Theme: Human (In) Compliance to Extortions: Algorithm Awareness Matters

Guest Speaker: Prof. Dr. ZHENG Jie, Shandong University

Time: July 5th, 2024 (Friday), 9:30-11:30 am.

Venue: Room 318, Business School Building

Organizer: The Department of Economics

Guest Bio:

Prof. ZHENG Jie, doctoral supervisor at the Graduate School of Economics of Shandong University, director of the Center for Research in Theoretical and Experimental Economics (CREATE) of Shandong University, and a Distinguished Professor at Shandong University. He received his B.A. and M.A. in Economics from Tsinghua University, and his M.A. and Ph.D. in Economics from the University of Washington. D. in Economics from the University of Washington, USA. He is the Associate Editor of Journal of Economic Behavior and Organization, Associate Editor of Research in Economics, and Guest Editor of several SSCI/SCI journals. His research fields include information economics, experimental economics, behavioral economics, and industrial economics. He has presided over a number of projects of the National Natural Science Foundation of China (with "excellent" evaluation), and has given keynote speeches and presentations at academic conferences. His research work has been published in Economic Research, American Economic Review (Papers and Proceedings), Games and Economic Behavior, Management Science, Nature Communications and other domestic and international economics, management, and natural sciences at home and abroad. 

Synopsis:

When confronting extortions or oppression, human may take actions to struggle against exploitation even at very high costs, eg. Spartacus Rebellion and workers organizing protests to demand better pay and benefits. Does such incompliance persist when the extortions are implemented by algorithms? This study uses an economic experiment to examine the behavioral responses of human subjects when confronted with an opponent employing an extortionate Zero-Determinant (ZD) strategy, either informed (under I condition) or uninformed (under U condition) that their opponent is an algorithm-based computer. The findings reveal a significant divergence in human behavior depending on their awareness of the opponent's algorithmic nature. In treatments with U condition, subjects demonstrated a propensity of incompliance, even at the expense of their own payoff. Conversely, in treatments with I condition, their rate of cooperation—and thus compliance—increased markedly. Further investigation into the relative income status (advantage, parity, or disadvantage) of human subjects vis-à-vis their opponents across various treatments suggested a nuanced impact of algorithm awareness on incompliance behavior. Subjects displayed a pronounced concern for their relative income status over their absolute income under U condition. However, this preoccupation with relative income status was supplanted by the pursuit of absolute payoff when exposed to I condition. This study provides insights into the influence of algorithm awareness on human economic behavior.