15min version

1. Using Values and Norms To
Model Realistic Agents
Rijk Mercuur, Virginia Dignum and Catholijn Jonker
Delft University of Technology
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2. Using Values and Norms To
Model Realistic Agents
Rijk Mercuur, Virginia Dignum and Catholijn Jonker
Delft University of Technology
what one finds important in life a standard or rule in society
reproduces human behavior
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3. Humans share their desserts
28%
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4. Classical economical agents do not share their
desserts and are therefore not realistic
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5. How to design
realistic agents?
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6. Policies succeed because of realistic agents
Organ DonorsDonorPercentage
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7. How to design
realistic agents?
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8. In the ultimatum game,
the realism of agents
can be improved
with values and norms.
Result:
Method:
Case:
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9. The ultimatum game is a psychological
experiment about sharing money
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10. The UG can have different amount of rounds
The one-shot UG or repeated UG
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11. In the 1x UG, we ask: why do humans make
fair demands?
Demand 56% of the $1000
Accept 81% of the demands
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N = 5950
Cooper, D. J., & Dutcher, E. G. (2011). The Dynamics of Responder Behavior in Ultimatum Games: A Meta-Study.

12. In the 10x UG, we ask: how come humans start to
demand and accept more after 10 rounds?
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13. In the ultimatum game,
the realism of agents
can be improved
with values and norms.
Result:
Method:
Case:
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14. The realism of agents is evaluated by
comparing human behavior to agent behavior
Micro data Macro data
Other (e.g.,
Turing Test)
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15. We simulate the behavior of a group of agents
We distinguishing two types of learning
Evolutionary learning In-Game learning
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16. To reproduce first round behavior we let
agents ‘evolve’ until they are stable
Demand 56%
Accept 81%
?
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17. To reproduce repeated UG behavior we check
human learning against agent learning
95% CI of human play
Simulated agent play
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18. In the ultimatum game,
the realism of agents
can be improved
with values and norms.
Result:
Method:
Case:
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19. Value and norms improve the realism of
agents
~< <
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20. In the 1xUG, Vanessa can reproduce the
acceptance rates whereas Leo cannot
<
Accept
81%

 

Demand
56%
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21. ‘Evolving’ Leo never let to the human
acceptance rates
Action Quality
0 10
10 10
20 10
30 10
… ….
1000 10
Round 1
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22. If Vanessa values fairness slightly higher than
wealth she reproduces the demand and
acceptance rates.
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23. In the 1xUG, Vanessa can reproduce the
acceptance rates whereas Leo cannot
<
Accept
81%

 

Demand
56%
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24. Value and norms improve the realism of
agents
~< <
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25. Vanessa outperforms Noël in 1x UG, but Noël
outperforms Vanessa in the 10x UG
> <
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26. Vanessa outperforms Noël in 1x UG
>
Demand
56%
Accept
81%




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27. Noël estimates the norm from others people
behavior, but has no direction
? ? Accept
50%
Demand
50%
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28. Vanessa outperforms Noël in 1x UG, but Noël
outperforms Vanessa in the 10x UG
> <
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29. Noël outperforms Vanessa in the 10x UG
<
 
 
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30. Vanessa always act out of her stable values so
her actions do not change
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31. If you set Noël to the right starting point the
learning of the demands is quite realistic
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32. Value and norms improve the realism of
agents
~< <
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33. VaNo decides pre-run if it will use values or
norms
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34. In the ultimatum game,
the realism of agents
can be improved
with values and norms.
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35. Implications
• Potential for agent-based simulations of psychological
experiments
• Shows some models are more realistic than others
• Simple models are not enough
• Groundwork for more complex social agents
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36. VaNo outperforms Vanessa, even if some of
the agents act out of values
<
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37. In the 1x UG, VaNo outperforms Noël
<
Evolves to
Demand
56%
Evolves to
Demand
50%
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38. In the 10x UG, Vano reproduces the learning
where Vanessa is stable
<
Reproduces
Human
Learning
No learning
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39. VaNo outperforms Noël in the 1xUG and
Vanessa in the 10x UG.
<<
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40. By ‘evolving’ Leo, he can learn to make human
demands, but never human acceptance rates
Action Quality
0 10
10 10
20 10
30 10
… ….
1000 10
Action Quality
0 10
10 10
20 10
30 10+reward
… ….
1000 10
Round 1 Round 2
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41. VaNo outperforms Noël in the 1xUG and
Vanessa in the 10x UG.
<<
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42. Value and norms improve the realism of
agents
~< <
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43. VaNo decides pre-run if it will use values or
norms
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