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Decision Skills Matter

Posted on by by Posted in Financial Decisions, Health Decisions, Intuition, Risk and Uncertainty

To what extent do decision skills matter in real live? Do these skills actually lead to better decision outcomes and fewer unpleasant life events?
Decision Skills Matter
Or, more specifically: do people who perform better on hypothetical decision tasks also make better real-world decisions, to the extent that they experience better outcomes over the course of their lives?

Let’s take a step back. Based on all the different theories of what counts as a “rational” choice, we know that some people perform better in the kinds of choices that are typically presented in research studies. There are people, for example, who are less affected than others by the way information is presented to them (in other words, they are better able to resist framing effects). Or, while most people are overconfident most of the time, some people actually have a pretty accurate level of confidence into their own judgments. There are also people who are better able to abandon a bad plan that involves sunk costs, while others are more prone to keep throwing good money after bad. We also know that these decision skills are related to other cognitive abilities, and that they can be taught and improved with explicit instructions and practice. (Check out the list of references below for just a sample from a large body of research.)

The question is though: do people who perform better on those sorts of tasks also make better real-world decisions? And most importantly, can those better decisions be measured by better outcomes? Are “skillful” decision-makers, as defined by those measures, perhaps better able to avoid bad life events?

Apparently, the answer is a robust YES, across different ways of measuring the quality of decisions and the quality of decision outcomes.

For example, in one study, the researchers gave people hypothetical tasks to measure their decision skills. The test they used is called A-DMC, for Adult Decision Making Competence, and it measures skills such as resistance to framing effects, ability to disregard sunk costs, over- and under-confidence, or the ability to process complex information in a decision.

The researchers then asked people about a variety of stressful life events that could result from poorly made decisions. The events ranged from serious (declaring bankruptcy, being diagnosed with Type 2 diabetes) to minor (getting blisters from sunburn, throwing out groceries you bought because they went bad). Other examples of stressful life events included missing a flight, getting kicked out of a bar, having your driver’s license revoked, or having spent a night in a jail cell.

It turned out that people who performed better in hypothetical decision tasks (as seen in high A-DMC scores) were indeed less likely to have experienced such negative life events.

Other research has also linked performance on decision-making competence tasks to better real-life outcomes, such as fewer suspensions among students.

It is important to note that not everyone is dealt the same hand when it comes to avoiding stressful life events. For example, people from disadvantaged socio-economic backgrounds are exposed to more negative life events. Also, poor decision outcomes are more common among younger people. However, the relationship between decision-making competence and better decision outcomes was still significant even after the researchers controlled their analysis for socio-economic status and age.

Granted, even the soundest decision-making processes cannot guarantee good outcomes. Given all the uncertainties in life, unpleasant surprises are often inevitable, even to skilled decision makers. However, what these studies confirm is that across time, people, and decisions, good decision processes predict good decision outcomes on average.

After knowing this, it bears repeating: decision-making competence can be taught and improved. Several independent research groups across different countries, using different types of interventions at schools, have shown clear improvements in decision skills as a result of targeted decision education.

