Abstract
In the previous chapter, we analyzed insurance decisions and found that the nature of the decision rule (i.e., whether it is intuitive or calculative) affects choices. We also found that statistical knowledge has an effect on whether people base their decisions on intuition or calculation. Those who were familiar with statistics (particularly such concepts as expected value, random variable, and expected utility theory) were significantly more calculative (i.e., less intuitive) than were those who did not know any of these concepts.1
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References
Parts of this chapter were published in P. J. H. Schoemaker, “The Role of Statistical Knowledge in Gambling Decisions: Moment vs. Risk Dimension Approaches,” Organizational Behavior and Human Performance 24 (1979): 1–17. Reprinted by permission of Academic Press, Inc.
An interesting discussion of the implications of differences in cognitive style (intuitive versus systematic) for decision making and the use of analytical techniques is provided by Keen and McKenney (1974).
Payne (1975) and Slovic (1967) also found that perceptions as to the riskiness of gambles are more related to risk dimensions than to moments.
Wright (1975) provides some empirical marketing data and a discussion of the type of compromise that consumers must make between “optimizing eventual consumption benefits and reducing the strains of decision making” (p. 62). He notes that surprisingly little attention has been paid to the effect of cognitive strain on decision processes. Indeed, to my knowledge, no studies involving gambles have explicitly examined that effect.
Dimension preference is not independent of a person’s risk-taking attitude, as is shown in the expected utility analysis of Appendix VI.
Although differences generally exist between volunteer and nonvolunteer subjects (Rosenthal and Rosnow, 1974), the implications are minimal for the present experiment.
Slovic, Lichtenstein, and Edwards’s (1965) finding that boredom leads to simpler strategies, and hence to less cognitive strain, supports the assumption of an inverse relationship. It may appear that this assumption is inconsistent with the hypothesis that trained subjects (who supposedly are more bored) use more complex decision rules. The paradox is resolved, however, when distinguishing between cognitive complexity and mathematical complexity. Although multiplicative models are mathematically more complex than additive ones, they may be cognitively easier for trained subjects than additive models are for untrained subjects. Indeed, Table 6.3 suggests that this is the case.
Readers concerned about the use of z-tests when expected cell frequencies are less than five are referred to Roscoe and Byars (1971) or Camilli and Hopkins (1978).
It should be noted that the additive model will generally provide a much better fit (i.e., higher R 2) than the multiplicative model when both are misspecified. To give an example, when the additive model was misfitted to a data set in which the bids were exactly equal to each duplex bet’s EV, the resulting R 2 equalled.78. The thirty-one bets used were those shown in Table 6.1. However, when the EV-variance model was misfitted to an artificial data set computed from an additive model with equal weights, the R 2 was as low as.23. Hence, the additive model appears significantly less sensitive to model misspecification than does the multiplicative one (see also Dawes and Corrigan, 1974).
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© 1980 Springer Science+Business Media Dordrecht
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Schoemaker, P.J.H. (1980). Statistical Knowledge and Gambling Decisions. In: Experiments on Decisions under Risk: The Expected Utility Hypothesis. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-5040-0_6
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DOI: https://doi.org/10.1007/978-94-017-5040-0_6
Publisher Name: Springer, Dordrecht
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