Michael Mauboussin Guide to Making Better Comparisons (Link)
Comparing via analogies (steps and common mistakes)
- Select the source for analogy
- Common pitfall at this stage: Undersampling due to availability bias
- Map the source to target to make inferences usually looking for similarities
- A common pitfall at this stage: mistaking correlation for causality
- Consider the first attempts at flight were people putting feathers on their arms, not studying lift
- Confusing a star performer with talent in a volatile field
- A common pitfall at this stage: mistaking correlation for causality
- Adjust for differences between source and target
- Tversky showed we place more emphasis on similarity than differences
- The framing problem: We are influenced by which differences and similarities we are prodded to focus on (ie framing)
- Learn by the success or failure of the analogy
- Pitfalls in heuristics and intuition
- Intuition works great in chess since it is a form of pattern recognition, but works poorly in investing where outcomes are non-linear
- Recency bias influences sample
- Choice-supportive or confirmation bias taps into our need to be consistent; we create stories after the fact to validate our decisions
- Hyperbolic discount rates prevail when we study inter-temporal decision making
- Stress is good response for crisis because it focuses on immediate needs; chronic stress causes decisions to be short-sighted
- Using poor reference points in comparison
- 2 companies in different industries can be more similar than their peers within their own industry; this shows how we can conflate attributes with what is actually driving value
- Not recognizing that widely varied values can be justifiable. For example, 2 companies with the same earnings growth can trade at justifiably different valuations if underlying returns on invested capital are very different
- Anchoring bias
- When I went to Capital Camp, Mauboussin discussed T Theory. The top row of a category have more in common with each other than the average in the category. This articulates how I think about investors! Warren Buffet, Annie Duke, and Sam Hinkie have more in common with each other than other people in the same category.
- Pitfalls in heuristics and intuition
So how to get better?
Instead of relying on analogies drawn from memory we can use “similarity-based” forecasting.
- Inputs
- A wide sample for the reference class rather than 1 or 2 examples from memory
- Additional refinement by weighting the results of the most similar samples more heavily
- Ways to quantify the similarity
- “nearest neighbor” algorithm (requires identifying relevant axes for the dimensions)
- “connectionist” technique for weighting features by similarities and differences
- Ways to quantify the similarity
- Additional refinement by weighting the results of the most similar samples more heavily
- A statistical “base rate” drawn from the outcomes of varied reference classes
- A wide sample for the reference class rather than 1 or 2 examples from memory