The Anchoring Effect in Decision-Making with Visual Analytics
Abstract
Anchoring effect is the tendency to focus too heavily on one piece of information when making decisions. In this paper, we present a novel, systematic study and resulting analyses that investigate the effects of anchoring effect on human decision-making using visual analytic systems. Visual analytics interfaces typically contain multiple views that present various aspects of information such as spatial, temporal, and categorical. These views are designed to present complex, heterogeneous data in accessible forms that aid decision-making. However, human decision-making is often hindered by the use of heuristics, or cognitive biases, such as anchoring effect. Anchoring effect can be triggered by the order in which information is presented or the magnitude of information presented. Through carefully designed laboratory experiments, we present evidence of anchoring effect in analysis with visual analytics interfaces when users are primed by representation of different pieces of information. We also describe detailed analyses of users’ interaction logs which reveal the impact of anchoring bias on the visual representation preferred and paths of analysis. We discuss implications for future research to possibly detect and alleviate anchoring bias.
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In higher-level decision tasks, visualization design influences data interpretation and decision making.
“In higher-level decision tasks, visualization design also influences data interpretation and decision making [9, 4].”
From Illusion of Causality in Visualized Data (2020) · cites this paper as [4]
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Making and Trusting Decisions in Visual Analytics
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Cognitive Biases and Data Visualization
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- Authors
- Cho, I., Wesslen, R., Karduni, A., Santhanam, S., Shaikh, S., & Dou, W.
- Affiliations
- University of North Carolina at Charlotte (US)
- University of North Carolina at Charlotte
- Venue
- 2017 IEEE Conference on Visual Analytics Science and Technology (VAST)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 116-126
- OpenAlex
- W2906640975
- Cited by
- 88
- References
- 49
- Citations by year
- 2026: 9 · 2025: 14 · 2024: 12 · 2023: 10 · 2022: 9 · 2021: 10 · 2020: 8 · 2019: 9 · 2018: 6 · 2017: 1
- FWCI
- 1.92
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Conference on Visual Analytics Science and Technology (VAST) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- anchoring effect, cognitive biases, visual analytics, human decision-making, heuristics, information presentation order, bias detection, bias mitigation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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