Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning and Efficiency in Games with Dynamic Population   We study the quality of outcomes in repeated games when the population of
players is dynamically changing and participants use learning algorithms to
adapt to the changing environment. Game theory classically considers Nash
equilibria of one-shot games, while in practice many games are played
repeatedly, and in such games players often use algorithmic tools to learn to
play in the given environment. Most previous work on learning in repeated games
assumes that the population playing the game is static over time.
  We analyze the efficiency of repeated games in dynamically changing
environments, motivated by application domains such as Internet ad-auctions and
packet routing. We prove that, in many classes of games, if players choose
their strategies in a way that guarantees low adaptive regret, then high social
welfare is ensured, even under very frequent changes. In fact, in large markets
learning players achieve asymptotically optimal social welfare despite high
turnover. Previous work has only showed that high welfare is guaranteed for
learning outcomes in static environments. Our work extends these results to
more realistic settings when participation is drastically evolving over time.

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The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol
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The categories of this paper are: plasm-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: q-alg
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The categories of this paper are: chao-dyn
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The categories of this paper are: cmp-lg
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The categories of this paper are: eess
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The categories of this paper are: hep-ex
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The categories of this paper are: adap-org
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The categories of this paper are: atom-ph
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The categories of this paper are: econ
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The categories of this paper are: q-bio
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The categories of this paper are: hep-th
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The categories of this paper are: nucl-ex