Presentation heuristics
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Heuristics are simple rules or mental shortcuts that allow humans to make decisions quickly and with limited information. The document discusses several types of heuristics including: the gaze heuristic, recognition heuristic, social heuristics like "do what the majority do", and heuristics based on reasons like take the best and tallying. It also covers cognitive biases like hindsight bias. Overall, the document examines how heuristics demonstrate bounded rationality and how humans use fast and frugal mental shortcuts to make decisions in an efficient manner.
Presentation heuristics
- 1. CONTENT Introduction Heuristic Types Gaze heuristic Recognition heuristic Social heuristic Heuristics Based on Reasons Take the best heuristic Tallying heuristic Hindsight bias Conclusion
- 3. WHAT to DO ?
- 4. Heuristics Versus Rules Heuristic is a rule: A rule is not necessarily a heuristic, unless it embodies 3 qualities
- 5. 1-Heuristics exploit evolved capacities 2-Heuristics exploit structures of 3- heuristics are distinct from “as optimization models
- 6. What is Unbounded Rationality ? all relevant infoInrm matoidonels of Bounded Unbounded Rationality Rationality (Simon’s term ): how do humans who have little time & knowledge infinite amount of time behave?
- 7. 3 Programs associated with bounded rationality 1 • The study of optimization under constraints. 2 • The study of cognitive illusion. 3 •The study of fast & frugal heuristic.
- 8. Simon’s vision of bounded rationality Simon: Human rational behavior is capabilities of the actor structure of task environment
- 9. Heuristic Types Gaze Heuristic
- 10. Gaze heuristic PRABOBOLA Initial Distance Initial Velocity Projection Angle Projection Speed Direction of The Wind No such robots exist yet!
- 11. Gaze heuristic RUNNING SPEED ANGLE OF GAZE REMAINS CONSTANT NOT COMPUTE THE POINT AT WHICH THE BALL WILL LAND, BUT HE WILL BE THERE FAST and FRUGAL
- 12. Gaze heuristic
- 13. Gaze heuristic
- 14. Gaze heuristic
- 15. Gaze heuristic
- 16. Heuristic Type Recognition Heuristics
- 17. TWO CITIES IN UNITED STATES San Antonio San Diego
- 18. Recognition heuristic GERMAN Which city has more inhabitant? SAN DIEGO 66 % 100 % 33 % SAN ANTONIO 0 % AMERICAN
- 19. WHAT HAPPENED …!? Less Knowledge More Correct Inference
- 20. Ecological Rationality: Correlation between recognition & criteria Recognition validity: α = R / ( R + W )
- 21. recognition information further knowledge less-is-more effect Less-is-more in groups
- 22. The less-is-more effect n n ( 1) 1 N n N n ( )( 1) ( 1) 2 ( 1) n N n 2 ( ) ( 1) N N N N N N c N: number of objects n: number of recognized objects α: recognition validity β: knowledge validity
- 23. The recognition heuristic will yield a less-is-more effect if α > β
- 24. Heuristic Type Social Heuristics
- 25. “Do what the majority do” heuristic Darwin Decision to marry Single or married
- 26. Ecological Rationality of do-what-the-majority do Heuristics Similar environments Stable environments Noisy environments
- 27. Heuristic Type Take the Best &Tallying
- 28. Take The Best heuristic (TTB) Validities Cues School A School B 0.9 reputation + - 0.6 distance - + 0.8 students - + ONE-REASONSE DAERCCIHSI OBNY VMAALKIDINITGY ONE-REASON STOPPING RULE
- 29. Validities Cues School A School B 0.9 reputation + - 0.6 distance - + 0.8 students - + Random search Stop after searching m cues Tallying rull Tallying heuristic
- 30. Ecological Rationality of Take The Best and Tallying Take The Best Noncompensatory Tallying Compensatory
- 31. Hindsight Bias
- 32. Hindsight Bias Which has more cholesterol , cake or pie? Cues Original Feedback Recall Saturated fat (80%) cake? Pie "cake" cake> pie Calories (70%) cake> pie Protein (60%) Choice cake cake Confidence 70% 80%
- 33. Which heuristic to use?
- 34. Using heuristic unconsciously adapt heuristics to changing environment with feedback.
- 35. Evaluating creditworthy How people adapt their heuristics to the structure of environment
- 36. Robustness • the ability to make predictions about the future or new events fitness • the ability to fit the past or already known data
- 37. Simplicity can lead to higher predictive accuracy
- 38. The building Blocks of Heuristics
- 40. CONCLUSION
- 41. CONCLUSION