic rules of statistics, probability, and logic. We have identified factors that should be irrelevant, but aren’t, such as the response mode, the problem representation, and the decision frame.

What are the legacies of this research? We have probed the boundaries of human rationality. We have discovered important limitations of cognitive processing, and we understand how poor judgment makes people their own worst enemies. But somewhere along the way, we lost sight of everything else.

While walking across campus to a colloquium one afternoon, a colleague asked me whether the speaker was a member of the JDM Society. When I told him “yes,” he said, “Then give me a quick preview. What is the error of the day?” He was perfectly serious. We are well known for setting traps and taking delight at human failure.

Haven’t we reached the point of diminishing returns? Demonstrations of one more error for the sake of an error, or one more violation for the sake of a violation, are nothing new. Not only are they not new, they add to an already lopsided view of human competence. We need theories of decision making that predict not only errors, biases, and violations of axioms, but also broader themes of psychological and social functioning. We know very little about the effects of emotions on choice. We know very little about the relationships between decision making and signal detection, memory retrieval, or categorization. Not only that, we know very little about the impact of social context. Why are certain errors, and not others, attenuated in experimental markets, and possibly other institutional settings?

One of the reasons we may have become so preoccupied with errors is because we applied to our descriptive theories the organizing principles from our normative theories. In normative theories, we classify decisions depending on the assignment of probabilities to states of nature (decision making under certainty, risk, uncertainty, or conflict), and these categories may not be optimal for descriptive theorizing. In the animal literature, decisions are often classified on the basis of the animal’s activities, such as foraging and mating. Perhaps functional distinctions might be appropriate in the human literature as well. How often have you heard complaints that our theories apply to purchasing decisions, but not decisions about marriage or children? How often have you heard complaints that our theories of gambles don’t generalize to medical treatments, job opportunities, or even vacation sites? Perhaps the missing links in our descriptive theories would become more apparent with a different set of organizing principles that highlight our activities, goals, and desires.

We have gotten a great deal of mileage out of errors. Decision making is discussed in many psychology texts. It is also cited in marketing, organizational behavior, political science, and microeconomics texts. Philosophers, economists, and statisticians are also developing richer and more interesting definitions of rationality. Finally, psychologists have begun to study human strengths as well as human weakness, and this work should have important consequences for artificial intelligence systems designed to complement and aid human decision making.

To have a lasting impact, we should continue to go beyond errors, mistakes, and other human failures and adopt a broader perspective. As John Locke said, “It is one thing to show a man that he is in error, and another to put him in possession of the truth.”

The point is, for better or for worse, the majority of JDM research has always been about the difference between formalisms and human behavior. The formalisms are drawn from economics, mathematics, and psychology as Edwards said, and I’d guess that the following list of formal models (with examples of JDM research areas in parens) is close to complete:

• probability (base-rate neglect / conservatism, confidence),
• logic (Wason selection task),
• subjective expected utility (Prospect Theory, Support Theory),
• choice axioms (Independence of irrelevant alternatives, attraction / compromise effects)
• statistics
  • sampling (Representativeness, law of small numbers, probability weighting, decisions from experience)
  • inference (lens model, fast&frugal heuristics)
  • estimation (Availability, Anchoring, risk perception)

Outside of this, there is a bit of descriptive work (Naturalistic DM, individual differences) and a bit of prescriptive work, though the latter is usually taken up in the field known as Decision Analysis. Like Mellers quite a few JDM researchers have not been happy with the organization around axiomatic norms, but if we are to define JDM by what it is has primarily been in the past, this generalization is hard to deny.

Since Meller’s letter, attention has moved from documenting differences to building more formal models of what people do, with Prospect Theory being the field’s most successful export.

As to the differences with Social Psychology, I think the blog post above addresses the differences in current practice.

As to the differences with Cognitive Psychology, Barsalou’s textbook puts JDM as a field within Cognitive Psychology and I think this is right: judging, choosing, and deciding are thought processes. Cognitive Psych is defined as covering perception, memory, thinking, language, and problem solving. Barsalou’s chapters are: categorization, representation, executive control, working memory, long-term memory, knowledge, language structure, language process, and thought. JDM typically falls under “thinking” / “thought”.

If forced to choose two books that represent what the field is about, I’d go with:

• Rational Choice In An Uncertain World, by Reid Hastie and Robyn M. Dawes
• Judgment under Uncertainty: Heuristics and Biases, edited by Daniel Kahneman, Paul Slovic, Amos Tversky

Photo credit:http://www.flickr.com/photos/captkodak/272746539/

TYPES OF DEFAULTS AND HOW TO SET THEM

Defaults are settings or choices that apply to individuals who do not take active steps to change them (Brown & Krishna, 2004). Collections of default settings, or “default configurations” determine the way products, services, or policies are initially encountered by consumers, while “reuse defaults” come into play with subsequent uses of a product. At the finest level, a single question can have “choice option default”, which on electronic forms can take the shape of a pre-checked box (Johnson, Bellman, and Lohse, 2002).

Defaults have been shown to have strong effects on real-world choices in domains including investment (Cronqvist & Thaler, 2004; Madrian & Shea, 2001), insurance (Johnson et al, 2003), organ donation (Johnson & Goldstein, 2004), marketing (Goldstein et al, 2008) and beyond.

They have a wide appeal among marketers and policy makers in that they guide choice while at the same time preserving freedom to choose. They are often regarded as the prototypical instruments of libertarian paternalism (Sunstein & Thaler, 2003).

