Alfarano, S., Lux, T., and Wagner, F. (2005). Estimation of agent-based models: The case of an asymmetric herding model. Computational Economics, 26(1):19–49.
Alfarano, S., Lux, T., and Wagner, F. (2006). Estimation of a simple agent-based model of financial markets: An application to australian stock and foreign exchange data. Physica A: Statistical Mechanics and its Applications, 370(1):38 – 42.
Amilon, H. (2008). Estimation of an adaptive stock market model with heterogeneous agents. Journal of Empirical Finance, 15(2):342 – 362.
- An, G. and Wilensky, U. (2009). From artificial life to in silico medicine. In Komosinski, M. and Adamatzky, A., editors, Artificial Life Models in Software, pages 183–214. Springer London, London.
Paper not yet in RePEc: Add citation now
- Anderson, P. W. et al. (1972). More is different. Science, 177(4047):393–396.
Paper not yet in RePEc: Add citation now
Archer, K. J. and Kimes, R. V. (2008). Empirical characterization of random forest variable importance measures.
Assenza, T., Gatti, D. D., and Grazzini, J. (2015). Emergent dynamics of a macroeconomic agent based model with capital and credit. Journal of Economic Dynamics and Control, 50:5–28.
Banerjee, A. V. (1992). A simple model of herd behavior. The Quarterly Journal of Economics, 107(3):797–817.
Barde, S. (2016a). Direct comparison of agent-based models of herding in financial markets. Journal of Economic Dynamics and Control, 73:329 – 353.
Barde, S. (2016b). A practical, accurate, information criterion for nth order markov processes. Computational Economics, pages 1–44.
Bargigli, L., Riccetti, L., Russo, A., and Gallegati, M. (2016). Network Calibration and Metamodeling of a Financial Accelerator Agent Based Model. Working papers, economics, Universita̠degli Studi di Firenze, Dipartimento di Scienze per l’Economia e l’Impresa.
- Bergstra, J. and Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13(Feb):281–305.
Paper not yet in RePEc: Add citation now
- Booker, A., Dennis, J.E., J., Frank, P., Serafini, D., Torczon, V., and Trosset, M. (1999). A rigorous framework for optimization of expensive functions by surrogates. Structural optimization, 17(1):1–13.
Paper not yet in RePEc: Add citation now
Boswijk, H., Hommes, C., and Manzan, S. (2007). Behavioral heterogeneity in stock prices. Journal of Economic Dynamics and Control, 31(6):1938 – 1970.
Bottazzi, G. and Secchi, A. (2006). Explaining the distribution of firm growth rates. The RAND Journal of Economics, 37(2):235–256.
- Breiman, L. (2001). Random forests. Machine learning, 45(1):5–32.
Paper not yet in RePEc: Add citation now
- Breiman, L., Friedman, J., Stone, C. J., and Olshen, R. A. (1984). Classification and regression trees. CRC press.
Paper not yet in RePEc: Add citation now
Brock, W. A. and Hommes, C. H. (1997). A rational route to randomness. Econometrica, 65(5):1059–1095.
Brock, W. A. and Hommes, C. H. (1998). Heterogeneous beliefs and routes to chaos in a simple asset pricing model. Journal of Economic Dynamics and Control, 22(8–9):1235 – 1274.
- Brown, D. G., Page, S., Riolo, R., Zellner, M., and Rand, W. (2005). Path dependence and the validation of agent-based spatial models of land use. International Journal of Geographical Information Science, 19(2):153– 174.
Paper not yet in RePEc: Add citation now
Caiani, A., Godin, A., Caverzasi, E., Gallegati, M., Kinsella, S., and Stiglitz, J. E. (2016). Agent based-stock flow consistent macroeconomics: Towards a benchmark model. Journal of Economic Dynamics and Control, 69:375–408.
- Carley, K. M., Fridsma, D. B., Casman, E., Yahja, A., Altman, N., Chen, L.-C., Kaminsky, B., and Nave, D. (2006). Biowar: scalable agent-based model of bioattacks. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 36(2):252–265.
