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.
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 based on the paper: “Multi-attribute utility models as cognitive search engines”, by Pantelis P. Analytis, Amit Kothiyal, and Konstantinos Katsikopoulos that appeared in Judgment and Decision Making, Vol. 9, No. 5, September 2014, pp. 403–419. This post does not look at persistence (post Persistence or delay (post Decision Delay) when you believe that you need more alternatives, but when to quit your search and stop within the available alternatives.
In optimal stopping problems, decision makers are assumed to search randomly to learn the utility of alternatives; in contrast, in one-shot multi-attribute utility optimization, decision makers are assumed to have perfect knowledge of utilities. The authors point out that these two contexts represent the boundaries of a continuum, of which the middle remains uncharted: How should people search intelligently when they possess imperfect information about the alternatives? They pose the example of trying to hire a new employee faced with several dozen applications listing their skills and credentials. You need interviews to determine each candidate’s potential. What is the best way to organize the interview process? First, you need to decide the order in which you will be inviting candidates. Then, after each interview you need to decide whether to make an offer to one of the interviewed candidates, thus stopping your search. The first problem is an ordering problem and the second a stopping problem. If credentials were adequate, you would not need an interview, and if credentials were worthless, you would invite people for interviews randomly.
This post is derived from “Chapter 5, Multiple Measure Strategy Classification-Outcomes, Decision Times, and Confidence Ratings” authored by Andreas Glockner from Foundations for Tracing Intuition– Challenges and Methods, edited by Andreas Glockner and Cilia Witteman 2010 Psychology Press NY. It shines a little light on how intuition experiments seeking to answer if a person is using a Take the Best strategy or a Parallel Constraint Satisfaction strategy, etc are actually done. It is written more understandably than a typical paper for a journal. It will hopefully give more meaning to the letters MM-ML.
The last post Persistence looked at persistence as being partially determined by the distribution of the waiting times for the reward. A fat tailed distribution might rationally steer one toward giving up after a short waiting period. Robin Hogarth (Educating Intuition) has recently published a paper: “Ambiguous Incentives and the Persistence of Effort: Experimental Evidence” in the Journal of Economic Behavior & Organization, Volume 100, April 2014, page 1-19, with Marie Claire Villeval that looks at economic activities where the reward is mundane–money. It is more aimed at looking at what determines our persistence from the employers point of view, but I believe it could be more broadly applicable.
Hogarth and Villeval explore ambiguous situations where economic agents reap the benefits of engaging in an activity across time until – unknown to them – there is a shift (the regime change) in the underlying process and pursuing the activity is no longer profitable. The term regime shift was new to me in the context. For an old city planner, regime shift meant a new mayor or change in the form of government. Apparently the ecological term more or less runs with the old definition and means abrupt long lasting non-linear change. Hogarth has helped me understand that humans have made an evolutionary career out of understanding linear change or functions that are linear over the relevant range, while we tend to be weak at non-linear functions. How long will the investor continue to place new orders and does this depend on the regularity of his previous outcomes? How long will an employee keep working in the same firm if she no longer receives a bonus? How is the decision affected by preferences regarding risk and ambiguity and/or the regularity with which bonuses have been paid in the past?
Waiting and delay lead naturally to the concept of persistence. It is interesting that waiting to or delaying before making a decision is procrastination, while waiting for a reward after you make the decision is persistence. Procrastination is generally panned while persistent is typically praised.
On July 5, 2014, during a repeat of the “Prairie Home Companion,” 40th anniversary celebration show, Dwayne’s mother called him about dumping his novel after only three years. She said: “You give up so fast. That is why you have never gotten married…You are addicted to new beginnings… And did you hear about that man who has been on the same radio show for forty years?” They go back and forth for a couple of minutes when she says: “Well at least he stuck with something” and her son replies that: “the key there is stuck as in unable to move.” Dwayne goes on to note that the guy has been married five or six times and his mom answers: “At least he kept trying.” We all know that the correct amount of persistence is a virtue.
This post is a continuation of the theme of when decisions are made and how we delay or wait or decide not to decide. This post is based on a 2014 paper by Teichert, Ferrera, and Grinband, “Humans Optimize Decision-Making by Delaying Decision Onset,” in PLoS ONE. Again, this paper is beyond my understanding at least as to the details. It has some excellent figures and graphics that are pretty, but I do not think that I really understand them. These are my shortcomings. What interests me about this is the contrast with my previous post Deciding not to Decide. This paper examines decision onset and nondecision time while “Deciding not to Decide” suggested an explicit decision to inhibit the decision. I find making an inhibitory decision a more satisfying explanation than delaying decision onset, although they could be the same thing or the situations may be so different that there is no real comparison.
This post is an executive summary of a 2013 paper about deciding not to decide. (“Deciding Not to Decide: Computational and Neural Evidence for Hidden Behavior in Sequential Choice,” by Sebastian Gluth, Jorg Rieskamp, and Christian Buchel, that appeared in PLoS Comput Bio 9(10). Quite frankly the detail of the paper is beyond me, but the general ideas are interesting.
Many decisions are not triggered by a single event but based on multiple sources of information. When purchasing a new computer, for instance, we certainly look at the price, but not without accounting for further aspects like capabilities, quality and appearance. According to Gluth et al, usually, these multi-attribute decisions evolve sequentially, that is, as long as the collected evidence is insufficient to motivate a particular choice we search for more information to resolve our uncertainty. Importantly, such ‘‘decisions not to decide’’ are not directly observable but can promote significant changes in behavior.
Continuing on the delay theme, this post is based on the paper: “Delay, Doubt, and Decision: How Delaying a Choice Reduces the Appeal of (Descriptively) Normative Options” written by Niels Van de Ven, Thomas Gilovich, and Marcel Zeelenberg, that appeared in Psychological Science in 2010.
The authors examined whether choosing to delay making a choice between a focal option and an alternative tends to make people subsequently less likely to choose what they would otherwise have chosen. They based their efforts on a regularity in elections in the United States that is known as the incumbent rule. It refers to the fact that undecided voters who end up casting ballots tend to vote against the incumbent. One analysis found that in 127 of 155 national, state,
and municipal elections, the majority of undecided voters went for the challenger. This may seem a little odd since decision researchers have documented a status quo bias in people’s
choices—a bias to stick with the status quo option rather than try something new. So why do undecided voters not favor the incumbent? Van de Ven et al contend that undecided voters
interpret the fact that they have yet to decide as information that calls into question the wisdom of picking the incumbent. Given that the incumbent is typically the more psychologically prominent candidate, and that people know they often follow an “if it ain’t broke, don’t fix it” rule, they may wonder why they have not already resolved to vote for the incumbent. In other words, they propose that the experience of doubt is experienced as doubt about the incumbent.
As you get older even those of us not labeled as procrastinators realize that some decisions never have to be made. You can wait a little bit and it becomes irrelevant or the decision becomes obvious. Using my adaptation of the parallel constraint satisfaction model, your intuitive processing often does not come up with a clear cut answer and sends the analytic system out for more information. This is a common point for us to insert delay if we can. Other times we make a decision and then get an opportunity to change it without any real penalty. Frank Partnoy’s book Wait- The Art and Science of Delay examines the overall issue mostly with a series of anecdotes. The book provides some insights.