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Incremental Elicitation of Preferences: Optimist or Pessimist?

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Algorithmic Decision Theory (ADT 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13023))

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In robust incremental elicitation, it is quite common to make recommendations and to select queries by using a minimax regret criterion, which corresponds to a pessimistic attitude. In this paper, we explore its optimistic counterpart, showing this new approach enjoys the same convergence properties. While this optimistic approach does not offer the same kind of guarantees than minimax approaches, it still offers some other interesting properties. Finally, we illustrate with some experiments that the best approach amongst the two approaches heavily depends on the underlying setting.

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  1. 1.

    The user is an oracle, and the chosen family of preference model includes the right model.

  2. 2.

    It does not, however, offer the same robust guarantees.


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Correspondence to Sébastien Destercke .

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Adam, L., Destercke, S. (2021). Incremental Elicitation of Preferences: Optimist or Pessimist?. In: Fotakis, D., Ríos Insua, D. (eds) Algorithmic Decision Theory. ADT 2021. Lecture Notes in Computer Science(), vol 13023. Springer, Cham.

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  • Print ISBN: 978-3-030-87755-2

  • Online ISBN: 978-3-030-87756-9

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