Exploring visualization support for early-career decision making

Many impactful personal decisions are made by individuals with little or no prior experience in decision making who must nevertheless navigate choices that are high stakes and complex. Unlike contexts where decision makers are experienced professionals, these individuals face unfamiliar situations in which they must weigh numerous trade-offs and preferences without clear guidance or prior knowledge. To investigate how multi-criteria decision making (MCDM) visualizations can support such individuals, we conducted two exploratory design studies: one with students making their first borrowing decisions and another with job seekers who had limited experience navigating the job market. Across both studies, participants initially focused narrowly on immediate, easily quantifiable aspects such as monthly repayment ability or yes/no filters on job attributes, while overlooking long-term consequences and more nuanced qualitative trade-offs. Our studies revealed several unstructured aspects of decision making in practice, including unclear preference hierarchies, difficulties in incorporating categorical and temporal criteria, and reliance on improvised datasets. Visualization probes encouraged participants to compare alternatives more systematically, surfacing neglected criteria and prompting more reflective reasoning. Together, these findings illustrate that personal, high-stakes decisions made in real-world contexts by individuals with limited prior experience challenge the fixed-data assumptions of MCDM and highlight new directions for visualization research. Supplementary materials are available at https://osf.io/9eu7x/overview?view_only=cc4d629e8e184e19ba08e4709e265d56 .

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Publication Details

Journal
Information Visualization
Published
2026-06-26
DOI
https://doi.org/10.1177/14738716261455686
Primary Topic
Data Visualization and Analytics
Type
article
Field-Weighted Citation Impact
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article

Exploring visualization support for early-career decision making

Lars Van Dijk, Evanthia Dimara, Angelos Chatzimparmpas, Yalan Zhang et al.
Information Visualization
Data Visualization and Analytics
article

Exploring visualization support for early-career decision making

Lars Van Dijk, Evanthia Dimara, Angelos Chatzimparmpas, Yalan Zhang, Robert Võeras, Başak Oral
article en

Abstract

Many impactful personal decisions are made by individuals with little or no prior experience in decision making who must nevertheless navigate choices that are high stakes and complex. Unlike contexts where decision makers are experienced professionals, these individuals face unfamiliar situations in which they must weigh numerous trade-offs and preferences without clear guidance or prior knowledge. To investigate how multi-criteria decision making (MCDM) visualizations can support such individuals, we conducted two exploratory design studies: one with students making their first borrowing decisions and another with job seekers who had limited experience navigating the job market. Across both studies, participants initially focused narrowly on immediate, easily quantifiable aspects such as monthly repayment ability or yes/no filters on job attributes, while overlooking long-term consequences and more nuanced qualitative trade-offs. Our studies revealed several unstructured aspects of decision making in practice, including unclear preference hierarchies, difficulties in incorporating categorical and temporal criteria, and reliance on improvised datasets. Visualization probes encouraged participants to compare alternatives more systematically, surfacing neglected criteria and prompting more reflective reasoning. Together, these findings illustrate that personal, high-stakes decisions made in real-world contexts by individuals with limited prior experience challenge the fixed-data assumptions of MCDM and highlight new directions for visualization research. Supplementary materials are available at https://osf.io/9eu7x/overview?view_only=cc4d629e8e184e19ba08e4709e265d56 .

Information Visualization
Utrecht University (NL), University of Science and Technology Beijing (CN)
Peace, Justice and strong institutions
Openalex Percentile: Top 4%
Data Visualization and Analytics
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