Part 2:
Gerd Gigerenzer (Max-Planck-Institute for Human Development, Berlin), Tom Griffiths (Princeton University, online)
Moderation: Christian Ruff (University of Zurich)
How can we reliably model a world of uncertainty, complexity, and incomplete information? What ensures the reliability and validity of models in critical fields like medicine, climate science, and epidemiology? As artificial intelligence, big data, and automation reshape scientific roles—from model creators to controllers—what implications does this shift have for scientific practice, especially as models increasingly exceed the bounds of cognitive and brain-compatibility? From another angle, what are the limits of models in the face of irreducible complexity? How do we model the mind’s reasoning under conditions of limited attention, bounded rationality, and incomplete information? Can psychological models of heuristics and biases be reconciled with computational ideals of rationality, as pursued in artificial intelligence? What roles do randomness and uncertainty play in shaping cognitive strategies and algorithmic methods of decision-making?
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