Separating the Wheat from the Chaff: Comparative Visual Cues for Transparent Diagnostics of Competing Models

Document Type

Article

Publication Date

1-1-2020

Abstract

Experts in data and physical sciences have to regularly grapple with the problem of competing models. Be it analytical or physics-based models, a cross-cutting challenge for experts is to reliably diagnose which model outcomes appropriately predict or simulate real-world phenomena. Expert judgment involves reconciling information across many, and often, conflicting criteria that describe the quality of model outcomes. In this paper, through a design study with climate scientists, we develop a deeper understanding of the problem and solution space of model diagnostics, resulting in the following contributions: i) a problem and task characterization using which we map experts' model diagnostics goals to multi-way visual comparison tasks, ii) a design space of comparative visual cues for letting experts quickly understand the degree of disagreement among competing models and gauge the degree of stability of model outputs with respect to alternative criteria, and iii) design and evaluation of MyriadCues, an interactive visualization interface for exploring alternative hypotheses and insights about good and bad models by leveraging comparative visual cues. We present case studies and subjective feedback by experts, which validate how MyriadCues enables more transparent model diagnostic mechanisms, as compared to the state of the art.

Identifier

85075640051 (Scopus)

Publication Title

IEEE Transactions on Visualization and Computer Graphics

External Full Text Location

https://doi.org/10.1109/TVCG.2019.2934540

e-ISSN

19410506

ISSN

10772626

PubMed ID

31478858

First Page

1043

Last Page

1053

Issue

1

Volume

26

Fund Ref

Battelle

This document is currently not available here.

Share

COinS