The field of artificial intelligence is enjoying renewed attention today, based on its pervasive influence on a wide range of technologies, and promise for transformative effects in the near-term future. Coincident with the economic impact of AI in this century has been an increasing embrace of economic reasoning in the design and analysis of AI systems. Developments in algorithmic game theory and mechanism design are shaping how autonomous software agents interact in the networked economies, just as such agents are proliferating in key sectors like Internet advertising and financial trading. Large-scale computing infrastructure enabling much of this activity can also be harnessed in service of reasoning in a principled way about the resulting complex strategic environments. I illustrate this through a large-scale simulation approach to analyze the implications of algorithmic and high-frequency trading in financial markets.

Michael P. Wellman is the Lynn A. Conway Collegiate Professor of Computer Science & Engineering at the University of Michigan. He received a PhD from the Massachusetts Institute of Technology in 1988 for his work in qualitative probabilistic reasoning and decision-theoretic planning. For the past 20+ years, his research has focused on computational market mechanisms and game-theoretic reasoning methods, with applications in electronic commerce, finance, and other domains of strategic decision making. Wellman previously served as Chair of the ACM Special Interest Group on Electronic Commerce (SIGecom), and as Executive Editor of the Journal of Artificial Intelligence Research. He is a Fellow of the Association for the Advancement of Artificial Intelligence and the Association for Computing Machinery, and 2014 recipient of the SIGAI Autonomous Agents Research Award.