Human Overrides in Autonomous Building Management: A Conceptual Framework Grounded in HVAC Fault Diagnostics and Controllable Operation Data

1. Introduction

Autonomous building management is usually framed as a technical problem of sensing, prediction, optimization, and control. That framing is incomplete for operational buildings. Building energy management systems coordinate HVAC and other subsystems through software, but users and operators can still intervene through local panels or remote interfaces. Advanced systems may forecast loads or environmental conditions from historical and current data, yet the control loop remains embedded in organizational work practices where human actors can accept, delay, modify, or cancel machine recommendations. [1]

Human intervention should therefore be treated as an expected authority transition inside a socio-technical control system, not left as an undocumented discretionary act. This need becomes sharper in HVAC settings because an operating condition that appears acceptable at a single controlled variable may still be diagnostically unreliable. Recent fault-detection work shows that sensor bias in closed-loop HVAC control can be hidden at the biased sensor itself because the controller compensates to maintain the apparent setpoint, while anomaly signatures migrate to correlated sensors and actuator behavior. Recent dataset work also shows that reproducible operation traces can be generated under both routine rule-based control and deliberately exploratory supervisory trajectories, which broadens the range of state-action conditions under which an override policy could be assessed. [2] [3]

This article addresses the problem of how an autonomous building management system should allocate authority among automatic execution, human review, and fallback control. The manuscript is conceptual research, not a report of completed field evaluation. Its scope is intentionally narrow: supervisory HVAC override logic in smart buildings. That scope is chosen because the strongest direct evidence supplied here concerns HVAC sensor faults and controllable operation datasets rather than autonomous building management in general.

2. Research question and evidence scope

The primary research question is: how should an autonomous HVAC supervisory controller decide whether to execute a recommended action automatically, request human review, revert to a transparent baseline controller, or enter a predefined safe mode? A secondary question is evidential: which available studies provide direct operational support for that decision problem, and which provide only transferable methodological or organizational foundations?

The article uses a targeted narrative synthesis rather than a systematic review. No search counts or exhaustiveness claims are made. The direct operational evidence comes from two studies. First, a BRICK-based fault-detection framework automatically constructs virtual temperature sensors from building metadata and uses residuals among correlated sensors to detect hidden temperature-sensor bias in HVAC systems. Second, a reproducible dataset-generation method creates multi-building, multi-climate HVAC operation traces under both default rule-based control and a stochastic exploratory supervisory controller. These studies bear directly on hidden anomaly detection, action-state coverage, and reproducible testing conditions for override governance. [2] [3]

Four other studies are used as transferable foundations. A heating-load prediction study is relevant because it combines probabilistic learning with residual modeling and argues that robustness and uncertainty matter in addition to point accuracy. A retrofit optimization study is relevant because it formulates trade-offs explicitly across energy, carbon, and comfort. Two BIM adoption studies are used only as contextual support for organizational implementation: they indicate that technological competence, leadership support, readiness, and mitigation of behavioral and managerial barriers influence whether digital methods are adopted and sustained. These BIM studies do not provide direct evidence about override performance in HVAC control. [1] [4] [5] [6]

The synthesis therefore distinguishes three evidential levels throughout: direct HVAC operational evidence, transferable methodological design ideas, and organizational implementation context. The proposed framework draws from all three levels but is empirically established by none of them in live building operation.

3. Direct operational evidence for override design

The strongest direct lesson for override design comes from the HVAC fault-detection study. Its central claim is that temperature-sensor bias in closed-loop HVAC systems is hard to detect by ordinary threshold monitoring because the feedback controller compensates and returns the faulty reading toward its apparent setpoint. The abnormality does not disappear; it is redistributed across correlated signals and actuator behavior. The study therefore trains LightGBM virtual temperature sensors on fault-free inter-sensor relationships derived from BRICK metadata, computes z-scored residuals as anomaly indicators, ranks sensors for fault isolation, and uses command-position discrepancy to distinguish sensor faults from mechanical faults. [2]

For override governance, the important point is the mechanism rather than the benchmark score. A controlled variable near setpoint cannot be treated as sufficient evidence of healthy operation. A supervisory controller may appear successful while relying on distorted sensing or compensatory actuator effort. Any proposed override trigger should therefore check cross-sensor consistency and command-position disagreement, not only the apparent behavior of a single controlled signal. [2]

