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How causal knowledge simplifies decision-making

Abstract

Making decisions can be hard, but it can also be facilitated. Simple heuristics are fast and frugal but nevertheless fairly accurate decision rules that people can use to compensate for their limitations in computational capacity, time, and knowledge when they make decisions [Gigerenzer, G., Todd, P. M., & the ABC Research Group (1999). Simple Heuristics That Make Us Smart. New York: Oxford University Press.]. These heuristics are effective to the extent that they can exploit the structure of information in the environment in which they operate. Specifically, they require knowledge about the predictive value of probabilistic cues. However, it is often difficult to keep track of all the available cues in the environment and how they relate to any relevant criterion. This problem becomes even more critical if compound cues are considered. We submit that knowledge about the causal structure of the environment helps decision makers focus on a manageable subset of cues, thus effectively reducing the potential computational complexity inherent in even relatively simple decision-making tasks. We review experimental evidence that tested this hypothesis and report the results of a simulation study. We conclude that causal knowledge can act as a meta-cue for identifying highly valid cues, either individual or compound, and helps in the estimation of their validities.

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References

  • Abela, J. R. Z., & Seligman, M. E. P. (2000). The hopelessness theory of depression: A test of the diathesis-stress component in the interpersonal and achievement domains. Cognitive Therapy & Research, 24, 361–378.

    Article  Google Scholar 

  • Ahn, W., & Kalish, C. W. (2000). The role of mechanism beliefs in causal reasoning. In F. C. Keil & R. A. Wilson (Eds.), Explanation and cognition (pp. 199–225). Cambridge, MA: MIT Press.

    Google Scholar 

  • Boyle, P. (1997). Cancer, cigarette smoking and premature death in Europe: A review including the recommendations of European cancer experts consensus meeting, Helsinki, October 1996. Lung Cancer, 17, 1–60.

    Google Scholar 

  • Brehmer, B. (1973). Note on the relation between single-cue probability learning and multiple-cue probability learning. Organizational Behavior and Human Performance, 9, 246–252.

    Article  Google Scholar 

  • 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, & Cognition, 26, 1332–1346.

    Article  Google Scholar 

  • Bröder, A. (2003). Decision making with the “Adaptive Toolbox”: Influence of environmental structure, intelligence, and working memory load. Journal of Experimental Psychology: Learning, Memory, & Cognition, 29, 611–625.

    Article  Google Scholar 

  • Bröder, A., & Schiffer, S. (2003a). Bayesian strategy assessment in multi-attribute decision making. Journal of Behavioral Decision Making, 16, 193–213.

    Article  Google Scholar 

  • Bröder, A., & Schiffer, S. (2003b). Take The Best versus simultaneous feature matching: Probabilistic inferences from memory and effects of representation format. Journal of Experimental Psychology: General, 132, 277–293.

    Article  Google Scholar 

  • Castellan, N. J. (1973). Multiple-cue probability learning with irrelevant cues. Organizational Behavior and Human Performance, 9, 16–29.

    Article  Google Scholar 

  • Cheng, P. W. (1997). From covariation to causation: A causal power theory. Psychological Review, 104, 367–405.

    Article  Google Scholar 

  • Cheng, P. W. (2000). Causality in the mind: Estimating contextual and conjunctive causal power. In F. C. Keil & R. A. Wilson (Eds.), Explanation and cognition (pp. 227–253). Cambridge, MA: MIT Press.

    Google Scholar 

  • Dieckmann, A., & Todd, P. M. (2004). Simple ways to construct search orders. Proceedings of the 26th Annual conference of the cognitive science society (pp. 309-314). Mahwah, NJ: Erlbaum.

  • Edgell, S. E. (1993). Using configural and dimensional information. In N. J. Castellan Jr. (Ed.), Individual and group decision making processes (pp. 43–64). Hillsdale, NJ: Erlbaum

    Google Scholar 

  • Edgell, S. E., & Hennessey, J. E. (1980). Irrelevant information and utilization of event base rates in nonmetric multiple-cue probability learning. Organizational Behavior and Human Performance, 26, 1–6.

    Article  Google Scholar 

  • Garcia-Retamero, R. (in press). The influence of knowledge about causal mechanisms on compound processing. The Psychological Record.

