Bridging Knowledge Gaps in Decision Making with Visual Analytics

The research challenges targeted in this thesis are (1) the extraction of knowledge from data with visual analytics to support the decision making process, and (2) the bridging of gaps between involved stakeholders. The first challenge has been addressed in the previous section. In this section, we present a concept on how visual analytics can support the knowledge transfer between stakeholders in the decision making process. The concept is strongly related to the ideas of collaborative visualization as presented by Isenberg et al. among others [IES^∗11]. However, so far collaborative visualization has been addressed from the temporal (synchronous vs. asynchronous work) and the spatial (co-located vs. distributed work) perspective. Varying user expertises and roles, as present in the decision making

3.3. Bridging Knowledge Gaps in Decision Making with Visual Analytics

process, have rarely been considered. In the following, we first describe an adapted decision making process (see Figure 3.8) and an adapted policy cycle (see Figure 3.9) that support the bridging of knowledge gaps between involved stakeholders by the extensive use of visual analytics technology.

Second, we describe how the complexity of decision support systems can be reduced by applying different visualization disciplines. And finally, we discuss synergy effects by incorporating visual analytics into the decision making process.

3.3.1. Bridging Knowledge Gaps in Organizational Decision Making

Our first concept extends the ‘classical’ decision making process with visual analytics technology (see Figure3.8). The concept addresses the bridging of knowledge gaps in model-driven data analysis sce-narios. We select a model-driven scenario, since it involves a large variety of user roles: decision makers, analysts, and modeling experts. However, we claim that the concept can be adapted to textual data (with text analysis methods as models) and empirical data (with data mining techniques as mod-els). In model-driven scenarios, a computational model is applied in the decision making process to create and evaluate alternative solutions to a given problem. However, the models’ complexity impede the consideration of the created knowledge in the decision making process. Hence, we suggest con-necting visualization techniques to computational models developed by modeling experts to improve their usability for non-experts. That way, the complexity of the models is hidden in the computational back-end, while only the information necessary for providing user input (e.g., control parameters, etc.)

Figure 3.8.:A concept to bridge knowledge gaps between stakeholders with visual analytics. The classical communication workflow from the decision maker over the analyst to the modeling expert and back is bridged with visualization techniques.

and analyzing the model output (e.g., simulation results, statistical measures, etc.) is displayed on the screen. The most crucial aspect of this concept is that users not familiar with the computational mod-eling can visually interact with these models and conduct analysis scenarios by themselves. Hence, analysts and decision makers can specify objectives and constraints via a visual interface (see Figure 3.8, on the left). They can ‘experiment’ with different settings, and generate alternative model outputs (see Figure3.8, on the right). Furthermore, due to a uniform visual representation of the model data, stakeholders with different backgrounds can validate the models’ utility and usability. For example, the decision maker can detect aspects not covered yet by the model that the modeling expert might include in an improved model. The communication of results is facilitated, since all stakeholders work with the same visual representation.

3.3.2. Bridging Knowledge Gaps in Policy Making

The model presented in the previous section can be easily transferred to political decision making (or policy making). Nevertheless, we introduce a second model that illustrates the idea of our approach.

Therefore, we expand the five-stage policy cycle by Anderson [And75] (see Figure3.9a) at the policy formulation and the policy adoption stages. These stages imply the definition of policy options, their analysis, and finally the decision which one to choose (see Figure3.9 b). Several stakeholders with different expertise are involved in these stages which result in knowledge gaps to overcome.

At the ‘agenda setting and problem definition’ stage public problems that shape the political agenda are identified by political decision makers (policy makers). At the ‘policy formulation’ stage, the problem is analyzed (policy analysis) and potential solutions to the problems are defined. The main stakeholders involved at this stage are the (policy) analyst and the modeling expert. The analyst concep-tualizes the problem identified in the previous stage and consults modeling experts as external adviser.

Based on the requirements specified by the analyst, the modeling experts design computational models that support the creation and analysis of policy options. The model results are communicated to the analyst who uses the extracted knowledge to define policy options. In the ‘policy adoption’ stage the defined policy options are communicated in a condensed way to the decision maker who decides which option to choose. The model includes two feedback loops. First, the analyst might define additional requirements based on the modeling results communicated by the modeling expert. Second, the deci-sion makers might request additional policy options from the policy analyst by refining the problem description. The policy cycle is completed with the policy implementation and the policy evaluation stages that are not considered in this model.

Similar to the previous model, this process can be augmented with visual analytics concepts. By connecting visualization techniques to the computational model, users can visual-interactively access the model parameters and the model output (see Figure 3.9 c). In the following, we discuss how different visualization disciplines are applied to support different analysis tasks.

3.3. Bridging Knowledge Gaps in Decision Making with Visual Analytics

Figure 3.9.: Adaptation of policy cycle. a) Standard policy cycle [And75]. b) Adaptation of policy cycle, providing more detail in the policy formulation and policy adoption stages, and two feedback loops. c) Linking visualization to the model to bridge knowledge gaps.

3.3.3. Complexity Reduction via Appropriate Visualization Disciplines

In the previous sections, we introduced two concepts on how to include visual analytics in business and political decision making processes to bridge knowledge gaps between stakeholders. The complexity of the analysis process can be further reduced by applying appropriate visualization disciplines in the decision making process. Depending on the user and task at hand, visualization disciplines of varying complexity can be applied.

