Now the confidence heuristic is not the only thing Trump takes advantage of, but we will leave those for another time. I will also avoid the question of whether or not Trump is actually confident. So what is the relationship of confidence and decision making? Daniel Kahneman in Thinking, Fast and Slow on page 13 describes:
a puzzling limitation of our mind: our excessive confidence in what we believe we know, and our apparent inability to acknowledge the full extent of our ignorance and the uncertainty of the world we live in. We are prone to overestimate how much we understand about the world and to underestimate the role of chance in events. Overconfidence is fed by the illusory certainty of hindsight.
Confidence is defined as our degree of belief that a certain thought or action is correct. There is confidence in your own individual decisions or perceptions and then the between person confidence where you defer your own decision making to someone else.
Why am I thinking of confidence? An article by Cass Sunstein explains it well. The article appeared in Bloomberg, Politics & Policy, October 18, 2018, Bloomberg Opinion, “Donald Trump is Amazing. Here’s the Science to Prove It.”
This post is largely a continuation of the Kenneth R Hammond post, but one prompted by recent current events. My opinion on gun control is probably readily apparent. But if it is not, let me say that I go crazy when mental health is bandied about as the reason for our school shootings or when we hear that arming teachers is a solution to anything. However, going crazy or questioning the sincerity of people with whom you are arguing is not a good idea. Dan Kahan (See my posts Cultural Cognition or Curiosity or his blog Cultural Cognition) has some great ideas on this, but Ken Hammond actually had accomplishments and they could help guide all of us today. I should note also that I was unable to quickly find the original sources so I am relying completely on: “Kenneth R. Hammond’s contributions to the study of judgment and decision making,” written by Mandeep K. Dhami and Jeryl L. Mumpower that appeared in Judgment and Decision Making, Vol. 13, No. 1, January 2018, pp. 1–22.
Why do almost all people tell the truth in ordinary everyday
life? […] The reason is, firstly because it is easier; for
lying demands invention, dissimulation, and a good memory
(Friedrich Nietzsche, page 54, Human, All Too Human: A Book for Free Spirits, 1878)
“I just fired the head of the F.B.I. He was crazy, a real nut job,” Mr. Trump said, according to the document, which was read to The New York Times by an American official. “I faced great pressure because of Russia. That’s taken off.”
Mr. Trump added, “I’m not under investigation.” (Pres. Donald Trump, discussion with Russian diplomats, May 10, 2017).
This post is based on the paper: ” ‘ I can see it in your eyes’: Biased Processing and Increased Arousal in Dishonest Responses,” authored by Guy Hochman, Andreas Glockner, Susan Fiedler, and Shahar Ayal, that appeared in the Journal of Behavioral Decision Making, December 2015.
This post is based on a comment paper: “Honest People Tend to Use Less–Not More—Profanity: Comment on Feldman et al.’s (2017) Study,” that appeared in Social Psychological and Personality Science 1-5 and was written by R. E. de Vries, B. E. Hilbig, Ingo Zettler, P. D. Dunlop, D. Holtrop, K. Lee, and M. C. Ashton. Why would honesty suddenly be important with respect to decision making when I have largely ignored it in the past? You will have to figure that out for yourself. It reminded me that most of our decision making machinery is based on relative differences. We compare, but we are not so good at absolutes. Thus, when you get a relentless fearless liar, the relative differences are widened and this is likely to spread out what seems to be a reasonable decision.
This post is based on a paper: “Learning from experience in nonlinear environments: Evidence from a competition scenario,” authored by Emre Soyer and Robin M. Hogarth, Cognitive Psychology 81 (2015) 48-73. It is not a new topic, but adds to the evidence of our nonlinear shortcomings.
