The Need for Meaningful Feedback

In complex systems, controlling processes and ensuring stability is no simple task. A core challenge lies in determining what states must be fed back to users to allow them to anticipate the outcomes of their actions. Whether driving a car or piloting an aircraft, feedback must reflect the system's dynamics to enable the user to foresee future states. To support the Harmonics Way principle of "Always know how things are going," the design of such systems must go beyond surface-level solutions, such as defense-in-depth protections, to focus on visualizing key risks and constraints through meaningful representations.

Key Feedback for Effective Control: Learning from Inertial Systems

Consider the case of driving a car. A driver cannot effectively control a vehicle by relying on positional feedback alone - velocity is also a factor to consider in predicting future positions. Feedback systems must reflect both the car's speed and the braking dynamics to help drivers judge safe distances and react accordingly. Without this layered feedback, safety becomes compromised. The same principle applies to other high-dimensional control systems, where multiple states—often difficult to measure—must be monitored in real-time.

Defense-in-Depth: A Buffer with Limitations

One common approach to managing risk in complex systems is defense in depth—creating buffers such as speed limits or safety zones to prevent dangerous situations. However, Flach points out a crucial flaw: over time, people tend to drift beyond these artificial constraints to maximize efficiency, reducing their influence on behavior and increasing the risk of accidents. Rasmussen’s dynamic safety model highlights the importance of making actual risk boundaries visible, rather than relying solely on abstract buffers.

This concept sets the stage for Ecological Interface Design (EID), an approach to developing safety-critical interfaces that emphasize clear visualizations of system states, risks, and constraints. In EID, the goal is to help users “see” the future impact of their actions through accurate and dynamic feedback.

The Principles of Representation: Semantic Mapping and Systematicity

For visual interfaces to support complex decision-making effectively, two principles are central: semantic mapping and systematicity. These principles ensure that visual representations correspond to the real-world dynamics of the system.

1. Semantic Mapping: Creating Meaningful Correspondence

The principle of semantic mapping emphasizes a one-to-one relationship between system states and their visual cues. This relationship ensures that the most important information for anticipating consequences, whether risks or opportunities, is immediately visible and understandable to the user. A well-designed interface reflects the system's dynamics so that users can intuitively grasp future outcomes based on what they see.

For example, in a driving scenario, a blind spot indicator provides direct, actionable feedback that a vehicle may be in a dangerous position—bridging the gap between unseen risks and visible information.

2. Systematicity: Maintaining Structural Integrity Across Multiple Levels

Systematicity emphasizes that visualizations should preserve a system's nested structure of constraints. Just as work environments have hierarchies of goals, constraints, and interactions, visual representations should reflect these relationships consistently. This visualization allows users to maintain local and global awareness—crucial in distributed work environments, where multiple users may control different system parts.

Similar to the way film editing preserves narrative coherence across scenes, interface designs must retain the integrity of higher-order goals across multiple displays or contexts. This integrity is essential for both individual users and teams to coordinate effectively.

Visualizing Risk and Control: From Wireframes to Multidisciplinary Design

In practice, the development of effective visualizations is closely tied to work analysis, the process of identifying the relevant state variables and constraints. However, designing effective interfaces is not merely a matter of applying insights from work analysis; it is a collaborative and iterative process. Early interface concepts often serve as valuable tools for eliciting further knowledge from domain experts, helping designers refine their understanding of the system's dynamics.

This participatory design approach ensures that visualizations reflect the true functional constraints of the work environment, enabling users to anticipate risks and take timely action. Concrete visualizations, such as wireframes or prototypes, play a vital role in fostering collaboration and discovering essential information feedback loops.

Conclusion: Visualizing for Safety and Control

Designing stable control systems requires more than buffers and safety margins. It demands visualizations that align with the system's functional dynamics. Both defense-in-depth protections and meaningful feedback must coexist to ensure effective control. Users need tools to foresee the consequences of their actions, make informed decisions, and act preemptively to avoid risks.

At the heart of effective visualization are semantic mapping principles and systematicity. By ensuring a meaningful correspondence between system dynamics and interface cues and preserving the structure of nested constraints across different system levels, designers can empower users to manage complex environments safely and efficiently.

Whether applied to computer interfaces, mental models, or collaborative tools, the quality of the representation determines the quality of control. The ability to anticipate risks and take action depends on our capacity to visualize the state space that underlies the dynamic work environment. In the end, thoughtful interface design enhances performance and can make the difference between safety and disaster in high-stakes scenarios.