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This chapter formally introduces the Prospect Theory and talks about the difference between it and the Expected Utility Theory. When doing a little background research, prospect theory (part of his research on decision making under uncertainty) was noted as contributing to his winning the Nobel prize in economics. 

In Expected Utility Theory, a gain is assessed by comparing the calculated value of two states. The value delivered by a decision is calculated by multiplying each of the possible outcomes by the likelihood each outcome will occur and then summing all of those values.  If you have a 50% chance to make $500 and a 50% chance of breaking even, the value is $250. When the value is positive the theory would predict that humans would always accept the gamble. Kahneman and Tversky observed that real-life behavior often differed from the behavior predicted by this Expected Utility Theory because of the context in which the choice is made makes a difference.  Changing our example to a 50/50 chance of either making $500 or losing $400, Expected Utility Theory would predict that a rational economic human would accept the gamble. However, if the person being asked to make accept the gamble has a net worth of $1,000 they would naturally be more risk-averse because the potential loss would be perceived to be psychologically larger. (more…)

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Kahneman opens the chapter by establishing the economic definition of a human as someone who is rational, selfish and whose tastes don’t change. This flies in the face of how a human is envisioned in psychological theory – not always rational, sometimes generous, and whose tastes change. I have always had trouble with rational human definition because I grew up in a family tied to the retail and wholesaling of clothing — at the very least I have direct evidence that people’s taste change which means part of the definition does not track. The idea that people act as a pure economic being is a tantalizing simplification when planning changes in an organization. Many changes agents try to sell the process change on a purely economic basis only to be shocked when there is resistance. (more…)

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As in Agile Coach, I am an expert in many aspects of software development. To develop that level of expertise, I have had to learn and practice a lot. I interface with many other experts and draw on their skills. The people I respect as experts have earned that title based on experience. The question in this chapter is when can expert intuition be trusted. This chapter focuses on the work that Kahneman did with Gary Kline.  Part of the premise of this chapter is that there is a difference between subjective and expert intuition. The chapter explores the difference, how expert intuition is formed, how it is evaluated, and whether we can trust it. (more…)

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Last night severe thunderstorms rolled through northern Ohio.  There were lots of power outages and trees that were blown over.  This morning when I went to the grocery store, the store’s systems could not accept debit cards. I immediately made up a story that connected the storms to system failure. As we have seen before, System 1 thinking takes disparate facts and creates a coherent believable story.  No conclusion is too big a jump for System 1 thinking. My story and my belief that I had predicted the most probable cause is an illusion of validity.    (more…)

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Part 3 of Thinking, Fast and Slow is titled Overconfidence.  Chapter 19 begins by exploring several biases that affect overconfidence. Earlier in the book, we explored how System 1 thinking connects events to generate a coherent story.  This chapter begins by building on the attributes of fast thinking by stating that humans interpret behavior as a manifestation of general propensities and personal traits. One of the classic biases that cause this type of thinking is the halo effect. I overheard an example of a negative halo effect this week as I walked behind a group of people in Chicago. The group, tourists, pointed at a person sleeping rough along the river and exclaimed that the person was lazy.  One attribute of the person’s behavior was generalized into a larger narrative.   (more…)

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I know this is not Saturday however, I was not able to post week 18 last weekend because I was traveling to Canada to pick up a puppy and bring her home.  So I am trying to catch up!  Week 18 of our re-read of Thinking, Fast and Slow tackles Chapter 18, Taming Intuitive Predictions. (more…)

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Why does leadership bring a release home to great adulation only to have the next release crash and burn? Did the leader’s skill change between releases or were other random factors, such as luck, involved. Kahneman suggests a simple formula as a thought experiment.  Success = skill + luck. Chapter 17 of Thinking, Fast and Slow, Regression To The Mean, discusses correlation and causal interpretation.   (more…)

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In Chapter 15 of Thinking, Fast and Slow we explore two types of fallacies. Logical and conjunction fallacies can impact any process improvement effort, typically in a manner that does not benefit change. 

The central plot device in this chapter is an experiment performed by Kahneman and Tversky that asked sets of respondents to rank attributes by representativeness and another group to rank by probability. The experiment begins with a description of the person, Linda,  (similar to the experiment at the center of Chapter 14). A set of statements about Linda’s potential profession is then listed. In this case (as compared to the experiment in Chapter 14), there are items in the list that require the application of logic to judge. For example, one item is “Linda is a bank teller” and a second is “Linda is a bank teller and is active in the feminist movement”. The lightbulb moment from the experiment was when there are two items that have a logical relationship, respondents distinguished between the two based on the story System 1 constructs.  (more…)

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Chapter 14 continues the discussion of cognitive biases and heuristics.  In Chapter 14 of Thinking, Fast and Slow we explore the representative heuristic.  

Tom W’s Specialty is an exercise/puzzle that Kahnamen uses throughout Chapter 14 to explore how the perception of representativeness impacts conclusions originally made from the base rate (the percentage of an attribute in the population). This might sound somewhat esoteric, but consider how many contracts are signed based on estimates that include representations of performance above and beyond the norm. Alternately how many agile teams pull more work items above their median performance based on representations of simplicity from stakeholders. The representative heuristic can negatively bias any intuitive decision.  (more…)

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Today we dive into the availability heuristic.  The availability heuristic is useful for understanding what people believe and how they will act.  A twist in using the availability heuristic is useful for disrupting beliefs based on impressions or events that can be quickly remembered. All leaders need to understand the impact of top of mind experiences on decision making and how to disrupt those biases, the availability heuristic is a tool for building that knowledge.  (more…)