Skip to main content Accessibility help
×
Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-24T17:47:40.657Z Has data issue: false hasContentIssue false

16 - Computational Models of Decision Making

from Part III - Computational Modeling of Basic Cognitive Functionalities

Published online by Cambridge University Press:  21 April 2023

Ron Sun
Affiliation:
Rensselaer Polytechnic Institute, New York
Get access

Summary

This chapter introduces computational models of decision making as worthy successors to the traditional, algebraic utility framework that has dominated the field. It provides an overview of several different computational modeling approaches before providing a detailed example of perhaps the most well-established of these, based on sequential sampling of information and evidence accumulation. It is shown how this approach can account for common paradoxes in decision behavior, and how it can be extended to a variety of tasks and response modes while retaining the same basic cognitive principles. The chapter concludes with an illustration of how process-tracing methods that capture the information acquisition and response processes can help to evaluate computational models of decision making and discriminate among them.

Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2023

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Allais, M. (1953). Le comportement de l’homme rationnel devant le risque: critique des postulats et axiomes de l’école américaine. Econometrica: Journal of the Econometric Society, 21(4), 503546.CrossRefGoogle Scholar
Anderson, J. R. (1996). ACT: a simple theory of complex cognition. American Psychologist, 51, 355365.Google Scholar
Beach, L. R., & Mitchell, T. R. (1978). A contingency model for the selection of decision strategies. Academy of Management Review, 3(3), 439449.Google Scholar
Bergner, A. S., Oppenheimer, D. M., & Detre, G. (2019). VAMP (Voting Agent Model of Preferences): a computational model of individual multi-attribute choice. Cognition, 192, 103971.Google Scholar
Berkowitsch, N. A., Scheibehenne, B., & Rieskamp, J. (2014). Rigorously testing multialternative decision field theory against random utility models. Journal of Experimental Psychology: General, 143(3), 1331.CrossRefGoogle ScholarPubMed
Bhatia, S. (2013). Associations and the accumulation of preference. Psychological Review, 120(3), 522.Google Scholar
Bhatia, S. (2014). Sequential sampling and paradoxes of risky choice. Psychonomic Bulletin & Review, 21(5), 10951111.Google Scholar
Bhatia, S., & Pleskac, T. J. (2019). Preference accumulation as a process model of desirability ratings. Cognitive Psychology, 109, 4767.CrossRefGoogle ScholarPubMed
Birnbaum, M. H. (2008). New paradoxes of risky decision making. Psychological Review, 115(2), 463.CrossRefGoogle ScholarPubMed
Birnbaum, M. H., & Stegner, S. E. (1979). Source credibility in social judgment: bias, expertise, and the judge’s point of view. Journal of Personality and Social Psychology, 37, 4874.CrossRefGoogle Scholar
Bostic, R., Herrnstein, R. J., & Luce, R. D. (1990). The effect on the preference-reversal phenomenon of using choice indifferences. Journal of Economic Behavior & Organization, 13(2), 193212.Google Scholar
Busemeyer, J. R., & Diederich, A. (2002). Survey of decision field theory. Mathematical Social Sciences, 43(3), 345370.CrossRefGoogle Scholar
Busemeyer, J. R., Gluth, S., Rieskamp, J., & Turner, B. M. (2019). Cognitive and neural bases of multi-attribute, multi-alternative, value-based decisions. Trends in Cognitive Sciences, 23(3), 251263.CrossRefGoogle ScholarPubMed
Busemeyer, J. R., & Johnson, J. G. (2008). Micro-process models of decision making. In R. Sun (Ed.), Cambridge Handbook of Computational Psychology, (pp. 302–321).Google Scholar
Busemeyer, J. R., & Townsend, J. T. (1992). Fundamental derivations from decision field theory. Mathematical Social Sciences, 23(3) (pp. 302–321).CrossRefGoogle Scholar
