This post is based on a paper that appeared in Judgment and Decision Making, Vol. 12, No. 4, July 2017, pp. 369–381, “How generalizable is good judgment? A multi-task, multi-benchmark study,” authored by Barbara A. Mellers, Joshua D. Baker, Eva Chen, David R. Mandel, and Philip E. Tetlock. Tetlock is a legend in decision making, and it is likely that he is an author because it is based on some of his past work and not because he was actively involved. Nevertheless, this paper, at least, provides an opportunity to go over some of the ideas in Superforecasting and expand upon them. Whoops! I was looking for an image to put on this post and found the one above. Mellers and Tetlock looked married and they are. I imagine that she deserved more credit in Superforecasting, the Art and Science of Prediction. Even columnist David Brooks who I have derided in the past beat me to that fact. (http://www.nytimes.com/2013/03/22/opinion/brooks-forecasting-fox.html)
The authors note that Kenneth Hammond’s correspondence and coherence (Beyond Rationality) are the gold standards upon which to evaluate judgment. Correspondence is being empirically correct while coherence is being logically correct. Human judgment tends to fall short on both, but it has gotten us this far. Hammond always decried that psychological experiments were often poorly designed as measures, but complimented Tetlock on his use of correspondence to judge political forecasting expertise. Experts were found wanting although they were better when the forecasting environment provided regular, clear feedback and there were repeated opportunities to learn. According to the authors, Weiss & Shanteau suggested that, at a minimum, good judges (i.e., domain experts) should demonstrate consistency and
discrimination in their judgments. In other words, experts should make similar judgments if cases are alike, and dissimilar judgments when cases are unalike. Mellers et al suggest that consistency and discrimination are silver standards that could be useful. (As an aside, I would suggest that Ken Hammond would likely have had little use for these. Coherence is logical consistency and correspondence is empirical discrimination.)
This post is a contination of the previous blog post Hogarth on Description. Hogarth and Soyer suggest that the information humans use for probabilistic decision making has two distinct sources: description of the particulars of the situations involved and through experience of past instances. Most decision aiding has focused on exploring effects of different problem descriptions and, as has been shown, is important because human judgments and decisions are so sensitive to different aspects of descriptions. However, this very sensitivity is problematic in that different types of judgments and decisions seem to need different solutions. To find methods with more general application, Hogarth and Soyer suggest exploiting the well-recognized human ability to encode frequency information, by building a simulation model that can be used to generate “outcomes” through a process that they call “simulated experience”.
Simulated experience essentially allows a decision maker to live actively through a decision situation as opposed to being presented with a passive description. The authors note that the difference between resolving problems that have been described as opposed to experienced is related to Brunswik’s distinction between the use of cognition and perception. In the former, people can be quite accurate in their responses but they can also make large errors. I note that this is similar to Hammond’s correspondence and coherence. With perception and correspondence, they are unlikely to be highly accurate but errors are likely to be small. Simulation, perception, and correspondence tend to be robust.
This post is a look at the book by Philip E Tetlock and Dan Gardner, Superforecasting– the Art and Science of Prediction. Phil Tetlock is also the author of Expert Political Judgment: How Good Is It? How Can We Know? In Superforecasting Tetlock blends discussion of the largely popular literature on decision making and his long duration scientific work on the ability of experts and others to predict future events.
In Expert Political Judgment: How Good Is It? How Can We Know? Tetlock found that the average expert did little better than guessing. He also found that some did better. In Superforecasting he discusses the study of those who did better and how they did it.
I occasionally like to go far afield from judgment and decision making, and here I go again. This post takes a look at Michio Kaku’s 2014 book, The Future of the Mind–The Scientific Quest To Understand, Enhance, And Empower The Mind, Doubleday, New York.
Decision models can sometimes seem very explanatory, but they seem so simple minded when I read in Kaku’s book that we have two separate centers of consciousness and that we may all have photographic memories.
This post looks at the medical/health component of decision making as addressed in Gerd Gigerenzer’s new book, Risk Saavy, How to Make Good Decisions. First, Gigerenzer has contributed greatly to improving health decision making. This blog includes three consecutive posts on the Statistics of Health Decision Making based on Gigerenzer’s work.
