Agent-Based Social Modelling and Simulation with Fuzzy Sets

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Agent-Based Social Modelling and Simulation with
Fuzzy Sets
ESSA 2007

• Samer Hassan Collado
• Luis Garmendia Salvador
• Juan Pavón Mestras

Dep. Ingeniería del Software e Inteligencia
Artificial
Acknowledgments. This work has been developed
with support of the project TIN2005-08501-C03-01,
funded by the Spanish Council for Science and
Technology.
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Index

• Why can the fuzzy logic be useful for Agent-Based
  Social Simulation?
• The case under study is a complex sociological
  problem the evolution of values in the Spanish
  post-modern society
• Fuzzification of ABSS, step by step
• Results a system that approaches more to reality

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Why Fuzzy Logic?

• The simulation of Multi-Agent Systems (MAS) is a
  powerful technique for studying complex systems
  behaviour
• Social Simulation allows the observation of
  emergent behaviour of a system of
  agents/individuals
• Limitation? when considering the evolution of
  complex mental entities, such as human believes
  and values
• Social sciences are characterized by uncertain
  and vague knowledge
• The fuzzy semantic predicates can determine this
  type of knowledge

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Why Fuzzy Logic?

• In the case study European Value Survey, World
  Value Survey
• Questions about the degree of happiness,
  satisfaction in aspects of life, or trust in
  several institutions (Very much Partially)
• Fuzzy logic can be applied to model different
  aspects of the MAS

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Case study

• Objective to simulate the process of change in
  values
• in a period 1980-2000
• in a society Spanish
• A problem with many factors involved Ideology,
  Economy, Demography, Values, Relationships,
  Inheritance many of them uncertain or diffuse
• Far from the typical industrial applications of
  ABSS that require software engineers task-driven
  agents, clear defined rules
• Input Data EVS 1980-2000

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Design of the MAS model

• World
• Demographic model
• Network relationships
• Friends groups
• Relatives

• Agent/Individual
• From EVS ? Agent MS atts ideology, religiosity,
  economic class, age, sex
• Different behaviour while life cycle youth,
  adult, old
• Demographic micro-evolution couples,
  reproduction, inheritance

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MAS system

• Hundreds of agents in continuous interaction
• Real-time graphics that show system evolution

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Fuzzifying the MAS Relationships

• Friendship its unrealistic just to be or not
  to be friends.
• Friendships is defined as a fuzzy relationship
  with real values between 0 and 1
• Rfriend UxU ? 0,1
• Immediate effect distinguishing between close
  friends and known people
• The same process could be done to family

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Fuzzifying the MAS fuzzy characteristics

• For fuzzy operations, it is needed to define
  fuzzy sets over the agents' characteristics/variab
  les
• Defining fuzzy sets over these variables
• i.e. ?religious U ? 0,1
• ?religious (ind) 0.2 means that ind is mainly
  not religious
• For instance, for age can be defined several
  fuzzy sets

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Fuzzifying the MAS Similarity

• Similarity operation rates how similar two
  agents are, based on their characteristics
• In the MAS is used for
• Finding possible friends
• Choosing couple
• Fuzzified as OWA (weighted aggregation) of
  similarities of attribute fuzzy sets
• Rsimilarity(Ind, Ind2) OWA (??att_i?defined,
  N(?att_i (Ind)-??att_i(Ind2)))

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Fuzzifying the MAS Couple

• Choosing couple is highly improved
• Now, we can know how compatible are two agents
  Rcompatible(Ind, Ind2) OWA ( Rfriend(Ind,
  Ind2), Rsimilarity(Ind, Ind2) )
• Rcouple (Ind, Ind2)
• Adult(Ind) AND
• Ind2 Max Rcompatible( Ind,
  Indi??Friends(Ind)
• where Rcouple (Indi) false AND
• Sex(Ind) ? Sex(Indi) AND
• Adult(Indi) )

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Fuzzifying the MAS other aspects

• Many other points where fuzzy logic can be
  applied
• Local influence is a fuzzy concept how much an
  agent influences its friends and family
• Inheritance between generations composition of
  parents variables (with random mutation factor)
• ??X attribute of Ind, ?x(Ind) ?x (Father
  (Ind)) o ?x (Mother (Ind))
• Fuzzy states can be implemented for smoother
  agents behaviour

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Extracting knowledge with fuzzy logic

• Fuzzy transitive property in friendship works
  the friend of my friend is somehow my friend
• But how much is that somehow?
• Having friend(A,B)0.4, friend(B,C)0.6
• friend(A,C) Min(0.4, 0.6) 0.4
• friend(A,C) Prod(0.4, 0.6) 0.24
• friend(A,C) Lw(0.4, 0.6) max(0, ab-1)0

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Extracting knowledge with fuzzy logic

• The T-transitive closure is a fuzzy operation
  that applies consecutively the transitive
  property
• In the case of friendship it can be applied to
  know how friends are all the non-connected
  agents. In friendship, T should be Prod
• Other powerful possibilities for extracting
  knowledge inference with rules, fuzzy
  implications, or fuzzy compositions

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Application and Results

• Implementation of some of these fuzzy
  applications has been done over the MAS studied
• Fuzzification of friendship
• Fuzzy sets over attributes
• New fuzzy similarity
• New matchmaking, that produced a great
  improvement in the micro aspect of finding
  couples
• T-transitive closure, with its consequent
  extraction of knowledge (agents know more people,
  with grading)

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For application in other contexts

• The example has shown how to fuzzify relations
  that determine agents interactions
• Agents attributes can be defined in terms of
  fuzzy sets
• Context-dependant functions, like inheritance,
  can be modelled as well as a typical fuzzy
  similarity operation
• Life states of agents are frequent in systems
  that evolve over time, especially in task solving
  environments
• A global fuzzy operation over all the agents was
  defined on a fuzzy relation to make inference
  with coherent results

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• Thanks for your attention!
• Samer Hassan Collado
• samer_at_fdi.ucm.es
• Dep. Ingenieria del Software e Inteligencia
  Artificial
• Universidad Complutense de Madrid

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Contents License

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