Paper not yet in RePEc: Add citation now
Castaldi, C. and Dosi, G. (2009). The patterns of output growth of firms and countries: Scale invariances and scale specificities. Empirical Economics, 37(3):475–495.
- Chen, S.-H., Chang, C.-L., and Du, Y.-R. (2012). Agent-based economic models and econometrics. The Knowledge Engineering Review, 27:187–219.
Paper not yet in RePEc: Add citation now
- Chen, T. and Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 785–794. ACM.
Paper not yet in RePEc: Add citation now
Chiarella, C., Iori, G., and PerelloÃŒÂ, J. (2009). The impact of heterogeneous trading rules on the limit order book and order flows. Journal of Economic Dynamics and Control, 33(3):525–537.
- Cireşan, D. C., Giusti, A., Gambardella, L. M., and Schmidhuber, J. (2013). Mitosis detection in breast cancer histology images with deep neural networks. In International Conference on Medical Image Computing and Computer-assisted Intervention, pages 411–418. Springer.
Paper not yet in RePEc: Add citation now
- Claesen, M., Simm, J., Popovic, D., Moreau, Y., and De Moor, B. (2014). Easy hyperparameter search using optunity. arXiv preprint arXiv:1412.1114.
Paper not yet in RePEc: Add citation now
- Conti, S. and O’Hagan, A. (2010). Bayesian emulation of complex multi-output and dynamic computer models. Journal of statistical planning and inference, 140(3):640–651.
Paper not yet in RePEc: Add citation now
- Dawid, H., Gemkow, S., Harting, P., Van der Hoog, S., and Neugart, M. (2014a). Agent-based macroeconomic modeling and policy analysis: the eurace@ unibi model. Technical report, Bielefeld Working Papers in Economics and Management.
Paper not yet in RePEc: Add citation now
Dawid, H., Harting, P., and Neugart, M. (2014b). Economic convergence: Policy implications from a heterogeneous agent model. Journal of Economic Dynamics and Control, 44:54–80.
- De Marchi, S. (2005). Computational and mathematical modeling in the social sciences. Cambridge University Press.
Paper not yet in RePEc: Add citation now
Dosi, G. (1988). Sources, procedures and microeconomic effects of innovation. Journal of Economic Literature, 26:126–71.
Dosi, G., Fagiolo, G., and Roventini, A. (2010). Schumpeter meeting Keynes: A policy-friendly model of endogenous growth and business cycles. Journal of Economic Dynamics and Control, 34(9):1748–1767.
Dosi, G., Fagiolo, G., Napoletano, M., and Roventini, A. (2013). Income distribution, credit and fiscal policies in an agent-based Keynesian model. Journal of Economic Dynamics and Control, 37(8):1598–1625.
Dosi, G., Fagiolo, G., Napoletano, M., Roventini, A., and Treibich, T. (2015). Fiscal and monetary policies in complex evolving economies. Journal of Economic Dynamics and Control, 52(C):166–189.
Dosi, G., Pereira, M. C., and Virgillito, M. E. (2017c). On the robustness of the fat-tailed distribution of firm growth rates: a global sensitivity analysis. Journal of Economic Interaction and Coordination, pages 1–21.
Dosi, G., Pereira, M., Roventini, A., and Virgillito, M. (2017a). When more flexibility yields more fragility: The microfoundations of keynesian aggregate unemployment. Journal of Economic Dynamics and Control, forthcoming.
Dosi, G., Pereira, M., Roventini, A., and Virgillito, M. E. (2016). The Effects of Labour Market Reforms upon Unemployment and Income Inequalities: an Agent Based Model. LEM Working Papers Series 2016-27, Scuola Superiore Sant’Anna.
Dosi, G., Pereira, M., Roventini, A., and Virgillito, M. E. (2017b). Causes and consequences of hysteresis: Aggregate demand, productivity and employment. LEM Working Papers Series 2017-07, Scuola Superiore Sant’Anna.
