The Psychology of Expertise: Cognitive Research and Empirical AI

By Robert R. Hoffman | Go to book overview

is intended as a tonic for those familiar with the decision theory literature because there it has often been reported that adults, including Stanford and Ann Arbor college students, are miserable at multicausal reasoning, even with simple additive models (e.g., Brehmer, 1980; Nisbett & Ross, 1980). In fact, in his review of the past 30 years of decision theory research, Brehmer offered the following dismal conclusion:

These results, taken as a whole, strongly suggest that people do not have the cognitive schemata needed for efficient performance in probabalistic tasks . . . even if they are given massive amounts of practice. (p. 233)

It may be that many more individuals than has been thought possess the capacity to engage in expert decision making, because the studies that led to the dismal findings were not motivationally enriched. Needlessly ungenerous estimations of individuals' cognitive complexity may inevitably follow from studies that do not supply adequate incentives for sufficient facts to be acquired, integrated, and differentiated.


Notes
1.
Expertise was based on precision at predicting post-time odds rather than on more "intuitive" measures such as the number of race winners picked or the amount of money won. This was done for two reasons. First, it is not possible to gather reliable data on the actual amount of money won or lost. Individuals may over- or underestimate their winnings for a variety of reasons. For example, they may fear that the interviewer will report them to the IRS, as all payoffs that exceed 300 to 1 are supposed to be cashed at the special IRS window; they may not wish word to circulate that they had won a large payoff, for fear that others will want to borrow from them; or they may wish to impress the interviewer with their prowess. So, for these and other reasons, the use of "earnings" as a criterion was not feasible. Concerning the use of "number of winners" as a criterion of expertise, there are other problems having to do with the nature of pari-mutuel wagering. In brief, short of correctly picking winners in over 95% of races (a feat that no one has yet come even close to!), the sheer number of winners that one picks is unimportant. What really matters in pari-mutuel wagering is to avoid overvalued horses and select undervalued ones, that is, ones that the betting public has not bet commensurate with their "true" odds. Even if one were to pick 50% winners (not difficult to do, as the favorite alone wins 38% of races at the tracks studied), one could still lose money. Yet, one could actually win money if one picked only 5% winners, provided they were sufficiently undervalued by the public. Although there are many different models of probability that can be applied to racing data, we may take a simple model of independence between two horses in the same race to illustrate why accurately assessing true odds is what racing is all about. Suppose that the number-one horse is the heavy favorite, say 1 to 5 (20¢ on a dollar, or $2.40 return on a $2.00 wager). But suppose the expert assesses this horse's chances of winning to be more like 5 to 2 ($7.00 return for a $2.00 wager). Now suppose that another horse in that race, the number-eight horse, is being bet at 10 to 1 ($22.00 return on a $2.00 wager), even though the expert assesses this horse's "true" odds closer to 4 to 1 ($10.00 return on a $2.00 wager). According to some models, the number-one horse will win 10 out of every 17 direct matchups with the number-eight horse (i.e., at 5:2 true odds, he will win 10 out of 35 races, whereas the number-eight horse, with true odds of 4:1, will win only 7 out of 35). Yet, at 1:5 post time odds, the number-one horse is a poor wager, even though in any given race he has a better chance to win than the number-eight horse (10 to 7). A gambler will definitely lose money betting horses like the number-one horse (e.g., in the example given here, one would lose $46 every 35 matchups, assuming a constant $2.00 wager at 1:5 posttime odds with a horse whose "true" odds were 5:2). Yet, the individual who accurately assessed the mismatch between the number-eight horse's posttime odds and his "true" odds, could win a lot of money even though he selected fewer winners than his counterpart. Thus, the measure of expertise that was used was odds estimation, not winners or earnings.
2.
Separate regression analyses were run for each subject in a linear model in which vectors represented the characteristics of each stock on the variables as well as on a composite seven-way interactive term. This latter term was constructed to produce P/E ratios in a manner similar to the way race horses' post-time odds were derived. The dependent variable in these analyses was the

-228-

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