The same study also states boundary conditions that materially constrain how its findings can be transferred. The evaluation relies on simulated data; only temperature-bias faults are tested; real buildings would add missing values, communication delays, noise, drift, and occupancy-driven variability; and at mild biases the nearest thermal neighbor can outrank the truly biased sensor in isolation. Those limitations imply that override decisions should be able to represent diagnostic ambiguity instead of assuming that every anomaly score corresponds to a uniquely identified fault. [2]

The operation-dataset study provides the second direct foundation. It proposes a reproducible pipeline using EnergyPlus-based and Modelica-based simulation environments to generate 15-minute multivariate HVAC operation traces across heterogeneous buildings and climates. Crucially, the workflow includes both a default rule-based controller and a stochastic exploratory supervisory policy that produces drift, ramps, oscillations, jumps, and noisy holds within operational bounds. The released variables include indoor temperature, outdoor weather, setpoints, and whole-building HVAC power. [3]

This dataset design matters for human override because rare authority transitions are unlikely to be represented by routine schedules alone. Review prompts and manual interventions are most plausible when conditions are unusual, when setpoint changes are non-routine, or when models encounter weakly represented parts of the state-action space. The source-target structure in the same dataset method also makes it possible to test whether an override rule developed in some simulated buildings remains coherent in a distinct target domain. [3]

4. Transferable foundations from prediction, optimization, and organizational adoption

A first transferable foundation is uncertainty-aware prediction. The heating-load study evaluates several machine-learning models and motivates a hybrid Natural Gradient Boosting plus Mixture Density Network architecture on the grounds that probabilistic estimation of the target distribution can be combined with residual modeling of nonlinear structure not captured by the first-stage predictor. The same study emphasizes robustness and error-oriented assessment, while also cautioning that its numerical results depend on the specific building and validation settings. The relevant transferable lesson here is that override logic should not rely on point forecasts alone when deciding whether automation is trustworthy enough to act without review. [1]

A second foundation is explicit multi-objective reasoning. The retrofit optimization study formulates a tri-objective problem over energy, life-cycle carbon emissions, and thermal discomfort, then uses surrogate modeling and NSGA-III to generate Pareto solutions. Although that study concerns retrofit design rather than operational control, it provides a useful structural lesson: when multiple operational goods conflict, the trade-off should be made explicit. Human overrides often arise precisely because local staff value one objective differently from the controller at a given moment, for example privileging comfort or equipment protection over short-term energy savings. [4]

A third foundation concerns implementation conditions. One BIM adoption study, drawing on TOE and diffusion reasoning, reports positive roles for technological competence, top management support, organizational readiness, external support, environmental sustainability orientation, and relative advantage in digital-method adoption. Another identifies behavioral, managerial, technical, and operational barriers, with behavioral and managerial barriers exerting strong influence in its setting. These studies do not demonstrate anything about HVAC override outcomes, but they support a narrower contextual claim: override governance will probably not be used consistently without defined roles, training capacity, leadership support, and management of resistance to new procedures. [5] [6]

5. Conceptual model of an Override-Aware Autonomous Building Management System

The proposed Override-Aware Autonomous Building Management System (OA-ABMS) is defined here as a supervisory control architecture that would condition action authorization on sensing confidence, forecast-derived risk, actuator consistency, and operator response status. It contains five logical layers: sensing, predictive diagnostics, supervisory evaluation, authority governance, and audit logging. The sensing layer acquires indoor temperatures, setpoints, weather variables, HVAC power, and available actuator positions. The predictive-diagnostics layer constructs metadata-defined virtual sensors and short-horizon forecasts. The supervisory-evaluation layer compares a candidate autonomous action with a transparent baseline controller. The authority-governance layer selects among automatic execution, review pending, fallback, manual control, or locked-safe mode. The audit layer records each trigger, state transition, acknowledgment, and reason code. [2] [3] [4]

A reproducible override architecture requires a precise rule for selecting which sensors count as critical. Let G be the BRICK metadata graph. For a supervised HVAC subsystem e, define the critical sensor set C(e) as all temperature sensors x_i that satisfy both of the following conditions: (1) graph inclusion: x_i is connected to e or to a downstream thermal zone z by one of the declared BRICK paths e-hasPoint->x_i, e-hasPart->u-hasPoint->x_i, or e-feeds->z-hasPoint->x_i; and (2) control relevance: x_i belongs to one of three eligible classes predefined before evaluation, namely supply-air temperature, mixed-air or return-air temperature, or zone/room air temperature used by the supervisory loop. Sensors are excluded if they are duplicate telemetry channels, diagnostic-only temporary points, or points lacking at least one nominal training segment without missingness. This rule is intended to reduce analyst discretion. Two operators applying the same metadata and class list should construct the same C(e). [2]

