Work

Individual differences in escalation of commitment: a multi-level adaptive learning perspective

Year: 2024

Type: article

Abstract: Previous studies have found stable individual differences in decision-making under escalation situations. Conventionally, the differences have been attributed to dispositional factors. In this paper, we offer multi-level adaptive learning as an alternative, positing that stable individual differences can develop (a) from an equal starting point at which there are no individual differences among all simulated learners, and (b) without the presumption of influences from dispositional factors. The results of three computer simulation studies showed that after sufficient learning trials, simulated individuals developed the key characteristics of stable individual differences in escalation of commitment: (a) a stable escalation tendency, (b) stable individual differences in escalation tendency, (c) decreases in stability of individual differences as more learning occurs, and (d) decreases in test–retest correlation as the test–retest interval increases. The findings suggest that adaptive learning can explain the emergence and development of individual differences in escalation of commitment without the assumption of dispositional factors.

Source: Journal of Computational Social Science

DOI: 10.1007/s42001-024-00339-7

Authors: Kin Fai Ellick Wong, Jessica Y. Y. Kwong, Michelle Yik

Institutions: Hong Kong University of Science and Technology, Chinese University of Hong Kong

Language: English

Topic: Decision-Making and Behavioral Economics

Subfield: General Decision Sciences

Field: Decision Sciences

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $3,090

Funders: Hong Kong University of Science and Technology, Hong Kong Government