A lot of research and business activity in the Cambridge/Boston area is engaged in economic and computational questions in regard to understanding and developing the economics of Internet activity. Examples of topics of interest include theoretical, modeling, algorithmic, and empirical work on electronic commerce, networked behavior, and social networks.
One of the main purposes of CAEC is to encourage collaboration between local researchers. Significant emphasis will be placed on a poster session and short talks. The overall structure of the day will involve three longer talks, by Avrim Blum, Cynthia Dwork, Mitsuru Igami and Elie Tamer, with a short talks session and a poster session over lunch, along with brief poster announcements.
CAEC17 will take place at Microsoft Research, New England, 1 Memorial Drive, Cambridge on Friday, December 1st 2017 and is supported by Microsoft Research.
Abstract. Artificial intelligence (AI) has achieved superhuman performance in a growing number of tasks, including the classical games of chess, shogi, and Go, but understanding and explaining AI remain challenging. This paper studies the machine-learning algorithms for developing the game AIs, and provides their structural interpretations. Specifically, chess-playing Deep Blue is a calibrated value function, whereas shogi-playing Bonanza represents an estimated value function via Rust's (1987) nested fixed-point method. AlphaGo's "supervised-learning policy network" is a deep neural network (DNN) version of Hotz and Miller's (1993) conditional choice probability estimates; its "reinforcement-learning value network" is equivalent to Hotz, Miller, Sanders, and Smith's (1994) simulation method for estimating the value function. Their performances suggest DNNs are a useful functional form when the state space is large and data are sparse. Explicitly incorporating strategic interactions and unobserved heterogeneity in the data-generating process would further improve AIs' explicability.