We develop a framework that leverages the smoothed complexity analysis by Spielman and Teng [60] to circumvent paradoxes and impossibility theorems in social choice, motivated by modern applications of social choice powered by AI and ML. For Condrocet's paradox, we prove that the smoothed likelihood of the paradox either vanishes at an exponential rate as the number of agents increases, or does not vanish at all. For the ANR impossibility on the non-existence of voting rules that simultaneously satisfy anonymity, neutrality, and resolvability, we characterize the rate for the impossibility to vanish, to be either polynomially fast or exponentially fast. We also propose a novel easy-to-compute tie-breaking mechanism that optimally preserves anonymity and neutrality for even number of alternatives in natural settings. Our results illustrate the smoothed possibility of social choice--even though the paradox and the impossibility theorem hold in the worst case, they may not be a big concern in practice.

Neural Information Processing Systems http://nips.cc/

Exploration in multi-agent reinforcement learning is a challenging problem, especially in environments with sparse rewards. We propose a general method for efficient exploration by sharing experience amongst agents.

Uncertainty modeling is critical in trajectory forecasting systems for both interpretation and safety reasons.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper presents a probabilistic model for language learning. The authors cover the nature in which a pair of cooperative agents may work together to create an agreed-upon language. One question I have is how this could possibly be implemented in real-world language learning situations. Your evaluation of the emergence of phenomenon seen in real world languages makes me think you are trying to model or learn something about what real world language evolution is like.

Language system dynamics is studied at several distinct time scales (Lewis, 2014).

However, this restriction prevents it from representing value functions in which an agent's ordering over its actions can depend on