This post is sooo… derivative, but I cannot help myself. Good judgment is dependent on good information. It has never been so obvious how much we rely on good referees to determine what is good information. Most persuasion is based on filtering the information to the persuader’s advantage, but it has been rare in my lifetime to use the strategy of just hammering the lie.
It is easy to imagine that our paleo brains were rewarded by believing the chief. We both had skin in the game. So our still tribal brains believe things that are repeated over and over, even lies. Unfortunately, our information sources have gotten further and further from us so that our futures are not intertwined, except in an existential way. Our information networks have expanded and more critically selectively expanded.
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 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 have mentioned Michael Mauboussin’s book The Success Equation before, but this will be the closest I come to a review. The title makes it sound like a self help book, but it is much more substantial. However, his notes and bibliography somehow miss both Ken Hammond and Robin Hogarth which frankly seems unlikely. Hogarth’s books Educating Intuition (post Learning, Feedback and Intuition) and Dance with Chance (post Dancing with Chance) have much in common.
Mauboussin most unique contribution from my view is to bring Bill James and his successors from baseball to the world of skill and luck and investment. And Mauboussin is amazingly honest about the luck involved in investment which is his world. He pretty much says that you cannot be an expert in his field but only experienced. Using sports, especially baseball, makes the book’s ideas much more understandable. That brings us to the idea for this post. Mauboussin calls it reversion to the mean and Kahneman calls it regression to the mean. Either way, baseball makes it more understandable.
This post looks at a paper by Andreas Glockner and Thorsten Pachur entitled: “Cognitive models of risky choice: Parameter stability and predictive accuracy of prospect theory,” that appeared in Cognition in 2012. The paper looks at the changeable parameters in prospect theory and tries to determine their explanatory value and also the extent which individuals have stable parameters. It also tests a number of heuristics along with expected value and expected utility theory by studying the responses of 66 college students at the University of Bonn.
Prospect theory is a descriptive model of decision making and considered by some the greatest psychological advance ever. A descriptive model tries to describe and predict actual behavior and not theoretically ideal behavior. It takes expected utility theory of Daniel Bernouilli who took the rational model of Blaise Pascal, and makes a couple of tweaks. It belongs to Daniel Kahneman and Amos Tversky, and earned Kahneman the Nobel Prize for economics. The tweaks are:
Daniel Kahneman has been practically ignored in this blog. His 2011 book: Thinking, Fast and Slow, is well written and an excellent resource. I certainly do not hold winning the Nobel Prize for Economics against him. I do wish he was more like Ken Hammond and gave me more background and more perspective on what questions that are likely to be answered in the future or what research he believes is interesting, or especially why Gigerenzer is wrong. System 1 and System 2 seem outdated and Kahneman seems to just ignore research like that of Glockner and Betsch Intuition in J/DM that sees the systems as more holistic.
This backward facing emotion is a combination of self-blame and disappointment. Interestingly, we all know that it influences our decision making. Daniel Kahneman in Thinking Fast and Slow notes that neither prospect theory or utility theory take regret into account. It is certainly a big part of television games shows like “Who Wants to be a Millionaire” as Kahneman’s example illustrates.
Choose between 90% chance to win $1 million OR $50 with certainty.
Choose between 90% chance to win $1 million OR $150,000 with certainty.
You know that in the second example, you will regret turning down a gift of $150k if you do not win the million. With regret, the experience of an outcome depends on a choice you could have taken, but did not. Kahneman notes that since regret adds more weight to the tool box and little to better prediction, it is not really worth the trouble to include it.