Daniela Grieco

Biography

Daniela Grieco is professor of economic policy at the University of Milan (Italy), Department of Law “Cesare Beccaria”. During her USIAS fellowship, she will be hosted by Professor Patrick Llerena at the Bureau of Theoretical and Applied Economics (BETA) of the University of Strasbourg.

She has over 15 years of research experience in behavioural and experimental economics and in the economics of innovation and creativity, with a particular focus on decision-making, team dynamics, and the interaction between humans and emerging technologies such as artificial intelligence (AI). Her work lies at the intersection of economics and behavioural sciences, addressing both fundamental theoretical questions and policy-relevant issues.

She holds a PhD in economics and a bachelor’s degree in economics and social sciences (summa cum laude) from Bocconi University. Before joining the University of Milan, she held permanent faculty positions at Bocconi University and research appointments at the Italian National Research Council (CNR). She is currently research quality coordinator for her department, a member of the Steering Committee of it’s Project of Excellence (2023–27), and serves on the Scientific Board of the Social Interaction Lab at the University of Strasbourg.

Daniela Grieco’s research has been published in leading international journals, including the Economic Journal, Journal of the European Economic Association, Experimental Economics and Journal of Economic Behavior & Organization. Her recent work focuses on creativity, incentives, and the impact of generative AI on individual and collective performance. She is the recipient of several competitive grants and honours from a number of organisations, including the Fondazione Cariplo, UNU‑WIDER, the European Economic Association, the Paris Dauphine University and the Bank of Italy; and has been invited to present her research at major international conferences and universities worldwide.

Fellowship 2026

Dates - 01/11/2026-30/04/2028

Project summary

COLLECTIVE CREATIVITY, TEAM INTERACTION, AND (UNEQUAL) ACCESS TO GENERATIVE ARTIFICIAL INTELLIGENCE

Creative work is increasingly carried out in teams and is now profoundly shaped by the rapid diffusion of generative artificial intelligence (AI). While AI tools promise to enhance productivity and idea generation, little is known about how they affect collective creativity, that is how people interact, exchange ideas, and jointly produce creative outcomes when working together. Existing research has focused mainly on individuals or on highly mediated forms of interaction, leaving a gap in our understanding of real-time team dynamics and the consequences of team members’ access to AI. This project addresses this gap by studying how human teams create together, with and without AI support, in environments that closely resemble real-world collaboration.

The project presents a series of large-scale laboratory experiments to investigate three cutting-edge issues: non-mediated face-to-face interaction in creative teams, the role of AI as a ‘teammate’ rather than a mere tool, and the effects of unequal AI access and training on collective performance. Methodologically, it advances the state of the art by exploiting innovative laboratory infrastructures that allow direct interaction among participants while capturing rich behavioural data (audio, video, and movement tracking). This enables a fine-grained analysis of communication, leadership, coordination, and reliance on AI—dimensions that are difficult to explore realistically in standard laboratory settings.

The experimental protocol has a 2×3 design, comparing mediated versus non-mediated interaction and varying access to AI (none, equal training, unequal training). Teams will perform incentivized creative and routine tasks, and their outputs will be evaluated by independent external judges. The expected outcomes include new evidence on how team interaction shapes creativity, when AI complements or hinders collaborative processes, and how unequal access to AI may amplify disparities in performance. These findings will be highly relevant not only for creativity and innovation research, but also for organizational design, AI governance, and policy debates on the equitable and responsible integration of AI into collective work.