GeneCity: A Multi Agent Simulation Environment for Hereditary Diseases

Simulating the psycho-societal aspects of a human community is an issue always intriguing and challenging, aspiring us to help better understand, macroscopically, the way(s) humans behave. The mathematical models that have extensively been used for the analytical study of the various related phenomena prove inefficient, since they cannot conceive the notion of population heterogeneity, a parameter highly critical when it comes to community interactions. Following the more successful paradigm of artificial societies, coupled with multi-agent systems and other Artificial Intelligence primitives, and extending previous epidemiological research work, we have developed GeneCity: an extended agent community, where agents live and interact under the veil of a hereditary epidemic. The members of the community, which can be either healthy, carriers of a trait, or patients, exhibit a number of human-like social (and medical) characteristics: wealth, acceptance and influence, fear and knowledge, phenotype and reproduction ability. GeneCity provides a highly-configurable interface for simulating social environments and the way they are affected with the appearance of a hereditary disease, either Autosome or X-linked. This paper presents an analytical overview of the work conducted and examines a testhypothesis based on the spreading of Thalassaemia major.

[1]  D. Buss,et al.  Sex differences in human mate preferences: Evolutionary hypotheses tested in 37 cultures , 1989, Behavioral and Brain Sciences.

[2]  Joshua M. Epstein,et al.  Growing Artificial Societies: Social Science from the Bottom Up , 1996 .

[3]  Malcolm A. Ferguson-Smith,et al.  Essential Medical Genetics , 1997, Annals of Internal Medicine.

[4]  John H. Holland.  Genetic Algorithms and Classifier Systems: Foundations and Future Directions , 1987, ICGA.

[5]  Andreas Flache,et al.  Understanding Complex Social Dynamics: A Plea For Cellular Automata Based Modelling , 1998, J. Artif. Soc. Soc. Simul..

[6]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[7]  W. O. Kermack,et al.  A contribution to the mathematical theory of epidemics , 1927 .

[8]  Paul J. Zak,et al.  Population Genetics and Economic Growth , 2000 .

[9]  J. Stender,et al.  Many Agent-Simulation and Artificial Life , 1994 .

[10]  William H. Lane,et al.  Stable Marriage Problem , 2001 .

[11]  Miles T. Parker,et al.  What is Ascape and Why Should You Care? , 2001, J. Artif. Soc. Soc. Simul..

[12]  R JenningsNicholas,et al.  Developing multiagent systems , 2003 .

[13]  Terry Jones,et al.  The Ecology of Echo , 1997, Artificial Life.

[14]  Chung-Yuan Huang,et al.  Simulating SARS: Small-World Epidemiological Modeling and Public Health Policy Assessments , 2004, J. Artif. Soc. Soc. Simul..

[15]  Henrik Eriksson,et al.  Knowledge modeling at the millennium : The design and evolution of Protégé-2000 , 1999 .

[16]  J. V. Rauff,et al.  Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence , 2005 .

[17]  Nelson Minar,et al.  The Swarm Simulation System: A Toolkit for Building Multi-Agent Simulations , 1996 .

[18]  W. Hamilton,et al.  The Evolution of Cooperation , 1984 .

[19]  Joshua M. Epstein,et al.  Chapter 12. TOWARD A CONTAINMENT STRATEGY FOR SMALLPOX BIOTERROR: AN INDIVIDUAL-BASED COMPUTATIONAL APPROACH , 2004 .

[20]  F. Eeckman,et al.  Evolution and Biocomputation: Computational Models of Evolution , 1995 .

[21]  Chris Goldspink,et al.  Modelling social systems as complex: Towards a social simulation meta-model , 2000, J. Artif. Soc. Soc. Simul..