by Ursina Teuscher (PhD), at Teuscher Decision Coaching, Portland OR

Selected References:
Blais, A.-R., Thompson, M. M., & Baranski, J. V. (2005). Individual differences in decision processing and confidence judgments in comparative judgment tasks: The role of cognitive styles. Personality and Individual Differences, 38(7), 1701–1713.
Brady, S. S., & Matthews, K. A. (2002). The influence of socioeconomic status and ethnicity on adolescents’ exposure to stressful life events. Journal of Pediatric Psychology, 27(7), 575–583.
Bruine de Bruin, W., Parker, A. M., & Fischhoff, B. (2007). Individual differences in adult decision-making competence. Journal of Personality and Social Psychology, 92(5), 938–956.
Del Missier, F., Mäntylä, T., Hansson, P., Bruine de Bruin, W., Parker, A. M., & Nilsson, L.-G. (2013). The multifold relationship between memory and decision making: An individual-differences study. Journal of Experimental Psychology: Learning, Memory, and Cognition, 39(5), 1344–1364.
Jacobson, D., Parker, A., Spetzler, C., Bruine de Bruin, W., Hollenbeck, K., Heckerman, D., & Fischhoff, B. (2012). Improved learning in U.S. history and decision competence with decision-focused curriculum. PloS One, 7(9), e45775.
Levin, I. P., Gaeth, G. J., Schreiber, J., & Lauriola, M. (2002). A New Look at Framing Effects: Distribution of Effect Sizes, Individual Differences, and Independence of Types of Effects. Organizational Behavior and Human Decision Processes, 88(1), 411–429.
Lui, V. W. C., Lam, L. C. W., Luk, D. N. Y., Chiu, H. F. K., & Appelbaum, P. S. (2010). Neuropsychological performance predicts decision-making abilities in Chinese older persons with mild or very mild dementia. East Asian Archives of Psychiatry, 20(3), 116–122.
Marin, L. M., & Halpern, D. F. (2011). Pedagogy for developing critical thinking in adolescents: Explicit instruction produces greatest gains. Thinking Skills and Creativity, 6(1), 1–13.
Parker, A. M., Bruine de Bruin, W., & Fischhoff, B. (2015). Negative decision outcomes are more common among people with lower decision-making competence: an item-level analysis of the Decision Outcome Inventory (DOI). Cognition, 6, 363.
Parker, A. M., de Bruin, W. B., & Fischhoff, B. (2007). Maximizers versus satisficers: Decision-making styles, competence, and outcomes. Judgment and Decision Making, 2(6), 342–350.
Reyna, V. F., & Farley, F. (2006). Risk and Rationality in Adolescent Decision Making Implications for Theory, Practice, and Public Policy. Psychological Science in the Public Interest, 7(1), 1–44.
Stanovich, K. E. (1999). Who Is Rational?: Studies of Individual Differences in Reasoning. Psychology Press.
Stanovich, K. E., Grunewald, M., & West, R. F. (2003). Cost–benefit reasoning in students with multiple secondary school suspensions. Personality and Individual Differences, 35(5), 1061–1072.
Stanovich, K. E., & West, R. F. (2008). On the relative independence of thinking biases and cognitive ability. Journal of Personality and Social Psychology, 94(4), 672–695.
Teuscher, U. (2003). Evaluation of a Decision Training Program for Vocational Guidance. International Journal for Educational and Vocational Guidance, 3(3), 177–192.

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Summer Reading List 2016

Posted on by by Posted in Book Recommendations, Creative Thinking, Leadership, Risk and Uncertainty

Some book recommendations on decision making, innovation and productivity:

Kayt Sukel (2016) The Art of Risk: The New Science of Courage, Caution, and Chance
A very readable overview of current research on the neuroscience of risk, illustrated with personal stories and some inspiring interviews with risk takers and scientists.

Charles Duhigg (2016). Smarter Faster Better: The Secrets of Being Productive in Life and Business.
Important insights into how organizations can foster better productivity and innovation. For my taste, the book relied very heavily on anecdotes though, to the extent that I found it difficult to identify key takeaways.

Philip Tetlock & Dan Gardner (2015). Superforecasting: The Art and Science of Prediction.
A convincing case that – while even experts usually make poor predictions about the future – forecasting is a skill that can be improved. Good forecasting doesn’t require powerful computers either. However, it does involve gathering evidence from a variety of sources, thinking probabilistically, working in teams, keeping score, and being willing to admit error and change course.

Drew Boyd & Jacob Goldenberg (2013). Inside the Box: A Proven System of Creativity for Breakthrough Results.
This book does a great job demystifying the creative process. It shows how innovation can come from a structured process, using a set of templates that channel creative thinking. The techniques are derived from research that discovered a surprising set of common patterns shared by inventive solutions.

Those are some of the books I’ve read recently and found worthwhile. Which other ones would you recommend I add to my own summer reading list?

by Ursina Teuscher (PhD), at Teuscher Decision Coaching, Portland OR


Age Differences in Decision Making Skills

Posted on by by Posted in Intuition, Risk and Uncertainty

A recent study confirms it again: older adults do well with decisions that require emotional skills.

Old age affects our decision-making skills in quite complex ways. Some cognitive skills decline with age, while emotional skills may even improve. This leads to interesting findings: older people do worse on some decision tasks, but they do just as well as younger adults on those same tasks when they get to experience them, rather than read instructions. This recent study, for example, used two ways to present gambling tasks. In the “description-based” task, people received information about different card decks: the probability of winning or losing, and the amount of money that could be won or lost with each card drawn from that particular deck. In the “experience-based” task they received none of that information – they were simply given four card decks, from which they had to start picking cards and figure out over time which card decks were more advantageous than others. In other words, people got to experience wins and losses over time and build an “intuition” as to which gambles are worth playing, and which are worth avoiding, without ever knowing the underlying probabilities for sure. (The researchers used the famous Iowa Gambling Task – which, I just discovered, you can get as a free iPad app).

