Information efficient learning of complexly structured preferences: Elicitation procedures and their application to decision making under uncertainty

• URL: http://arxiv.org/abs/2110.12879v1
• Date: Tue, 19 Oct 2021 07:01:24 GMT
• Title: Information efficient learning of complexly structured preferences: Elicitation procedures and their application to decision making under uncertainty
• Authors: Christoph Jansen, Hannah Blocher, Thomas Augustin, Georg Schollmeyer
• Abstract summary: We propose efficient methods for elicitation of complexly structured preferences. We show that under certain conditions optimal decisions can be found without fully specifying the preference system.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this paper we propose efficient methods for elicitation of complexly structured preferences and utilize these in problems of decision making under (severe) uncertainty. Based on the general framework introduced in Jansen, Schollmeyer and Augustin (2018, Int. J. Approx. Reason), we now design elicitation procedures and algorithms that enable decision makers to reveal their underlying preference system (i.e. two relations, one encoding the ordinal, the other the cardinal part of the preferences) while having to answer as few as possible simple ranking questions. Here, two different approaches are followed. The first approach directly utilizes the collected ranking data for obtaining the ordinal part of the preferences, while their cardinal part is constructed implicitly by measuring meta data on the decision maker's consideration times. In contrast, the second approach explicitly elicits also the cardinal part of the decision maker's preference system, however, only an approximate version of it. This approximation is obtained by additionally collecting labels of preference strength during the elicitation procedure. For both approaches, we give conditions under which they produce the decision maker's true preference system and investigate how their efficiency can be improved. For the latter purpose, besides data-free approaches, we also discuss ways for effectively guiding the elicitation procedure if data from previous elicitation rounds is available. Finally, we demonstrate how the proposed elicitation methods can be utilized in problems of decision under (severe) uncertainty. Precisely, we show that under certain conditions optimal decisions can be found without fully specifying the preference system.

Related papers

• An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting [53.36437745983783]
  We first construct a max-margin optimization-based model to model potentially non-monotonic preferences. We devise information amount measurement methods and question selection strategies to pinpoint the most informative alternative in each iteration. Two incremental preference elicitation-based algorithms are developed to learn potentially non-monotonic preferences.
  arXiv  Detail & Related papers  (2024-09-04T14:36:20Z)
• Experience in Engineering Complex Systems: Active Preference Learning with Multiple Outcomes and Certainty Levels [1.5257326975704795]
  Black-box optimization refers to the problem whose objective function and/or constraint sets are either unknown, inaccessible, or non-existent. The algorithm so-called Active Preference Learning has been developed to exploit this specific information. Our approach aims to extend the algorithm in such a way that can exploit further information effectively.
  arXiv  Detail & Related papers  (2023-02-27T15:55:37Z)
• Social Mechanism Design: A Low-Level Introduction [31.564788318133264]
  We show that agents have preferences over both decision outcomes and the rules or procedures used to make decisions. We identify simple, intuitive preference structures at low levels that can be generalized to form the building blocks of preferences at higher levels. We analyze algorithms for acceptance in two different domains: asymmetric dichotomous choice and constitutional amendment.
  arXiv  Detail & Related papers  (2022-11-15T20:59:34Z)
• Generalizing Bayesian Optimization with Decision-theoretic Entropies [102.82152945324381]
  We consider a generalization of Shannon entropy from work in statistical decision theory. We first show that special cases of this entropy lead to popular acquisition functions used in BO procedures. We then show how alternative choices for the loss yield a flexible family of acquisition functions.
  arXiv  Detail & Related papers  (2022-10-04T04:43:58Z)
• Bi-objective Ranking and Selection Using Stochastic Kriging [0.0]
  We consider bi-objective ranking and selection problems in which the two objective outcomes have been observed with uncertainty. We propose a novel Bayesian bi-objective ranking and selection method that sequentially allocates extra samples to competitive solutions. Experimental results show that the proposed method outperforms the standard allocation method, as well as a well-known state-of-the-art algorithm.
  arXiv  Detail & Related papers  (2022-09-05T23:51:07Z)
• Meta-Learning Approaches for a One-Shot Collective-Decision Aggregation: Correctly Choosing how to Choose Correctly [0.7874708385247353]
  We present two one-shot machine-learning-based aggregation approaches. The first predicts, given multiple features about the collective's choices, which aggregation method will be best for a given case. The second directly predicts which decision is optimal, given, among other things, the selection made by each method.
  arXiv  Detail & Related papers  (2022-04-03T15:06:59Z)
• Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment [79.5678820246642]
  We show that certain action-value methods are more sample efficient than policy-gradient methods on transfer problems that require only sparse changes to a sequence of previously optimal decisions. We generalize the recently proposed societal decision-making framework as a more granular formalism than the Markov decision process.
  arXiv  Detail & Related papers  (2021-06-28T21:29:13Z)
• Leveraging Expert Consistency to Improve Algorithmic Decision Support [62.61153549123407]
  We explore the use of historical expert decisions as a rich source of information that can be combined with observed outcomes to narrow the construct gap. We propose an influence function-based methodology to estimate expert consistency indirectly when each case in the data is assessed by a single expert. Our empirical evaluation, using simulations in a clinical setting and real-world data from the child welfare domain, indicates that the proposed approach successfully narrows the construct gap.
  arXiv  Detail & Related papers  (2021-01-24T05:40:29Z)
• Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utility [54.179859639868646]
  Bipartite ranking aims to learn a scoring function that ranks positive individuals higher than negative ones from labeled data. There have been rising concerns on whether the learned scoring function can cause systematic disparity across different protected groups. We propose a model post-processing framework for balancing them in the bipartite ranking scenario.
  arXiv  Detail & Related papers  (2020-06-15T10:08:39Z)
• Learning with Differentiable Perturbed Optimizers [54.351317101356614]
  We propose a systematic method to transform operations into operations that are differentiable and never locally constant. Our approach relies on perturbeds, and can be used readily together with existing solvers. We show how this framework can be connected to a family of losses developed in structured prediction, and give theoretical guarantees for their use in learning tasks.
  arXiv  Detail & Related papers  (2020-02-20T11:11:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.