research-article

Free access

Supporting informed decision-making under uncertainty and risk through interactive visualisation

Authors:

Mohammad Daradkeh,

Clare Churcher,

Alan McKinnonAuthors Info & Claims

AUIC '13: Proceedings of the Fourteenth Australasian User Interface Conference - Volume 139

Pages 23 - 32

Published: 29 January 2013 Publication History

Abstract

Informed decisions are based on the availability of information and the ability of decision-makers to manipulate this information. More often than not, the decision-relevant information is subject to uncertainty arising from different sources. Consequently, decisions involve an undeniable amount of risk. An effective visualisation tool to support informed decision-making must enable users to not only distil information, but also explore the uncertainty and risk involved in their decisions. In this paper, we present VisIDM, an information visualisation tool to support informed decision-making (IDM) under uncertainty and risk. It aims to portray information about the decision problem and facilitate its analysis and exploration at different levels of detail. It also aims to facilitate the integration of uncertainty and risk into the decision-making process and allow users to experiment with multiple "what-if" scenarios. We evaluate the utility of VisIDM through a qualitative user study. The results provide valuable insights into the benefits and drawbacks of VisIDM for assisting people to make informed decisions and raising their awareness of uncertainty and risk involved in their decisions.

References

[1]

Asahi, T., Turo, D., & Shneiderman, B. (1995). Using Treemaps to Visualize the Analytic Hierarchy Process. Information Systems Research, 6(4), pages 357--375.

[2]

Bautista, J. L., & Carenini, G. (2006). An integrated task-based framework for the design and evaluation of visualizations to support preferential choice. In Proceedings of the working conference on Advanced visual interfaces (AVI 06), pages 217--224, Venezia, Italy. ACM.

Digital Library

[3]

Bekker, H., Thornton, J. G., Airey, C. M., Connelly, J. B., Hewison, J., Robinson, M. B., Lilleyman, J., MacIntosh, M., Maule, A. J., Michie, S., & Pearman, A. D. (1999). Informed Decision Making: an Annotated Bibliography and Systematic Review. Health Technology Assessment, 3(1), pages 1--156.

[4]

Bostrom, A., Anselin, L., & Farris, J. (2008). Visualizing Seismic Risk and Uncertainty: a review of related research. Annals of the New York Academy of Sciences, 1128(1), pages 29--40. Blackwell Publishing Inc.

[5]

Copeland, T. E., & Weston, J. F. (1983). Solutions Manual for Financial Theory and Corporate Policy (2 ed.): Addison-Wesley Publishing Company.

[6]

Daradkeh, M. (2012). Information Visualisation to Support Informed Decision-Making under Uncertainty and Risk. Lincoln University, Lincoln, New Zealand.

[7]

French, S. (2003). Modelling, making inferences and making decisions: The roles of sensitivity analysis. TOP, 11(2), pages 229--251.

[8]

Gresh, D., Deleris, L. A., Gasparini, L., & Evans, D. (2011). Visualizing risk. in Proceedings of IEEE Information Visualization Conference 2011 (InfoVis 2011), Providence, RI, USA. IEEE computer society.

[9]

Keeney, R. L., Hammond, J. S., & Raiffa, H. (1999). Smart Choices: A Guide to Making Better Decisions. Boston: Harvard University Press.

[10]

Koller, T., Goedhart, M., & Wessels, D. (2005). Valuation: measuring and managing the value of companies (4 ed.): Hoboken: Wiley & Sons.

[11]

Magni, C. A. (2009). Investment Decisions, Net Present Value and Bounded Rationality. Quantitative Finance, 9(8), pages 967--979.

[12]

Maier, H. R., Ascough Ii, J. C., Wattenbach, M., Renschler, C. S., Labiosa, W. B., & Ravalico, J. K. (2008). Chapter Five Uncertainty in Environmental Decision Making: Issues, Challenges and Future Directions. Environmental Modelling, Software and Decision Support, 3, pages 69--85.

[13]

Marco, B., Fred, G., Gary, K., & Haibo, W. (2008). Simulation Optimization: Applications in Risk Management. International Journal of Information Technology & Decision Making (IJITDM), 07(04), pages 571--587.

[14]

Nobre, F. F., Trotta, L. T. F., & Gomes, L. F. A. M. (1999). Multi-criteria decision making -- an approach to setting priorities in health care. Statistics in Medicine, 18(23), pages 3345--3354.

[15]

Patton, M. Q. (2005). Qualitative Research: John Wiley & Sons, Ltd.

[16]

Saaty, T. L. (1980). The Analytic Hierarchy Process. New York: McGraw-Hill.

[17]

Schwartz, A., & Hasnain, M. (2002). Risk perception and risk attitude in informed consent. Risk, Decision and Policy, 7(2), pages 121--130.

[18]

Stone, E. R., Sieck, W. R., Bull, B. E., Frank Yates, J., Parks, S. C., & Rush, C. J. (2003). Foreground:background salience: Explaining the effects of graphical displays on risk avoidance. Organizational Behavior and Human Decision Processes, 90(1), pages 19--36.

