Abstract
Research on adaptive rationality has focused principally on inference, judgment, and decision-making that lead to behaviors and actions. These processes typically require cognitive representations as input, and these representations must presumably be acquired via learning. Nonetheless, there has been little work on the nature of, and justification for, adaptively rational learning processes. In this paper, we argue that there are strong reasons to believe that some learning is adaptively rational in the same way as judgment and decision-making. Indeed, overall adaptive rationality can only properly be assessed for pairs of learning and decision processes. We thus present a formal framework for modeling such pairs of cognitive processes, and thereby assessing their adaptive rationality relative to the environment and the agent’s goals. We then use this high-level formal framework on specific cases by (a) demonstrating how natural formal constraints on decision-making can lead to substantive predictions about adaptively rational learning and representation; and (b) characterizing adaptively rational learning for fast-and-frugal one-reason decision-making.
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Notes
That is, methods for which there are proofs of their asymptotic and/or short-run reliability.
In particular, the sums of probabilities within each partition element must be the same.
There are many ways to have “representations at the level of partition elements,” including both coarsened representations and inattention to particular distinctions in the world.
More precisely, for each representation r, let O r be the vector of “optimal responses” for the N goals (i.e., O r = < a 1,…,a N > where a i = D(r, V Gi )). It is straightforward to prove: if all partitions are equally costly, then r and s are in the same element of the optimal partition if and only if O r = O s .
R could be the space of possible cue validities or another similar representational structure.
References
Bailenson, J. N., Shum, M. S., Atran, S., Medin, D. L., & Coley, J. D. (2002). A bird’s eye view: Biological categorization and reasoning within and across cultures. Cognition, 84, 1–53.
Bonawitz, E., Denison, S., Chen, A., Gopnik, G., & Griffiths, T. L. (2011). A simple sequential algorithm for approximating Bayesian inference. Proceedings of the Thirty-third Cognitive Science Society.
Bröder, A. (2000). Assessing the empirical validity of the “Take-the-Best” heuristic as a model of human probabilistic inference. Journal of Experimental Psychology. Learning, Memory, and Cognition, 26, 1332–1346.
Bröder, A. (2003). Decision making with the “adaptive toolbox”: influence of environmental structure, intelligence, and working memory load. Journal of Experimental Psychology. Learning, Memory, and Cognition, 29, 611–625.
Chin-Parker, S., & Ross, B. H. (2002). The effect of category learning on sensitivity to within-category correlations. Memory and Cognition, 30, 353–362.
Dana, J. (2008). What makes improper linear models tick. In J. I. Krueger (Ed.), Rationality and social responsibility: Essays in honor of Robyn Mason Dawes. Mahwah: Lawrence Erlbaum Associates.
Danks, D. (2014). Unifying the mind: Cognitive representations as graphical models. Cambridge: The MIT Press.
Danks, D. (in press). Goal dependence in (scientific) ontology. Synthese.
Dawes, R. M. (1979). The robust beauty of improper linear models in decision making. American Psychologist, 34, 571–582. doi:10.1037/0003-066X.34.7.571.
Denison, S., Bonawitz, E. B., Gopnik, A., & Griffiths, T. L. (2013). Rational variability in children’s causal inferences: The sampling hypothesis. Cognition, 126(2), 285–300.
Dieckmann, A., & Todd, P. M. (2012). Simple rules for ordering cues in one-reason decision making. In P. M. Todd & G. Gigerenzer (Eds.), Ecological rationality: Intelligence in the world (pp. 274–308). Oxford: Oxford University Press.
Dougherty, M. R., Franco-Watkins, A. M., & Thomas, R. (2008). Psychological plausibility of the theory of probabilistic mental models and the fast and frugal heuristics. Psychological Review, 115(1), 199–213.
Geman, S., Bienenstock, E., & Doursat, R. (1992). Neural networks and the bias/variance dilemma. Neural Computation, 4, 1–58.
Gigerenzer, G. (1996). On narrow norms and vague heuristics: A rebuttal to Kahneman and Tversky. Psychological Review, 103, 592–596.
Gigerenzer, G., & Brighton, H. (2009). Homo heuristicus: Why biased minds make better inferences. Topics in Cognitive Science, 1, 107–143.
Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review, 103(4), 650–669.
Gigerenzer, G., Todd, P. M., & the ABC Research Group. (1999). Simple heuristics that make us smart. New York: Oxford University Press.
Goldstein, D. G., & Gigerenzer, G. (2002). Models of ecological rationality: The recognition heuristic. Psychological Review, 109, 75–90.
Hammond, K. R. (1996). Human judgment and social policy: Irreducible uncertainty, inevitable error, unavailable injustice. New York: Oxford University Press.
Hertwig, R., Fanselow, C., & Hoffrage, U. (2003). Hindsight bias: How knowledge and heuristics affect our reconstruction of the past. Memory, 11, 357–377.
Hertwig, R., Hoffrage, U., & the ABC Research Group. (2012). Simple heuristics in a social world. Oxford: Oxford University Press.
