A new algorithm for calibrating agent-based models is proposed, which employs a popular gradient boosting framework. Machine learning techniques are not used to develop a surrogate model, but rather assist in narrowing down the parameter space during the search for optimal parameters. Our...

In order to verify the effects of machine learning in a market structure, an evolutionary model containing firms that use a genetic algorithm to decide their investment in innovative R&D was developed. These firms share the market, with two other types of firms, those with a fixed rate of...

The "histogram method" (Young, 2010), while the standard approach for analyzing distributional dynamics in heterogeneous agent models, is linear in optimal policies. We introduce a novel method that captures nonlinearities of distributional dynamics. This method solves the distributional...

The main objective of this study is to present a two-step approach to generate estimates of economic growth based on agents' expectations from tendency surveys. First, we design a genetic programming experiment to derive mathematical functional forms that approximate the target variable by...

The main objective of this study is to present a two-step approach to generate estimates of economic growth based on agents' expectations from tendency surveys. First, we design a genetic programming experiment to derive mathematical functional forms that approximate the target variable by...

Mathematical programming (MP) is a widespread approach to depict production and investment decisions of agents in agent-based models (ABM) related to agriculture. However, introducing dynamics and indivisibilities in MP models renders their solution computing time intensive. We present a...