AbstractWe show that a computational model of affective moral decision making can fit human behavior data obtained from an empirical study on criminal decision making. By applying parameter tuning techniques on data from an initial sample, optimal fits of the affective moral decision making model were found supporting the influences of honesty/humility, perceived risk and negative state affect on criminal choice. Using the parameter settings from the initial sample, we were able to predict criminal choices of participants in the holdout sample. The prediction errors of the full model were fairly low. Moreover, they compared favorably to the prediction errors produced by constrained variants of the model where either the moral, rational or affective influences or a combination of these had been removed.
BackgroundSubstantial evidence emotions are fundamental in criminal decision makingBut emotions rarely in criminal choice modelsStudy relation Ratio+Emotions+Moral
A Computational Model of Affective Moral Decision Making that predicts Human Criminal Choices
Matthijs A. Pontier1,2, Jean-Louis Van Gelder1,3, Reinout E. de Vries1,4
1 VU University, Amsterdam, 2CAMeRA@VU http://camera-vu.nl/matthijs/ [email protected], 3NSCR, 4Faculty of Psychology
MethodWe integrated Moral Reasoning and the Emotional Intelligence of Silicon Coppélia into Moral Coppélia to be able to predict human criminal decisions
ExpectedSatisfaction(action) =
wmor*Morality(action) + wrat*ExpectedUtility(action) +
wemo*EESA(action)
Ratio + Affect in Silicon Coppelia
Ratio Emo R+E Moral M+R M+E M+R+E
morcc 0 0 0.68 0.42 0.453 0.435
wmor 0 0 0 1.00 0.96 0.97 0.87
partrat 1 0 0.34 0 1 0 0.64
partemo 0 1 0.66 0 0 1 0.36
R2 initial
0.755 0.879 0.922 0.934 0.987 0.980 0.988
R2 holdout
0.719 0.906 0.932 0.928 0.980 0.978 0.982
Results: Predicting human criminal decisions
Match:• Honesty/Humility to Weightmorality
• Perceived Risk to Expected Utility• Negative State Affect to EESA
Discussion• Validation of Moral Coppélia • Adds to criminological theory• Useful in applications:
•Serious games•Entertainment•Coaching
Expected Emotional State Affect: EESA(action) = Desired(emotional state) – Expected(emotional state)
