Success and failure in expert reasoning

Johnson, Paul E.; Grazioli, Stefano; Jamal, Karim; Zualkernan, Imran A.

doi:10.1016/0749-5978(92)90061-b

Cited by 45 publications

(32 citation statements)

References 17 publications

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“…Differences in scoring were resolved by discussion between the two coders. The results of the reliability analysis were within the range commonly found in the field (Ericsson & Simon, 1993), and similar to what has been obtained in investigations of problem solving in auditing and other professional domains (e.g., Johnson, Duran, Hassebrock, Moller, Prietula, Feltovich & Swanson, 1981;Johnson, Grazioli, Jamal & Zualkernan, 1992).…”

Section: Notessupporting

confidence: 65%

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Detecting deception: adversarial problem solving in a low base-rate world

et al. 2001

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The work presented here investigates the process by which one group of individuals solves the problem of detecting deceptions created by other agents. A field experiment was conducted in which twenty-four auditors (partners in international public accounting firms) were asked to review four cases describing real companies that, unknown to the auditors, had perpetrated financial frauds. While many of the auditors failed to detect the manipulations in the cases, a small number of auditors were consistently successful. Since the detection of frauds occurs infrequently in the work of a given auditor, we explain success by the application of powerful heuristics gained from experience with deceptions in everyday life. These heuristics implement a variation of Dennett's intentional stance strategy, which is based on interpreting detected inconsistencies in the light of the Deceiver's (i.e., management's) goals and possible actions. We explain failure to detect deception by means of perturbations (bugs) in the domain knowledge of accounting needed to apply these heuristics to the specific context of financial statement fraud. We test our theory by showing that a computational model of fraud detection that employs the proposed heuristics successfully detects frauds in the cases given to the auditors. We then modify the model by introducing perturbations based on the errors made by each of the auditors in the four cases. The resulting models account for 84 of the 96 observations (i.e., 24 auditors x four cases) in our data.

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Section: Notessupporting

confidence: 65%

“…An error analysis (Johnson, Grazioli, Jamal & Zualkernan, 1992) was performed based on the method for fraud detection shown in Fig. 1.…”

Section: Performance Model Of Fraud Detectionmentioning

confidence: 99%

Detecting deception: adversarial problem solving in a low base-rate world

et al. 2001

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“…It has been estimated that human error accounts for approximately 90% of industrial and system failures [25]. However, there is a lack of an extensive empirical body of knowledge detailing how or why errors occur, as well as the absence of an adequate body of theoretical knowledge to explain error occurrence [25]. The best way to interpret errors, therefore, may be as "failures of reasoning" [25].…”

Section: Introductionmentioning

confidence: 99%

“…Individuals with higher levels of deductive logic ability may adopt a more analytical approach to problem solving and engage in more systematic processing of information. It has been estimated that human error accounts for approximately 90% of industrial and system failures [25]. However, there is a lack of an extensive empirical body of knowledge detailing how or why errors occur, as well as the absence of an adequate body of theoretical knowledge to explain error occurrence [25].…”

Section: Introductionmentioning

confidence: 99%

“…However, there is a lack of an extensive empirical body of knowledge detailing how or why errors occur, as well as the absence of an adequate body of theoretical knowledge to explain error occurrence [25]. The best way to interpret errors, therefore, may be as "failures of reasoning" [25]. The ability to reason, that is, to form conclusions based on incoming information, facilitates successful interaction with the environment [26].…”

Section: Introductionmentioning

confidence: 99%

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The Effect of Contextual and Personal Factors on the Use of Probabilistic Recommenders in E-Markets

O'Hare¹,

Phillips²,

Moss³

2009

TOERGJ

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E-markets of the future may extend to real-time, on-line auctions of, for example, real estate or antiques. To operate successfully in real-time e-markets, knowledge-based recommenders may become indispensable. Real-time emarket trading decisions typically take place in dynamic and uncertain environments in which high stakes, time pressure, multiple players, and feedback loops are key characteristics. A 2x2x2 design was used in which risk, time pressure and availability of a probabilistic recommender were manipulated. The effect of the recommender on the confidence and performance of three groups of decision-makers (novices, journeymen and expert) was examined. Finally, deductive logic ability was assessed. 84 participants engaged in a laboratory-based computer simulation of a customized version of Blackjack that approximated a real-world e-market decision environment. Recommender use differed with expertise level. Novices were significantly more confident when the recommender was available and used its advice to avoid decision errors. The recommender did not alter the confidence of journeymen and experts but they used it to avoid errors as contextual factors became more complex. There were significant group differences in recommender use to improve strategic outcomes: recommender use enhanced experts' performance, damaged journeymen's and had little effect on novices'. In addition, deductive logic ability was significantly related to fewer decision errors. These results suggest that, to be effective in dynamic e-market environments, knowledge-based recommenders should be tailored to individual differences, particularly decision-makers' expertise and reasoning ability.

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