Design illusions in cyber physical systems reveal hidden risks and solutions
Towards Illusions Awareness in Cyber-Physical System's Design
Computation and Language
Summary
Cyber-Physical Systems are machines that continuously sense, think, and act in environments they can't fully predict. Because designers make assumptions about how these systems will work, sometimes those assumptions turn out wrong, causing a difference between expected and real behavior. The authors call this hidden overconfidence 'design illusions' and argue that making these illusions clear can help improve designs. They propose a way to identify and use these illusions as knowledge to create better systems.
What this means in practice
- •For embedded systems engineers: Improve design decisions by explicitly identifying and structuring invalidated assumptions during system development to reduce unexpected failures.
- •For industrial automation teams: Manage operational risks by leveraging knowledge of design illusions to better anticipate and handle system behavior discrepancies in complex environments.
A position paper. It proposes an approach and reports no results.
Authors
Anna Di Placido, Nicolas Ferry, Julien Deantoni
Abstract
Cyber-Physical Systems (CPS) operate through a continuous sense-compute-act loop within an open context environment, making it impossible to anticipate all the situations the system will face. To cope with this openness, stakeholders rely on assumptions, formalized into design models. However, these assumptions may no longer hold once the system is confronted with runtime reality, resulting in a discrepancy between expected and observed behaviour known in literature as the reality gap. Existing approaches mainly focus on reducing or overcoming it by making simulations more faithful to reality, with no unified methodology to structure and exploit invalidated assumptions that give rise to this gap as reusable design knowledge. We refer to the persistent reliance on invalidated assumptions -and the resulting false confidence in the design model's operational validity -as design illusions, and argue that they need to be made explicit, structured, and exploited as knowledge to support better design decisions. We propose a conceptual pipeline for illusions-awareness that identifies, classifies, characterizes, and leverages illusions to transform them into actionable design knowledge.