Cognitive biases in decision-making under uncertainty with interactive data visualizations

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Wesslen, R. (2021). Cognitive biases in decision-making under uncertainty with interactive data visualizations. Unc Charlotte Electronic Theses And Dissertations.
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Abstract

In this thesis, we hypothesize that data visualization users are subject to systematic errors, or cognitive biases, in decision-making under uncertainty. Based on research from psychology, behavioral economics, and cognitive science, we design five experiments to measure the role of anchoring bias, confirmation bias, belief bias, and myopic loss aversion under different uncertain decision tasks like social media event detection, misinformation identification, and financial portfolio allocation. This thesis makes three major contributions. First, we find evidence of cognitive biases in data visualization through multiple behavioral trace data including user decisions, user hover and click interactions, qualitative feedback, and belief elicitation techniques. Second, we design multiple experiments with interactive data visualization systems across different design complexities (coordinated multiple views to single plot), data types (social network, linguistic, geospatial, temporal, statistical), and evaluate them on user populations that range from novice to expert (crowdsourced, undergraduate, data scientist, domain expert). Third, we evaluate the experiments using multiple statistical and probabilistic techniques to measure the effects of cognitive biases including classical statistical tests, (Bayesian) mixed effects modeling, hierarchical clustering, natural language processing, and Bayesian cognitive modeling. These experiments show the promising role data visualizations and human-computer techniques could mitigate such biases and lead to better decision-making under uncertainty.

Details
Author

Wesslen, Ryan

Title

Cognitive biases in decision-making under uncertainty with interactive data visualizations

Physical Description

1 online resource (213 pages) : PDF

Date

2021

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

In this thesis, we hypothesize that data visualization users are subject to systematic errors, or cognitive biases, in decision-making under uncertainty. Based on research from psychology, behavioral economics, and cognitive science, we design five experiments to measure the role of anchoring bias, confirmation bias, belief bias, and myopic loss aversion under different uncertain decision tasks like social media event detection, misinformation identification, and financial portfolio allocation. This thesis makes three major contributions. First, we find evidence of cognitive biases in data visualization through multiple behavioral trace data including user decisions, user hover and click interactions, qualitative feedback, and belief elicitation techniques. Second, we design multiple experiments with interactive data visualization systems across different design complexities (coordinated multiple views to single plot), data types (social network, linguistic, geospatial, temporal, statistical), and evaluate them on user populations that range from novice to expert (crowdsourced, undergraduate, data scientist, domain expert). Third, we evaluate the experiments using multiple statistical and probabilistic techniques to measure the effects of cognitive biases including classical statistical tests, (Bayesian) mixed effects modeling, hierarchical clustering, natural language processing, and Bayesian cognitive modeling. These experiments show the promising role data visualizations and human-computer techniques could mitigate such biases and lead to better decision-making under uncertainty.

Genre

doctoral dissertations

Subjects--Topics

Computer science
Cognitive psychology
Information science

Degree

Ph.D.

Keywords

Cognitive Biases
Human-Computer Interaction
Information Visualization
Interactive Data Visualizations
Uncertainty Visualizations
Visual Analytics

Subject Area

Computer Science

Advisor(s)

Dou, Wenwen

Committee Members

Thill, Jean-Claude
Shaikh, Samira
Markant, Douglas
Cho, Isaac

Degree Note

Thesis (Ph.D.)--University of North Carolina at Charlotte, 2021.

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Identifier

Wesslen_uncc_0694D_12887

Permalink

http://hdl.handle.net/20.500.13093/etd:2741