We differentiate between two main usage scenarios of visualization techniques: (1) the visual-interactive exploration and sense-making as analysis tasks and (2) the visual communication as knowl-edge transfer task (cf. Figure2.4by Few [Few09]). In the first case, the users require visual-interactive control of the computation model. Users are enabled to define input parameters and run the model to create alternative output data. The interactive control of the model is realized by using concepts from the field of visual analytics, which are coined by Bertini and Lalanne as “white-box-integration”

[BL10]. The visual analytics concept connecting visualization with computational models is already applied to different application domains. We propose to introduce this concept to the decision making domain. In the second case, users require visual access to the analysis results generated with the model.

The output data is presented with information visualization techniques. Users can visually explore the results of the model by search and filter operations. The information may be visualized with

differ-ent visualization techniques depending on the users’ expertise and knowledge. The main focus of these techniques lies on the usability of the system. The intuitive usage of the visualization has to be ensured.

In the following, we describe how the appropriate selection of visualization techniques can further support the bridging of knowledge gaps between stakeholders.

Visualization for Decision Makers (and Public stakeholders)

The visualization design for the decision maker consists of easy-to-understand interfaces only depicting the information relevant for the decision process. It enables the decision maker to get quick access to analysis results. This interface bridges knowledge gap between decision maker and the modeling expert (see competence gap). Moreover, the decision maker can give high-level feedback to the modeling expert if some information is missing, or if the model needs to be refined (see iteration gap). As another

‘gap bridger’, the translation of analysis results to decision options can be derived by the visualization.

This bridges the knowledge gap between analyst and decision maker (see analysis and competence gap). The considered visualization techniques are mainly static visualization (or infographics), and easy-to-use information visualization techniques. As an example, the ManyEyes system enables a user friendly access to visualization techniques with the option for the users to upload and visualize their own datasets [VWvH^∗07].

Visualization for Analysts

The visualization design for the analyst consists of the basis functionality provided to the decision maker, and advanced interaction techniques, that offer a closer connection to the model. This inter-face bridges the knowledge gap between the analyst and the modeling expert (competence gap). The analyst is enabled to validate the model from the domain perspective, and refine it, e.g., by changing input parameters (iteration gap and analysis gap). The communication between the analyst and the modeling expert is supported, since both can work with the same information representation. Again, the access to the complex model is facilitated. This enables the analyst to interact with the model, gain an understanding of the model, and finally produce analysis results without the help of the modeling expert (see analysis gap). The considered visualization techniques come from both fields, information visualization and visual analytics. Examples for visual analytics systems designed to support analysts can be found in the fishery policy domain [BMPM12], and the energy domain [Hea12].

Visualization for Modeling Experts

The visualization design for the modeling expert comprises the highest functionality. Depending on the requirements of the modeling expert, a visual-interactive editing of the model may be realized. Visual-izing the model input and output supports the modeling expert in refining the model and validating its functionality (see iteration gap). That way, new analysis results are produced and communicated to the analyst and the decision maker via their respective visualization design (see analysis gap, and

compe-3.3. Bridging Knowledge Gaps in Decision Making with Visual Analytics

tence gap). This bridges the knowledge gap between modeling experts, analysts, and decision makers, who collaboratively refine the functionality of the model, and validate the correctness of the model (see iteration gap). The considered visualization techniques for the modeling expert are mainly from the field of visual analytics. In [MK08], a visual system for the data-driven verification of hypothesis is provided. In [IMI^∗10], an interactive data analysis process is supported with visualization techniques.

3.3.4. Synergy Effects of Applying Visual Analytics to Decision Making

In order to address the challenges imposed on decision making and policy making, we proposed the incorporation of visual analytics into decision making (see Figure3.8) and policy making (see Figure 3.9) processes. Hereby, visual analytics serves as an important component of decision support systems itself. In the following, we summarize the benefits resulting from this integration:

Communication. The communication between relevant fields, e.g., science and policy making, is facilitated. Visualization may serve as a mediator of information between two distinct environments.

Through the unified visual presentation of information, different stakeholders are enabled to discuss at the same knowledge level. Thereby, the communication between scientists and decision makers in the decision making process is supported.

Complexity.Through the abstraction of user tasks and the design of visual analytics systems adapted to the expertise of the targeted users the complexity of the underlying models can be hidden. With visual analytics complex data operations can be executed on the machine, while the parameters to control their execution can be intuitively displayed on the screen. Visual interfaces provide the information on the level of detail needed by the respective user role.

Subjectivity.The aspect of subjectivity can be reduced since different stakeholders get access to the same information provided in an ‘objective’ way via visual analytics techniques. The provided infor-mation can be discussed among the stakeholders to balance subjective interpretations of the findings.

Validation. The outcomes of the decision making process can be transparently presented to all involved stakeholders including public stakeholders. That way decisions can be justified since they have been made based on an objective analysis. This can improve the trust in scientific results, and political decision making.

Transparency and reproducibility of results. If open access to the visual analytics system is provided, public stakeholders (e.g., journalists, interest groups, etc.) are enabled to generate analysis results on their own, and therefore, better understand the rational background of strategical decisions.