Anchoring and adjustment in probabilistic inference in auditing

Abstract

Auditors are faced with the task of formulating opinions about the fairness of their clients' financial statements. In doing so, they use their professional judgment to determine the type and amount of information to collect, the timing and manner of collecting it, and the implications of the information collected. This information is rarely, if ever, perfectly reliable or perfectly predictive of the "true" state of a client's financial statements. Nevertheless, auditors may be held liable at common law or under the federal securities laws should the audited financial statements prove to be unrepresentative of this true state. Thus, it is important for auditors to have the ability to formulate appropriately judgments based on probabilistic data.

In this paper, we describe the results of experiments designed to assess whether auditors formulate judgments in accordance' with normative principles of decision making or whether a particular alternative to the normative model of decision making under uncertainty 's employed. In the next section, we discuss several alternatives to normative decision models, focusing on the anchoring and adjustment heuristic which forms the basis for our experiments.