Friends Forever: Social Relationships with a Fuzzy Agent-Based Model Samer Hassan Mauricio Salgado Juan Pav ó n Universidad Complutense de Madrid University of Surrey

Samer Hassan HAIS Contents Understanding Friendship Dynamics The ABM Mentat Fuzzification process Results Conclusions

Samer Hassan HAIS Understanding Friendship Dynamics “Meeting” & “Mating”: strangers => acquaintances => friends => partner “Meeting”: depends on opportunities alone space & time “Mating”: depends on both opportunities & attraction

Samer Hassan HAIS Understanding Friendship Dynamics Proximity principle: The more similar two individuals are, the stronger their chances of becoming friends Features channel individual preferences Homogeneous friendship choices

Samer Hassan HAIS Contents Understanding Friendship Dynamics The ABM Mentat Fuzzification process Results Conclusions

Samer Hassan HAIS The ABM Mentat Aim: simulate the process of change in moral values in a period in a society Plenty of factors involved Now focusing on demography

Samer Hassan HAIS Mentat: architecture Agent: Mental State attributes Life cycle patterns Demographic micro-evolution: Couples Reproduction Inheritance

Samer Hassan HAIS Mentat: architecture World: 3000 agents Grid 100x100 Demographic model Network: Communication with Moore Neighbourhood Friends network Family network

Samer Hassan HAIS Mentat: Relationships Meeting Agents randomly distributed in space Mating Similarity operator => Boolean friendship Matchmaking Couple chosen among “ candidates ” Quantity? The more friends, the more couples Quality? Couples should be similar

Samer Hassan HAIS Mentat: Relationships Meeting Agents randomly distributed in space Mating Similarity operator => Friendship Matchmaking Couple chosen among “ candidates ” Quantity? The more friends, the more couples Quality? Couples should be similar

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Samer Hassan HAIS Contents Understanding Friendship Dynamics The ABM Mentat Fuzzification process Results Conclusions

Samer Hassan HAIS Be Fuzzy, my Friend Similar, Friend: fuzzy concepts Fuzzification Improve accuracy of similarity Improve realism of friendship Improve quality couples

Samer Hassan HAIS Fuzzy Similarity Fuzzifying variables μ economy :U → [0,1] μ economy (ind) = 0.7 Fuzzifying Similarity operator

Samer Hassan HAIS Fuzzy Friendship Boolean => Fuzzy relationship But friendship occurs through time: Dynamic evolution! Hypothesis: Logistic function

Samer Hassan HAIS Fuzzy Friendship Evolution

Samer Hassan HAIS Couples Couple relationship cannot be fuzzified But process improved: Accuracy of similarity New info: friendship

Samer Hassan HAIS Contents Understanding Friendship Dynamics The ABM Mentat Fuzzification process Results Conclusions

Samer Hassan HAIS Results Implementations (FS, FF): FS: attributes & similarity fuzzified FF: friendship fuzzified, evolving & affecting partner choice Configurations: RND-Fr: promoting random friends (agents can be linked to a non-similar neighbour) SIM-Fr: promoting similarity-based friends (agents will give priority to the most similar neighbours)

Samer Hassan HAIS Results

Samer Hassan HAIS Contents Understanding Friendship Dynamics The ABM Mentat Fuzzification process Results Conclusions

Samer Hassan HAIS Conclusions Fuzzification fits well in social processes Achieved improvements in Accuracy of similarity Realism of friendship Continuous Evolving Quality of the couples Future work Stability of friendship Weak links

Samer Hassan HAIS Thanks for your attention! Samer Hassan Universidad Complutense de Madrid University of Surrey

Samer Hassan HAIS Contents License This presentation is licensed under a Creative Commons Attribution You are free to copy, modify and distribute it as long as the original work and author are cited