Through default-setting policies, choice architects exhibit influence over resulting choices. The palette of policies includes simple defaults (choosing one default for all audiences), random defaults (assigning a configuration at random, for instance, as an experiment), forced choice (withholding the product or service by default, and releasing it only after an active choice is made), and sensory defaults (those that change according to what can be inferred about the user, for example, web sites that change language based on the visitor’s IP address).

Products and services that are re-used can also avail themselves of persistent or reverting defaults (which, respectively, remember or forget the last changes made to the default configuration) and predictive defaults (which intelligently alter reuse defaults based on observation of the user).

Those setting defaults should be aware of the ethical risks involved (Smith, Goldstein & Johnson, 2010). The ethical acceptability of using a default to guide choice has much to do with the reason why the default has an effect in the first place. When consumers are aware that defaults may be recommendations in some cases and manipulation attempts in other cases (Brown & Krishna), they exhibit a level of “marketplace metacognition” that suggests they retain autonomy and freedom of choice. However, if defaults are effective because consumers are not aware that they have choices, or because the transaction costs of changing from the default are too high, defaults impinge upon consumer autonomy. An often prudent policy, though not a cure-all, is to set the default to the alternative most people prefer when making an active choice, without time pressure, in the absence of any default. Running an experiment on a sample of the greater population can determine these preferences, and can be done in little time and at a low cost in the age of Internet experimentation (Gosling & Johnson, 2010).

REFERENCES

Brown, Christina L. and Aradhna Krishna (2004), “The Skeptical Shopper: A Metacognitive Account for the Effects of Default Options on Choice,” Journal of Consumer Research, 31 (3), 529-539.
Cronqvist, Henrik and Richard H. Thaler (2004), “Design Choices in Privatized Social Security Systems: Learning from the Swedish Experience,” American Economic Review, 94 (2), 424-428.
Goldstein, Daniel G., Eric J. Johnson, Andreas Herrman, and Mark Heitmann (2008), “Nudge Your Customers Toward Better Choices,” Harvard Business Review, December, 99-105.
Gosling, Samuel D. and John A. Johnson (2010), Advanced methods for conducting online behavioral research. Washington, DC: American Psychological Association.
Johnson, Eric J., Steven Bellman, and Gerald L. Lohse (2002), “Defaults, Framing, and Privacy: Why Opting In Is Not Equal To Opting Out,” Marketing Letters, 13 (1), 5–15.
Johnson, Eric J. and Daniel G. Goldstein (2003), “Do Defaults Save Lives?” Science, 302, 1338-1339.
Johnson, Eric J., John Hershey, Jacqueline Meszaros, and Howard Kunreuther (1993), “Framing, Probability Distortions, and Insurance Decisions,” Journal of Risk and Uncertainty, 7, 35-53.
Madrian, Brigitte C. and Dennis F. Shea, D. F. (2001), “The Power of Suggestion: Inertia in 401(k) Participation and Savings Behavior,” Quarterly Journal of Economics, 116 (4), 1149-1187.
Thaler, Richard, Daniel Kahneman and Jack L. Knetsch (1992), “The Endowment Effect, Loss Aversion and Status Quo Bias,” in Richard Thaler, The Winner’s Curse, Princeton: Princeton University Press, 63-78.
Samuelson, William and Richard Zeckhauser (1988), “Status Quo Bias in Decision Making,” Journal of Risk and Uncertainty, 1 (1), 7-59.
Smith, N. Craig, Daniel G. Goldstein, and Eric J. Johnson (2010). Choice without Awareness: Ethical and Policy Implications of Defaults. Working paper.
Sunstein, Cass R. and Richard H. Thaler (2003), “Libertarian Paternalism Is Not an Oxymoron,” The University of Chicago Law Review, 70 (4), 1159-1202.

ONE POST, TWO JOBS

The YALE SCHOOL OF MANAGEMENT is seeking additional faculty members at all levels in the areas of economics and organizational behavior. Ph.D. or equivalent is required; research and teaching interest in theory and application preferred, as well as an interdisciplinary orientation. Appointments will be made for the 2011 – 2012 academic year.

To apply online click here.

Please note that only electronic applications are accepted this year.

The deadlines for receipt of all materials is October 15, 2010.

For more information visit: http://mba.yale.edu/faculty/faculty_openings.shtml

Yale is an equal opportunity, affirmative action employer and especially encourages applications from women and members of minority groups.

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THE DEPARTMENT OF SOCIAL AND DECISION SCIENCES AT CARNEGIE MELLON UNIVERSITY seeks candidates to fill a junior tenure-track position in decision making and public policy.

Candidates should have a strong commitment both to applying decision-making research to public policy and to creating the scientific foundations for such applications. Their letter of application should describe a research program designed to influence public policy and contribute to basic knowledge. Although policy interests could be in any area, the department has strengths in environment, energy, health, safety, finance, national security, and risk. Teaching would support the department’s educational programs.

The department is interdisciplinary, with faculty members from psychology, economics, political science, decision science, and history. Several have joint appointments in other departments, notably Engineering and Public Policy. Collaboration is a hallmark of the Department and University.

For more information, please visit: http://www.hss.cmu.edu/departments/sds/

Applicants should send a CV, two papers, three letters of recommendation, and a statement of research interests to:

Chair, Behavioral Decision Research and Policy Search Committee
Carnegie Mellon University
Department of Social and Decision Sciences
Pittsburgh, PA 15213-3890.

Please submit applications by December 1.

Carnegie Mellon University is an Affirmative Action/ Equal Opportunity employer. We encourage minorities, women, and individuals with disabilities to apply.

CMU Baker & Porter Halls photo credit: http://www.flickr.com/photos/aschultz/3254899110/