- Effken, J. A., Carley, K. M., Lee, J.-S., Brewer, B. B., and Verran, J. A. (2012). Simulating nursing unit performance with orgahead: strengths and challenges. Computers, informatics, nursing: CIN, 30(11):620.
Paper not yet in RePEc: Add citation now
Fabretti, A. (2012). On the problem of calibrating an agent based model for financial markets. Journal of Economic Interaction and Coordination, 8(2):277–293.
Fagiolo, G. and Dosi, G. (2003). Exploitation, exploration and innovation in a model of endogenous growth with locally interacting agents. Structural Change and Economic Dynamics, 14(3):237–273.
Fagiolo, G. and Roventini, A. (2012). Macroeconomic policy in dsge and agent-based models. Revue de l’OFCE, 124:67–116.
Fagiolo, G. and Roventini, A. (2017). Macroeconomic policy in dsge and agent-based models redux: New developments and challenges ahead. Journal of Artificial Societies and Social Simulation, 20(1).
Fagiolo, G., Birchenhall, C., and Windrum, P. (2007). Empirical validation in agent-based models: Introduction to the special issue. Computational Economics, 30(3):189–194.
Fagiolo, G., Napoletano, M., and Roventini, A. (2008). Are output growth-rate distributions fat-tailed? some evidence from oecd countries. Journal of Applied Econometrics, 23(5):639–669.
- Feurer, M., Klein, A., Eggensperger, K., Springenberg, J., Blum, M., and Hutter, F. (2015). Efficient and robust automated machine learning. In Advances in Neural Information Processing Systems, pages 2962–2970.
Paper not yet in RePEc: Add citation now
Franke, R. (2009). Applying the method of simulated moments to estimate a small agent-based asset pricing model. Journal of Empirical Finance, 16(5):804 – 815.
Franke, R. and Westerhoff, F. (2012). Structural stochastic volatility in asset pricing dynamics: Estimation and model contest. Journal of Economic Dynamics and Control, 36(8):1193–1211.
- Freund, Y. (1990). Boosting a weak learning algorithm by majority. In COLT, volume 90, pages 202–216.
Paper not yet in RePEc: Add citation now
- Freund, Y., Schapire, R. E., et al. (1996). Experiments with a new boosting algorithm. In Icml, volume 96, pages 148–156.
Paper not yet in RePEc: Add citation now
- Gallegati, M. and Kirman, A. (2012). Reconstructing economics. Complexity Economics, 1(1):5–31.
Paper not yet in RePEc: Add citation now
Gilli, M. and Winker, P. (2003). A global optimization heuristic for estimating agent based models. Computational Statistics & Data Analysis, 42(3):299 – 312. Computational Ecomometrics.
- Goldberg, A. B., Zhu, X., Furger, A., and Xu, J.-M. (2011). Oasis: Online active semi-supervised learning. In AAAI.
Paper not yet in RePEc: Add citation now
Grazzini, J. (2012). Analysis of the emergent properties: Stationarity and ergodicity. Journal of Artificial Societies and Social Simulation, 15(2):7.
Grazzini, J. and Richiardi, M. (2015). Estimation of ergodic agent-based models by simulated minimum distance. Journal of Economic Dynamics and Control, 51:148 – 165.
Grazzini, J., Richiardi, M. G., and Tsionas, M. (2017). Bayesian estimation of agent-based models. Journal of Economic Dynamics and Control, 77:26 – 47.
- Grimm, V. and Railsback, S. F. (2013). Individual-based modeling and ecology. Princeton university press.
Paper not yet in RePEc: Add citation now
Guerini, M. and Moneta, A. (2016). A Method for Agent-Based Models Validation. LEM Papers Series 2016/16, Laboratory of Economics and Management (LEM), Sant’Anna School of Advanced Studies, Pisa, Italy.
- Herlands, W., Wilson, A., Nickisch, H., Flaxman, S., Neill, D., Van Panhuis, W., and Xing, E. (2015). Scalable gaussian processes for characterizing multidimensional change surfaces. arXiv preprint arXiv:1511.04408.
