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Trust Miscalibration Is Sometimes Necessary: An Empirical Study and a Computational Model
Michael G Collins1, Ion Juvina1
1ASTECCA Laboratory, Department of Psychology, Wright State University, Dayton, OH, United States.
Frontiers in Psychology
|August 27, 2021
Summary
Appropriately calibrated trust is usually best, but sometimes over- or under-trusting is needed for success. This study shows people strategically adjust trust in dynamic situations for better long-term outcomes.
Area of Science:
- Cognitive Science
- Computational Modeling
- Behavioral Economics
Background:
- Trust calibration is generally considered optimal for human-human and human-machine interactions.
- Miscalibrated trust (over- or under-trust) is typically viewed as detrimental, leading to over-reliance or under-utilization.
Purpose of the Study:
- To test predictions of a computational cognitive model regarding trust calibration in strategic interactions.
- To investigate whether temporary violations of trust calibration are critical for sustained success in dynamic environments.
Main Methods:
- Utilized an extended trust game, the multi-arm trust game, incorporating choices in interaction partners.
- Employed computational modeling and empirical studies to analyze trust investment strategies.
Main Results:
- Found that participants strategically adjusted trust portfolios under increased trust necessity.
- Demonstrated participants discounted trust in previously trusted partners and developed trust with previously distrusted ones.
Conclusions:
- Exceptions to trust calibration principles are crucial for understanding long-term trust dynamics.
- Strategic trust reconstruction is vital for navigating interdependence and uncertainty in interactive environments.
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