In 1980, Brehmer questioned whether people can learn from experience – more specifically, whether they can learn to make appropriate inferential judgments in probabilistic environments outside the psychological laboratory. His assessment was quite pessimistic. Other scholars have also highlighted difficulties in learning from experience. Klayman, for example, pointed out that in naturally occurring environments, feedback can be scarce, subject to distortion, and biased by lack of appropriate comparative data. Hogarth asked when experience-based judgments are accurate and introduced the concepts of kind and wicked learning environments (see post Learning, Feedback, and Intuition). In kind learning environments, people receive plentiful, accurate feedback on their judgments; but in wicked learning environments they don’t. Thus, Hogarth argued, a kind learning environment is a necessary condition for learning from experience whereas wicked learning environments lead to error. This paper explores the boundary conditions of learning to make inferential judgments from experience in kind environments. Such learning depends on both identifying relevant information and aggregating information appropriately. Moreover, for many tasks in the naturally occurring environment, people have prior beliefs about cues and how they should be aggregated.
This post is from Judgment and Decision Making, Vol. 11, No. 6, November 2016, pp. 601–610, and is based on the paper: “The irrational hungry judge effect revisited: Simulations reveal that the magnitude of the effect is overestimated,” written by Andreas Glöckner. Danziger, Levav and Avnaim-Pesso analyzed legal rulings of Israeli parole boards concerning the effect of serial order in which cases are presented within ruling sessions. DLA analyzed 1,112 legal rulings of Israeli parole boards that cover about 40% of the parole requests of the country. They assessed the effect of the serial order in which cases are presented within a ruling session and took advantage of the fact that the ruling boards work on the cases in three sessions per day, separated by a late morning snack and a lunch break. They found that the probability of a favorable decision drops from about 65% to 5% from the first ruling to the last ruling within each session. This is equivalent to an odds ratio of 35. The authors argue that these findings provide support for extraneous factors influencing judicial decisions and speculate that the effect might be driven by mental depletion. Glockner notes that the article has attracted attention and the supposed order effect is considerably cited in psychology.
This post is based on a paper written by Fabienne Picard and Karl Friston, entitled: “Predictions, perceptions, and a sense of self,” that appeared in Neurology® 2014;83:1112–1118. Karl Friston is one of the prime authors of predictive processing and Fabienne Picard is a doctor known for studying epilepsy. The ideas here are not new or even new to this blog, but the paper and specifically the figure below provide a good summary of the ideas of predictive processing. Andy Clark’s Surfing Uncertainty is the place to go if the subject interests you.
This post is based on “Providing information for decision making: Contrasting description and simulation,” Journal of Applied Research in Memory and Cognition 4 (2015) 221–228, written by
Robin M. Hogarth and Emre Soyer. Hogarth and Soyer propose that providing information to help people make decisions can be likened to telling stories. First, the provider – or story teller – needs to know what he or she wants to say. Second, it is important to understand characteristics of the audience as this affects how information is interpreted. And third, the provider must match what is said to the needs of the audience. Finally, when it comes to decision making, the provider should not tell the audience what to do. Although Hogarth and Soyer do not mention it, good storytelling draws us into the descriptions so that we can “experience” the story. (see post 2009 Review of Judgment and Decision Making Research)
Hogarth and Soyer state that their interest in this issue was stimulated by a survey they conducted of how economists interpret the results of regression analysis. The economists were given the outcomes of the regression analysis in a typical, tabular format and the questions involved interpreting the probabilistic implications of specific actions given the estimation results. The participants had available all the information necessary to provide correct answers, but in general they failed to do so. They tended to ignore the uncertainty involved in predicting the dependent variable conditional on values of the independent variable. As such they vastly overestimated the predictive ability of the model. Another group of similar economists who only saw a bivariate scatterplot of the data were accurate in answering the same questions. These economists were not generally blinded by numbers as some in the population, but they still needed the visually presented frequency information.
This post is the first of two that look at a book review written by Karl Friston. Friston is the primary idea man behind embodied cognition (see post Embodied (grounded) Prediction (cognition) so far as I can tell. A book review is a chance to read his ideas in a little less formal and easier to understand environment. He reviews The Age of Insight: the Quest to Understand the Unconscious in Art, Mind, and Brain, from Vienna 1900 to the Present by Eric R. Kandel 2012.