Situating the Problematics of Judgment-Based Deselection: A Heuristics and Biases Approach

Abstract

This paper presents an analysis of decision-making in the context of judgment-based (subjective) deselection using decision theory concepts developed by Daniel Kahneman in collaboration with others. Judgment-based deselection is considered from multiple perspectives using this approach. Situating the deselection context within loss aversion, status quo bias, and the endowment effect (products of prospect theory and behavioral economics) may explain the psychological reluctance accompanying deselection. The role of feedback as a condition for developing intuitive expertise demonstrates the limited potential for developing expertise in subjective deselection. Heuristics and biases employed in decision-making and decision fatigue may account for inconsistencies in deselection decisions.

Keywords:

• Deselection
• weeding
• heuristics
• biases
• prospect theory
• bounded rationality

Acknowledgements

I would like to thank my colleague Alexandre Fortier for comments on a draft of this paper; my colleague Anne Moreau for her persistence in fulfilling many ILL and document delivery requests; and Rose Fuchs for her feedback.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 McHale et al. (2017) offer a useful exception.

2 See also Walker et al.(2010, 3), recommendation 2: “Make past monograph usage, understood in context, one facet in decision making about future CUL acquisitions and investment in the collection.”

3 The worry about wasting resources is a version of the sunk cost effect; see Thaler (1980).

4 Kahneman, Sibony, and Sunstein (2021, 89–90) identify mood, stress, fatigue, the weather, and “the order in which cases are examined” as factors in occasion noise, the phenomenon of making different judgments “when faced with the same facts on two occasions” (81).

5 Christian (2020, 92) notes that the approach of “pitting expert judgment against simple mathematical models” goes back even further, to the early 1940s.

6 In the context of academic libraries, the proactive, continuous, and ongoing collection assessment and maintenance approaches of Lantzy, Matlin, and Opdal (2020) and Luther and Guimarães (2016) offer a counterpoint to emergency weeding projects.

7 Johnson (2014, 199) provides another example: “The 1970s energy crisis produced interest in peat and wind as sources of energy and sent researchers after publications that had not been requested in sixty years.”