HiddenCove

adaptation vs. stability: building systems that evolve with context rather than seeking unchanging order.
algorithmic governance: governance shaped by algorithmic systems, where power can accrue through data, metrics, and optimization.
audit trail: a chronological record of system activities, decisions, and data changes that enables reconstruction of events and accountability; essential for traceability in regulated environments
automation modes: distinct operational states in automated systems where control authority shifts between human operators and automated functions; can be hidden or partially legible, creating confusion during anomalies
balancing (negative) feedback loops: loops that counteract change and stabilize the system (e.g., mutualism and scarcity damping inequality).
buffers / slack: spare capacity, time, or resources between system components that absorb variation and create space for intervention; eliminated by optimization pressures, potentially reducing system resilience
business continuity: organizational capacity to maintain or rapidly resume critical functions after disruption; focuses on recovery time objectives and continuity strategies rather than prevention alone
circuit breakers: designed interruption points that allow systems to pause, isolate, or halt cascading failures.
compliance vs. trust: when procedural conformity is treated as equivalent to genuine trust this typically produces brittle systems.
continuous monitoring: ongoing observation of system states, behaviors, and performance indicators to detect anomalies, degradation, or emerging risks in real-time rather than through periodic audits
continuum: the idea that “there are no separate systems” and everything is connected across gradients rather than hard divides.
custodianship: a governance stance that emphasizes responsibility for maintaining, limiting, or retiring risky systems rather than mastering them.
degraded state: a mode of operation in which a system continues functioning with reduced safeguards, redundancy, or visibility.
delay: the lag between action and its observable effect, which can distort understanding and control.
design for feedback (not prediction): prioritizing mechanisms that learn and adapt over attempts at perfect foresight.
distortions: biases and outright errors in information that misguide decisions and weaken responsiveness.
dogma: fixed belief that resists critique and change, blocking the flow of information.
dynamic equilibrium: a shifting balance maintained through ongoing feedback and adjustment, not static harmony.
epistemic humility: acknowledgment that our knowledge and our models are partial and that systems will surprise us; considered important for learning.
essentialism: reducing reality to a single defining property or value.
ethical stance: a stance that is taken because or for a certain idea of what is ethical or moral.
fail-safe mechanisms: design features that default to a safe state when failures occur; rely on predictable failure modes and may not address system accidents arising from interactive complexity
feedback: information returning to a system or person about its behavior, enabling correction or amplification.
fragility: a state where a system cannot adapt, and risks failure when conditions within the system, or in its wider environment, change.
hidden modes: system states or automation conditions that are active but not easily visible or legible to operators.
ICS (Industrial Control Systems): cyber-physical systems that monitor and control industrial processes in critical infrastructure (energy, water, manufacturing); tightly coupled and increasingly complex as IT/OT converge
incident escalation: the process by which minor failures, deviations, or anomalies combine and amplify into more serious events; particularly dangerous in tightly coupled systems where response windows narrow rapidly
incident response: organizational capability to detect, interpret, contain, and recover from system failures or security events; effectiveness depends on traceability, clarity of system states, and ability to intervene without full understanding
information flows: the movement of data, signals, and stories through a system; the lifeblood of adaptation and change.
inside-out participation: engaging as both observer and participant within systems, listening as much as acting.
interactive complexity: a property of systems where components interact in unexpected, non-linear ways outside normal or intended sequences.
interdependence: mutual reliance among parts of a system.
last-link mindset: a failure analysis approach that locates responsibility at the final human or technical action closest to the accident.
learning vs. control: favoring iterative improvement and openness over rigid command-and-control structures.
leverage points: places in a system where small shifts yield large effects.
listening systems: systems designed to hear themselves—through active, diverse feedback that informs adaptation, adjustment, change and renewal.
map vs. territory: a warning that models and metrics can colonize reality when they are mistaken for it.
metrics vs. meaning: the risk that quantitative indicators substitute for substantive judgment or values.
moral humility: recognition of limits, interdependence, and participation in the face of moral decision-making.
nonlinear causality: cause and effect relationship that does not unfold in simple, straight chronological lines, but rather indirect, delayed, or emergent.
normal accident: an accident that is structurally inevitable in certain systems due to their interactive complexity and tight coupling.
optimization loops: reinforcing feedback that privileges efficiency and stability over adaptability.
oscillation: movement back and forth between states or poles.
ossification: the process by which ideals harden into dogma, narrowing discourse and choking feedback.
ownership of feedback loops: the question of who controls how information circulates and which voices are encoded in systems.
paradigm: the underlying worldview or set of assumptions from which a system’s goals, rules, and feedback loops arise.
paradigm shift: transformation at the level of the underlying worldview or set of assumptions that reconfigures the whole system’s behavior.
redundancy: duplication of critical components, processes, or data to maintain function if one fails; effective for component failures but less so for system accidents where multiple redundant elements can fail simultaneously through unexpected interactions
reflection: the capacity to examine one’s own behavior and assumptions, beyond reactive control.
reinforcing (positive) feedback loops: loops that amplify change (e.g., wealth begets power, power begets wealth).
resilience: a system’s capacity to absorb shocks, adapt, and keep functioning; lost when feedback is suppressed or distorted.
response window: the limited time available for operators to detect, interpret, and intervene before escalation becomes irreversible.
rollback: the ability to reverse system changes, configurations, or transactions to a previously known acceptable state; provides intervention capability when full understanding is unavailable during incidents
root cause analysis: investigation method that seeks to identify the fundamental reason for a failure; runs the risk of defaulting to last-link thinking in complex systems where multiple interacting factors combine to produce accidents
socio-technical system: a system encompassing both technical components (machines, software, infrastructure) and social elements (people, organizations, practices)
surveillance: data collection and monitoring that can tighten control loops and skew incentives.
system: a pattern of relationships, not a mere collection of parts; a dynamic whole shaped by stocks, interactions, flows, and feedback loops.
system accident: a failure arising from the unanticipated interaction of multiple normal or minor failures within a tightly coupled, complex system.
system boundaries: conceptual lines used for modeling; incomplete and permeable because systems interpenetrate.
system health: the quality of a system’s functioning, indicated by resilience, openness to feedback, adaptability, and learning.
tight coupling: a condition in which system components are linked with little or no slack, buffer, or delay, causing effects to propagate rapidly.
traceability: the ability to reconstruct system states, decisions, data, and changes across time and organizational boundaries.