Let t index control intervals of length Delta. Let x_i(t) be the observed value of critical sensor i and V_i(t) its virtual estimate from correlated sensors selected through G. Define the standardized residual r_i(t) = (x_i(t) - V_i(t)) / sigma_i, where sigma_i is the standard deviation of nominal residuals for sensor i on training data. Let w_i be nonnegative class weights with sum_i in C(e) w_i = 1. The anomaly score is S(t) = sum_i in C(e) w_i * min(|r_i(t)|, r_max). If more than p percent of sensors in C(e) are missing at time t, then S(t) is declared unavailable rather than imputed for trigger purposes. This avoids forcing a review decision from an anomaly metric known to be weakly observed. [2]

Let Y_h(a,t) denote the forecast distribution of indoor temperature under action a for horizons h = 1,...,H, and let T_set,h(a,t) be the corresponding active setpoint. Let delta_c be a prespecified comfort tolerance band. Define expected discomfort risk as ER(a,t) = (1/H) * sum_h Pr(|Y_h(a,t) - T_set,h(a,t)| > delta_c). This gives a coherent inference path from a probabilistic forecast to a review trigger. Forecast uncertainty is defined separately as U(a,t) = (1/H) * sum_h [Q_0.95(Y_h(a,t)) - Q_0.05(Y_h(a,t))], the mean width of the forecast prediction interval over the horizon. If a deterministic forecaster is used in an ablation, ER(a,t) is replaced by the indicator average (1/H) * sum_h I(|y_h(a,t) - T_set,h(a,t)| > delta_c), and the protocol should note that uncertainty-sensitive triggers are then unavailable. [1]

The supervisory objective is defined explicitly so that optimization and override are not conflated. Let J(a,t) = lambda_r * ER(a,t) + lambda_e * E_norm(a,t) + lambda_b * B_norm(a,t), where ER is expected discomfort risk, E_norm is normalized predicted HVAC energy use over the horizon, and B_norm is normalized actuator burden measured as cumulative absolute command change over the horizon. The nonnegative weights lambda_r, lambda_e, and lambda_b sum to 1. To remove scale ambiguity, define E_h(a,t) as predicted whole-building HVAC power at horizon step h and let E_sum(a,t) = sum_h E_h(a,t). For each building scenario, let E_min and E_max be fixed reference values estimated once from Stage 1 replay under valid intervals using the 0.01 and 0.99 empirical quantiles of E_sum(a_base,t) under the rule-based baseline, with the requirement E_max > E_min; then set E_norm(a,t) = clip((E_sum(a,t) - E_min) / (E_max - E_min), 0, 1). Likewise, let u(a,t) be the supervisory setpoint trajectory implied by action a over horizons 0,...,H and define the raw burden B_raw(a,t) = sum_h=1..H |u_h(a,t) - u_{h-1}(a,t)|. Let B_ref > 0 be a fixed scenario-specific normalization constant equal to the 0.99 empirical quantile of B_raw(a_base,t) from the same Stage 1 replay, and set B_norm(a,t) = clip(B_raw(a,t) / B_ref, 0, 1). If actuator-position trajectories are unavailable, B_raw is computed from commanded supervisory setpoint changes rather than physical actuator motion, while physical command-position discrepancy remains a separate trigger through D_A(t). J is not itself an override trigger; it is a comparative score used to evaluate the autonomous recommendation a_auto against the baseline action a_base under a declared policy weighting. [4] [1] [3]

6. Override decision rules and state-transition logic

The proposal would use a fixed state machine with five states: AUTO, REVIEW_PENDING, FALLBACK, MANUAL, and LOCKED_SAFE. AUTO means the autonomous recommendation may be executed without waiting for a human response. REVIEW_PENDING means the recommendation is held while an operator may approve, modify, or reject it within the acknowledgment window W_ack. FALLBACK means the transparent baseline controller a_base governs supervisory actions. MANUAL means a human operator has taken explicit authority for supervisory decisions. LOCKED_SAFE means neither autonomy nor the active baseline schedule is judged configuration-safe, so the system applies a preapproved emergency supervisory schedule snapshot.