Age differences in decision making skills
While older adults (aged 64-90) managed to win less money overall in the description based task, they did just as well as younger adults (aged 18-32) in the experience-based task.

This is in line with the idea that our decision-making skills rely on two systems:

  1. The affective or experiential mode, which is fast, automatic, intuitive, and builds from our experiences in similar situations.
  2. The deliberative mode, which is is effortful, conscious, analytical, logical, relatively slow, controlled, limited by our working memory capacity, and therefore linked to general intelligence.

As we get older, it is normal for our working memory capacity to decline, in particular the speed with which we can juggle information, and therefore it is not surprising that our deliberative decision-making skills also suffer. However, our affective or experiential abilities seem to remain intact into old age.

by Ursina Teuscher (PhD), at Teuscher Decision Coaching, Portland OR


Selected References:
Bechara, A., Damasio, A. R., Damasio, H., & Anderson, S. W. (1995). Insensitivity to future consequences following damage to human prefrontal cortex. In J. Mehler & S. Franck (Eds.), Cognition on cognition (pp. 3–11). Cambridge, MA, US: The MIT Press.
Bechara, A., Damasio, H., Tranel, D., & Damasio, A. R. (2005). The Iowa Gambling Task and the somatic marker hypothesis: some questions and answers. Trends in Cognitive Sciences, 9(4), 159–162. http://doi.org/10.1016/j.tics.2005.02.002
Bruine de Bruin, W., Parker, A. M., & Fischhoff, B. (2007). Individual differences in adult decision-making competence. Journal of Personality and Social Psychology, 92(5), 938–956. http://doi.org/10.1037/0022-3514.92.5.938
Cauffman, E., Shulman, E. P., Steinberg, L., Claus, E., Banich, M. T., Graham, S., & Woolard, J. (2010). Age differences in affective decision making as indexed by performance on the Iowa Gambling Task. Developmental Psychology, 46(1), 193–207. http://doi.org/10.1037/a0016128
Huang, Y. H., Wood, S., Berger, D. E., & Hanoch, Y. (2015). Age differences in experiential and deliberative processes in unambiguous and ambiguous decision making. Psychology and Aging, 30(3), 675–687. http://doi.org/10.1037/pag0000038
Johnson, M. M. S. (1990). Age Differences in Decision Making: A Process Methodology for Examining Strategic Information Processing. Journal of Gerontology, 45(2), P75–P78. http://doi.org/10.1093/geronj/45.2.P75
MacPherson, S. E., Phillips, L. H., & Della Sala, S. (2002). Age, executive function and social decision making: A dorsolateral prefrontal theory of cognitive aging. Psychology and Aging, 17(4), 598–609. http://doi.org/10.1037/0882-7974.17.4.598
Peters, E., Hess, T. M., Västfjäll, D., & Auman, C. (2007). Adult Age Differences in Dual Information Processes: Implications for the Role of Affective and Deliberative Processes in Older Adults’ Decision Making. Perspectives on Psychological Science, 2(1), 1–23. http://doi.org/10.1111/j.1745-6916.2007.00025.x
Salthouse, T. A., & Babcock, R. L. (1991). Decomposing adult age differences in working memory. Developmental Psychology, 27(5), 763–776. http://doi.org/10.1037/0012-1649.27.5.763


Numerical Skills for Financial Decisions

Posted on by by Posted in Financial Decisions, Health Decisions, Risk and Uncertainty

Relatively few people own private long-term care insurance, even though long-term care is one of the largest financial risks currently facing older people. A new study suggests that poor numerical skills may explain a part of that phenomenon: people with better numerical skills (even after controlling for education and cognitive function) are more likely to hold long-term care insurance.

Given the complexity of this particular financial decision, it is not surprising that a lack of numerical skills would create barriers. Assessing the value of a private long-term care insurance policy involves a variety of calculations, such as determining the probability of needing care, evaluating the likely lifetime expense of premiums against the payments one could expect to receive, and comparing the costs and benefits of insurance against other strategies to manage the same risks. It is easy to feel overwhelmed by all this. A lack of skills to deal with all those numbers would certainly prevent people from making the best decisions for themselves.

As for so many problems, investing into education seems a good idea if we want to empower everyone to prepare well for their own future. In addition though, I think it’s important to directly offer people help with some of those complex decisions processes.

