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

Donald R. Jones, Patrick Wheeler, Radha Appan, Naveed Saleem

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

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.

Original languageEnglish
Pages (from-to)1917-1930
Number of pages14
JournalDecision Support Systems
Volume42
Issue number3
DOIs
StatePublished - Dec 2006

Keywords

  • Decision making
  • Decision strategies
  • Judgment bias
  • Model advice

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