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8:30-9:00: Arrival and Breakfast
SESSION 1: Efficient Compressed Representations (Chair: Rava Azeredo da Silveira, ENS)
9:00-10:00: Keynote Lecture: Naftali Tishby [Hebrew University], “Information Constrained Decision Making and Planning”
10:00-10:40: Noga Zaslavsky [Hebrew University and UC Berkeley], “Efficient Coding and the Evolution of Semantic Systems”
11:10-11:50: Matthew Botvinick [Google DeepMind], “Deep Learning and Structure-sensitive Credit Assignment”
11:50-12:30: Sophia Sanborn [UC Berkeley], “Efficient Coding Under Reward: Compressing to Learn in Hierarchical Reinforcement Learning”
12:30-13:00: General Discussion
SESSION 2: Imprecise Coding of Subjective Value (Chair: Michael Woodford, Columbia)
14:00-15:00: Keynote Lecture: Andrew Caplin [NYU], “Measuring Costs of Attention in Lab and Field”
15:00-15:40: Filip Matějka [CERGE-EI], “An Attention-Based Theory of Mental Accounting”
16:10-16:50: Alan Stocker [U Penn], “Behavioral and Neural Signatures of Resource
Limitations in Perceptual Inference”
16:50-17:30: Rafael Polania [ETH Zurich], “Efficient Coding of Subjective Value”
17:30-18:00: General Discussion
Economists, computer scientists and computational neuroscientists have all been interested in models in which the structure of the internal representations on which decisions are based is assumed to represent an efficient use of finite cognitive resources. Theories of this kind go by different names (“efficient coding,” “rational inattention,” “information bottleneck,” etc.), and the nature of the assumed resource constraint also differs in different strands of the literature. This conference, the latest in a series of Columbia University interdisciplinary conferences on the cognitive foundations of decision making, brings together leading researchers representing several of these approaches, to discuss both their foundations and important applications.
The conference will include keynote lectures by Naftali Tishby (Hebrew University) and Andrew Caplin (New York University), and additional talks by Matt Botvinick (DeepMind), Rafael Polania (ETH Zurich), Sophia Sanborn (UC Berkeley), Alan Stocker (University of Pennsylvania), and Noga Zaslavsky (Hebrew University).