  • Garcia-Retamero, R., Hoffrage, U., & Dieckmann, A. (in press a). When one cue is not enough: Combining fast and frugal heuristics with compound cue processing. The Quarterly Journal of Experimental Psychology.

  • Garcia-Retamero, R., Hoffrage, U., Dieckmann, A., & Ramos, M. (in press b). Compound cue processing within the fast and frugal heuristics approach in nonlinearly separable environments. Learning and Motivation.

  • Garcia-Retamero, R., Takezawa, M., & Gigerenzer, G. (2006). How to learn good cue orders: When social learning benefits simple heuristics. In R. Sun, & N, Miyake (Eds.), Proceedings of the 28th annual conference of the cognitive science society (pp. 1352–1358). Mahwah, New Jersey, USA.

  • Garcia-Retamero, R., Wallin, A., & Dieckmann, A. (2006). Does causal knowledge help us be faster and more frugal in our decisions? Manuscript submitted for publication.

  • Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review, 103, 650–669.

    Article  Google Scholar 

  • Gigerenzer, G., & Goldstein, D. G. (1999). Betting on one good reason: The Take The Best heuristic. In G. Gigerenzer, P. M. Todd, & the ABC Research Group (Eds.), Simple Heuristics That Make Us Smart (pp. 75–95). New York: Oxford University Press.

  • Gigerenzer, G., Todd, P. M., & the ABC Research Group (1999). Simple Heuristics That Make Us Smart. New York: Oxford University Press.

  • Glymour, C. (1998). Learning causes: Psychological explanations of causal explanation. Minds & Machines, 8, 39–60.

    Article  Google Scholar 

  • Glymour, C., & Cheng, P. W. (1999). Causal mechanism and probability: A normative approach. In K. Oaksford & N. Chater (Eds.), Rational models of cognition (pp. 295–313). Oxford: Oxford University Press.

    Google Scholar 

  • Gopnik, A., Glymour, C., Sobel, D. M., Schulz, L. E., Kushnir, T., & Danks, D. (2004). A theory of causal learning in children: Causal maps and bayes nets. Psychological Review, 111, 3–32.

    Article  Google Scholar 

  • Gopnik, A., & Schulz, L. (in press). Causal learning: Psychology, philosophy, and computation. Oxford: Oxford University Press.

  • Harre, R., & Madden, E. H. (1975). Causal powers: A theory of natural necessity. Totowa, NJ: Rowman & Littlefield.

    Google Scholar 

  • Hoffrage, U., Garcia-Retamero, R., & Czienskowski, U. (2005). The robustness of The Take The Best Configural heuristic in linearly and nonlinearly separable environments. In B. G. Bara, L.␣Barsalou, & M. Bucciarelli, (Eds.), Proceedings of the 27th annual conference of the cognitive science society (pp. 971–976). Mahwah, New Jersey: Lawrence Erlbaum Associates.

  • Hume, D. (1987). A treatise of human nature (2nd ed.). Oxford: Clarendon Press (Original work published 1739).

  • Juslin, P., & Persson, M. (2002). PROBabilities from EXemplars (PROBEX): A “lazy” algorithm for probabilistic inference from generic knowledge. Cognitive Science, 26, 563–607.

    Article  Google Scholar 

  • Kant, I. (1965). Critique of pure reason. London: Macmillan (Original work published 1781).

    Google Scholar 

  • Kehoe, E. J., & Graham, P. (1988). Summation and configuration: Stimulus compounding and negative patterning in the rabbit. Journal of Experimental Psychology: Animal Behavior Processes, 14, 320–333.

    Article  Google Scholar 

  • Kimmel, H. D., & Lachnit, H. (1991). Acquisition of a unique cue in positive and negative patterning? Integrative Physiological and Behavioral Science, 26, 32–38.

    Google Scholar 

  • Koslowski, B. (1996). Theory and evidence: The development of scientific reasoning. Cambridge, MA: MIT Press.

    Google Scholar 

  • Koslowski, B., & Masnick, A. (2002). The developmental of causal reasoning. In U. Goswami (Ed.), Blackwell handbook of childhood cognitive development (pp. 257–281). Malden, MA: Blackwell.

    Google Scholar 

  • Kruschke, J. K., & Johansen, M. K. (1999). A model of probabilistic category learning. Journal of Experimental Psychology: Learning, Memory, & Cognition, 25, 1083–1119.

