Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-cd9895bd7-dzt6s Total loading time: 0 Render date: 2024-12-28T19:29:30.517Z Has data issue: false hasContentIssue false

Chapter 15 - The Information Cost–Benefit Trade-Off as a Sampling Problem in Information Search

from Part IV - Truncation and Stopping Rules

Published online by Cambridge University Press:  01 June 2023

By
Linda McCaughey ,
Johannes Prager  and
Klaus Fiedler
Edited by
Klaus Fiedler ,
Peter Juslin  and
Jerker Denrell
Klaus Fiedler
Affiliation:
Universität Heidelberg
Peter Juslin
Affiliation:
Uppsala Universitet, Sweden
Jerker Denrell
Affiliation:
University of Warwick
Get access

Summary

Information amount is a crucial determinant of decision outcomes. But how much information one should collect before arriving at a decision depends on a cost–benefit trade-off: Is the expected benefit of increased decision accuracy that can be gained from additional information higher than the additional information costs? To investigate this trade-off with temporal costs for information, we developed a speed–accuracy trade-off paradigm with sample-based decisions, in which the total payoff was the product of the average payoff per decision and the number of decisions completed in a restricted period. Increasing n served to increase the accuracy of choices, but also to decrease the number of completed choices. Yet, whereas the number of completed choices decreases linearly with increasing n, accuracy increases in a clearly sublinear fashion. As a consequence, the sample-based choice task calls for more weight given to speed than to accuracy. However, overly conservative sampling strategies prevented almost all participants from exploiting the speed advantage despite various guiding interventions. Even when the task was enriched by the social aspect of a teammate or rival, who demonstrated the optimal trade-off, participants remained too focussed on accuracy. We also investigate the cost–benefit trade-off with financial information costs, for which participants’ performance was less biased. We propose this to be related to how evaluable the information’s costs were relative to its benefits. Issues of adaptivity in contrast with optimality are addressed in a final discussion.

Keywords

information searchinformation costopportunity costspeed–accuracy trade-offcost–benefit trade-offoptimalityadaptivityevaluabilitymetacognitionoptional stopping
Type
Chapter
Information
Sampling in Judgment and Decision Making , pp. 334 - 356
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

