1 Contributo su Rivista

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Predicting Exporters with Machine Learning

In this contribution, we exploit machine learning techniques to evaluate whether and how close firms are to become successful exporters. First, we train various algorithms using financial information on both exporters and non-exporters in France in 2010–2018. Thus, we show that it is possible to predict the distance non-exporters are from export status. In particular, we find that a Bayesian Additive Regression Tree with Missingness In Attributes (BART-MIA) performs better than other techniqu...

Assortativity in cognition

In pairwise interactions, where two individuals meet and play a social game with each other, assortativity in cognition means that pairs where both decision-makers use the same cognitive process are more likely to occur than what happens under random matching. In this paper, we show theoretically that assortativity in cognition may arise as a consequence of assortativity in other dimensions. Moreover, we analyze an applied model where we investigate the effects of assortativity in cognition o...

The supramodality “spillover” from neuroscience to cognitive sciences: a commentary on Calzavarini (2023)

This is a commentary on Calzavarini (Citation2023), Rethinking Modality-Specificity in the Cognitive Neuroscience of Concrete Word Meaning: A Position Paper 10.1080/23273798.2023.2173789.

Auto-tuning of reference models in direct data-driven control

Designing controllers directly from data often requires choosing a reference closed-loop model, whose behavior should be reproduced as tightly as possible by the actual closed-loop system via the selected controller structure (e.g., PID). Within a linear setting, we present a derivative-based approach to jointly select the reference model and controller parameters directly from data. The proposed strategy allows one to maximize closed-loop performance while enforcing user-defined constraints,...

Counter-Example Guided Inductive Synthesis of Control Lyapunov Functions for Uncertain Systems

We propose a counter-example guided inductive synthesis (CEGIS) scheme for the design of control Lyapunov functions and associated state-feedback controllers for linear systems affected by parametric uncertainty with arbitrary shape. In the CEGIS framework, a learner iteratively proposes a candidate control Lyapunov function and a tailored controller by solving a linear matrix inequality (LMI) feasibility problem, while a verifier either falsifies the current candidate by producing a counter-...

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