What do you think? How do you navigate such a complex financial decision? 


Reference:
McGarry, B. E., Temkin-Greener, H., Chapman, B. P., Grabowski, D. C., & Li, Y. (2016). The Impact of Consumer Numeracy on the Purchase of Long-Term Care Insurance. Health Services Research, n/a–n/a (article first published online: 22 JAN 2016). http://doi.org/10.1111/1475-6773.12439

by Ursina Teuscher (PhD), at Teuscher Decision Coaching, Portland OR


Instead of a Book Recommendation

Posted on by by Posted in Book Recommendations, Health Decisions, Risk and Uncertainty

This month I’m recommending an article instead of a book, by Gigerenzer and his colleagues, who have been pioneers in advocating for statistical literacy:
Gigerenzer, G., Gaissmaier, W., Kurz-Milcke, E., Schwartz, L. M., & Woloshin, S. (2007). Helping Doctors and Patients Make Sense of Health Statistics. Psychological Science in the Public Interest, 8(2), 53–96.

The article shows impressively how not only patients, but also journalists and physicians lack a basic understanding of health statistics, which can have serious consequences for healthcare and medical decision making.
The authors make a very strong case that this confusion is not necessary: it is created by nontransparent presentation of information (intentional or not), and the skill of thinking about these statistics and probabilities could relatively easily be taught – but isn’t.

Please let me know if you are interested in reading the article but don’t have access to the full text here.

by Ursina Teuscher (PhD), at Teuscher Decision Coaching, Portland OR


Medical Decisions: How to Judge Risks

Posted on by by Posted in Health Decisions, Risk and Uncertainty

Thanks for submitting your answer to the poll!

The question was: Would you get a cancer screening done if it reduced your chance of dying from this type of cancer by one third?

If you were undecided, you may have wanted more information about the type of cancer. But there is a more important reason why you should not have decided based on the information you got in this scenario: the number (risk reduction by “one third”) is not meaningful at all, because it only gave you information about relative risk reduction, rather than absolute risk reduction. If you get this kind of information from your health care provider, you should ask: “Ok, and what are my chances of dying from this type of cancer without the treatment?” Or more generally, ask for your base risk: your risk without the treatment. Only with that information can you judge the benefit of a treatment. For example, if my initial risk of getting this type of cancer is 6%, the screening will reduce my risk to 4% (the absolute risk reduction would be 2% in that case). If my initial risk is 0.3%  (in other words, if only 3 in 1,000 people from my risk group are developing this cancer), the screening would reduce my risk to 0.2%, which means the absolute risk reduction in that case is only 0.1%. In other words, only 1 in 10,000 people would benefit from the screening in that case. In this second case, the screening would have a much smaller benefit, even if it also reduces my relative risk by one third. (Notice, however, how even the assumption of a base risk of 6%, which would be a very high base risk for any specific type of cancer, makes the treatment seem less urgent than it seemed in the original question.)

Here are different versions of how the information can be presented to you.

  • Relative risk reduction: “If you have this test every 2 years, it will reduce your chance of dying from this cancer by around one third over the next 10 years.”
  • Absolute risk reduction: “If you have this screening test every 2 years, it will reduce your chance of dying from this cancer from around 3 in 1,000 to around 2 in 1,000 over the next 10 years.”
  • Number needed to treat: “If around 1,000 people have this screening test every 2 years, 1 person will be saved from dying from this cancer every 10 years.”

For all those questions, the benefits of the test are identical, except that they are expressed either as a relative risk reduction, as an absolute risk reduction, or as the number of people needed to be treated (screened) to prevent one death from cancer.

Unfortunately, studies overwhelmingly show that many patients do not understand this difference between relative and absolute risk reduction, and that they do indeed evaluate treatments much more favorably if the benefits are presented as relative risk reductions.

What is even more concerning is that doctors and other health professionals (including nurse educators, and even reviewers of grant proposals), succumb to the same bias. Multiple studies have shown that health professionals, just like patients, get confused by numbers about risk reduction, and rate the effectiveness of a treatment much higher when the benefits are described in terms of a relative risk reduction, rather than as an absolute risk reduction or a number needed to treat.

It is therefore quite clear that reporting relative risk reductions without clearly specifying the base rates is bad practice, because not only is the information so incomplete as to be meaningless, but it also leads people – patients as well as providers – to overestimate the benefits of treatments.