[19]

Tegarden, D. P. (1999). Business information visualization. Communications of the AIS 1(1), Article 4.

Digital Library

[20]

Tversky, A., & Kahneman, D. (1974). Judgment under Uncertainty: Heuristics and Biases. Science, 185(4157), pages 1124--1131.

[21]

Tziralis, G., Kirytopoulos, K., Rentizelas, A., & Tatsiopoulos, I. (2009). Holistic investment assessment: optimization, risk appraisal and decision making. Managerial and Decision Economics, 30(6), pages 393--403.

[22]

Williamson, C., & Shneiderman, B. (1992). The dynamic HomeFinder: evaluating dynamic queries in a real-estate information exploration system. In Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval, pages 338--346, New York, NY, USA. ACM.

Digital Library

[23]

Yi, J. S. (2008). Visualized decision making: development and application of information visualization techniques to improve decision quality of nursing home choice. Georgia Institute of Technology.

[24]

Yi, J. S., Melton, R., Stasko, J., & Jacko, J. A. (2005). Dust & magnet: multivariate information visualization using a magnet metaphor. Information Visualization, 4(4), pages 239--256.

Digital Library

[25]

Zhu, B., & Chen, H. (2008). Information Visualization for Decision Support. In Handbook on Decision Support Systems 2. International Handbooks Information System (pp. 699--722): Heidelberg. Springer Berlin.

Cited By

Dimara EBezerianos ADragicevic PMark GFussell SLampe Cschraefel mHourcade JAppert CWigdor D(2017)Narratives in Crowdsourced Evaluation of VisualizationsProceedings of the 2017 CHI Conference on Human Factors in Computing Systems10.1145/3025453.3025870(5475-5484)Online publication date: 2-May-2017
https://dl.acm.org/doi/10.1145/3025453.3025870
Zhang YBellamy RKellogg WBegole BKim JInkpen KWoo W(2015)Designing Information for Remediating Cognitive Biases in Decision-MakingProceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems10.1145/2702123.2702239(2211-2220)Online publication date: 18-Apr-2015
https://dl.acm.org/doi/10.1145/2702123.2702239

Supporting informed decision-making under uncertainty and risk through interactive visualisation

Recommendations

Exploring the Use of an Information Visualization Tool for Decision Support under Uncertainty and Risk
ICEMIS '15: Proceedings of the The International Conference on Engineering & MIS 2015

In this paper, we present an evaluation study as part of the research effort toward the development of RiDeViz. RiDeViz is an information visualization tool designed to support decision-making under uncertainty and risk. We studied how people use the ...
Visualisation tools for exploring the uncertainty-risk relationship in the decision-making process: a preliminary empirical evaluation
AUIC '10: Proceedings of the Eleventh Australasian Conference on User Interface - Volume 106

Models play an important part in the decision-making process. However, due to uncertainty in a model's input variables, making decisions involves a degree of risk. We have developed two visualization prototypes for exploring the influence of uncertainty ...
A Belief-Based Account of Decision Under Uncertainty

We develop a belief-based account of decision under uncertainty. This model predicts decisions under uncertainty from (i) judgments of probability, which are assumed to satisfy support theory; and (ii) decisions under risk, which are assumed to satisfy ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image DL Hosted proceedings

AUIC '13: Proceedings of the Fourteenth Australasian User Interface Conference - Volume 139

January 2013

143 pages

ISBN:9781921770241

Editors:
Ross T. Smith
University of South Australia, Adelaide, South Australia, Australia
,
Burkhard C. Wünsche
University of Auckland, Auckland, New Zealand

Publisher

Australian Computer Society, Inc.

Australia

Publication History

Published: 29 January 2013

Author Tags

Qualifiers

Research-article

Acceptance Rates

AUIC '13 Paper Acceptance Rate 12 of 31 submissions, 39%;

Overall Acceptance Rate 40 of 100 submissions, 40%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
412
Total Downloads

Downloads (Last 12 months)65
Downloads (Last 6 weeks)6

Reflects downloads up to 03 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Dimara EBezerianos ADragicevic PMark GFussell SLampe Cschraefel mHourcade JAppert CWigdor D(2017)Narratives in Crowdsourced Evaluation of VisualizationsProceedings of the 2017 CHI Conference on Human Factors in Computing Systems10.1145/3025453.3025870(5475-5484)Online publication date: 2-May-2017
https://dl.acm.org/doi/10.1145/3025453.3025870
Zhang YBellamy RKellogg WBegole BKim JInkpen KWoo W(2015)Designing Information for Remediating Cognitive Biases in Decision-MakingProceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems10.1145/2702123.2702239(2211-2220)Online publication date: 18-Apr-2015
https://dl.acm.org/doi/10.1145/2702123.2702239

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Figures

Tables

Media

View Table of Conten