Structure of Uncertainty and Decision Making: An Experimental Investigation*

Abstract

Decisions in the real world usually involve imprecise information or uncertainty about the precesses by which outcomes may be determined. This research reports the results of a laboratory experiment which examined whether the structure of uncertainty, namely, both the center and the range of the probability distribution describing the uncertainty, is an important determinant of choice. Specifically, it examines how the uncertainty of audit by the Internal Revenue Service of income tax returns affects taxpayers' decisions about intentional noncompliance. The context is relevant as almost nothing is known about how taxpayers assess detection risks using the probability information they have.The study focuses on intentional noncompliance. The factors affecting it are distinct and separate from those affecting unintentional noncompliance. Other factors that affect intentional tax noncompliance, such as risk, tax rates, and penalty rates, were controlled in the experiment. It was hypothesized that the lower the mean and the lesser the range (ambiguity) of the perceived audit probability, the greater the international noncompliance. As hypothesized, the analysis indicates that both the mean and the range of the perceived audit probability rate affect intentional noncompliance, though the effect of ambiguity is greater at a relatively higher level of mean. This result suggests that the strength of the information describing an uncertain event is captured better by both the mean and the range of the uncertainty than either of those components singly.

Risk, Ambiguity, and Decision Choice: Some Additional Evidence*

“…For example, the Plant C scenario with a reliability range of 70% to 90% is considered more ambiguous than the scenario for Plant A in which the probability is stated as 80%. This is consistent with prior research findings that the mean and the range of the probability distribution describing an uncertain event have different information content with perceived ambiguity increasing in range (Ghosh & Crain, 1993). This is also consistent with Kahn and Sarin's (1988) operational definition of ambiguity in terms of "second-order uncertainty or, in other words, by a probability distribution of perceived frequencies, [or] .…”

“…This lack of information about secondorder probabilities (for AMBIGUITY SETTING) created settings characterized by "lack of information," or where information is "vague, instead of precise," suggested to be contributors to ambiguity (Budescu & Wallsten, 1987). Ghosh and Crain (1993) found that the strength of the information describing an uncertain event is captured better by both the mean and the range of the uncertainty than by either components alone. Thus, by not providing the mean information, we intended to emphasize the ambiguity in the situation.…”

The Effects of Feedback Equivocality on Escalation of Commitment: An Empirical Investigation of Decision Dilemma Theory1

“…However, it should be noted that in the economics literature, such uncertainty is not necessarily unresolvable. Similarly, in the accounting literature, uncertainty is operationalized as the range of a data set (Ghosh & Crain, 1993) and in the forecasting literature as the coefficient of variation of a stream of data (Collopy & Armstrong, 1992). Certainly, these definitions speak to the degree of variability that may exist in feedback; however, the issue of patterning or trend is not addressed by these definitions.…”

Supplier Selection Behavior Under Uncertainty: Contextual and Cognitive Effects on Risk Perception and Choice

“…In this study, we focus on specific contextual and cognitive processes that influence supplier selection behavior under uncertainty (i.e., with variance in potential outcomes) (Ghosh & Crain, 1993). We investigate how contextual factors commonly experienced by supply managers, such as a purchased component's strategic importance, its sourcing difficulty, and a manager's compensation scheme influence risk perception and subsequently affect the choice of a supplier (Olsen & Ellram, 1997;Villena, Gomez-Mejia, & Revilla, 2009).…”