1. A Computational Model
of Affective Moral
Decision Making
that predicts
Human Criminal Choices
Matthijs Pontier, matthijspon@gmail.com
Jean-Louis Van Gelder
Reinout E. de Vries

2. Overview of this presentation
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SELEMCA
Moral Reasoning
Silicon Coppelia: Model of Emotional Intelligence
Moral Reasoning + Silicon Coppelia = Moral Coppelia
Predicting Crime with Moral Coppelia
Conclusions
Future Work
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3. SELEMCA
• Develop ‘Caredroids’: Robots or Computer Agents
that assist Patients and Care-deliverers
• Focus on patients who stay in long-term care facilities
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4. Possible functionalities
• Care-broker: Find care that matches need patient
• Companion: Become friends with the patient to
prevent loneliness and activate the patient
• Coach: Assist the patient in making healthy choices:
Exercising, Eating healthy, Taking medicine, etc.
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5. Applications: Care Agents
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6. Applications: Care Robots
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7. Background Machine Ethics
• Machines are becoming more autonomous
 Rosalind Picard (1997): ‘‘The greater the freedom of
a machine, the more it will need moral standards.’’
• Machines interact more with people
 We should manage that machines do not harm us or
threaten our autonomy
• Machine ethics is important to establish perceived
trust in users
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8. Moral reasoning system
We developed a rational moral reasoning system that
is capable of balancing between conflicting moral goals.
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9. Limitations rational moral reasoning
• Only moral reasoning results in very cold decisionmaking, only in terms of rights and duties
• Wallack, Franklin & Allen (2010): “Ethical agents
require Emotional Intelligence as well as other
‘supra-rational’ faculties, such as a sense of self and
a Theory of Mind”
• Tronto (1993): “Care is only thought of as good care
when it is personalized”
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10. Solution: Add Emotional Processing
• Previously, we developed Silicon Coppelia,
a model of Emotional Intelligence.
• This can be projected in others for Theory of Mind
• Learns from experience  Personalization
 Connect Moral Reasoning to Silicon Coppelia
• More human-like moral reasoning
• Personalize moral decisions and communication
about moral reasoning
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11. Silicon Coppelia
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12. Speed-dating Experiment
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13. Improving Emotion Regulation
in Moral Coppelia
• Expected Emotion(action, emotion) =
(1-β) * AEB(action, emotion) + β * current_emotion
• EESA(action) =
1-
* (Desired(emotion(i)) – EE(action, i))
• ExpectedSatisfaction(action) =
wmor*Morality(action) +
wrat*ExpectedUtility(action) +
wemo*EESA(action)
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14. Moral Coppelia
Decisions based on:
1. Rational influences
• Does action help me to reach my goals?
2. Affective influences
• Does action lead to desired emotions?
• Does action reflect Involvement I feel towards user?
• Does action reflect Distance I feel towards user?
3. Moral reasoning
• Is this action morally good?
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15. Background Criminology
• Substantial evidence emotions are fundamental in
criminal decision making
• But emotions rarely in criminal choice models
 Study relation Ratio + Emotions + Morality
 Apply Moral Coppelia to criminology data
 Predict criminal decisions participants
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16. Matching data to model
Match:
• Honesty/Humility
to Weightmorality
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to Expected Utility
to EESA
Perceived Risk
Negative State Affect
Parameter Tuning:
1. Find optimal fits for initial sample
2. Predict decisions for holdout sample
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17. Predicting Criminal Choices
Ratio
Emo
R+E
Moral
M+R
M+E
M+R+E
morcc
0
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
0.8792
0.9222
0.9336
0.9871
0.9798
0.9881
0.9060
0.9323
0.9281
0.9803
0.9778
0.9821
R2
0.7553
initial
R2
0.7192
holdout
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18. Conclusions
• Validation of Moral Coppélia:
Moral Coppélia can be used to predict
human criminal choices
• Adds to theory criminology
• Useful in applications
• Serious games: virtual crook
• Entertainment
• Theory of Mind
• Predict crime?
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19. Future Work
• Publish Book: “Machine Medical Ethics”
• More detailed model of Autonomy
• Develop Applications that require
Affective Moral Behavior, such as
• Persuasive Technology:
Moral dilemmas about Helping vs Manipulating
• Integrate current system with
Health Care Intervention models
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20. Thank you!
Matthijs Pontier
matthijspon@gmail.com
http://camera-vu.nl/matthijs
http://www.linkedin.com/in/matthijspontier
http://crispplatform.nl/projects/selemca
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