[Invited Talk] Quantifying Human Behaviors in Complex Games
- Topic : [Invited Talk] Quantifying Human Behaviors in Complex Games
- Speaker: Prof. Cheng Shih-Fen (Singapore Management University)
- Date: Friday, October 7th, 2022
- Time: 2:20 pm - 3:30 pm
- Venue: R102, CSIE - DerTian Hall, NTU 臺灣大學 德田館 102會議室
In domains where agents interact strategically, game theory is applied widely to predict how agents would act. However, game-theoretic predictions are based on the assumption that agents are fully rational, which unfortunately is not possible when human decision makers are involved. To address this limitation, a number of behavioral game-theoretic models are defined to account for the limited rationality of human decision makers. One popular such model is the "cognitive hierarchy" (CH) model introduced by Camerer et al. (2004), which allows us to explicitly specify different rationality levels for agents in a game. In the CH model, non-strategic agents are regarded as level-0, and their strategies are generated irrespective of other agents (e.g., uniformly randomly or greedily). For strategic agents at level-k (with k greater than 0), they would assume that other agents would be behaving at levels less than k, and compute best responses. The CH model is studied extensively and shown to be an ideal model for capturing strategic human behaviors. Researchers over the years have proposed various ways to further improve the CH model, which are mostly through the improvements in the computations of best responses and level-0 strategies.
In particular, we propose to develop a road allocation system based on the auction mechanism design theory for the self-driving system that will act as a decentralized multi-agent system where each autonomous car is assumed to be a self-interested entity. In contrast to current behavior of human drivers who use web-applications to determine their driving routes (e.g., WAZE) in order to avoid traffic jams, we envision, that in the near future we will have the ability and technology to prevent traffic jams using an auction based mechanism within the self-driving system.
Shih-Fen Cheng is an Associate Professor of Computer Science at the Singapore Management University. He received his Ph.D. degree in industrial and operations engineering from the University of Michigan, Ann Arbor, and B.S.E. degree in mechanical engineering from the National Taiwan University.
His research focuses on the modeling and optimization of complex systems in engineering and business domains, with application in the areas of urban computing and human decision-making. He is particularly concerned about the real-world impact of his research, as illustrated by his recent research on taxi and ride-hailing industry. His research outputs and deployed system have received prestigious international awards from CIKM, AAMAS, and INFORMS. He regularly publishes in top AI conferences such as IJCAI, AAAI, and AAMAS; he also publishes widely in journals such as Transportation Science, ACM Transactions on Intelligent Systems and Technology, IEEE Transactions on Intelligent Transportation Systems, and IIE Transactions. He is long-time members of INFORMS, AAAI, and IEEE, and serves as Senior Editor for Electronic Commerce Research and Applications.