Understanding and attenuating decision bias in the use of model advice and other relevant information

  • Authors:

  • Donald R. Jones;Patrick Wheeler;Radha Appan;Naveed Saleem

  • Affiliations:

  • Information Systems and Quantitative Sciences Area, Jerry S. Rawls College of Business Administration, Lubbock, Texas;School of Accountancy, University of Missouri-Columbia, Columbia, Missouri;Nance College of Business, Cleveland State University, Cleveland, Ohio;Accounting, Legal Studies and Information Systems Department, University of Houston-Clear Lake, Houston, TX

  • Venue:
  • Decision Support Systems
  • Year:
  • 2006

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Abstract

A human judge faced with model advice, modeled information (used by the model to compute the advice), and unmodeled information (known by the human but not included in the model) should use a "divide-and-conquer" strategy in which the human judge relies completely on the model to process the modeled information and focuses all energy on assessing and adjusting for the unmodeled information [D.R. Jones, D. Brown, The division of labor between human and computer in the Presence of Decision Support System Advice, Decision Support Systems 33 (2002) 375-388]. This paper extends Jones and Brown [D.R. Jones, D. Brown, The division of labor between human and computer in the Presence of Decision Support System Advice, Decision Support Systems 33 (2002) 375 388] in two studies. In Study 1, we find that, in lieu of the divide-and-conquer strategy, human judges give weight to all three types of inputs and that giving weight to the modeled information degrades performance. In Study 2, we find that (1) as strategies approach the divide-and-conquer strategy judgment performance improves, and (2) the divide-and-conquer strategy can be encouraged by a combination of instruction and a decision support feature. Application of these results could improve judgment in a variety of important contexts.