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Learning in Repeated Games: Human Versus Machine
While Artificial Intelligence has successfully outperformed humans in complex
combinatorial games (such as chess and checkers), humans have retained their
supremacy in social interactions that require intuition and adaptation, such as
cooperation and coordination games. Despite significant advances in learning
algorithms, most algorithms adapt at times scales which are not relevant for
interactions with humans, and therefore the advances in AI on this front have
remained of a more theoretical nature. This has also hindered the experimental
evaluation of how these algorithms perform against humans, as the length of
experiments needed to evaluate them is beyond what humans are reasonably
expected to endure (max 100 repetitions). This scenario is rapidly changing, as
recent algorithms are able to converge to their functional regimes in shorter
time-scales. Additionally, this shift opens up possibilities for experimental
investigation: where do humans stand compared with these new algorithms? We
evaluate humans experimentally against a representative element of these
fast-converging algorithms. Our results indicate that the performance of at
least one of these algorithms is comparable to, and even exceeds, the
performance of people
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