 Moreover, in parallel developments, the classical conception of rationality is under attack on many fronts. Herbert Simon (1947) introduced the ideas of bounded rationality and satisficing. Simon later championed the need for a heuristic approach to problem solving by humans and computers (Newell &amp; Simon 1972). Various flavors of artificial intelligence then led the way in the methodology of problem solving, with heuristics as a central topic and no longer the temporary scaffolding of positivism and Popper. Simon’s program in adaptive, “ecological rationality” is now being expanded by Gerd Gigerenzer and the Adaptive Behavior and Cognition group in Berlin (Gigerenzer et al. 1999). Simon’s approach and the “heuristics and biases” program of Daniel Kahneman and Amos Tversky (Kahneman et al. 1982), plus work by the latter on prospect theory, triggered the emergence of behavioral economics, which rejects the neo-classical homo economicus rationality model. Philosopher Christopher Cherniak’s Minimal Rationality (1986) also brought out sharply how idealized were traditional philosophical assumptions about rationality. In other directions, some computer scientists are challenging the anthropocentrism of received conceptions of rational inference by asking why artificial intelligence, including deep learning, should be restricted to human forms of reasoning. Meanwhile, biologists and philosophers are studying the evolution of rationality (Okasha &amp; Binmore 2012), and ethologists ask why we should withhold attributions of rationality to animals from chimps and elephants to octopuses, simply because they seem to lack a human sort of conceptual language.