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 – http://bit.ly/2UL4D6h    

Week 2: The Characters Of The Story – http://bit.ly/2PwItyX  

Week 3: Attention and Effort – http://bit.ly/2H45x5A 

Week 4: The Lazy Controller – http://bit.ly/2LE3MQQ 

Week 5: The Associative Machine – http://bit.ly/2JQgp8I 

Week 6: Cognitive Ease – http://bit.ly/2VTuqVu 

Week 7: Norms, Surprises, and Causes – http://bit.ly/2Molok2 

Week 8: A Machine for Jumping to Conclusions – http://bit.ly/2XOjOcx 

Week 9: How Judgement Happens and Answering An Easier Question – http://bit.ly/2XBPaX3 

Week 10:  Law of Small Numbers – http://bit.ly/2JcjxtI 

Week 11: Anchors – http://bit.ly/30iMgUu 

Week 12: The Science of Availability – http://bit.ly/30tW6TN 

Week 13: Availability, Emotion, and Risk – http://bit.ly/2GmOkTT 

Week 14: Tom W’s Speciality – http://bit.ly/2YxKSA8 

Week 15: Linda: Less Is More – http://bit.ly/2T3EgnV 

Week 16: Causes Trump Statistics – http://bit.ly/2OTpAta 

Week 17: Regression To The Mean – http://bit.ly/2ZdwCgu 

Week 18: Taming Intuitive Predictions — http://bit.ly/2kAHClJ 

Week 19: The Illusion of Understanding – http://bit.ly/2lK954p  

Week 20: The Illusion of Validity –   http://bit.ly/2mfyrYh  

Week 21: Intuitions vs Formulas – http://bit.ly/2kx7kri 

Week 22: Expert Intuition – http://bit.ly/2ooe50h   

Week 23: Chapter 23: The Outside View http://bit.ly/35dOibJ

Week 24: Chapter 24 The Engine of Capitalism –  http://bit.ly/2WgNSgV

Week 25: Chapter 25  Bernoulli’s Errorshttp://bit.ly/32bJ8dV 

Week 26: Chapter 26 – Prospect Theoryhttp://bit.ly/2Nx3tWI  

Week 27: Chapter 27 – Endowment Effecthttp://bit.ly/2QwHgdz 

Week 28: Chapter 28 – Bad Events  http://bit.ly/33hAgUi

Week 29: The Fourfold Patternhttp://bit.ly/2Y3doHg 

Week 30: Rare Eventshttp://bit.ly/2LANUMr