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Agent-Based Social Modelling and Simulation with Fuzzy Sets

ESSA 2007. Agent-Based Social Modelling and Simulation with Fuzzy Sets. Samer Hassan Collado Luis Garmendia Salvador Juan Pavón Mestras. Dep. Ingeniería del Software e Inteligencia Artificial.

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Agent-Based Social Modelling and Simulation with Fuzzy Sets

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

  2. 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 HAIS 2007

  3. 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 HAIS 2007

  4. 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 HAIS 2007

  5. 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 HAIS 2007

  6. Design of the MAS model • 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 • World: • Demographic model • Network relationships: • Friends groups • Relatives HAIS 2007

  7. MAS system • Hundreds of agents in continuous interaction • Real-time graphics that show system evolution HAIS 2007

  8. Fuzzifying the MAS: Relationships • Friendship: it’s 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 HAIS 2007

  9. Age Youth Adult Old 10 1 0 0 20 0.8 0.8 0.1 30 0.5 1 0.2 40 0.2 1 0.4 50 0.1 1 0.6 Fuzzifying the MAS: fuzzy characteristics • For fuzzy operations, it is needed to define fuzzy sets over the agents' characteristics/variables • 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: HAIS 2007

  10. 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))) HAIS 2007

  11. 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) } ) HAIS 2007

  12. 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 HAIS 2007

  13. 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, a+b-1)=0 HAIS 2007

  14. 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 HAIS 2007

  15. 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) HAIS 2007

  16. 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 HAIS 2007

  17. Thanks for your attention! Samer Hassan Collado samer@fdi.ucm.es Dep. Ingenieria del Software e Inteligencia Artificial Universidad Complutense de Madrid HAIS 2007

  18. Contents License • This presentation is licensed under a Creative Commons Attribution 3.0 http://creativecommons.org/licenses/by/3.0/ • You are free to copy, modify and distribute it as long as the original work and author are cited HAIS 2007

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