Balci, F., Simen, P., & Niyogi, R., et al. (2011). Acquisition of decision making criteria: Reward rate ultimately beats accuracy. Attention, Perception, and Psychophysics, 73(2), 640–657. https://doi.org/10.3758/s13414-010-0049-7Google Scholar
Connolly, T., & Serre, P. (1984). Information search in judgment tasks: The effects of unequal cue validity and cost. Organizational Behavior and Human Performance, 34(3), 387–401. https://doi.org/10.1016/0030-5073(84)90045-XCrossRefGoogle Scholar
Denrell, J. & Le Mens, G. (2023) The hot stove effect. In Fiedler, Klaus, Juslin, Peter, & Denrell, Jerker (Eds.), Sampling in judgment and decision making (pp. 90–111). Cambridge: Cambridge University Press.Google Scholar
Denrell, J., & March, J. G. (2001). Adaptation as information restriction: The hot stove effect. Organization Science, 12(5), 523–538. https://doi.org/10.1287/orsc.12.5.523.10092Google Scholar
Dhami, M. K., Hertwig, R., & Hoffrage, U. (2004). The role of representative design in an ecological approach to cognition. Psychological Bulletin, 130(6), 959–988. https://doi.org/10.1037/0033-2909.130.6.959Google Scholar
Edwards, W. (1965). Optimal strategies for seeking information: Models for statistics, choice reaction-times, and human information-processing. Journal of Mathematical Psychology, 2(2), 312. https://doi.org/10.1016/0022-2496(65)90007-6Google Scholar
Evans, N. J., Bennett, A. J., & Brown, S. D. (2019). Optimal or not: Depends on the task. Psychonomic Bulletin and Review, 26(3), 1027–1034. https://doi.org/10.3758/s13423-018-1536-4Google Scholar
Evans, N. J., & Brown, S. D. (2017). People adopt optimal policies in simple decision-making, after practice and guidance. Psychonomic Bulletin and Review, 24(2), 597–606. https://doi.org/10.3758/s13423-016-1135-1Google Scholar
Fiedler, K., McCaughey, L., Prager, J., Eichberger, J., & Schnell, K. (2021). Speed–accuracy trade-offs in sample-based decisions. Journal of Experimental Psychology: General, 150(6), 1203–1224, https://doi.org/10.1037/xge0000986Google Scholar
Fiedler, K., Prager, J., & McCaughey, L (in press). Metacognitive Myopia: A Major Obstacle on the Way to Rationality. Current Directions in Psychological Science, 311–333.Google Scholar
Fischhoff, B., Slovic, P., & Lichtenstein, S. (1979). Subjective sensitivity analysis. Organizational Behavior and Human Performance, 23(3), 339–359. https://doi.org/10.1016/0030-5073(79)90002-3Google Scholar
Fried, L. S., & Peterson, C. R. (1969). Information seeking: Optional versus fixed stopping. Journal of Experimental Psychology, 80(3), 525–529. https://doi.org/10.1037/h0027484Google Scholar
Gigerenzer, G. (2004). Striking a blow for sanity in theories of rationality. In Augier, M. & March, J. G. (Eds.), Models of a man: Essays in memory of Herbert A. Simon (pp. 389–409). Cambridge, MA: MITGoogle Scholar
Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review, 103, 650–669.Google Scholar
Goldstein, D. G., & Gigerenzer, G. (2002). Models of ecological rationality: The recognition heuristic. Psychological Review, 109(1), 75.Google Scholar
Harris, C. A., & Custers, R. (2023). Biased preferences through exploitation. In Fiedler, Klaus, Juslin, Peter, & Denrell, Jerker (Eds.), Sampling in judgment and decision making (pp. 00–00). Cambridge: Cambridge University Press.Google Scholar
Hausfeld, J., & Resnjanskij, S. (2018) Risky decisions and the opportunity costs of time. Ifo Working Paper No. 269. Munich: Ifo Institute.Google Scholar
Hershman, R. L., & Levine, J. R. (1970). Deviations from optimum information-purchase strategies in human decision-making. Organizational Behavior and Human Performance, 5(4), 313–329. https://doi.org/10.1016/0030-5073(70)90023-1CrossRefGoogle Scholar
Hsee, C. K., & Zhang, J. (2010). General evaluability theory. Perspectives on Psychological Science, 5(4), 343–355. https://doi.org/10.1177/1745691610374586Google Scholar
Jarvstad, A., Rushton, S. K., Warren, P. A., & Hahn, U. (2012). Knowing when to move on: Cognitive and perceptual decisions in time. Psychological Science, 23(6), 589–597. https://doi.org/10.1177/0956797611426579Google Scholar
Larrick, R. P., & Soll, J. B. (2008). The MPG illusion. Science, 320, 1593–1594. http://dx.doi.org/10.1126/science.1154983Google Scholar
McCaughey, L., Prager, J., & Fiedler, K (2022). Rivals reloaded: Adapting tosample-based speed–accuracy trade-offs through competitive pressure. Manuscript submitted for publication.Google Scholar
McCaughey, L., Prager, J., & Fiedler, K. (2022). Adapting to information search costs in sample-based decisions. Manuscript in preparation.Google Scholar
Madan, C. R., Spetch, M. L., & Ludvig, E. A. (2015). Rapid makes risky: Time pressure increases risk seeking in decisions from experience. Journal of Cognitive Psychology, 5911(June), 1–8. https://doi.org/10.1080/20445911.2015.1055274Google Scholar
Navarro, D. J., Newell, B. R., & Schulze, C. (2016). Learning and choosing in an uncertain world: An investigation of the explore–exploit dilemma in static and dynamic environments. Cognitive Psychology, 85, 43–77. https://doi.org/10.1016/j.cogpsych.2016.01.001Google Scholar
Payne, J. W., Bettman, J. R., & Luce, M. F. (1996). When time is money: Decision behavior under opportunity–cost time pressure. Organizational Behavior and Human Decision Processes, 66(2), 131–152. https://doi.org/10.1006/obhd.1996.0044Google Scholar
Phillips, N. D., Hertwig, R., Kareev, Y., & Avrahami, J. (2014). Rivals in the dark: How competition influences search in decisions under uncertainty. Cognition, 133(1), 104–119. https://doi.org/10.1016/j.cognition.2014.06.006CrossRefGoogle ScholarPubMed
Pitz, G. F. (1968). Information seeking when available information is limited. Journal of Experimental Psychology, 76(1), 25–34. https://doi.org/10.1037/h0025302CrossRefGoogle Scholar
Pitz, G. F., Reinhold, H., & Scott Geller, E. (1969). Strategies of information seeking in deferred decision making. Organizational Behavior and Human Performance, 4(1), 1–19. https://doi.org/10.1016/0030-5073(69)90028-2Google Scholar
Rieskamp, J., & Hoffrage, U. (2008). Inferences under time pressure: How opportunity costs affect strategy selection. Acta Psychologica, 127(2), 258–276. https://doi.org/10.1016/j.actpsy.2007.05.004Google Scholar
Sedlmeier, P., Hertwig, R., & Gigerenzer, G. (1998). Are judgment of the positional frequencies of letters systematically biased due to availability? Journal of Experimental Psychology: Learning Memory, and Cognition, 24(3), 754–770. https://doi.org/10.1037/0278-7393.24.3.754Google Scholar
Slovic, P., & Lichtenstein, S. (1971). Comparison of Bayesian and regression approaches to the study of information processing in judgment. Organizational Behavior and Human Performance, 6(6), 649–744. https://doi.org/10.1016/0030-5073(71)90033-XCrossRefGoogle Scholar
Snapper, K. J., & Peterson, C. R. (1971). Information seeking and data diagnosticity. Journal of Experimental Psychology, 87(3), 429–433. https://doi.org/10.1037/h0030557CrossRefGoogle Scholar
Svenson, O., & Eriksson, G. (2017). Mental models of driving and speed: Biases, choices and reality. Transport Reviews, 37, 653–666. http://dx.doi.org/10.1080/01441647.2017.1289278Google Scholar
Todd, P. M., & Gigerenzer, G. (2007). Environments that make us smart: Ecological rationality. Current Directions in Psychological Science, 16(3), 167–171. https://doi.org/10.1111/j.1467-8721.2007.00497.xGoogle 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
×