Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Presentation heuristics

1,413 views

Published on

  • Be the first to comment

  • Be the first to like this

Presentation heuristics

  1. 1. CONTENT  Introduction  Heuristic Types Gaze heuristic Recognition heuristic Social heuristic Heuristics Based on Reasons Take the best heuristic Tallying heuristic Hindsight bias Conclusion
  2. 2. Introduction
  3. 3. WHAT to DO ?
  4. 4. Heuristics Versus Rules Heuristic is a rule: A rule is not necessarily a heuristic, unless it embodies 3 qualities
  5. 5. 1-Heuristics exploit evolved capacities 2-Heuristics exploit structures of 3- heuristics are distinct from “as optimization models
  6. 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. 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. 8. Simon’s vision of bounded rationality  Simon: Human rational behavior is capabilities of the actor structure of task environment
  9. 9. Heuristic Types Gaze Heuristic
  10. 10. Gaze heuristic PRABOBOLA Initial Distance Initial Velocity Projection Angle Projection Speed Direction of The Wind No such robots exist yet!
  11. 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. 12. Gaze heuristic
  13. 13. Gaze heuristic
  14. 14. Gaze heuristic
  15. 15. Gaze heuristic
  16. 16. Heuristic Type Recognition Heuristics
  17. 17. TWO CITIES IN UNITED STATES San Antonio San Diego
  18. 18. Recognition heuristic GERMAN Which city has more inhabitant? SAN DIEGO 66 % 100 % 33 % SAN ANTONIO 0 % AMERICAN
  19. 19. WHAT HAPPENED …!? Less Knowledge More Correct Inference
  20. 20. Ecological Rationality:  Correlation between recognition & criteria  Recognition validity: α = R / ( R + W )
  21. 21. recognition information further knowledge less-is-more effect Less-is-more in groups
  22. 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. 23. The recognition heuristic will yield a less-is-more effect if α > β
  24. 24. Heuristic Type Social Heuristics
  25. 25. “Do what the majority do” heuristic Darwin Decision to marry Single or married
  26. 26.  Ecological Rationality of do-what-the-majority do Heuristics Similar environments Stable environments Noisy environments
  27. 27. Heuristic Type Take the Best &Tallying
  28. 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. 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. 30. Ecological Rationality of Take The Best and Tallying Take The Best Noncompensatory Tallying Compensatory
  31. 31. Hindsight Bias
  32. 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. 33. Which heuristic to use?
  34. 34.  Using heuristic unconsciously  adapt heuristics to changing environment with feedback.
  35. 35. Evaluating creditworthy How people adapt their heuristics to the structure of environment
  36. 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. 37. Simplicity can lead to higher predictive accuracy
  38. 38. The building Blocks of Heuristics
  39. 39. Heuristics In Hospital
  40. 40. CONCLUSION
  41. 41. CONCLUSION

×