Hills, T. T., & Hertwig, R. (2010). Information search in decisions from experience: Do our patterns of sampling foreshadow our decisions? Psychological Science, 21, 1787–1792.
Hoffman, A. B., & Rehder, B. (2010). The costs of supervised classification: The effect of learning task on conceptual flexibility. Journal of Experimental Psychology: General, 139(2), 319–340.
Hoffrage, U., Hertwig, R., & Gigerenzer, G. (2000). Hindsight bias: A by-product of knowledge updating? Journal of Experimental Psychology. Learning, Memory, and Cognition, 26, 566–581.
Juslin, P., & Persson, M. (2002). PROBabilities from EXemplars (PROBEX): a “lazy” algorithm for probabilistic inference from generic knowledge. Cognitive Science, 26, 563–607.
Kahneman, D., & Tversky, A. (1996). On the reality of cognitive illusions. Psychological Review, 103, 582–591.
Katsikopoulos, K. V., Schooler, L. J., & Hertwig, R. (2010). The robust beauty of ordinary information. Psychological Review, 117, 1259–1266.
Lagnado, D. A., & Sloman, S. A. (2006). Time as a guide to cause. Journal of Experimental Psychology. Learning, Memory, and Cognition, 32, 451–460.
Lagnado, D. A., Waldmann, M. R., Hagmayer, Y., & Sloman, S. A. (2007). Beyond covariation: Cues to causal structure. In A. Gopnik & L. E. Schulz (Eds.), Causal learning: Psychology, philosophy, and computation (pp. 154–172). Oxford: Oxford University Press.
Lovie, A., & Lovie, P. (1986). The flat maximum effect and linear scoring models for prediction. Journal of Forecasting, 5, 159–168. doi:10.1002/for.3980050303.
Markman, Art B., & Ross, B. H. (2003). Category use and category learning. Psychological Bulletin, 129, 592–613.
Martignon, L., & Hoffrage, U. (1999). Where and why is Take The Best fast, frugal and fit? In G. Gigerenzer & P. M. Todd (Eds.), Simple heuristics that make us smart (pp. 119–140). Oxford: Oxford University Press.
Martignon, L., & Hoffrage, U. (2002). Fast, frugal, and fit: Simple heuristics for paired comparison. Theory and Decision, 52, 29–71.
Newell, B. R., Rakow, T., Weston, N. J., & Shanks, D. R. (2004). Search strategies in decision making: The success of “success”. Journal of Behavioral Decision Making, 17(2), 117–137.
Newell, B. R., & Shanks, D. R. (2003). Take the best or look at the rest? Factors influencing ‘one-reason’ decision making. Journal of Experimental Psychology. Learning, Memory, and Cognition, 29, 53–65.
Rieskamp, J., & Otto, P. E. (2006). SSL: A theory of how people learn to select strategies. Journal of Experimental Psychology: General, 135, 207–236.
Ross, B. H. (1997). The use of categories affects classification. Journal of Memory and Language, 37, 240–267.
Ross, B. H. (2000). The effects of category use on learned categories. Memory and Cognition, 28(1), 51–63.
Samuels, R., Stich, S., & Bishop, M. (2002). Ending the rationality wars: How to make disputes about human rationality disappear. In R. Elio (Ed.), Common sense, reasoning, and rationality (pp. 236–268). New York: Oxford University Press.
Schooler, L. J., & Anderson, J. R. (1997). The role of process in the rational analysis of memory. Cognitive Psychology, 32, 219–250.
Shi, L., Griffiths, T. L., Feldman, N. H., & Sanborn, A. N. (2010). Exemplar models as a mechanism for performing Bayesian inference. Psychonomic Bulletin & Review, 17(4), 443–464.
Simon, H. A. (1976). From substantive to procedural rationality. In S. J. Latsis (Ed.), Method and appraisal in economics. Cambridge: Cambridge University Press.
Sloman, S. A., & Lagnado, D. A. (2005). Do we “do”? Cognitive Science, 29, 5–39.
Todd, P. M., & Dieckmann, A. (2005). Heuristics for ordering cue search in decision making. In Lawrence K. Saul, Yair Weiss, & Leon Bottou (Eds.), Advances in Neural Information Processing Systems 17 (pp. 1393–1400). Cambridge: MIT Press.
von Winterfeldt, D., & Edwards, W. (1986). Decision analysis and behavioral research. Cambridge: Cambridge University Press.
Acknowledgments
Thanks to participants at the 2013 workshop on Finding Foundations for Bounded and Adaptive Rationality for helpful comments and criticisms, particularly Stefan Herzog, Ralf Mayrhofer, and Peter Todd. Research for this paper was partially supported by a James S. McDonnell Foundation Scholar Award to Danks.
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Wellen, S., Danks, D. Adaptively Rational Learning. Minds & Machines 26, 87–102 (2016). https://doi.org/10.1007/s11023-015-9370-1
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DOI: https://doi.org/10.1007/s11023-015-9370-1