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Robust decision making in uncertain environments

Henry Brighton. Robust decision making in uncertain environments. Motivation. Practically all cognitive tasks involve uncertainty: E.g., vision, language, memory, learning, decision making. Humans and other animals are well adapted to uncertain environments.

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Robust decision making in uncertain environments

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  1. Henry Brighton Robust decision making in uncertain environments

  2. Motivation • Practically all cognitive tasks involve uncertainty: • E.g., vision, language, memory, learning, decision making. • Humans and other animals are well adapted to uncertain environments. • Artificial Intelligence (AI) considers the same tasks: • These problems appear to be computationally demanding. • “Every problem we look at in AI is NP-complete”(Reddy, 1998). • How do humans and other animals deal with uncertainty? • The study of simple heuristic mechanisms. • Robust responses to uncertainty via simplicity.

  3. Catching a ball When a man throws a ball high in the air and catches it again, he behaves as if he had solved a set of differential equations in predicting the trajectory of the ball... At some subconscious level, something functionally equivalent to the mathematical calculation is going on. -- Richard Dawkins, The Selfish Gene

  4. Gaze heuristic Fix your gaze on the ball, start running, and adjust your running speed so that the angle of gaze remains constant.

  5. Gaze heuristic Fix your gaze on the ball, start running, and adjust your running speed so that the angle of gaze remains constant.

  6. Gaze heuristic Fix your gaze on the ball, start running, and adjust your running speed so that the angle of gaze remains constant.

  7. Gaze heuristic Fix your gaze on the ball, start running, and adjust your running speed so that the angle of gaze remains constant. • Bats, birds, and dragonflies maintain a constant optical angle between themselves and their prey. • Dogs do the same, when catching a Frisbee (Shaffer et al., 2004). • Ignore: velocity, angle, air resistance, speed, direction of wind, and spin.

  8. Heuristics ignore information ? Peahen mate choice (Petrie & Halliday, 1994). Heuristic strategies are: • Computationally efficient, consuming few resources. • Ignore information, and seek “good enough” solutions. • Many examples in biology, termed “rules of thumb”.

  9. Why use heuristics? The accuracy-effort trade-off Accuracy Cost Effort • Information search and computation cost time and effort. • Therefore, minds rely on simple heuristics that are less accurate than strategies that use more information and computation. • This view is widely held within cognitive science, economics, and beyond.

  10. The study of heuristics Three widely held assumptions: • Heuristics are always second-best. • We use heuristics only because of our cognitive limitations. • More information, more computation, and more time would always be better. A stronger hypothesis, the possibility that less-is-more: More information or computation can decrease accuracy; therefore, minds rely on simple heuristics in order to be more accurate than strategies that use more information and time. Heuristics as functional responses to environmental uncertainty.

  11. An example: take-the-best Cues Objects Cue validities: Consider the most valid unexamined cue Does this cue discriminate? A: Choose object with positive cue value Q: Which city has a greater population, Berlin or Cologne? Y N Y Are there any other cues? N A: Guess Y

  12. The performance of take-the-best Take-the-best: • Fits the data poorly. • Predicts exceptionally well. • The uncertainty of samples • Regularity vs. randomness. Predictions Sample B Train models Sample A

  13. Heuristics and robustness Robust systems maintain their function despite changes in operating conditions. Aircraft functioning Generalization error Changes to operating conditions Atmospheric disturbances Changes in samples The robustness of heuristics: • A sample of observations only provides an uncertain indicator of latent environmental regularities. • Ignoring information is one way of increasing robustness.

  14. No system is robust under all conditions Environmental operating conditions High predictability TTB dominates (white) Proportion of the learning curve dominated by TTB TTB inferior (black) Low predictability Low redundancy High redundancy

  15. The big picture: Dealing with uncertainty “Small worlds” versus “Large worlds” (Savage, 1954) Small worlds – “Laboratory conditions.” • Maximize expected utility. • Bayesian updating of probability distributions. • Need to know the relevant probabilities/options/actions. Optimization Large worlds – “The real world.” • Probabilities/options/actions not known with certainty. • Robustness becomes more important. • The accuracy-effort trade-off no longer holds. Satisficing (Simon, 1990)

  16. Summary: Heuristics and uncertainty An introduction to the study of heuristics: • Why do organisms rely on heuristics in an uncertain world? • Heuristics are not poor substitutes for more sophisticated, resource intensive mechanisms. • Ignoring information and performing less processing can lead to greater accuracy and increased robustness. • Many examples of less-is-more… Gigerenzer, G. & Brighton, H. (2009). Homo Heuristicus: Why biased minds make better inferences. Topics in Cognitive Science, 1, 107-143.

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