Combinatorial approach to inference in partially identified incomplete structural models: Inference in games
We propose a computationally feasible inference method in finite games of complete information. Galichon and Henry, 2011 and Beresteanu, Molchanov, and Molinari, 2011 show that the empirical content in such models is characterized by a collection of moment inequalities whose number increases exponentially with the number of discrete outcomes. We propose an equivalent characterization based on classical combinatorial optimization methods that allows the construction of confidence regions with an efficient bootstrap procedure that runs in linear computing time. The method can be applied to the empirical analysis of cooperative and noncooperative games, instrumental variable models of discrete choice, and revealed preference analysis. We propose an application to the determinants of long term elderly care choices.
We present a method for synthesising control strategies for continuous dynamical systems. We use Uppaal Tiga for the synthesis in combination with a set-based Euler method for guaranteeing that the synthesis is safe. We present both a general method and a method which provides tighter bounds for monotone systems. As a case-study, we synthesize a guaranteed safe strategy for a simplified adaptive cruise control application. We show that the guaranteed strategy is only slightly more conservative than the strategy generated in the original adaptive cruise control paper which uses a discrete non guaranteed strategy. Also, we show how reinforcement learning may be used to obtain optimal sub-strategies.