    Article  Google Scholar 

  • Lachnit, H., & Kimmel, H. D. (1993). Positive and negative patterning in human classical skin conductance response conditioning. Animal Learning & Behavior, 21, 314–326.

    Google Scholar 

  • Lagnado, D. A., & Sloman, S. (2004). The advantage of timely intervention. Journal of Experimental Psychology: Learning, Memory, & Cognition, 30, 856–876.

    Article  Google Scholar 

  • Läge, D., Hausmann, D., Christen, S., & Daub, S. (2005). Take The Best: How much do people pay for validity? Manuscript submitted for publication

  • Martignon, L., & Hoffrage, U. (2002). Fast, frugal and fit: Simple heuristics for paired comparison. Theory and Decision, 52, 29–71.

    MATH  Article  Google Scholar 

  • Mackie, J. L. (1974). The cement of the universe: A study of causation. Oxford, England: Clarendon Press.

    Google Scholar 

  • Medin, D. L., & Schwanenflugel, P. J. (1981). Linear separability in classification learning. Journal of Experimental Psychology: Human Learning and Memory, 7, 355–368.

    Article  Google Scholar 

  • 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, 117–137.

    Article  Google Scholar 

  • 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, & Cognition, 29, 53–65.

    Article  Google Scholar 

  • Newell, B. R., Weston, N. J., & Shanks, D. R. (2003). Empirical tests of a fast-and-frugal heuristic: Not everyone “Takes-The-Best”. Organizational Behavior and Human Decision Processes, 91, 82–96.

    Article  Google Scholar 

  • Novick, L. R., & Cheng, P. W. (2004). Assessing interactive causal influence. Psychological Review, 111, 455–485.

    Article  Google Scholar 

  • Pearl, J. (2000). Causality. New York: Oxford University Press.

    MATH  Google Scholar 

  • Reichenbach, H. (1956). The direction of time. Berkeley: University of California Press.

    Google Scholar 

  • Rieskamp, J., & Hoffrage, U. (1999). When do people use simple heuristics, and how can we tell? In G. Gigerenzer, P. M. Todd, & the ABC Research Group (Eds.), Simple Heuristics That Make Us Smart (pp. 141–167). New York: Oxford University Press.

  • Rieskamp, J., & Otto, P. E. (2006). SSL: A theory of how people learn to select strategies. Journal of Experimental Psychology: General, 135, 207–236.

    Article  Google Scholar 

  • Rumelhart, D. R., Hinton, G. E., & Williams, R. J. (1986). Learning internal representations by error propagation. In D. E. Rumelhart, J. L. McClelland, & the PDP Research Group (Eds.), Parallel distributed processing (pp. 318–362), Vol 1. Cambridge, MA: MIT Press.

  • Schlottmann, A. (1999). Seeing it happen and knowing how it works: How children understand the relation between perceptual causality and underlying mechanism. Developmental Psychology, 35, 303–317.

    Article  Google Scholar 

  • Shanks, D. R., Charles, D., Darby, R. J., & Azmi, A. (1998). Configural processes in human associative learning. Journal of Experimental Psychology: Learning, Memory, & Cognition, 24, 1353–1378.

    Article  Google Scholar 

  • Shanks, D. R., & Dickinson, A. (1987). Associative accounts of causality judgment. In G. H. Bower (Ed.), The psychology of learning and motivation: Advances in research and theory (pp. 229–261) Vol. 21. San Diego, CA: Academic Press.

    Google Scholar 

  • Shultz, T. R. (1982). Rules of causal attribution. Monographs of the Society for Research in Child Development, 47, 1–51.

    Article  Google Scholar 

  • Smith, J. D., Murray, M. J., & Minda, J. P. (1997). Straight talk about linear separability. Journal of Experimental Psychology: Learning, Memory, & Cognition, 23, 659–680.

    Article  Google Scholar 

  • Spellman, B. A. (1996). Conditionalizing causality. In D. R. Shanks, K. J. Holyoak, & D. L. Medin (Eds.), The psychology of learning and motivation (pp. 167–206) Vol 34. San Diego: Academic Press.

    Google Scholar 

  • Spirtes, P., Glymour, C., & Scheines, R. (1993). Causation, prediction, and search (Springer lecture notes in statistics). New York: Springer-Verlag.

    Google Scholar 

  • Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, prediction, and search (2nd ed.). Cambridge, MA: MIT Press.

