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DESCRIPTION:Homo Heuristicus: Decision Making under Uncertainty\n\nBy Gerd 
 Gigerenzer\n\nUniversity of Potsdam\n\nDate: February 6\, 2025\n	Time: 11:0
 0 AM to 1:00 PM\n\nRegister & watch the webinar\n\nView poster\n\n\n \n\nA
 bstract\n\nIn well-defined situations with known risks\, the axioms of cla
 ssical decision theory can guide optimal decision-making. However\, when S
 avage introduced his axioms\, he clarified that they apply to risk but not
  to uncertainty and intractability. Uncertainty refers to ill-defined situ
 ations where the future states of the world (their exhaustive and mutually
  exclusive set)\, their outcomes\, and associated probabilities are either
  unknown or unknowable. Intractability\, on the other hand\, involves well
 -defined but overly complex situations\, such as in games like chess or Go
 \, where finding optimal solutions is impractical. Though Knight\, Keynes\
 , and Simon had drawn similar distinctions\, most models of uncertainty ha
 ve reduced it to risk\, such as by using second-order probabilities\, equa
 l priors\, or Bayesian subjective probabilities. In contrast\, I argue for
  a genuine theory of decision-making under uncertainty\, grounded in the e
 mpirical study of heuristic-based decisions. This approach includes three 
 key research areas. The first is descriptive: What heuristics do individua
 ls and organizations have in their adaptive toolbox\, and how do they choo
 se between them? The second is prescriptive: In what contexts are heuristi
 cs more likely to succeed than more complex strategies? This line of inqui
 ry\, known as the study of ecological rationality\, examines the match bet
 ween strategies (heuristics or others) and the structure of environments. 
 The third area is engineering and intuitive design: How can we create heur
 istic systems that aid experts and non-experts in making better decisions?
  To achieve this\, three methodological tools are essential: formal models
  of heuristics (to go beyond vague terms like 'System 1')\, competitive te
 sting of heuristics against complex strategies (instead of merely relying 
 on null hypothesis testing)\, and evaluating the predictive power of heuri
 stics (rather than just fitting them to data). Through examples from finan
 ce\, management\, and sports\, I demonstrate that heuristics often predict
  as accurately\, if not better\, than complex strategies\, including some 
 machine learning algorithms.\n
DTSTART:20250206T160000Z
DTEND:20250206T180000Z
SUMMARY:MCCHE Precision Convergence Webinar Series with Gerd Gigerenzer
URL:https://www.mcgill.ca/channels/channels/event/mcche-precision-convergen
 ce-webinar-series-gerd-gigerenzer-362939
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