Epistemic Considerations on Expert Disagreement, Normative Justification, and Inconsistency Regarding Multi-criteria Decision Making

Garbayo, Luciana

doi:10.1007/978-3-319-04280-0_5

Cited by 9 publications

(6 citation statements)

References 10 publications

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“…Thus, this article is also motivated by the idea that epistemology of disagreement [7][8][9] can be modeled computationally. On the abstract level, we view medical disagreement as "near-peer" disagreement [10][11][12], where we see expert groups as having partly overlapping knowledge. This article shows that such more realistic and fine-grained models can also be studied computationally, quantitatively, and at scale.…”

Section: Motivationmentioning

confidence: 99%

A Method for Computing Conceptual Distances between Medical Recommendations: Experiments in Modeling Medical Disagreement

et al. 2021

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Using natural language processing tools, we investigate the semantic differences in medical guidelines for three decision problems: breast cancer screening, lower back pain and hypertension management. The recommendation differences may cause undue variability in patient treatments and outcomes. Therefore, having a better understanding of their causes can contribute to a discussion on possible remedies. We show that these differences in recommendations are highly correlated with the knowledge brought to the problem by different medical societies, as reflected in the conceptual vocabularies used by the different groups of authors. While this article is a case study using three sets of guidelines, the proposed methodology is broadly applicable. Technically, our method combines word embeddings and a novel graph-based similarity model for comparing collections of documents. For our main case study, we use the CDC summaries of the recommendations (very short documents) and full (long) texts of guidelines represented as bags of concepts. For the other case studies, we compare the full text of guidelines with their abstracts and tables, summarizing the differences between recommendations. The proposed approach is evaluated using different language models and different distance measures. In all the experiments, the results are highly statistically significant. We discuss the significance of the results, their possible extensions, and connections to other domains of knowledge. We conclude that automated methods, although not perfect, can be applicable to conceptual comparisons of different medical guidelines and can enable their analysis at scale.

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Section: Motivationmentioning

confidence: 99%

A Method for Computing Conceptual Distances between Medical Recommendations: Experiments in Modeling Medical Disagreement

et al. 2021

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“…A theoretical model of expert disagreement was proposed in Christensen et al (2013) and Lackey (2014), and analyzed in Garbayo (2014) and Garbayo et al (2018). Earlier, the epistemology of agreement/disagreement and expertise was discussed in Goldman (2001), where the "ideal" model of expertise (e.g.…”

Section: Modeling Epistemic Near-peersmentioning

confidence: 99%

“…This article should also be viewed as a case study in computational implementation of the near-peer epistemic model of expert disagreement proposed in several of our earlier work (Garbayo (2014), Garbayo et al (2018), Garbayo et al (2019), Garbayo (2019)). The near-peer model can be viewed as a refinement of the standard epistemic peer model (e.g.…”

Section: Introductionmentioning

confidence: 97%

Computing Conceptual Distances between Breast Cancer Screening Guidelines: An Implementation of a Near-Peer Epistemic Model of Medical Disagreement

Hematialam,

Garbayo,

Gopalakrishnan

et al. 2020

Preprint

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Using natural language processing tools, we investigate the differences of recommendations in medical guidelines for the same decision problem -breast cancer screening. We show that these differences arise from knowledge brought to the problem by different medical societies, as reflected in the conceptual vocabularies used by the different groups of authors. The computational models we build and analyze agree with the near-peer epistemic model of expert disagreement proposed by Garbayo. Even though the article is a case study focused on one set of guidelines, the proposed methodology is broadly applicable.In addition to proposing a novel graph-based similarity model for comparing collections of documents, we perform an extensive analysis of the model performance. In a series of a few dozen experiments, in three broad categories, we show, at a very high statistical significance level of 3-4 standard deviations for our best models, that the high similarity between expert annotated model and our concept based, automatically created, computational models is not accidental. Our best model achieves roughly 70% similarity. We also describe possible extensions of this work.

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“…Because of that, the dynamics of belief updating on even complete networks may result in high average coherence but not in a single consistent belief suite across the population. Despite strong network linkage and a dynamics driven by coherence, a signicant number of internally coherent but distinct and mutually incompatible belief suites may remain (see also Feldman and Wareld 2010;Garbayo 2014).…”

Section: The Dynamics Of Coherencementioning

confidence: 99%

Coherence and Correspondence in the Network Dynamics of Belief Suites

Grim¹,

Modell²,

Breslin³

et al. 2016

Episteme

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Coherence and correspondence are classical contenders as theories of truth. In this paper we examine them instead as interacting factors in the dynamics of belief across epistemic networks. We construct an agent-based model of network contact in which agents are characterized not in terms of single beliefs but in terms of internal belief suites. Individuals update elements of their belief suites on input from other agents in order both to maximize internal belief coherence and to incorporate ‘trickled in’ elements of truth as correspondence. Results, though often intuitive, prove more complex than in simpler models (Hegselmann and Krause 2002, 2006; Grim et al. 2015). The optimistic finding is that pressures toward internal coherence can exploit and expand on small elements of truth as correspondence is introduced into epistemic networks. Less optimistic results show that pressures for coherence can also work directly against the incorporation of truth, particularly when coherence is established first and new data are introduced later.

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Epistemic Considerations on Expert Disagreement, Normative Justification, and Inconsistency Regarding Multi-criteria Decision Making

Cited by 9 publications

References 10 publications

A Method for Computing Conceptual Distances between Medical Recommendations: Experiments in Modeling Medical Disagreement

A Method for Computing Conceptual Distances between Medical Recommendations: Experiments in Modeling Medical Disagreement

Computing Conceptual Distances between Breast Cancer Screening Guidelines: An Implementation of a Near-Peer Epistemic Model of Medical Disagreement

Coherence and Correspondence in the Network Dynamics of Belief Suites

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