SEEL Staff


Aidin Hajikhameneh


Aidin joined the Department of Economics at San Jose State University in 2018. He earned his Ph.D. from Simon Fraser University in 2016. He recently completed a two-year post-doctoral fellowship at the Institute for the Study of Religion, Economics and Society at Chapman University. His research interests stretch over the fields of experimental economics, behavioral economics, and economic history. Broadly speaking, through the fusion of economic history and laboratory experiment, he is interested in delineating the role of culture, religion, and enforcement institutions in process of economic decision making.


Hajikhameneh, A. and Rubin, J., 2019. Exchange in the Absence of Legal Enforcement: Reputation and Multilateral Punishment under Uncertainty. The Journal of Law, Economics, and Organization, 35(1), pp. 192-237.

Hajikhameneh, A. and Kimbrough, E.O., 2019. Individualism, collectivism, and trade. Experimental Economics, 22(2), pp. 294-324.


Justin Rietz


Justin is an Assistant Professor of economics at San Jose State University. His research focuses on experimental macroeconomics, complex systems / agent-based modeling and monetary theory, including money search models that explore multi-currency economies and cryptocurrencies such as, Bitcoin. Previously, he worked Silicon Valley's software industry as a software product manager.


BA, Stanford University, Economics
MBA, UC Berkeley Haas School of Business
MA, San Jose State University, Economics
PhD, UC Santa Cruz, Economcs


Rietz, J. "Secondary Currency Acceptance: Experimental Evidence with a Dual Currency Search Model." Under Review. (2019).

Austin, T., Merrill, P., Rietz, J., Thakker, J.K., & Park, Y. (2019).  Locking Tokens for Free Blockchain Transactions. IEEE DAPPCON 2019. San Francisco, CA.

Merrill, P., Austin, T., Rietz, J. and Pearce, J.  (2019). Ping-Pong Governance: Token Locking for Enabling Blockchain Self-Governance.  MARBLE 2019. Santorini, Greece.

Rietz, J. “Secondary Currency Acceptance in an Agent-Based Model with Adaptive Learning.”  Working paper.