Busemeyer, J. R., & Townsend, J. T. (1993). Decision field theory: a dynamic-cognitive approach to decision making in an uncertain environment. Psychological Review, 100(3), 432.Google Scholar
Busemeyer, J. R., Wang, Z., & Townsend, J. T. (2006). Quantum dynamics of human decision-making. Journal of Mathematical Psychology, 50(3), 220241.Google Scholar
Cheng, J., & González‐Vallejo, C. (2017). Action dynamics in intertemporal choice reveal different facets of decision process. Journal of Behavioral Decision Making, 30(1), 107122.CrossRefGoogle Scholar
Colas, J. T. (2017). Value-based decision making via sequential sampling with hierarchical competition and attentional modulation. PloS One, 12(10), e0186822.Google Scholar
Diederich, A. (1997). Dynamic stochastic models for decision making under time constraints. Journal of Mathematical Psychology, 41(3), 260274.Google Scholar
Diederich, A., & Busemeyer, J. R. (1999). Conflict and the stochastic-dominance principle of decision making. Psychological Science, 10(4), 353359.CrossRefGoogle Scholar
Diederich, A., & Busemeyer, J. R. (2003). Simple matrix methods for analyzing diffusion models of choice probability, choice response time, and simple response time. Journal of Mathematical Psychology, 47(3), 304322.Google Scholar
Diederich, A., & Trueblood, J. S. (2018). A dynamic dual process model of risky decision making. Psychological Review, 125(2), 270.Google Scholar
Ellsberg, D. (1961). Risk, ambiguity, and the Savage axioms. The Quarterly Journal of Economics, 75(4), 643669.Google Scholar
Fiedler, S., & Glöckner, A. (2012). The dynamics of decision making in risky choice: an eye-tracking analysis. Frontiers in Psychology, 3, 335.CrossRefGoogle ScholarPubMed
Fifić, M., Houpt, J. W., & Rieskamp, J. (2019). Response times as identification tools for cognitive processes underlying decisions. In M. Schulte-Mecklenbeck, A. Kuehberger, & J. G. Johnson (Eds.), A Handbook of Process Tracing Methods for Decision Research (p. 184). New York, NY: Psychology Press.Google Scholar
Frame, M. E. (2019). EEG and ERPs as neural process tracing methodologies in decision-making research. In M. Schulte-Mecklenbeck, A. Kuehberger, & J. G. Johnson (Eds.), A Handbook of Process Tracing Methods (pp. 217233). London: Routledge.Google Scholar
Frame, M. E., Johnson, J. G., & Thomas, R. D. (2018). A neural indicator of response competition in preferential choice. Decision, 5(4), 272.CrossRefGoogle Scholar
Gao, J., & Lee, J. D. (2006). Extending the decision field theory to model operators’ reliance on automation in supervisory control situations. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 36(5), 943959.Google Scholar
Gigerenzer, G., & Gaissmaier, W. (2011). Heuristic decision making. Annual Review of Psychology, 62, 451482.Google Scholar
Glöckner, A., & Betsch, T. (2008). Multiple-reason decision making based on automatic processing. Journal of Experimental Psychology: Learning, Memory, and Cognition, 34(5), 1055.Google Scholar
Glöckner, A., Heinen, T., Johnson, J. G., & Raab, M. (2012). Network approaches for expert decisions in sports. Human Movement Science, 31(2), 318333.Google Scholar
Glöckner, A., Hilbig, B. E., & Jekel, M. (2014). What is adaptive about adaptive decision making? A parallel constraint satisfaction account. Cognition, 133(3), 641666.Google Scholar
Gold, J. I., & Shadlen, M. N. (2007). The neural basis of decision making. Annual Review of Neuroscience, 30, 535574.Google Scholar
Gonzalez, R., & Wu, G. (1999). On the shape of the probability weighting function. Cognitive Psychology, 38(1), 129166.Google Scholar
Grossberg, S., & Gutowski, W. E. (1987). Neural dynamics of decision making under risk: affective balance and cognitive-emotional interactions. Psychological Review, 94(3), 300.CrossRefGoogle ScholarPubMed
Hotaling, J. M., Busemeyer, J. R., & Li, J. (2010). Theoretical developments in decision field theory: comment on Tsetsos, Usher, and Chater (2010). Psychological Review, 117(4), 12941298.Google Scholar
