Tove Helldin

Effects of visualizing uncertainty on decision-making in a target identification scenario

Abstract This paper presents an empirical study that addresses the effects the visualization of uncertainty has on decision-making. We focus our investigations on an area where uncertainty plays an important role and the decision time is limited. For that, we selected an air defense scenario, where expert operators have a few minutes to make a well-informed decision based on uncertain sensor data regarding the identity of an object and where the consequences of a late or wrong decision are severe. An approach for uncertainty visualization is proposed and tested using a prototype that supports the interactive analysis of multivariate spatio-temporal sensor data. The uncertainty visualization embeds the accuracy of the sensor data values using the thickness of the lines in the graphical representation of the sensor values. Semi-transparent filled circles represent the uncertain position, while a track quality value between 0 and 1 accounts for the quality of the estimated track for each target. Twenty-two experienced air traffic operators were divided into two groups (with and without uncertainty visualization) and carried out identification and prioritization tasks using the prototype. The results show that the group aided by visualizations of uncertainty needed significantly fewer attempts to make a final identification, and a significant difference between the groups when considering the identities and priorities assigned was observed (participants with uncertainty visualization selected higher priority values and more hostile and suspect identities). These results may show that experts put themselves in the “worst-case scenario” in the presence of uncertainty when safety is an issue. Additionally, the presentation of uncertainty neither increased the participants׳ expressed workload, nor the time needed to make a classification. However, the inclusion of the uncertainty information did not have a significant effect on the performance (true positives, false negatives and false positives) or the participants׳ expressed confidence in their decisions.

Information fusion supporting team situation awareness for future fighting aircraft

In the military aviation domain, the decision maker, i.e. the pilot, often has to process huge amounts of information in order to make correct decisions. This is further aggravated by factors such as time-pressure, high workload and the presence of uncertain information. A support system that aids the pilot to achieve his/her goals has long been considered vital for performance progress in military aviation. Research programs within the domain have studied such support systems, though focus has not been on team collaboration. Based on identified challenges of assessing team situation awareness we suggest an approach to future military aviation support systems based on information fusion. In contrast to most previous work in this area, focus is on supporting team situation awareness, including team threat evaluation. To deal with these challenges, we propose the development of a situational adapting system, which presents information and recommendations based on the current situation.

Transparency of military threat evaluation through visualizing uncertainty and system rationale

Threat evaluation (TE) is concerned with determining the intent, capability and opportunity of detected targets. To their aid, military operators use support systems that analyse incoming data and make inferences based on the active evaluation framework. Several interface and interaction guidelines have been proposed for the implementation of TE systems; however there is a lack of research regarding how to make these systems transparent to their operators. This paper presents the results from interviews conducted with TE operators focusing on the need for and possibilities of improving the transparency of TE systems through the visualization of uncertainty and the presentation of the system rationale.

Evaluating Multi-Attributes on Cause and Effect Relationship Visualization

This paper presents findings about visual representations of cause and effect relationships direction, strength, and uncertainty based on an online user study. While previous researches focus on a ...

Understanding Indirect Causal Relationships in Node-Link Graphs

To find correlations and cause and effect relationships in multivariate data sets is central in many data analysis problems. A common way of representing causal relations among variables is to use node‐link diagrams, where nodes depict variables and edges show relationships between them. When performing a causal analysis, analysts may be biased by the position of collected evidences, especially when they are at the top of a list. This is of crucial importance since finding a root cause or a derived effect, and searching for causal chains of inferences are essential analytic tasks when investigating causal relationships. In this paper, we examine whether sequential ordering influences understanding of indirect causal relationships and whether it improves readability of multi‐attribute causal diagrams. Moreover, we see how people reason to identify a root cause or a derived effect. The results of our design study show that sequential ordering does not play a crucial role when analyzing causal relationships, but many connections from/to a variable and higher strength/certainty values may influence the process of finding a root cause and a derived effect.

Supporting Fighter Pilot Decision Making Through Team Option Awareness

Fighter pilots must often make decisions fast, under time-pressure and based on uncertain or incomplete data. Thus, decision-making in this environment poses several challenges on the pilots such as how to fulfil the goal of the mission, while at the same time limit the potential costs and risks taken to fulfil this goal. Another challenge involves the dynamic coordination of actions within the team of pilots needed to succeed with the mission efficiently. This paper discusses challenges and opportunities of introducing a decision-support tool in the fighter aircraft, aiding the pilots determine the best courses of action with regard to the teams resources, opportunities and the possible risks involved. To do so, we apply the concept of option awareness, guiding the future development of decision support in the fighter aircraft domain.

Visual Data Analysis

Data Science offers a set of powerful approaches for making new discoveries from large and complex data sets. It combines aspects of mathematics, statistics, machine learning, etc. to turn vast amounts of data into new insights and knowledge. However, the sole use of automatic data science techniques for large amounts of complex data limits the human user’s possibilities in the discovery process, since the user is estranged from the process of data exploration. This chapter describes the importance of Information Visualization (InfoVis) and visual analytics (VA) within data science and how interactive visualization can be used to support analysis and decision-making, empowering and complementing data science methods. Moreover, we review perceptual and cognitive aspects, together with design and evaluation methodologies for InfoVis and VA.

Absolute and relative preferences in AHP-like matrices

The Analytical Hierarchy Process (AHP) has been extensively used to interview experts in order to find the weights of the criteria. We call AHP-like matrices relative preferences of weights. In thi ...

Identifying Root Cause and Derived Effects in Causal Relationships

This paper focuses on identifying factors that influence the process of finding a root cause and a derived effect in causal node-link graphs with associated strength and significance depictions. We discuss in detail the factors that seem to be involved in identifying a global cause and effect based on the analysis of the results of an online user study with 44 participants, who used both sequential and non-sequential graph layouts. In summary, the results show that participants show geodesic-path tendencies when selecting causes and derived effects, and that context matters, i.e., participant’s own beliefs, experiences and knowledge might influence graph interpretation.

Towards a Clinical Support System for the Early Diagnosis of Sepsis

Early and accurate diagnosis of sepsis is critical for patient safety. However, this is a challenging task due to the very general symptoms associated with sepsis, the immaturity of the tools used by the clinicians as well as the time-delays associated with the diagnostic methods used today. This paper explores current literature regarding guidelines for clinical decision support, and support for sepsis diagnosis in particular, together with guidelines extracted from interviews with four clinicians and one biomedical analyst working at a hospital and clinical laboratory in Sweden. The results indicate the need for the development of visual and interactive aids for enabling early and accurate diagnosis of sepsis.