@article{
 title = {Agent-Based Model Exploration and Calibration using Machine Learning Surrogates},
 type = {article},
 year = {2016},
 id = {df9b1313-a55f-3596-aafd-71ec250c5d5f},
 created = {2016-10-31T11:02:53.000Z},
 file_attached = {false},
 profile_id = {8856f0a8-e934-3b54-83b3-1dc0f138e432},
 group_id = {bd0f1172-1660-3fe3-b7d0-9f2d0b4051ad},
 last_modified = {2017-03-14T12:29:36.496Z},
 tags = {DOLFINS_T1.3,DOLFINS_T2.3,DOLFINS_WP1},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Lamperti2016},
 abstract = {Bringing Agent-Based Models closer to the data is an open challenge. While facilitating the comparison to more standard approaches, getting closer to the data promotes Agent-Based Models as a methodology. In this paper, we explicitly tackle parameter space exploration and the issue of calibrating agent-based models using real data. Traditionally, three computationally expensive steps are involved: running the Agent-Based Model, measuring the calibration quality to real data and locating the parameters of interest. We demonstrate dramatic improvements in computation time by replacing the expensive Agent-Based Model and calibration measure with a machine learning surrogate that approximates a fast approximation of 1st and total order sensitivities over the parameter space. Our approach facilitates parameter exploration by a policy-maker, while providing a powerful filter to gain intuition and insight into Agent-Based Models with relatively large parameter spaces. We illustrate our approach by means of two agent-based models calibrated against different objective functions and evaluated on a large out-of-sample sensitivity analysis.},
 bibtype = {article},
 author = {Lamperti, Francesco},
 journal = {paper-progress}
}

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