Integrating behavioural insights and machine learning for smarter regulatory interventions

Descripción del proyecto

Entrenar el aprendizaje automático en ciencias del comportamiento para facilitar la gobernanza regulatoria

Las decisiones regulatorias que abordan cuestiones sociales complejas a menudo se basan en soluciones convencionales y juicios intuitivos que no tienen en cuenta factores conductuales, culturales y contextuales. Para abordar esta cuestión, el proyecto BEHAVREG, financiado por el Consejo Europeo de Investigación, creará una novedosa herramienta de aprendizaje automático integrada con datos de comportamiento para predecir los resultados de cumplimiento de las opciones normativas y facilitar una toma de decisiones eficaz. Aprovechando amplios conjuntos de datos sobre evaluaciones de impacto preliminares y revisiones posteriores a la implementación, se desarrollarán modelos predictivos que evalúen la eficacia de las intervenciones regulatorias en diferentes contextos. Básicamente, el sistema alertará a los reguladores sobre posibles consecuencias adversas o no deseadas, lo cual les permitirá repensar una intervención antes de seguir adelante. En general, se espera que este sistema innovador mejore el diseño regulatorio, mejorando los resultados de cumplimiento y la confianza pública.

Objetivo

Effective regulatory decision-making is essential for addressing complex societal challenges, yet current approaches often rely on intuition and standardized solutions that fail to account for behavioral, cultural, and contextual factors. BEHAVREG will transform regulatory governance by developing an innovative decision aid tool that integrates machine learning with behavioral science to predict likely compliance outcomes of regulatory choices and enable more effective intervention design. This project leverages comprehensive datasets, including ex-ante impact assessments and post-implementation reviews, to develop sophisticated predictive models that assess intervention effectiveness across diverse contexts. The tool analyzes critical factors such as compliance costs, social control mechanisms, institutional trust, and cultural norms to provide nuanced recommendations that move
beyond binary regulatory choices.
BEHAVREG's machine learning algorithms, trained on structured historical data, will identify patterns to improve prediction accuracy and evaluate regulatory trade-offs. This will empower regulators to select appropriate interventions—whether mandates, incentives, nudges, or information campaigns—precisely tailored to specific regulatory environments. Importantly, the system can predict potential backfire effects and unintended consequences, enabling regulators to anticipate challenges before implementation. By bridging the gap between theoretical insights and practical implementation, BEHAVREG delivers evidence-based predictions and actionable recommendations. This innovation will optimize regulatory design across sectors and cultures and will improve compliance outcomes, reduce enforcement costs, and enhance public trust—representing a significant advancement in regulatory governance with global implications for policy development and implementation.