emergence of norms through social learning
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Emergence of Norms through Social Learning. Partha Mukherjee, Sandip Sen and St éphane Airiau Mathematical and Computer Sciences Department University of Tulsa, Oklahoma, USA. Introduction. - PowerPoint PPT PresentationTRANSCRIPT
IJCAI’07
Emergence of Norms through Social Learning
Partha Mukherjee, Sandip Sen and Stéphane AiriauMathematical and Computer Sciences DepartmentUniversity of Tulsa, Oklahoma, USA
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Introduction
Norm: “a convention as an equilibrium that everyone expects in interactions that have more than one equilibrium” [Young, 1996]
Use a population of learning agents to simulate a population that faces a
problem modeled by a game and study the emergence of norms
Use a population of learning agents to simulate a population that faces a
problem modeled by a game and study the emergence of norms
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Example of a norm: picking the side of the road
Agents need to decide on one of several equally desirable alternatives.
This game can be extended to m actions
4
4
-1
-1
-1
-1
4
4
L
R
R L
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Previous Work
Previous work on learning norms assume observation of other interactions between agents.
How norms will emerge if all interactions were private?
Social Learning (IJCAI-07): agents play a bimatrix game, at each interaction, an agent plays against another agent, taken at random, in the population
Empirical study: Study effect of population size, number of actions available, effect of learning algorithms, presence of non-learning agents, multiple relatively isolated populations
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Social Learning
Population of N learning agentsA 2-player, k-action game M M is common knowledgeEach agent has a learning algorithm
(fixed, intrinsic) to play M as a row or a column player
Repeatedly, agents play the game M against an unknown, random opponent.
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Protocol of play
For each iteration, for each agentPick randomly one agent in its
neighborhoodFor each pair, one agent is randomly
considered row, the other column playerEach agent pick an action, and can observe
only the action of the other agent constituting the pair
Each agent gets the reward accordingly, and updates its learning mechanism
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Interactions are limited to neighboring agents
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Effect of neighboring size
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Learning Dynamics
D=1
D=15
It 145 It 355 It 480
Driving on the left Driving on the right
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Influence of non-learners
Non-learners use identical strategiesD=5
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Influence of non-learnersUsing different strategies
Driving on the left Driving on the right
D=1
D=15
It 45 It 535 It 905
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Conclusion
Bottom up process for the emergence of social norms
Depends only on private expertise Agents can learn and sustain useful social
norms Agent population with smaller neighborhoods
converge faster to a norm