Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Using the ll-known Artificial Anasazi simulation for a case study,  investigate the use of genetic algorithms (GAs) for performing two common tasks related to robustness checking of agent-based models: parameter calibration and sensitivity analysis.  In the calibration task,  demonstrate that a GA approach is able to find parameters that are equally good or better at minimizing error versus historical data, compared to a previous factorial grid-based approach. The GA approach also allows us to explore a wider range of parameters and parameter settings.  Previous univariate sensitivity analysis on the Artificial Anasazi model did not consider potentially complex/nonlinear interactions beten parameters.  With the GA-based approach,  perform multivariate sensitivity analysis to discover how greatly the model can diverge from historical data, while the parameters are constrained within a close range of previously calibrated values.   show that by varying multiple parameters within a 10% range, the model can produce dramatically and qualitatively different results, and further demonstrate the utility of sensitivity analysis for model testing, by the discovery of a small coding error.  Through this case study,  discuss some of the issues that can arise with calibration and sensitivity analysis of agent-based models.
['Forrest Stonedahl', 'Uri Wilensky']
I am writing a research paper on a idea Evolutionary Robustness Checking in the Artificial Anasazi Model
[{"from": "human", "value": "I am writing a research paper on a idea Evolutionary Robustness Checking in the Artificial Anasazi Model"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Using the ll-known Artificial Anasazi simulation for a case study,  investigate the use of genetic algorithms (GAs) for performing two common tasks related to robustness checking of agent-based models: parameter calibration and sensitivity analysis.  In the calibration task,  demonstrate that a GA approach is able to find parameters that are equally good or better at minimizing error versus historical data, compared to a previous factorial grid-based approach. The GA approach also allows us to explore a wider range of parameters and parameter settings.  Previous univariate sensitivity analysis on the Artificial Anasazi model did not consider potentially complex/nonlinear interactions beten parameters.  With the GA-based approach,  perform multivariate sensitivity analysis to discover how greatly the model can diverge from historical data, while the parameters are constrained within a close range of previously calibrated values.   show that by varying multiple parameters within a 10% range, the model can produce dramatically and qualitatively different results, and further demonstrate the utility of sensitivity analysis for model testing, by the discovery of a small coding error.  Through this case study,  discuss some of the issues that can arise with calibration and sensitivity analysis of agent-based models."}]