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A target-based foundation for the "hard-easy effect" bias

Published on Oct 1, 2014
· DOI :10.1007/978-3-319-46319-3_41
Robert F. Bordley4
Estimated H-index: 4
(UM: University of Michigan),
Marco LiCalzi14
Estimated H-index: 14
(Ca' Foscari University of Venice),
Luisa Tibiletti9
Estimated H-index: 9
(UNITO: University of Turin)
Abstract
The “hard-easy effect” is a well-known cognitive bias on self-confidence calibration that refers to a tendency to overestimate the probability of success in hard-perceived tasks, and to underestimate it in easy-perceived tasks. This paper provides a target-based foundation for this effect, and predicts its occurrence in the expected utility framework when utility functions are S-shaped and asymmetrically tailed. First, we introduce a definition of hard-perceived and easy-perceived task based on the mismatch between an uncertain target to meet and a suitably symmetric reference point. Second, switching from a target-based language to a utility-based language, we show how this maps to equivalence between the hard-perceived target/gain seeking and the easy-perceived target/loss aversion. Third, we characterize the agent’s miscalibration in self-confidence. Sufficient conditions for acting according to the “hard-easy effect” and the “reversed hard-easy effect” biases are set out. Finally, we derive sufficient conditions for the “hard-easy effect” and the “reversed hard-easy effect” to hold. As a by-product we identify situations in enterprise risk management where misconfidence in judgments emerges. Recognizing these cognitive biases, and being mindful of to be normatively influenced by them, gives the managers a better framework for decision making.
  • References (32)
  • Citations (2)
References32
Newest
#1Jong Seok Lee (U of M: University of Memphis)H-Index: 18
#2Mark Keil (GSU: Georgia State University)H-Index: 54
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#1Michael M. Roy (NWU: North-West University)H-Index: 11
#2Michael J. Liersch (NYU: New York University)H-Index: 7
Last.Stepehen Broomell (CMU: Carnegie Mellon University)H-Index: 1
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#1Arvid O. I. Hoffmann (UM: Maastricht University)H-Index: 13
#2Sam F. Henry (UM: Maastricht University)H-Index: 1
Last.Nikos Kalogeras (UM: Maastricht University)H-Index: 13
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#1Stephen V. Burks (UMN: University of Minnesota)H-Index: 18
#2Jeffrey P. Carpenter (Middlebury College)H-Index: 35
Last.Aldo Rustichini (UMN: University of Minnesota)H-Index: 50
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#1Sandra Ludwig (LMU: Ludwig Maximilian University of Munich)H-Index: 5
#2Philipp C. Wichardt (University of Bonn)H-Index: 9
Last.Hanke WickhorstH-Index: 2
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#1A. Peter McGraw (CU: University of Colorado Boulder)H-Index: 22
#2Jeff T. Larsen (TTU: Texas Tech University)H-Index: 27
Last.David A. Schkade (UCSD: University of California, San Diego)H-Index: 43
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#1R. Preston (California Institute of Technology)H-Index: 37
#2Hugo M. Mialon (Emory University)H-Index: 10
Last.Sue H. Mialon (Emory University)H-Index: 7
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#1B. Abdous (Université du Québec à Trois-Rivières)H-Index: 1
#2Radu Theodorescu (Laval University)H-Index: 9
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