He points out both the weaknesses of screening tests and our understanding of the results. We have to overcome our tendency to see linear relationships when they are nonlinear. Doctors are no different. The classic problem is an imperfect screening test for a relatively rare disease. You cannot think in fractions or percentages. You must think in absolute frequencies. Breast cancer screening is one example. Generally, it can catch about 90% of breast cancers and only about 9% test positive who do not have breast cancer. So if you have a positive test, that means chances are you have breast cancer. No! You cannot let your intuition get involved especially when the disease is more rare than the test’s mistakes. If we assume that 10 out of 1000 women have breast cancer, then 90% or 9 will be detected, but about 90 of the 1000 women will test positive who do not have disease. Thus only 9 of the 99 who test positive actually have breast cancer. I know this, but give me a new disease or a slightly different scenario and let a month pass, I will still be tempted to shortcut the absolute frequencies and get it wrong.
Gerd Gigerenzer has a 2014 book out entitled: Risk Saavy,How to Make Good Decisions, that is a refinement of his past books for the popular press. It is a little too facile, but it is worthwhile. Gigerenzer has taught me much, and he will likely continue. He is included in too many posts to provide the links here (you can search for them). My discussion of the book will be divided into two posts. This one will be a general look, while the next post will concentrate on Gigerenzer’s take on medical decision making.
As in many books like this, the notes provide insight. Gigerenzer points out his disagreements with Kahneman with respect to heuristics all being part of the unconscious system. As he notes heuristics, for instance the gaze heuristic, can be used consciously or unconsciously. This has been a major issue in my mind with Kahneman’s System 1 and System 2. Kahneman throws heuristics exclusively into the unconscious system. I also side with Gigerenzer over Kahneman, Ariely, and Thaler that the unconscious system is associated with bias. As Gigerenzer states: “A system that makes no errors is not intelligent.” He interestingly points out the use of the gaze heuristic by Sully Sullenberger to decide to not return to LaGuardia, but instead to land in the Hudson River.
This post is based on the paper: “The Affect Gap in Risky Choice: Affect-Rich Outcomes Attenuate Attention to Probability Information,” authored by Thorsten Pachur, Ralph Hertwig, and Roland Wolkewitz that appeared in Decision, 2013, Volume 1, No. 1, p 64-78. This is a continuation of the affect/ emotion theme. It is more of a valence based idea than Lerner’s Appraisal Tendency Framework. This is more thinking about emotion than actually experiencing it although the two can come together.
Often risky decisions involve outcomes that can create considerable emotional reactions. Should we travel by plane and tolerate a minimal risk of a fatal terrorist attack or take the car and run the risk of traffic jams and car accidents? How do people make such decisions? Decisions under risk typically obey the principle of the maximization of expectation.
The expectation expresses the average of an option’s outcomes, each weighted by its
probability. This, of course, underlies expected utility theory and cumulative prospect theory and these models do a good job in accounting for choices among relatively affect-poor
This post is based on a paper by Rebecca Ferrer, William Klein, Jennifer Lerner, Valerie Reyna, and Dacher Keltner: “Emotions and Health Decison-Making, Extending the Appraisal Tendency Framework to Improve Health and Healthcare,” in Behavioral Economics and Public Health, 2014. I note that Valerie Reyna is one of the authors of fuzzy trace theory (see post Fuzzy Trace Theory-Meaning, Memory, Development and subsequent posts.) which I find interesting.
The authors use the appraisal tendency framework (ATF) to predict how emotions may interact with situational factors to improve or degrade health-related decisions. The paper examines four categories of judgments and thought processes as related to health decisions: risk perception, valuation and reward-seeking, interpersonal attribution, and depth of information processing. They illustrate ways in which a better understanding of emotion can improve judgments and choices regarding health.
The ATF assumes that specific emotions give rise to corresponding cognitive and motivational processes that are related to the target of the emotion (i.e., the situation, person, or other stimulus that elicited the emotion). In contrast to theories that predict how broad mood states (positive or negative) may influence judgment and decision making, the ATF offers specific predictions for how discrete emotions will influence judgment and decision making (See Tables 1 and 2).
David Brooks has a way of irritating me. For some reason, he seems like a very serious person so I cannot dismiss him out of hand. But, on June 16, 2014, he wrote “The Structures of Growth—Learning is no Easy Task,” in the New York Times, about certain human activities having logarithmic learning functions and others as having exponential functions. I realize that I am envious of his being able to push such sloppy work out the door to millions of readers. It is just a column, but read it for yourself.
His basis was a blog by Scott H. Young in early 2013, who as far as I can tell made much less outlandish representations about learning or domains of growth. Young explains that anything that you try to improve will have a growth curve, and that it is a mistake to assume that it will be linear. Young says that athletic performance, productivity, and mastery of a complex skill tend to be logarithmic. Early progress on logarithmic growth activities can make you overconfident if you do not realize that the curve will soon flatten. He notes that exponential functions tend to be limited to ranges and apply to technological improvement, business growth, wealth, and rewards to talent.