Related Experiment Video
Updated: Jul 5, 2026
13:04
Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
Published on: September 19, 2012
A frequency/consequence-based technique for visualizing and communicating uncertainty and perception of risk.
David Slavin1, W Troy Tucker, Scott Ferson
1Institute of Biomedical Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK. david.slavin@decsionability.com
Annals of the New York Academy of Sciences
|May 13, 2008
Summary
This study introduces risk imaging to communicate uncertainty by analyzing harm frequency and adversity. This visual approach aids decision-making by clarifying risk perceptions and management choices.
Area of Science:
- Risk assessment and communication
- Decision analysis
- Health informatics
Background:
- Communicating risk uncertainty is challenging.
- Existing methods often fail to capture the nuances of risk perception.
- A novel approach is needed to visualize and compare risk information.
Purpose of the Study:
- To present a risk imaging technology for communicating uncertainty.
- To decompose risk into frequency and adversity components.
- To visualize risk profiles and individual risk perceptions.
Main Methods:
- Risk is decomposed into harm frequency (ratio scale) and adversity (ordinal scale).
- Uncertainty in frequency (sampling, measurement, bias) and adversity (opinion, measurement, dimensions) is quantified.
- Risk is imaged as a risk profile, visualized with uncertainty bounds.
- Risk profiles are adapted to visualize specific risk perceptions.
Main Results:
- The risk imaging technology provides a visual representation of hazard risk profiles.
- Uncertainty bounds effectively illustrate the range of potential harms.
- Visualizations can be tailored to represent different risk perceptions.
- The method was illustrated using clinical trial data.
Conclusions:
- Risk imaging offers a novel method for communicating risk uncertainty.
- Visualizing risk profiles facilitates comparison across management choices and risk perceivers.
- This approach can enhance risk communication and decision-making processes.
Related Concept Videos
01:17Design Example: Analyzing Capacity Contours for Flood Risk Assessment
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
00:59Uncertainty: Overview
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
00:54Uncertainty: Confidence Intervals
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor 't,' or...
00:59Propagation of Uncertainty from Random Error
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
01:10Propagation of Uncertainty from Systematic Error
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
01:17Probability Histograms
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.