OPIM
OPIM 290 STUDY GUIDE

# Decision making in high chance environments 1 do not

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Decision making in High Chance environments 1. Do not incentivize outcomes, but rather other indicators of competence. 2. Make decisions based on secondary considerations. 3. Prioritize accumulating opportunities over trying to choose the best one.

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Takeaways 1. Chance is streakier and more powerful than we expect it to be. 2. Streakiness is inevitable and not necessarily evidence of change. 3. Even large disparities in performance may not need explanation. 4. Small sample sizes = more chance. 5. Test chance by correlating measures with themselves. Lecture 4 Lower sample size = less precise estimates Lower sample size = less reliable knowledge; more chance Disparities in performance are more likely to be caused by chance when: 1. Sample size is small. 2. Differences in ability are small. The probability of an occurrence depends on how you define it. “that” ≠ “something like that” It is WRONG to calculate the odds of a pattern AFTER you have noticed it. But this is what we often do. We notice an unusual pattern and THEN we compute the odds of that exact thing happening. If you notice an unusual pattern and you want to test whether it is meaningful, you can: 1. Test the same prediction in a new dataset (replication) 2. Test a new prediction from the same hypothesis. Patterns WILL arise whenever you mine a large data set. Easy to do with large data sets: Data mine a random subset. If you find something interesting see if it replicates in the other subset. For practical purposes, a statistical relationship is true if you can predict it in advance. Takeaways 1. Smaller samples = more noise/chance. 2. Chance produces unusual outcomes. 3. “That” is different than “Something like that” 4. Testing for statistical significance hinges on specifying your hypotheses in advance. Seek replications for unusual patterns.
Lecture 5 Decisions are easier when we can predict the future. We can best predict the future by making sound inferences. Heuristics • Mental shortcuts/rules of thumb for making judgments • Heuristics save time • Heuristics are “good enough” much of the time • Heuristics are prone to systematic error Availability Heuristic We are more likely to overestimate the likelihood, frequency, and causal impact of things that: 1. Have Come To Mind Frequently or Recently 2. Are The Focus Of Our Attention 3. Spring Easily To Mind Anchoring People’s estimates of unknown quantities are easily biased by what values they consider…even when those values are obviously arbitrary and irrelevant. • They are sometimes believed to be informative – to signal that the true answer is close by. • In cases of extreme uncertainty, any value that comes to mind will seem plausible. • Anchors are defaults: we need good reasons to give answers that are far away from them (but not to give answers that are close to them).

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