Control Condition Please wait 3 seconds Feedback promotes more planning Lieder KruegerCallaway amp Griffiths 2017 leading to higher performance Lieder KruegerCallaway amp Griffiths 2017 ID: 1027273
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1. Testing the approachExperimental ConditionControl ConditionPlease wait 3 seconds.
2. Feedback promotes more planning(Lieder*, Krueger*Callaway*, & Griffiths, 2017)
3. … leading to higher performance(Lieder*, Krueger*Callaway*, & Griffiths, 2017)
4. ConclusionsResource rationality provides a framework that is realistic yet still systematic as a source of modelsMany standard heuristics are not just “kluges” but are resource-rational for some architectureThis approach lets us tackle questions about how people use their cognitive resources, and how to design systems to overcome those limitations
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6. Availability of extreme eventsAvailability bias: people over-estimate the probability of events that come to mind easily(Tversky & Kahneman, 1973)Extreme events come to mind easily…
7. Availability of extreme eventsTask: Estimate the expected utility of an actionArchitecture: Generate (weighted) samples of possible outcomes of the actionCost: Increases linearly in the number of samples (as with opportunity cost)(Lieder, Hsu, & Griffiths, 2014)
8. Unbiased sampling is bad…
9. Importance samplingp(x)q(x)
10. The optimal distributionVariance is minimized by…But, the result is biased: with small samples, we will over-represent extreme events(Lieder, Hsu, & Griffiths, 2014)
11. Over-representing extreme eventsρ = 0.53(Lieder, Hsu, & Griffiths, 2014)
12. A simple optimal heuristicWhen making a decision…generate from the utility-weighted distributiontally the number of pros and consReproduces phenomena in decision-makingfourfold pattern of risk preferences (Tversky & Kahneman, 1992)Allais paradox (Allais, 1953)outperforms cumulative prospect theory in the Technion dataset (Erev et al., 2010)
13. Fourfold pattern of risk preferencesOverweight gain/loss of risky gamble if p small(since |u(o)-u(p.o)| is greater than |u(p.o)|)Underweight gain/loss of risky gamble if p large
14. Allais paradoxL1: 66% chance of $2400, 33% chance of $2500, 1% chance of $0L2: 100% chance of $2400 L1: 33% chance of $2500, 67% chance of $0L2: 34% chance of $2400, 66% chance of $0
15. Allais paradox
16. Technion resultsr = 0.88
17. Anchoring and adjustmentWhat’s the freezing point of vodka?How long is Mars’ orbit around the sun? People answer these questions by starting with a more familiar example (an anchor) and adjusting away from it (leading to bias)(Epley & Gilovich, 2006)
18. Anchoring and adjustmentTask: Estimate a quantity based on memory and other cuesArchitecture: Markov chain Monte Carlo via the Metropolis-Hastings algorithmCost: Increases linearly in the number of samples (as with opportunity cost)(Lieder, Griffiths, & Goodman, 2012)
19. Metropolis-Hastings
20. Bias is resource-rational(Lieder, Griffiths, & Goodman, 2012)
21. Anchoring and adjustment(data from Epley & Gilovich, 2006)(Lieder, Griffiths, & Goodman, 2012)
22. Other predictionsCognitive load, time pressure, and alcohol reduce adjustment (Epley & Gilovich, 2006)Bias increases with anchor extremity (Russo & Schoemaker, 1989)Uncertainty increases anchoring (Jacowitz & Kahneman, 1995)Knowledge can abolish the anchoring bias (Wilson et al., 1996)
23. Effects of time and error cost
24. Waiting for the bus
25. Experiment results
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28. Investment taskTwo strategies: Take The Best (TTB) vs. weighted combinationTwo environments: noncompensatory vs. compensatory(Lieder & Griffiths, submitted)
29. Model predictions (data from Reiskamp & Otto, 2006)
30. Flexible strategy use(Lieder & Griffiths, submitted)
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