Paper not yet in RePEc: Add citation now
- Ilachinski, A. (1997). Irreducible semi-autonomous adaptive combat (isaac): An artificial-life approach to land warfare. Technical report, DTIC Document.
Paper not yet in RePEc: Add citation now
Kukacka, J. and Barunik, J. (2016). Estimation of financial agent-based models with simulated maximum likelihood. IES Working Paper 7/2016, Charles University of Prague.
Lamperti, F. (2016). Empirical Validation of Simulated Models through the GSL-div: an Illustrative Application.
Lamperti, F. (2017). An information theoretic criterion for empirical validation of simulation models. Econometrics and Statistics, forthcoming.
Lamperti, F. and Mattei, C. E. (2016). Going Up and Down: Rethinking the Empirics of Growth in the Developing and Newly Industrialized World. LEM Papers Series 2016/01, Laboratory of Economics and Management (LEM), Sant’Anna School of Advanced Studies, Pisa, Italy.
Lamperti, F., Dosi, G., Napoletano, M., Roventini, A., and Sapio, A. (2017). Faraway, so close: coupled climate and economic dynamics in an agent based integrated assessment model. Lem working papers series, Scuola Superiore Sant’Anna.
Leal, S. J., Napoletano, M., Roventini, A., and Fagiolo, G. (2014). Rock around the clock: an agent-based model of low-and high-frequency trading. Journal of Evolutionary Economics, pages 1–28.
Lee, J.-S., Filatova, T., Ligmann-Zielinska, A., Hassani-Mahmooei, B., Stonedahl, F., Lorscheid, I., Voinov, A., Polhill, J. G., Sun, Z., and Parker, D. C. (2015). The complexities of agent-based modeling output analysis. Journal of Artificial Societies and Social Simulation, 18(4):4.
- LEM Papers Series 2016/18, Laboratory of Economics and Management (LEM), Sant’Anna School of Advanced Studies, Pisa, Italy.
Paper not yet in RePEc: Add citation now
- Li, X., Engelbrecht, A., and Epitropakis, M. G. (2013). Benchmark functions for cec’2013 special session and competition on niching methods for multimodal function optimization. RMIT University, Evolutionary Computation and Machine Learning Group, Australia, Tech. Rep.
Paper not yet in RePEc: Add citation now
- Louppe, G., Wehenkel, L., Sutera, A., and Geurts, P. (2013). Understanding variable importances in forests of randomized trees. In Advances in neural information processing systems, pages 431–439.
Paper not yet in RePEc: Add citation now
Lux, T. and Marchesi, M. (2000). Volatility clustering in financial markets: a microsimulation of interacting agents. International journal of theoretical and applied finance, 3(04):675–702.
- Macy, M. W. and Willer, R. (2002). From factors to actors: Computational sociology and agent-based modeling. Annual review of sociology, pages 143–166.
Paper not yet in RePEc: Add citation now
- Marks, R. E. (2013). Validation and model selection: Three similarity measures compared. Complexity Economics, 2(1):41–61.
Paper not yet in RePEc: Add citation now
- Morokoff, W. J. and Caflisch, R. E. (1994). Quasi-random sequences and their discrepancies. SIAM Journal on Scientific Computing, 15(6):1251–1279.
Paper not yet in RePEc: Add citation now
- Moss, S. (2008). Alternative approaches to the empirical validation of agent-based models. Journal of Artificial Societies and Social Simulation, 11(1):5.
Paper not yet in RePEc: Add citation now
- Petrovic, S., Osborne, M., and Lavrenko, V. (2011). Rt to win! predicting message propagation in twitter. ICWSM, 11:586–589.
Paper not yet in RePEc: Add citation now
- Platt, J. et al. (1999). Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in large margin classifiers, 10(3):61–74.
Paper not yet in RePEc: Add citation now
Popoyan, L., Napoletano, M., and Roventini, A. (2017). Taming Macroeconomic Instability: Monetary and Macro Prudential Policy Interactions in an Agent-Based Model. Journal of Economic Behavior & Organization, 134:117–140.
