Better Governance: Professional Practice in Governance and Public Organizations

Daniel Armani is an applied econometrician and data scientist whose work examines how governance, AI, and blockchain technologies shape collective action and economic performance. His research focuses on automated governance and decentralized autonomous organizations (DAOs), which he designs and evaluates using online experimentation and agent-based simulation. He has conducted research on collective intelligence and governance mechanisms at Michigan State University, the University of Texas at Austin (as Principal Investigator), and the University of Delaware, and has taught at all three institutions. He holds a Master's in Socio-economic Systems and a PhD in Management Information Systems.