[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83404-en":3,"doc-seo-83404-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},83404,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Robust Bayesian Decision Making under Adversarial Uncertainty","Scientific experiments often aim to maximize information gain, yet many real settings value reliable downstream decisions more than nominal inference accuracy. Existing decision-aware experimental design and active learning typically assume well-specified outcome models and stability of the optimal decision under perturbations. In practice, outcomes can be driven by hidden or weakly modeled effects that change which decision is optimal and can mislead conclusions. A sequential adversarially robust decision-aware design is studied, modeling worst-case unexpected effects via adversarial variables. Bayesian theory yields a criterion targeting decision stability. Experiments on synthetic and real-world datasets show robustness-aware design maintains stable, reliable decisions under adversarial variation.","Robust Bayesian Decision Making under Adversarial Uncertainty  \nHaripriya Harikumar 1 Sammie Katt2,3 Yasir Zubayr Barlas 1 Samuel Kaski 1,2,3  \n1Department of Computer Science, The University of Manchester, UK  \n2ELLIS Institute Finland  \n3Department of Computer Science, Aalto University, Espoo, Finland  \narXiv :2607 .08590v2 [ cs .LG] 13 Jul 2026  \nAbstract  \nScientific experiments are often designed to maximize information gain, yet in many applications the primary objective is to support reliable downstream decision-making. Existing decision-aware experimental design and active learning methods typically assume well-specified outcome models and implicitly rely on the stability of the optimal decision under real-world perturbations. In practice, however, experimental outcomes are frequently influenced by hidden or weakly modeled effects, which can substantially alter decision optimality and lead to misleading conclusions. We study sequential adversarially robust decision-aware experimental design, where data acquisition has to take into account information gain against plausible worst-case unexpected effects, modeled here as variation in adversarial variables. Building on Bayesian decision theory, we formalize an adversarially robust optimal decision under this setting and derive a principled Bayesian experimental design criterion. The criterion explicitly targets decision stability rather than nominal optimality. Experiments on synthetic and real-world scientific datasets show that conventional decision-aware design can converge rapidly to high confidence yet fragile decisions, while our robustness-aware approach yields decisions that are significantly more stable and reliable under adversarial variation.  \n1 INTRODUCTION  \nScience advances through controlled experimentation [Fisher, 1935, Cheng and Shen, 2005, Melendez et al., 2021]: experiments are designed by manipulating a set of controllable variables, observing outcomes, and drawing conclusions about an underlying process. Classical exper-  \nFigure 1: Three treatment plans (in green, orange, and blue curves) and their corresponding outcomes on the y-axis. A clinician must select a treatment for a new patient (indicated by the black dashed line). The x-axis denotes adversarially perturbed variable associated with patients. Although the blue treatment appears optimal (highest outcome) for the new patient, the smoother nominal utility (green) leads to decisions that remain stable across the perturbation region (shaded grey area denoted as xa −ϵ and xa +ϵ ), motivating adversarially robustness-aware decision learning.  \nimental design [Chaloner and Verdinelli, 1995, Ryan and Morgan, 2007] methods aim to test hypotheses or maximize information gain. In practice, however, experimentation is costly, resources are limited, and not all relevant variables can be identified, measured, or controlled. As a result, experimental outcomes are often influenced by weakly modeled adversarial factors that distort outcomes and degrade the reliability of downstream decisions [Corbett-Davies et al., 2017, Lacoste-Julien et al., 2011] . This challenge is particularly significant in high-dimensional settings, where some potential adversarial variables may systematically affect outcomes in ways that are difficult to model probabilistically. Such factors may be external, latent, partially observable, or implicitly coupled to the experimental process [Grünwald and van Ommen, 2017, Rainforth et al., 2024], and their influence often becomes apparent only after decisions are made. Consequently, experimental designs that are op-  \ntimal for parameter inference or predictive accuracy can lead to fragile or misleading decision-making when these influences are present. This raises a fundamental question: how can experiments be designed to support decisions that remain reliable under variations in adversarial variables?  \nThe same abstract problem occurs in decision-making in personalized medicine, for ","cbCaip1wX6ynrXB6","https://ap.wps.com/l/cbCaip1wX6ynrXB6","pdf",9059775,4,1,25,"English","en",105,"# Introduction\n## Motivation and challenge\n## Personalized medicine and decision reliability\n## Adversarially robust decision learning","[{\"question\":\"Why can decision-aware experimental design fail in practice?\",\"answer\":\"Because experimental outcomes may be influenced by hidden or weakly modeled adversarial factors, which can change which decision is truly optimal and make conclusions misleading.\"},{\"question\":\"How does the proposed framework model adversarial uncertainty?\",\"answer\":\"It models unexpected effects as variation in adversarial variables and incorporates them into sequential decision-aware experimental design under a worst-case perspective.\"},{\"question\":\"What does the robustness-aware criterion optimize instead of nominal optimality?\",\"answer\":\"It explicitly targets decision stability under adversarial variation, rather than maximizing nominal optimality under well-specified 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can decision-aware experimental design fail in practice?","Question",{"text":75,"@type":76},"Because experimental outcomes may be influenced by hidden or weakly modeled adversarial factors, which can change which decision is truly optimal and make conclusions misleading.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed framework model adversarial uncertainty?",{"text":80,"@type":76},"It models unexpected effects as variation in adversarial variables and incorporates them into sequential decision-aware experimental design under a worst-case perspective.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the robustness-aware criterion optimize instead of nominal optimality?",{"text":84,"@type":76},"It explicitly targets decision stability under adversarial variation, rather than maximizing nominal optimality under well-specified 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