Global sensitivity/uncertainty analysis for agent-based models

Agent-based models simulate simultaneous actions and interactions of multiple agents, in an attempt to re-create and predict the appearance of complex phenomena. We propose to use global sensitivity analysis as a tool for analyzing and evaluating agent-based models. A general approach for applying the global sensitivity analysis to agent-based models is presented and tested on the example of a socio-cultural agent-based model we developed earlier [45]. We identify the most significant parameters in the model and uncover their contributions to the outputs of interest. Methodology of model reduction for agent-based models is discussed and demonstrated for the aforementioned model.

[1]  Paola Annoni,et al.  Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index , 2010, Comput. Phys. Commun..

[2]  Chun-Che Huang,et al.  Using intelligent agents to manage fuzzy business processes , 2001, IEEE Trans. Syst. Man Cybern. Part A.

[3]  Paola Annoni,et al.  Sixth International Conference on Sensitivity Analysis of Model Output How to avoid a perfunctory sensitivity analysis , 2010 .

[4]  Dirk Helbing,et al.  Simulating dynamical features of escape panic , 2000, Nature.

[5]  R. Axelrod.  The Complexity of Cooperation , 2011 .

[6]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[7]  Laura Painton Swiler,et al.  Calibration, validation, and sensitivity analysis: What's what , 2006, Reliab. Eng. Syst. Saf..

[8]  Jose L. Segovia-Juarez,et al.  Identifying control mechanisms of granuloma formation during M. tuberculosis infection using an agent-based model. , 2004, Journal of theoretical biology.

[9]  Uta Berger,et al.  Pattern-Oriented Modeling of Agent-Based Complex Systems: Lessons from Ecology , 2005, Science.

[10]  Richard O. Mason,et al.  Applying ethics to information technology issues , 1995, CACM.

[11]  D. M. Titterington,et al.  Neural Networks: A Review from a Statistical Perspective , 1994 .

[12]  A. Troisi,et al.  An agent-based approach for modeling molecular self-organization. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Herbert A. Simon,et al.  Applications of machine learning and rule induction , 1995, CACM.

[14]  Michael Wooldridge,et al.  Introduction to multiagent systems , 2001 .

[15]  Kathrin Happe,et al.  Agent-Based Modelling and Sensitivity Analysis by Experimental Design and Metamodelling: An Application to Modelling Regional Structural Change , 2005 .

[16]  Maria Fonoberova,et al.  Model reduction for agent-based social simulation: coarse-graining a civil violence model. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  Eric Bonabeau,et al.  Agent-based modeling: Methods and techniques for simulating human systems , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[19]  Jon C. Helton,et al.  Survey of sampling-based methods for uncertainty and sensitivity analysis , 2006, Reliab. Eng. Syst. Saf..

[20]  Sergei S. Kucherenko,et al.  Derivative based global sensitivity measures and their link with global sensitivity indices , 2009, Math. Comput. Simul..

[21]  Blake LeBaron,et al.  Empirical regularities from interacting long- and short-memory investors in an agent-based stock market , 2001, IEEE Trans. Evol. Comput..

[22]  Gerhard Weiss,et al.  Multiagent systems: a modern approach to distributed artificial intelligence , 1999 .

[23]  Ping Zhu,et al.  Metamodel-based lightweight design of B-pillar with TWB structure via support vector regression , 2010 .

[24]  Ilya M. Sobol,et al.  Sensitivity Estimates for Nonlinear Mathematical Models , 1993 .

[25]  Alexander J. Smola,et al.  Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.

[26]  I. Sobola,et al.  Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates , 2001 .

[27]  Vincent Ginot,et al.  Selecting parameters for calibration via sensitivity analysis: An individual-based model of mosquitofish population dynamics , 2008 .

[28]  Sanjay B. Joshi,et al.  Metamodeling: Radial basis functions, versus polynomials , 2002, Eur. J. Oper. Res..

[29]  Douglas E. Jones,et al.  Parameter estimation and sensitivity analysis in an agent-based model of Leishmania major infection. , 2010, Journal of theoretical biology.

[30]  Liliana Perez,et al.  An agent-based approach for modeling dynamics of contagious disease spread , 2009, International journal of health geographics.

[31]  Timothy W. Simpson,et al.  Metamodels for Computer-based Engineering Design: Survey and recommendations , 2001, Engineering with Computers.

[32]  Kwang Mong Sim,et al.  Toward market-driven agents for electronic auction , 2001, IEEE Trans. Syst. Man Cybern. Part A.

[33]  A. Saltelli,et al.  Making best use of model evaluations to compute sensitivity indices , 2002 .

[34]  M. Kaegi,et al.  Analyzing maintenance strategies by agent-based simulations: A feasibility study , 2009, Reliab. Eng. Syst. Saf..

[35]  Hu Wang,et al.  Probability-based least square support vector regression metamodeling technique for crashworthiness optimization problems , 2011 .

[36]  Joshua M. Epstein,et al.  Generative Social Science: Studies in Agent-Based Computational Modeling (Princeton Studies in Complexity) , 2007 .

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

[38]  Dwight W Read,et al.  A multitrajectory, competition model of emergent complexity in human social organization , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[39]  R. H. Myers,et al.  Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .

[40]  Marco Dorigo,et al.  Swarm intelligence: from natural to artificial systems , 1999 .

[41]  Stefano Tarantola,et al.  Sensitivity analysis practices: Strategies for model-based inference , 2006, Reliab. Eng. Syst. Saf..

[42]  Maria Fonoberova,et al.  Nonlinear Dynamics of Crime and Violence in Urban Settings , 2012, J. Artif. Soc. Soc. Simul..

[43]  Joshua M. Epstein,et al.  Modeling civil violence: An agent-based computational approach , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[44]  Philippe De Wilde.  Fuzzy utility and equilibria , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[45]  J. Friedman.  Multivariate adaptive regression splines , 1990 .

[46]  M. Zako,et al.  Structural optimization using Kriging approximation , 2003 .

[47]  Carolyn Conner Seepersad,et al.  Metamodeling Techniques for Multidimensional Ship Design Problems , 2010 .