Distributed agencies applied to complex social systems, a multi-dimensional approach

Proceedings - 2nd International Conference on Next Generation Information Technology, ICNIT 2011

Volumn , Issue , 2011, Pages 213-219

  Marquez, Bogart Yail ^a   Espinoza Hernandez, Ivan ^a   Castanon Puga, Manuel ^a   Castro, Juan R ^a   Suarez, Eugenio D ^b  

^a UNIVERSIDAD AUTÓNOMA DE BAJA CALIFORNIA   (Mexico)

^b TRINITY UNIVERSITY   (United States)

Author keywords

Complex Social System; Data Mining; Distributed Agencies; Fuzzy Logic; Poverty

Indexed keywords

COMPLEX SOCIAL SYSTEMS; DISTRIBUTED AGENCIES; GEOGRAPHIC INFORMATION; INTERMEDIATE PARTS; MICRO LEVEL; MULTI-DIMENSIONAL APPROACH; NATURAL LANGUAGES; POVERTY; SOCIAL PROBLEMS; SOCIAL SIMULATIONS; TECHNIQUES USED;

COMPUTER SIMULATION LANGUAGES; FUZZY LOGIC;

INFORMATION TECHNOLOGY;

EID: 80052132434     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (29)

1. Akindola, R.B., Towards a Definition of Poverty Poor People's Perspectives and Implications for Poverty Reduction. Journal of Developing Societies, 2009. 25: p. 121-150.
2. Ding, Y.-C. and Y.-K. Zhang, The Simulation of Urban Growth Applying Sleuth Ca Model to the Yilan Delta in Taiwan. Journal Alam Bina, Jilid 09, No: 01, 2007., 2007.
3. Zaded, L.A., Fuzzy sets. Vol. 8. 1965: Information and Control.
4. Sigmund, K. (1998) Complex Adaptive Systems and the Evolution of Reciprocation 16.
5. Brownlee, J., Complex Adaptive Systems Technical Report 070302A 2007
6. Lansing, S. (2003) Complex Adaptive Systems. DOI: 10.1146.
7. Miler, J. and S. Page, Complex Adptative Systems, An introduction to computational models of social life. Princeton University Press ed. 2007. 284.
8. Brownlee, J., Complex Adaptive Systems. 2007, Complex Intelligent Systems Laboratory, Centre for Information Technology Research, Faculty of Information Communication Technology, Swinburne University of Technology: Melbourne, Australia.
9. Drogoul, A. and J. Ferber, Multi-Agent Simulation as a Tool for Modeling Societies: Application to Social Differentiation in Ant Colonies. 1992.
10. Burns, T.R. and E. Roszkowska, Social Judgment in Multi-Agent Systems, in Cognition and multi-agent interaction: from cognitive modeling to social simulation, R. Sun, Editor. 2006, Cambridge University Press: New York, NY, USA.
11. Suarez, E.D., A. Rodríguez-Díaz, and M. Castañón-Puga, Fuzzy Agents, in Soft Computing for Hybrid Intelligent Systems, O. Castillo, et al., Editors. 2007, Springer: Berlin. p. 269-293.
12. Suarez, E.D., M. Castañon-Puga, and B.Y. Marquez, Analyzing the Mexican microfinance industry using multi-level multi-agent systems, in Proceedings of the 2009 Spring Simulation Multiconference. 2009, Society for Computer Simulation International: San Diego, California. p. 1-4.
13. Zadeh, L.A., From Computing with Numbers to Computing with Words - From Manipulation of Measurements to Manipulation of Perceptions, in Intelligent Systems and Soft Computing: Prospects, Tools and Applications. 2000, Springer-Verlag.
14. Drennan, M., The Human Science of Simulation: a Robust Hermeneutics for Artificial Societies. Journal of Artificial Societies and Social Simulation, 2005. 8(1).
15. Marquez, B.Y., et al., On the Modeling of a Sustainable System for Urban Development Simulation Using Data Mining and Distributed Agencies, in 2nd International Conference on Software Engineering and Data Mining 2010: Chengdu, China.
16. Dubey, P., Z. Chen, and Y. Shi, Using Branch-Grafted R-trees for Spatial Data Mining, in International Conference on Computational Science (ICCS). 2004, Springer-Verlag: Berlin Heidelberg. p. 657-660.
17. Peng, Y., et al., A Descriptive Framework for the Field Of Data Mining and Knowledge Discovery. International Journal of Information Technology & Decision Making, 2008. vol 7 p. 639-682.
18. INEGI, "II Conteo de Población y Vivienda 2005". Instituto Nacional de Estadística Geografía e Informática. 2006.
19. Wilensky, U. NetLogo Software. 1999; Available from: http://ccl. northwestern.edu/netlogo.
20. Castro, J.R., O. Castillo, and L.G. Martínez, Interval Type-2 Fuzzy Logic Toolbox. Engineering Letters, 2007. 15.
21. Rantala, J. and H. Koivisto, Optimised Subtractive Clustering for Neuro-Fuzzy Models, in 3rd WSEAS International Conference on Fuzzy Sets and Fuzzy Systems. 2002: Interlaken, Switzerland.
22. Stefanescu, S., Applying Nelder Mead's Optimization Algorithm for Multiple Global Minima. Romanian Journal of Economic Forecasting., 2007: p. 97-103
23. Gilbert, N., Agent-Based Models. 2007, Los Angeles: Sage Publications Inc.
24. Wooldridge, M. and N. Jennings, Intelligent Agents: Theory and Practice. Knowledge Engineering Review, 1995.
25. Egghe, L., R. Rousseau, and S. Rousseau, TOP-curves: Research Articles. J. Am. Soc. Inf. Sci. Technol., 2007. 58(6): p. 777-785.
26. Botti, V. and V. Julián, Estudio de métodos de desarrollo de sistemas multiagente, in Inteligencia Artificial, Revista Iberoamericana de Inteligencia Artificial. 2003.
27. Russell, S. and P. Norvig, Inteligencia Artificial. Un Enfoque Moderno. 2o ed, ed. M. Pearson Educacion. S.A. 2004: Pearson Prentice Hall.
28. Suarez, E.D., A. Rodríguez-Díaz, and M. Castañón-Puga, Fuzzy Agents, in Soft Computing for Hybrid Intelligent Systems, O. Castillo, et al., Editors. 2007, Springer: Berlin / Heidelberg.
29. Gilbert, N., Computational social science: Agent-based social simulation, in Agent-based modelling and simulation, D. Phan and F. Amblard, Editors. 2007, Bardwell: Oxford. p. 115-134.