ESSA 2007 AgentBased Social Modelling and Simulation with

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 TIN 2005 -08501 -C 03 -01, funded by the Spanish Council for Science and Technology.

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 Samer Hassan HAIS 2007 2

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 Samer Hassan HAIS 2007 3

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 Samer Hassan HAIS 2007 4

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: taskdriven agents, clear defined rules… Input Data: EVS 1980 -2000 Samer Hassan HAIS 2007 5

Design of the MAS model Agent/Individual: Demographic model From EVS Agent MS atts: ideology, religiosity, economic class, age, sex… Different behaviour while life cycle: youth, adult, old Demographic microevolution: couples, reproduction, inheritance Samer Hassan World: Network relationships: • Friends groups • Relatives HAIS 2007 6

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

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 : Ux. U [0, 1] Immediate effect: distinguishing between “close friends” and “known people” The same process could be done to family Samer Hassan HAIS 2007 8

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: Age Youth Adult Old 10 1 0 0 20 0. 8 0. 1 30 0. 5 1 0. 2 40 0. 2 1 0. 4 50 0. 1 1 0. 6 Samer Hassan HAIS 2007 9

Fuzzifying the MAS: 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: Samer Hassan Rsimilarity(Ind, Ind 2)= OWA ( att_i defined, N( att_i (Ind)- att_i(Ind 2))) HAIS 2007 10

Fuzzifying the MAS: Couple Choosing couple is highly improved: Now, we can know how “compatible” are two agents: Rcompatible(Ind, Ind 2) : = OWA ( Rfriend(Ind, Ind 2), Rsimilarity(Ind, Ind 2) ) Rcouple (Ind, Ind 2) : = Adult(Ind) AND Ind 2 = Max Rcompatible( Ind, { Indi Friends(Ind) where: Rcouple (Indi) == false AND Sex(Ind) Sex(Indi) AND Adult(Indi) } ) Samer Hassan HAIS 2007 11

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 Samer Hassan HAIS 2007 12

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 Samer Hassan HAIS 2007 13

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 Samer Hassan HAIS 2007 14

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) Samer Hassan HAIS 2007 15

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 Samer Hassan HAIS 2007 16

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

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 Samer Hassan HAIS 2007 18