The comparator remains constant across the framework, but two schedule objects must be distinguished. The baseline action a_base(t) is the currently valid rule-based controller schedule for the present occupied or unoccupied period in the reproduced dataset or deployed building. It is a normal operating controller, used both as the reference action in evaluation and as the first fallback controller when review is required but automatic optimization is withheld. By contrast, the safe-state schedule S_safe is a separately approved emergency supervisory snapshot used only when neither the autonomous recommendation nor the baseline schedule is configuration-safe. This distinction prevents FALLBACK and LOCKED_SAFE from collapsing into the same state. [3]

The decision sequence is proposed as follows. Step 1: validate data integrity, including timestamp order, stale values, and sensor missingness. Step 2: validate the currently registered baseline schedule a_base and the existence and integrity of the emergency snapshot S_safe. Step 3: compute V_i(t), r_i(t), and S(t) on C(e). Step 4: generate Y_h(a,t), ER(a,t), and U(a,t) for both a_auto and a_base over horizon H. Step 5: when actuator position is available, compute discrepancy D_A(t) = |A_cmd(t) - A_pos(t)|. Step 6: compare J(a_auto,t) and J(a_base,t). Step 7: apply review, fallback, and safe-state transition rules. Step 8: log all state changes, operator responses, and reason codes. This sequence keeps configuration validation, anomaly detection, probabilistic forecasting, optimization, and authority allocation logically distinct. [2] [1] [3]

Mandatory review would be triggered at time t if any of the following proposed conditions holds: R1, S(t) > theta_S; R2, U(a_auto,t) > theta_U; R3, D_A(t) > theta_A for k consecutive intervals when actuator position exists; or R4, ER(a_auto,t) > theta_R and Delta_J(t) < theta_J, where Delta_J(t) = J(a_base,t) - J(a_auto,t). R4 therefore has a specific interpretation: if the autonomous action carries elevated expected discomfort risk while improving the supervisory objective less than the required margin theta_J relative to the baseline, the system should request review rather than proceed automatically. Independently of R1-R4, a configuration-safety trigger C1 is true whenever the baseline schedule a_base is invalid under the checks defined below. C1 does not by itself request review of a_auto; it escalates the controller to a safer state because the designated fallback object is not trustworthy. Thresholds theta_S, theta_U, theta_A, theta_R, and theta_J are policy parameters to be calibrated prospectively; no empirical values are claimed here.

State transitions are proposed exactly as follows. From AUTO to REVIEW_PENDING: if any review condition R1-R4 is true and C1 is false. From AUTO to LOCKED_SAFE: if C1 is true, regardless of whether a review condition is simultaneously true, because the designated fallback controller is not configuration-safe. From REVIEW_PENDING to AUTO: if an operator approves a_auto within W_ack and C1 remains false and no higher-priority data-integrity fault appears. From REVIEW_PENDING to MANUAL: if an operator enters a modified action or explicit manual takeover within W_ack. From REVIEW_PENDING to FALLBACK: if an operator explicitly rejects a_auto within W_ack and C1 is false; or if W_ack expires without acknowledgment and C1 is false. From REVIEW_PENDING to LOCKED_SAFE: if an operator explicitly rejects a_auto within W_ack and C1 is true; or if W_ack expires without acknowledgment and C1 is true. From FALLBACK to LOCKED_SAFE: if C1 becomes true while in FALLBACK. From FALLBACK to AUTO: only if an authorized operator clears the event, C1 is false, and all review conditions remain false for q consecutive intervals. From FALLBACK to MANUAL: if an operator takes manual authority. From MANUAL to FALLBACK: if the operator relinquishes manual authority and C1 is false. From MANUAL to LOCKED_SAFE: if the operator relinquishes manual authority and C1 is true, or if a non-bypassable data-integrity fault designated in policy occurs during MANUAL. From LOCKED_SAFE to FALLBACK or AUTO: only after a valid baseline or autonomous configuration is restored and an authorized operator issues clearance; return to AUTO additionally requires all review conditions to remain false for q consecutive intervals. Late acknowledgments after the system has already entered FALLBACK or LOCKED_SAFE do not retroactively re-execute the expired autonomous action; they are logged as late approvals or late rejections and may support clearance decisions, but state recovery still requires the ordinary q-interval stability condition plus operator authorization.

