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Toward an Understanding of Dynamic Moral Decision Making: Model-Free and Model-Based Learning

Abstract

In business settings, decision makers facing moral issues often experience the challenges of continuous changes. This dynamic process has been less examined in previous literature on moral decision making. We borrow theories on learning strategies and computational models from decision neuroscience to explain the updating and learning mechanisms underlying moral decision processes. Specifically, we present two main learning strategies: model-free learning, wherein the values of choices are updated in a trial-and-error fashion sustaining the formation of habits and model-based learning, wherein the brain updates more general cognitive maps and associations, thus sustaining flexible and state-dependent behaviors. We then summarize studies explaining the neuro-computational processes of both learning strategies—the calculation of prediction errors and valuation. We conclude by emphasizing how the incorporation of dynamic aspects in moral decision making could open new avenues for understanding moral behaviors in a changing world.

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Notes

  1. Notice that in this context, the term “value” refers to the magnitude of outcome and should not be confused with other interpretations such as social values etc.

  2. The concept of utility here is more generic than the way is used by “Utilitarian” approaches.

Abbreviations

DN:

Decision neuroscience

RPE:

Reward prediction error

SPE:

State prediction error

VMPFC:

Ventro-medial prefrontac cortex

SMH:

Somatic marker hypothesis

DLPFC:

Dorso lateral prefrontal cortex

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Acknowledgments

The authors would like to thank the Editors as well as two anonymous reviewers for their constructive comments in an earlier version of this paper. The preparation of this article was partially supported by the Academic Research Fund (AcRF) Tier 2 (MOE2012-T2-1-051) of the Ministry of Education, Singapore, awarded to Ying-yi Hong and George Christopoulos and by the Academic Research Fund (AcRF) Tier 1 (RG 1/11 M4010946.010) of the Ministry of Education, Singapore, awarded to the first author.

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Christopoulos, G.I., Liu, XX. & Hong, Yy. Toward an Understanding of Dynamic Moral Decision Making: Model-Free and Model-Based Learning. J Bus Ethics 144, 699–715 (2017). https://doi.org/10.1007/s10551-016-3058-1

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Keywords

  • Corruption
  • Bribery
  • Decision making
  • Decision neuroscience
  • Learning
  • Model-based