Project description
Training machine learning in behavioural science to facilitate regulatory governance
Regulatory decisions that address complex societal issues are often based on conventional solutions and intuitive judgements that fail to consider behavioural, cultural and contextual factors. To address this, the ERC-funded BEHAVREG project will create a novel machine learning tool integrated with behavioural data to predict compliance outcomes of regulatory options and facilitate effective decision-making. Exploiting extensive datasets on preliminary impact assessments and post-implementation reviews, it will develop predictive models that evaluate the efficacy of regulatory interventions in different contexts. Essentially, the system will alert regulators to potential adverse or unintended consequences, allowing them to rethink an intervention before moving forward. Overall, this innovative system is expected to improve regulatory design, improving compliance outcomes and public trust.
Objective
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.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-ERC-POC - HORIZON ERC Proof of Concept Grants
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2025-POC
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
52900 Ramat Gan
Israel
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.