Professor of Behavioural Science and Computational Social Science
Department of Sociology and Social Research
University of Trento
Behavioural and Implementation Science Interventions (BISI), Yong Loo Lin School of Medicine
National University of Singapore
What I study
Designing and analysing randomised controlled trials for behavioural interventions and public policy. Translating evidence into deliverable strategies through frameworks for time-sensitive interventions, personalised interventions, and implementation science.
Applying computational methods—from sequence analysis, network models, and agent-based models to large language models and AI—to understand social phenomena and human behaviour at scale.
Experimental design, online experiments, multiverse analysis, metascience, and quantitative methods for social science. Author of open-source R packages including multiverseRCT, BriDGE, and sequenceRCT.
Policy and Society
Behavioural interventions must navigate not only evidence of what works, but also political legitimacy and implementation feasibility.
This article develops a three-dimensional framework for behavioural policy design that integrates evidence, legitimacy, and delivery. Moving beyond the conventional focus on 'what works', the framework highlights how behavioural interventions must also navigate political acceptability and implementation feasibility. By mapping these three dimensions together, the article provides policymakers and researchers with a structured approach to designing behavioural policies that are not only effective but also politically viable and practically deliverable.
Read ArticleSelected work
Veltri, G. A., Mureddu, F., Innocenti, A., Sirizzotti, M., & Venturini, E. (2026). Regulatory frameworks and digital identity in the metaverse: an exploratory virtual reality experiment on trust, privacy, and openness. Frontiers in Virtual Reality, 7:1779260. DOI
Veltri, G. A. (2026). S-Frame vs. I-Frame Interventions: The Transformation-Robustness Trade-Off in Behavioral Policy. Journal of Artificial Societies and Social Simulation. In press.
Veltri, G. A. (2026). Navigating evidence, legitimacy and delivery: A three-dimensional framework for behavioural policy design. Policy and Society. DOI
Veltri, G.A.(2026). Time-Sensitive RCTs in Behavioural Public Policy: A Pragmatic Framework Using Sequence Methods, Personalisation, and Reinforcement Learning. Frontiers in Behavioral Economics. In press. DOI
De Duro, E. S., Franchino, E., Improta, R., Veltri, G. A., & Stella, M. (2026). Cognitive networks identify AI biases on societal issues in Large Language Models. EPJ Data Science. DOI
Veltri, G.A. (2025). The Effects of Data Preprocessing Choices on Behavioral RCT Outcomes: A Multiverse Analysis. Multivariate Behavioral Research. DOI
Veltri, G.A. (2025). From Evidence to Delivery: An Implementation‑Science Blueprint for Behavioural Policy. Behavioural Public Policy. DOI
Andrei, F., & Veltri, G. A. (2025). Signalling strategies and opportunistic behaviour: Insights from dark-net markets. PLOS ONE, 20(3), e0319794. DOI
Work in progress
Carrillo, A., Citraro, S., Ardebili, A. A., Taietta, E., Rossetti, G., Ferrara, E., Veltri, G. A., & Stella, M. (2026). LLMs can persuade only psychologically susceptible humans on societal issues, via trust in AI and emotional appeals, amid logical fallacies. arXiv. Preprint
Veltri, G. A., & Gilbert, J. (2026). Results from Randomized Controlled Trials are Highly Sensitive to Data Preprocessing Decisions: A Multiverse Analysis of 97 Outcomes. MetaArXiv. Preprint
Carrillo, A., Taietta, E., Ardebili, A. A., Veltri, G. A., & Stella, M. (2026). Talk2AI: A Longitudinal Dataset of Human–AI Persuasive Conversations. arXiv. Preprint
Andrei, F., Tizzoni, M., & Veltri, G. A. (2026). Dengue risk perception and public preferences for vector control in Italy and France: utility and regret-based choice experiments. medRxiv. Preprint