Book Cover

Happy Saturday! Next week begins the poll for the next book. 

Chapter 31 begins by reminding the reader of the idea that people tend to be risk-averse in the domain of gains and risk-seeking in the domain of losses. Stated less academically, people will accept less for an outcome from a sure bet than if the same bet was is less than certain. On the loss side, most people will avoid a certain loss to take a gamble on an outcome that has some chance at a positive outcome even when the probability is low and the loss will be higher. As Kahneman has pointed out in earlier chapters, people are not perfect logisticians when it comes to making decisions. 

With the basic idea of how the majority of people apply risk-aversion and risk-seeking behavior established Kahneman discusses an experiment that couples two gambles/decisions together. Putting two sets of decisions together lets the experimenter determine how people process scenarios with multiple decisions. The first gamble is between a certain gain and the possibility of a larger gain or nothing. The second gamble is a similar dichotomy between a certain loss and a possible loss (the exact equations are shown in the first few paragraphs of Chapter 31). In the example, the best solution is to take the risk on the gain and accept certainty on the loss, however when the experiment is run most people decouple the decisions and arrive at a result that delivers less value. Taking decisions as they arrive reflects a narrow versus broad approach. Considering related decisions as a whole is more apt to allow decision-makers to avoid potential cognitive biases (or at least minimize their impact).  

Earlier Kahneman established that gamblers (decision-makers) often exhibit a bias for loss aversion in the domain of gains and risk-seeking in the domain of losses. Couple these biases with a propensity to make decisions as they come (a narrow approach) rather than a whole,  increases the potential to leave money on the table in complex decision-making scenarios. This might sound academic, however, this type of behavior can affect product and portfolio prioritization. For example, both in qualitative and qualitative prioritization many organizations weight attributes to more “accurately” reflect their perception of return. These weights, unless carefully qualified, can reflect the same biases they are trying to avoid. Even when attributes are weighted, the horsetrading the accompanies prioritization allows bias to enter the process. 

Kahneman’s solution in this chapter is to establish risk policies (Paul Gibbon’s in Impact, the book I am currently reading, broaches the subject of de-biasing rather than establishing risk policies). A risk policy creates a broad frame for decision making. A risk policy I established many years ago to keep from being nickeled and dimed by car salespeople is never to buy any extra warranties or insurances from the dealership. Risk policies for prioritization (products, features, or stories) includes deciding which attributes will be used to compare work items and then comparing groups of work rather than considering each work item individually. This is one reason for the product to have a single backlog. The goal for a risk policy in work prioritization to avoid the optimism of the planning fallacy and exaggerated caution induced by loss aversion.

Remember, if you do not have a favorite, dog-eared copy of Thinking, Fast and Slow, please buy a copy.  Using the links in this blog entry helps support the blog and its alter-ego, The Software Process and Measurement Cast. Buy a copy on Amazon, It’s time to get reading!


The installments:

Week 1: Logistics and Introduction –    

Week 2: The Characters Of The Story –  

Week 3: Attention and Effort – 

Week 4: The Lazy Controller – 

Week 5: The Associative Machine – 

Week 6: Cognitive Ease – 

Week 7: Norms, Surprises, and Causes – 

Week 8: A Machine for Jumping to Conclusions – 

Week 9: How Judgement Happens and Answering An Easier Question – 

Week 10:  Law of Small Numbers – 

Week 11: Anchors – 

Week 12: The Science of Availability – 

Week 13: Availability, Emotion, and Risk – 

Week 14: Tom W’s Speciality – 

Week 15: Linda: Less Is More – 

Week 16: Causes Trump Statistics – 

Week 17: Regression To The Mean – 

Week 18: Taming Intuitive Predictions — 

Week 19: The Illusion of Understanding –  

Week 20: The Illusion of Validity –  

Week 21: Intuitions vs Formulas – 

Week 22: Expert Intuition –   

Week 23: Chapter 23: The Outside View

Week 24: Chapter 24 The Engine of Capitalism –

Week 25: Chapter 25  Bernoulli’s Errors 

Week 26: Chapter 26 – Prospect Theory  

Week 27: Chapter 27 – Endowment Effect 

Week 28: Chapter 28 – Bad Events

Week 29: The Fourfold Pattern 

Week 30: Rare Events