Sustainable System Modelling for Urban Development Using Distributed Agencies

Developing a sustainable simulation system consists essentially of generating sustainable, artificial worlds with the capacity to produce results similar to those observed in the real world. This allows for varying parameters in a controlled, reusable experimental environment, something that cannot be easily achieved through mathematical models. The field of simulation is broad and multidisciplinary and has had an impressive growth since the 90’s. While the area of simulation has been expanding to new horizons in traditional systems research, there are yet a series of unsolved epistemological issues David et al. (2010).

[1]  L. Ernesto,et al.  Indicadores de uso sustentable del agua en Ciudad Juárez, Chihuahua , 2007 .

[2]  Bogart Yail Márquez,et al.  Methodology for the Modeling of Complex Social System Using Neuro-Fuzzy and Distributed Agencies , 2022 .

[3]  David L. Williams,et al.  Rationale for research on including sustainable agriculture in the high school agricultural education curriculum , 1998 .

[4]  S. Stefanescu Applying Nelder Mead’s Optimization Algorithm for Multiple Global Minima , 2007 .

[5]  Michael Drennan,et al.  The Human Science of Simulation: a Robust Hermeneutics for Artificial Societies , 2004, J. Artif. Soc. Soc. Simul..

[6]  P. Torrens,et al.  Geosimulation: Automata-based modeling of urban phenomena , 2004 .

[7]  Zhengxin Chen,et al.  A Descriptive Framework for the Field of Data Mining and Knowledge Discovery , 2008, Int. J. Inf. Technol. Decis. Mak..

[8]  N. Theresa Hoagland,et al.  Sustainable systems theory: ecological and other aspects , 2005 .

[9]  Manuel Castañón-Puga,et al.  Fuzzy Models for Complex Social Systems Using Distributed Agencies in Poverty Studies , 2011, ICSECS.

[10]  Zhengxin Chen,et al.  Using Branch-Grafted R-trees for Spatial Data Mining , 2004, International Conference on Computational Science.

[11]  Bogart Yail Márquez,et al.  On the simulation of a sustainable system using modeling dynamic systems and distributed agencies , 2010, INC2010: 6th International Conference on Networked Computing.

[12]  Nuno David,et al.  Epistemological Perspectives on Simulation III: An introduction , 2010, J. Artif. Soc. Soc. Simul..

[13]  Francis Heylighen,et al.  Five Questions on Complexity , 2007, nlin/0702016.

[14]  Robert Lempert,et al.  Agent-based modeling as organizational and public policy simulators , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[15]  Manuel Castañón-Puga,et al.  Analyzing the Mexican microfinance industry using multi-level multi-agent systems , 2009, SpringSim '09.

[16]  Paul Davidsson,et al.  Agent Based Social Simulation: A Computer Science View , 2002, J. Artif. Soc. Soc. Simul..

[17]  Ricardo del Olmo Martínez,et al.  Modelado y simulación basada en agentes con SIG para la gestión de agua en espacios metropolitanos , 2006 .

[18]  G. Nigel Gilbert,et al.  Agent-Based Models , 2007 .

[19]  V. Julián,et al.  Agentes Inteligentes: el siguiente paso en la Inteligencia Artificial: el siguiente paso en la Inteligencia Artificial , 2000 .

[20]  Bogart Yail Márquez,et al.  On the modeling of a sustainable system for urban development simulation using data mining and distributed agencies , 2010, The 2nd International Conference on Software Engineering and Data Mining.

[21]  K. Boulding General Systems Theory---The Skeleton of Science , 1956 .

[22]  Witold Pedrycz,et al.  Soft Computing for Hybrid Intelligent Systems , 2008, Soft Computing for Hybrid Intelligent Systems.

[23]  D. M. Hutton Organizations as Complex Systems: An Introduction to Knowledge Cybernetics , 2007 .

[24]  Manuel Castañón-Puga,et al.  Modeling the Employment Using Distributed Agenciesand Data Mining , 2011 .

[25]  Klaus Jaffe,et al.  Co-Operative Punishment Cements Social Cohesion , 2010, J. Artif. Soc. Soc. Simul..

[26]  W. Ross Ashby,et al.  Principles of the Self-Organizing System , 1991 .