Huber, J., Payne, J. W., & Puto, C. (1982). Adding asymmetrically dominated alternatives: violations of regularity and the similarity hypothesis. Journal of Consumer Research, 9(1), 9098.CrossRefGoogle Scholar
Johnson, J. G. (2006). Cognitive modeling of decision making in sports. Psychology of Sport and Exercise, 7(6), 631652.Google Scholar
Johnson, J. G., & Busemeyer, J. R. (2005). A dynamic, stochastic, computational model of preference reversal phenomena. Psychological Review, 112(4), 841.CrossRefGoogle ScholarPubMed
Johnson, J. G., & Busemeyer, J. R. (2016). A computational model of the attention process in risky choice. Decision, 3(4), 254.Google Scholar
Johnson, J. G., & Frame, M. E. (2019). Using process tracing data to define and test process models. In Schulte-Mecklenbeck, M., Kuhberger, A., & Johnson, J. G. (Eds.), A Handbook of Process Tracing Methods (2nd ed.) (pp. 374387). New York, NY: Routledge.Google Scholar
Kahneman, D., & Tversky, A. (1979). Prospect theory: an analysis of decision under risk, Econometrica, 47, 263291.Google Scholar
Kahneman, D., & Tversky, A. (2013). Prospect theory: an analysis of decision under risk. In L. C. MacLean & W. T. Ziemba (Eds.), Handbook of the Fundamentals of Financial Decision Making: Part I (pp. 99127).Google Scholar
Keeney, R. L., & Raiffa, H. (1993). Decisions with Multiple Objectives: Preferences and Value Trade-offs. Cambridge: Cambridge University Press.Google Scholar
Kieslich, P. J., Henninger, F., Wulff, D. U., Haslbeck, J. M., & Schulte-Mecklenbeck, M. (2019). Mouse tracking: a practical guide to implementation and analysis. In Schulte-Mecklenbeck, M., Kuhberger, A., & Johnson, J. G. (Eds.), A Handbook of Process Tracing Methods (2nd ed.) (pp. 111130). New York, NY: Routledge.CrossRefGoogle Scholar
Koop, G. J., & Johnson, J. G. (2013). The response dynamics of preferential choice. Cognitive Psychology, 67(4), 151185.CrossRefGoogle ScholarPubMed
Krajbich, I., Armel, C., & Rangel, A. (2010). Visual fixations and the computation and comparison of value in simple choice. Nature Neuroscience, 13(10), 1292.Google Scholar
Krajbich, I., Lu, D., Camerer, C., & Rangel, A. (2012). The attentional drift-diffusion model extends to simple purchasing decisions. Frontiers in Psychology, 3, 193.Google Scholar
Krajbich, I., & Rangel, A. (2011). Multialternative drift-diffusion model predicts the relationship between visual fixations and choice in value-based decisions. Proceedings of the National Academy of Sciences, 108(33), 1385213857.Google Scholar
Kvam, P. D., & Busemeyer, J. R. (2020). A distributional and dynamic theory of pricing and preference. Psychological Review, 127(6), 1053. https://doi.org/0.1037/rev0000215CrossRefGoogle ScholarPubMed
Laird, J. E. (2012). The Soar Cognitive Architecture. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Lee, S., & Son, Y. J. (2020). Extended decision field theory with social-learning for long-term decision-making processes in social networks. Information Sciences, 512, 12931307. https://doi.org/10.1016/j.ins.2019.10.025Google Scholar
Lejarraga, T., Dutt, V., & Gonzalez, C. (2012). Instance‐based learning: a general model of repeated binary choice. Journal of Behavioral Decision Making, 25(2), 143153.CrossRefGoogle Scholar
Lichtenstein, S., & Slovic, P. (1971). Reversals of preference between bids and choices in gambling decisions. Journal of Experimental Psychology, 89(1), 46.Google Scholar
Lieder, F., & Griffiths, T. L. (2017). Strategy selection as rational metareasoning. Psychological Review, 124(6), 762.Google Scholar
Lindman, H. R. (1971). Inconsistent preferences among gambles. Journal of Experimental Psychology, 89(2), 390.Google Scholar
Link, S. W., & Heath, R. A. (1975). A sequential theory of psychological discrimination. Psychometrika, 40(1), 77105.Google Scholar
Marewski, J. N., & Mehlhorn, K. (2011). Using the ACT-R architecture to specify 39 quantitative process models of decision making. Judgment and Decision Making, 6(6), 439519.Google Scholar