Yet unfortunately, this bad practice is still very common, as Gerd Gigerenzer and his colleagues report in an enlightening article. [Please get in touch if you would like to read it and don’t have access to the full text]. According to their review, even articles in leading medical journals often only report relative risk reduction. So do brochures and pamphlets that provide information for patients, although this is perhaps less surprising when the brochures are issued by those who benefit financially from providing treatments.

I’m with Gigerenzer when he advocates for better education and better practices in medical communication. In the meantime, the take home message is simple: when offered any risk reducing treatment, always find out as much as you can about your base risk before making a decision.

by Ursina Teuscher (PhD), at Teuscher Decision Coaching, Portland OR


Selected References:
Covey, J. (2007). A meta-analysis of the effects of presenting treatment benefits in different formats. Medical Decision Making, 27, 638–654.
Gigerenzer, G., Gaissmaier, W., Kurz-Milcke, E., Schwartz, L. M., & Woloshin, S. (2007). Helping Doctors and Patients Make Sense of Health Statistics. Psychological Science in the Public Interest, 8(2), 53–96.
Naylor, C.D., Chen, E., & Strauss, B. (1992). Measured enthusiasm: Does the method of reporting trial results alter perceptions of therapeutic effectiveness? Annals of Internal Medicine, 117, 916– 921.
Muhlhauser, I., Kasper, J., & Meyer, G. (2006). FEND: Understanding of diabetes prevention studies: Questionnaire survey of professionals in diabetes care. Diabetologia, 49, 1742–1746.
Sarfati, D., Howden-Chapman, P., Woodward, A., & Salmond, C. (1998). Does the frame affect the picture? A study into how attitudes to screening for cancer are affected by the way benefits are expressed. Journal of Medical Screening, 5, 137–140.

 

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Decision Trees Made Easy

Posted on by by Posted in Risk and Uncertainty

Decision Tree for a 10-Year Old

This may well be the most unusual article I’ve ever come across in any peer-reviewed journal (it was published in an open access journal, so you can get the full text pdf here):

Mullin, Barbara & Roger, commented by Jack Dowie and Rex V. Brown.
Mhairi’s Dilemma: A study of decision analysis at work.
Judgment and Decision Making, 3, (8) (2008), 679–689.

It’s a case description of how a dad (Roger Mullin, a teacher of decision support systems) used a decision tree to help his 10-year old daughter, Mhairi, make a very difficult emotional decision: she had to decide whether to attend a dear friend’s funeral or not.

Decision trees are, in my own experience, one of the hardest tools to teach, as powerful as they are. They involve calculations with probabilities, which people are notoriously bad at – even people much older and more educated than the child in this case. Accordingly, they are not used that often, even by professionals. As Mullin states himself: “For over 20 years of teaching decision making, I have been struck by how resistant many professionals are to analytical approaches. A common complaint, whether from medical practitioners, social workers, police officers, business executives or other well educated groups, is that it is too difficult for their clients or even colleagues to understand and far too difficult to involve them in the process.”

Roger Mullin has my fullest respect and admiration for using this tool, not only with his child, but for a decision so emotional and hard to quantify. In this report (which includes his wife’s perspective and reflections), Roger Mullin makes a very strong case that a formal analysis is not at all incompatible with being sensitive, caring and effective.

Decision Trees Made Easy for the Rest of Us – Video

I have searched widely for good explanations of decision trees, and found many dry and complicated papers along the way. However, here is a video that is refreshingly simple (though not always pretty):


It provides straightforward instructions how to draw a decision tree, and how to calculate expected values with it.

If you give it a try for your own decisions, I’d love to hear about your experience.

by Ursina Teuscher (PhD), at Teuscher Decision Coaching, Portland OR

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Shortcut to Conflict Resolution With Game Theory

Posted on by by Posted in Group/Interactive Decisions, Conflict Resolution, Risk and Uncertainty

“Fair Outcomes”, an online system, offers an ingenious alternative to conflict resolution, based on the principles of game theory. The ABA journal has just published an article about it.

In contrast to litigation, arbitration, mediation, negotiation, and traditional sealed-bid/split-the difference arrangements, this system offers no incentive or excuse for either party to bluff or posture – or to try to posture through a refusal to use it.

The beauty is that it not only can save both parties an enormous amount of legal fees, but that the outcome is usually more favorable to each party than what that party
had proposed.

The system is very transparent and well documented, so if you’re interested in the topic, take some time to browse the website and think it through. If you prefer videos, here is a series of presentations that explain the underlying principles.


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