Take a look at the new version of this page: [ beta version ]. Tell us what you think.

ABSTRACT

Motivation -- To address both the theoretical and practical issues related to graphical representation of probabilities in the attempt to create 'corrective' representations that can counter-effect documented biases in judgment and decision-making.

Research approach -- 64 students were asked to answer questions dealing with statistical information that was presented either numerically or graphically, replicating two well-knows experiments in the field of judgement and decision-making.

Findings/Design -- The results of the pilot study suggest that graphical representation may help to counter the effect of documented biases.

Research limitations/Implications -- The pilot study has only marginal significance due to the relatively small sample size.

Originality/Value -- The research aims to suggest optimal graphical representations to help people in their decision-making process.

Take away message -- Graphical representations may be an untapped resource that can be used along with or instead of numerical representations in decision-making.

REFERENCES

• 1: Allais, M. (1953). Le comportement de l'homme rationnel devant de risque: Critique des postulats et axiomes de l'école américaine. Econometrica, 21, 503--546.
• 2: Jacques Bertin, Semiology of graphics, University of Wisconsin Press, 1983
• 3: Brase, G. L., (2002). Which statistical formats facilitate what decisions? The perception and influence of different statistical information formats. Journal of Behavioral Decision Making, 15(5), 381--401
• 4: Burkell, J. (2004). What are the chances? Evaluating risk and benefit information in consumer health materials. The Journal of the Medical Library Association, 92(2), 200--208.
• 5: Erev, I., Cohen, B., (1990). Verbal versus numerical probabilities: Efficiency, biases, and the preference paradox, Organizational Behavior and Human Decision Processes, 45, 1--18.
• 6: Gigerenzer, G., Edwards, A., (2003). Simple tools for understanding risks: From innumeracy to insight. British Medical Journal, 327, 41--44.
• 7: Kahneman, D., Tversky, A., (1979). Prospect Theory: An analysis of decision under risk. Econometrica, 47, 2.
• 8: Kahneman, D., Slovic, P., Tversky, A., (1982). Judgment under Uncertainty: Heuristics and Biases. Cambridge, MA: Cambridge University Press.
• 9: Larkin, J. H., Simon, H. A. (1987). Why a diagram is (sometimes) worth 10,000 words. Cognitive Science, 11(1), 65--100.
• 10: Lipkus, I. M., Hollands, J. G., (1999). The visual communication of risk. Journal of the National Cancer Institute Monographs, 25, 149--163.
• 11: Olson, M. J., Budescu, D. V., (1997). Patterns of preference for numerical and verbal probabilities. Journal of Behavioral Decision Making, 10, 117--131.
• 12: Stone, E. R., Yates, J. F., Parker, A. M., (1997). Effects of numerical and graphical displays on professed risk-taking behavior. Journal of Experimental Psychology: Applied, 3, 243--256.
• 13: Windschitl, P. D., Wells, G., (1996). Measuring psychological uncertainty: Verbal versus numeric. Journal of Experimental Psychology: Applied, 2, 343--364.

INDEX TERMS

Keywords:
decision-making, graphical representation of statistical information, probability representation, risk communication, uncertainty visualization