    MATH  Google Scholar 

  • Steyvers, M., Tenenbaum, J. B., Wagenmakers, E. J., & Blum, B. (2003). Inferring causal networks from observations and interventions. Cognitive Science, 27, 453–489.

    Article  Google Scholar 

  • Tenenbaum, J. B., Griffiths, T. L., & Niyogi, S. (in press). Intuitive theories as grammars for causal inference. In A. Gopnik & L. Schulz (Eds.), Causal learning: Psychology, philosophy, and computation. Oxford: Oxford University Press.

  • Todd, P. M., & Gigerenzer, G. (2000). Précis of simple heuristics that make us smart. Behavioral & Brain Sciences, 23, 727–780.

    Article  Google Scholar 

  • Waldmann, M. R. (1996). Knowledge-based causal induction. In D. R. Shanks, K. J. Holyoak, & D. L. Medin (Eds.), The psychology of learning and motivation (pp. 47–88) Vol. 34. San Diego, CA: Academic Press.

    Google Scholar 

  • Waldmann, M. R., & Hagmayer, Y. (2001). Estimating causal strength: The role of structural knowledge and processing effort. Cognition, 82, 27–58.

    Article  Google Scholar 

  • Waldmann, M. R., & Hagmayer, Y. (2005). Seeing versus doing: Two models of accessing causal knowledge. Journal of Experimental Psychology: Learning, Memory, & Cognition, 31, 216–227.

    Article  Google Scholar 

  • Waldmann, M. R., Holyoak, K. J. (1992). Predictive and diagnostic learning within causal models: Asymmetries in cue competition. Journal of Experimental Psychology: General, 121, 222–236.

    Article  Google Scholar 

  • Waldmann, M. R., Holyoak, K. J., & Fratianne, A. (1995). Causal models and the acquisition of category structure. Journal of Experimental Psychology: General, 124, 181–206.

    Article  Google Scholar 

  • Waldmann, M. R., & Martignon, L. (1998). A Bayesian network model of causal learning. In M. A. Gernsbacher, & S. J. Derry (Eds.), Proceedings of the 20th annual conference of the cognitive science society (pp. 1102–1107). Mahwah, NJ: Erlbaum.

    Google Scholar 

  • Walker, E. F., & Diforio, D. (1997). Schizophrenia: A neural diathesis-stress model. Psychological Review, 104, 667–685.

    Article  Google Scholar 

  • Wallin, A., & Gärdenfors, P. (2000). Smart people who make simple heuristics work. Behavioral and Brain Sciences, 23, 765.

    Article  Google Scholar 

  • Wattenmaker, W. D., Dewey, G. I., Murphy, T. D., & Medin, D. M. (1986). Linear separability and concept learning: Context, relational properties, and concept naturalness. Cognitive Psychology, 18, 158–194.

    Article  Google Scholar 

  • White, P. A. (1995). Use of prior beliefs in the assignment of causal roles: Causal powers versus regularity-based accounts. Memory & Cognition, 23, 243–254.

    Google Scholar 

  • Williams, D. A., & Braker, D. S. (1999). Influence of past experience on the coding of compound stimuli. Journal of Experimental Psychology: Animal Behavior Processes, 25, 461–474.

    Article  Google Scholar 

  • Wisniewski, E. J. (1995). Prior knowledge and functionally relevant features in concept learning. Journal of Experimental Psychology: Learning, Memory, & Cognition, 21, 449–468.

    Article  Google Scholar 

  • Zurada, J. M. (1992). Introduction to artificial neural systems. New York: West.

    Google Scholar 

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Acknowledgements

We thank Gerd Gigerenzer and Peter Todd for their helpful discussion of our results. We are deeply indebted to Chris White for his helpful comments on early drafts of the present paper. Finally, we also thank Anita Todd for editing the manuscript.

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Correspondence to Rocio Garcia-Retamero.

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Garcia-Retamero, R., Hoffrage, U. How causal knowledge simplifies decision-making. Minds & Machines 16, 365–380 (2006). https://doi.org/10.1007/s11023-006-9035-1

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Keywords

  • Causal knowledge
  • Compound cue
  • Cue selection
  • Fast and frugal heuristics
  • Search processes
  • Take the Best
  • Take the Best Configural
  • Validity estimation