_:n89269401ac6c48048bb87540088b25dcxb0
<http://schema.org/description>
"Agent-Based Models (ABMs) are used in several fields to study the evolution\nof complex systems from micro-level assumptions. However, ABMs typically can\nnot estimate agent-specific (or \"micro\") variables: this is a major limitation\nwhich prevents ABMs from harnessing micro-level data availability and which\ngreatly limits their predictive power. In this paper, we propose a protocol to\nlearn the latent micro-variables of an ABM from data. The first step of our\nprotocol is to reduce an ABM to a probabilistic model, characterized by a\ncomputationally tractable likelihood. This reduction follows two general design\nprinciples: balance of stochasticity and data availability, and replacement of\nunobservable discrete choices with differentiable approximations. Then, our\nprotocol proceeds by maximizing the likelihood of the latent variables via a\ngradient-based expectation maximization algorithm. We demonstrate our protocol\nby applying it to an ABM of the housing market, in which agents with different\nincomes bid higher prices to live in high-income neighborhoods. We demonstrate\nthat the obtained model allows accurate estimates of the latent variables,\nwhile preserving the general behavior of the ABM. We also show that our\nestimates can be used for out-of-sample forecasting. Our protocol can be seen\nas an alternative to black-box data assimilation methods, that forces the\nmodeler to lay bare the assumptions of the model, to think about the\ninferential process, and to spot potential identification problems."
<http://cdnjs.deepai.org/publication/on-learning-agent-based-models-from-data>