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SUMMARY:How Well Can Humans Approximate Optimality In Computationally Hard
  Problems?
DTSTART:20220916T103000
DTEND:20220916T120000
DTSTAMP:20260408T060255Z
UID:e9ddd5b234e68ae5d66e7690e0fa2d5472a23cecca06ed599cabddc6
CATEGORIES:Conferences - Seminars
DESCRIPTION:Peter Bossaerts\, University of Cambridge\nMany decisions peop
 le face are complex\, even intractable. It is often suggested that decisio
 n-makers use heuristics to overcome complexity\, and that the resulting be
 havior closely approximates optimality. The second claim deserves scrutiny
  because the theory of computation has concluded that solutions to certain
  classes of problems cannot reasonably be approximated. Still\, the theory
  applies to algorithms of abstract computers -- as opposed to humans -- an
 d classes are formed based on worst-case analysis. Here we report results 
 of an experiment in which participants were asked to solve a number of tas
 ks that differed in the degree of approximation complexity. We find that p
 erformance is as predicted by the theory. We elucidate how classification 
 based only on worst cases can predict average human performance. Our findi
 ngs demonstrate that properties of the task at hand explain performance wi
 thout having to understand which heuristics are used\, or which computatio
 nal constraints humans face.\n 
LOCATION:UniL Campus\, Room Extra 126
STATUS:CONFIRMED
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