Marley, A. A. J., & Colonius, H. (1992). The “horse race” random utility model for choice probabilities and reaction times, and its competing risks interpretation. Journal of Mathematical Psychology, 36, 120.CrossRefGoogle Scholar
Noguchi, T., & Stewart, N. (2018). Multialternative decision by sampling: a model of decision making constrained by process data. Psychological Review, 125(4), 512.CrossRefGoogle Scholar
Nosofsky, R. M., & Palmeri, T. J. (1997). An exemplar-based random walk model of speeded classification. Psychological Review, 104(2), 266.Google Scholar
Nunez, M. D., Vandekerckhove, J., & Srinivasan, R. (2017). How attention influences perceptual decision making: single-trial EEG correlates of drift-diffusion model parameters. Journal of Mathematical Psychology, 76, 117130.Google Scholar
Oppenheimer, D. M., & Kelso, E. (2015). Information processing as a paradigm for decision making. Annual Review of Psychology, 66, 277294.CrossRefGoogle ScholarPubMed
Otter, T., Allenby, G. M., & Van Zandt, T. (2008). An integrated model of discrete choice and response time. Journal of Marketing Research, 45(5), 593607.Google Scholar
Payne, J. W. (1976). Task complexity and contingent processing in decision making: an information search and protocol analysis. Organizational Behavior and Human Performance, 16(2), 366387.Google Scholar
Payne, J. W., Bettman, J. R., & Johnson, E. J. (1988). Adaptive strategy selection in decision making. Journal of experimental psychology: Learning, Memory, and Cognition, 14(3), 534.Google Scholar
Payne, J. W., & Braunstein, M. L. (1978). Risky choice: an examination of information acquisition behavior. Memory & Cognition, 6(5), 554561.Google Scholar
Payne, J. W., Payne, J. W., Bettman, J. R., & Johnson, E. J. (1993). The Adaptive Decision Maker. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Pleskac, T. J., & Busemeyer, J. R. (2010). Two-stage dynamic signal detection: a theory of choice, decision time, and confidence. Psychological Review, 117(3), 864.CrossRefGoogle ScholarPubMed
Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85(2), 59108.CrossRefGoogle Scholar
Ratcliff, R., Smith, P. L., Brown, S. D., & McKoon, G. (2016). Diffusion decision model: current issues and history. Trends in Cognitive Sciences, 20(4), 260281.CrossRefGoogle ScholarPubMed
Rieskamp, J., Busemeyer, J. R., & Mellers, B. A. (2006). Extending the bounds of rationality: evidence and theories of preferential choice. Journal of Economic Literature, 44(3), 631661.Google Scholar
Rieskamp, J., & Otto, P. E. (2006). SSL: a theory of how people learn to select strategies. Journal of Experimental Psychology: General, 135(2), 207.Google Scholar
Roe, R. M., Busemeyer, J. R., & Townsend, J. T. (2001). Multialternative decision field theory: a dynamic connectionst model of decision making. Psychological Review, 108(2), 370.CrossRefGoogle ScholarPubMed
Rottenstreich, Y., & Hsee, C. K. (2001). Money, kisses, and electric shocks: on the affective psychology of risk. Psychological Science, 12(3), 185190.Google Scholar
Schulte-Mecklenbeck, M., Johnson, J. G., Böckenholt, U., et al. (2017). Process-tracing methods in decision making: on growing up in the 70s. Current Directions in Psychological Science, 26(5), 442450.Google Scholar
Shah, A. K., & Oppenheimer, D. M. (2007). Easy does it: the role of fluency in cue weighting. Judgment and Decision Making, 2(6), 371379.Google Scholar
Simonson, I. (1989). Choice based on reasons: the case of attraction and compromise effects. Journal of Consumer Research, 16(2), 158174.Google Scholar
Smith, P. L., & Ratcliff, R. (2004). Psychology and neurobiology of simple decisions. Trends in Neurosciences, 27(3), 161168.Google Scholar
Stewart, N., & Simpson, K. (2008). A decision-by-sampling account of decision under risk. In N. Chater & M. Oaksford (Eds.), The Probabilistic Mind. Prospects for Bayesian Cognitive Science (pp. 261276). Oxford: Oxford University Press.Google Scholar
