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.
Behavioural Public Policy. In press.
The same seeding intervention can stall, drift, or cascade depending on the social-learning rules that govern a population.
This article develops a stylised agent-based model to examine how four canonical social-learning rules—conformist transmission, prestige-biased copying, payoff-biased copying, and random copying—shape the impact of behavioural policy seeding interventions. By comparing treatment arms that seed 5% baseline adopters plus 20% of remaining non-adopters against 5%-only control arms, the model traces adoption trajectories across homogeneous and mixed populations. Findings show that conformist dynamics produce threshold effects that erase treatment gains, while prestige-biased and random copying can preserve positive lift when diffusion remains incomplete. The article translates these dynamics into policy heuristics: evaluate interventions relative to local diffusion potential, make payoff signals visible, and tailor seeding to the prevailing mix of learning rules.
Selected 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