- Rasmussen, C. E. and Williams, C. K. I. (2006). Gaussian processes for machine learning. MIT Press.
Paper not yet in RePEc: Add citation now
Recchioni, M. C., Tedeschi, G., and Gallegati, M. (2015). A calibration procedure for analyzing stock price dynamics in an agent-based framework. Journal of Economic Dynamics and Control, 60:1 – 25.
- Ross, S., Gordon, G. J., and Bagnell, D. (2011). A reduction of imitation learning and structured prediction to no-regret online learning. In AISTATS, volume 1(2), page 6.
Paper not yet in RePEc: Add citation now
- Ryabko, D. (2016). Things bayes can’t do. In International Conference on Algorithmic Learning Theory, pages 253–260. Springer.
Paper not yet in RePEc: Add citation now
Salle, I. and Yildizoglu, M. (2014). Efficient Sampling and Meta-Modeling for Computational Economic Models. Computational Economics, 44(4):507–536.
- Saltelli, A., Annoni, P., Azzini, I., Campolongo, F., Ratto, M., and Tarantola, S. (2010). Variance based sensitivity analysis of model output. design and estimator for the total sensitivity index. Computer Physics Communications, 181(2):259–270.
Paper not yet in RePEc: Add citation now
- Settles, B. (2010). Active learning literature survey. Technical Report 55-66, University of Wisconsin, Madison.
Paper not yet in RePEc: Add citation now
Squazzoni, F. (2010). The impact of agent-based models in the social sciences after 15 years of incursions. History of Economic Ideas, pages 197–233.
- Subbotin, M. T. (1923). On the law of frequency of error. Matematicheskii Sbornik, 31(2):296–301.
Paper not yet in RePEc: Add citation now
ten Broeke, G., van Voorn, G., and Ligtenberg, A. (2016). Which sensitivity analysis method should i use for my agent-based model? Journal of Artificial Societies & Social Simulation, 19(1).
Tesfatsion, L. and Judd, K. L. (2006). Handbook of computational economics: agent-based computational economics, volume 2. Elsevier.
Thiele, J. C., Kurth, W., and Grimm, V. (2014). Facilitating parameter estimation and sensitivity analysis of agent-based models: A cookbook using netlogo and r. Journal of Artificial Societies and Social Simulation, 17(3):11.
van der Hoog, S. (2016). Deep Learning in Agent-Based Models: A Prospectus. Technical report, Faculty of Business Administration and Economics, Bielefeld University.
- Van Rijsbergen, C. (1979). Information Retrieval. London: Butterworths.
Paper not yet in RePEc: Add citation now
Weeks, M. (1995). Circumventing the curse of dimensionality in applied work using computer intensive methods. The Economic Journal, 105(429):520–530.
- Wilson, A. G., Dann, C., and Nickisch, H. (2015). Thoughts on massively scalable gaussian processes. arXiv preprint arXiv:1511.01870.
Paper not yet in RePEc: Add citation now
Winker, P., Gilli, M., and Jeleskovic, V. (2007). An objective function for simulation based inference on exchange rate data. Journal of Economic Interaction and Coordination, 2(2):125–145.
- Wolpert, D. H. (2002). The supervised learning no-free-lunch theorems. In Soft Computing and Industry, pages 25–42. Springer.
Paper not yet in RePEc: Add citation now
- Wong, K.-C. (2015). Evolutionary multimodal optimization: A short survey. arXiv preprint arXiv:1508.00457.
Paper not yet in RePEc: Add citation now
- Zhu, X. (2005). Semi-supervised learning literature survey. Technical report, University of Wisconsin-Madison. ABOUT OFCE The Paris-based Observatoire français des conjonctures économiques (OFCE), or French Economic Observatory is an independent and publicly-funded centre whose activities focus on economic research, forecasting and the evaluation of public policy.
Paper not yet in RePEc: Add citation now