Stewart, N., Hermens, F., & Matthews, W. J. (2016). Eye movements in risky choice. Journal of Behavioral Decision Making, 29(2–3), 116136.Google Scholar
Sun, R. (2016). Anatomy of the Mind: Exploring Psychological Mechanisms and Processes with the Clarion Cognitive Architecture. Oxford: Oxford University Press.Google Scholar
Thorngate, W. (1980). Efficient decision heuristics. Behavioral Science, 25(3), 219225.Google Scholar
Townsend, J. T., & Ashby, F. G. (1983). Stochastic modeling of elementary psychological processes. Cambridge University Press Archive.Google Scholar
Townsend, J. T., & Busemeyer, J. R. (1989) Approach-avoidance: return to dynamic decision behavior. In Izawa, C., (Ed.), Current Issues in Cognitive Processes: The Tulane Flowerree Symposium on Cognition. Hillsdale, NJ: Erlbaum.Google Scholar
Trueblood, J. S., Brown, S. D., & Heathcote, A. (2014). The multiattribute linear ballistic accumulator model of context effects in multialternative choice. Psychological Review, 121(2), 179.Google Scholar
Tsetsos, K., Usher, M., & Chater, N. (2010). Preference reversal in multiattribute choice. Psychological Review, 117(4), 1275.Google Scholar
Turner, B. M., Schley, D. R., Muller, C., & Tsetsos, K. (2018). Competing theories of multialternative, multiattribute preferential choice. Psychological Review, 125(3), 329.Google Scholar
Turner, B. M., van Maanen, L., & Forstmann, B. U. (2015). Informing cognitive abstractions through neuroimaging: the neural drift diffusion model. Psychological Review, 122(2), 312.Google Scholar
Tversky, A. (1972). Elimination by aspects: a theory of choice. Psychological Review, 79(4), 281.Google Scholar
Tversky, A. (1977). Features of similarity. Psychological Review, 84(4), 327.CrossRefGoogle Scholar
Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297323.Google Scholar
Tversky, A., & Simonson, I. (1993). Context-dependent preferences. Management Science, 39(10), 11791189.Google Scholar
Usher, M., & McClelland, J. L. (2001). The time course of perceptual choice: the leaky, competing accumulator model. Psychological Review, 108(3), 550.Google Scholar
Usher, M., & McClelland, J. L. (2004). Loss aversion and inhibition in dynamical models of multialternative choice. Psychological Review, 111(3), 757.Google Scholar
van Vugt, M. K., Simen, P., Nystrom, L. E., Holmes, P., & Cohen, J. D. (2012). EEG oscillations reveal neural correlates of evidence accumulation. Frontiers in Neuroscience, 6, 106.Google Scholar
van Vugt, M. K., Simen, P., Nystrom, L., Holmes, P., & Cohen, J. D. (2014). Lateralized readiness potentials reveal properties of a neural mechanism for implementing a decision threshold. PloS One, 9(3), e90943.Google Scholar
von Neumann, J., & Morgenstern, O. (1944). Theory of Games and Economic Behavior. Princeton, NJ: Princeton University Press.Google Scholar
Wallsten, T. S., & Barton, C. (1982). Processing probabilistic multidimensional information for decisions. Journal of Experimental Psychology: Learning, Memory, and Cognition, 8(5), 361.Google Scholar
Weber, E., & Kirsner, B. (1997). Reasons for rank-dependent utility evaluation. Journal of Risk and Uncertainty, 14(1), 4161.Google Scholar
Wedell, D. H. (2015). Multialternative choice models. The Wiley Blackwell Handbook of Judgment and Decision Making, 2, 117140.Google Scholar
Wollschläger, L. M., & Diederich, A. (2019). Similarity, attraction, and compromise effects: original findings, recent empirical observations, and computational cognitive process models. American Journal of Psychology (online). https://doi.org/10.5406/amerjpsyc.133.1.0001Google Scholar
Yechiam, E., Busemeyer, J. R., Stout, J. C., & Bechara, A. (2005). Using cognitive models to map relations between neuropsychological disorders and human decision-making deficits. Psychological Science, 16(12), 973978.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×