The safe-state behavior also needs operational precision. Let S_safe be a signed, timestamped supervisory schedule snapshot approved before deployment and stored separately from the active autonomous policy and from the ordinary baseline schedule a_base. S_safe is therefore an emergency schedule object, not a substitute name for a_base. It contains four fields for the relevant day type: occupied status, heating setpoint, cooling setpoint, and maximum permitted supervisory setpoint rate of change. A baseline schedule is treated as invalid if its configuration timestamp exceeds tau_cfg days without revalidation, if its checksum does not match the active configuration registry, or if one of its required occupied or unoccupied entries is missing for the current clock time. In LOCKED_SAFE mode, the supervisory layer freezes schedule class and setpoint values at those stored in S_safe for the current day type; low-level local control loops continue to update from real sensor feedback, and alarms continue to be processed, but no autonomous optimization or exploratory setpoint modification is allowed. If no approved S_safe exists, deployment should not proceed beyond advisory mode. This rule also resolves the non-review case: if baseline invalidity is detected in AUTO even when R1-R4 are all false, the controller enters LOCKED_SAFE rather than remaining in AUTO without a valid fallback object.

7. Prospective evaluation protocol

Because no experiments are reported here, a prospective evaluation protocol replaces a results section. Stage 1 would be offline replay on reproducible simulation datasets. The multi-building, multi-climate operation traces generated under rule-based and stochastic supervisory control would be used in shadow mode to compute how often OA-ABMS would request review, fall back, or enter locked-safe conditions under routine and non-routine trajectories. The purpose of this stage would be diagnostic, not confirmatory: it would test whether the state machine behaves coherently and whether threshold combinations produce pathological behavior such as nearly continuous review requests. [3]

Stage 2 would be closed-loop simulation with supervisory decision making enabled and with injected sensing anomalies where the simulation environment supports them. The HVAC fault benchmark is the natural starting point because its direct relevance concerns hidden temperature-sensor bias under closed-loop compensation. At minimum, three conditions should be compared prospectively: baseline rule-based control only; autonomous supervisory control without override governance; and autonomous supervisory control with OA-ABMS governance. The protocol should state in advance that inferences from this stage would initially be limited to simulated conditions and to the fault classes actually injected. [2] [3]

Stage 3 would be advisory-mode field deployment only. In that stage the system would issue review prompts, predicted risk summaries, and recommended fallback actions, while leaving live authority with the existing operational controller and staff. Observations from this stage could later be used to characterize prompt frequency, acknowledgment timing, interpretability failures, and data-quality obstacles in real buildings. No claim about acceptability, workload reduction, trust improvement, or field effectiveness is warranted until such observations are actually collected and analyzed. [2]

Primary outcome measures should be prespecified. They include review-trigger rate per operating day, fallback-entry rate, locked-safe entry rate, acknowledgment latency, late-acknowledgment rate, proportion of forecast discomfort events preceded by review prompts, energy deviation relative to a_base, and audit completeness. Audit completeness is defined as the proportion of all review, fallback, manual, and locked-safe events for which machine-readable reason code, timestamp, prior state, resulting state, and final authority holder are all present. Secondary measures may include transfer stability from source buildings to the target building, sensitivity to incomplete metadata, and climate-wise threshold behavior. [3] [1]

Threshold selection should be preregistered before any comparative evaluation. The default operational rule is as follows. First, estimate theta_S, theta_U, and theta_A separately for each source building-climate scenario from Stage 1 replay under nominal rule-based control by taking the 0.95 empirical quantile of each trigger variable over valid intervals only; then pool across scenarios by taking the median of those scenario-specific quantiles as the deployed default. Second, fix theta_R in advance as a policy discomfort ceiling. Third, calibrate theta_J on the same Stage 1 replay by evaluating a prespecified nonnegative grid of candidate margins against the review rule R4 and selecting the smallest margin for which the median daily review rate, computed within each source building-climate scenario and then summarized by the median across scenarios, does not exceed a prespecified operational burden cap; if several candidates satisfy the cap, choose the smallest, and if none satisfy it, choose the largest candidate and record that the cap was infeasible under the remaining parameters. Fourth, fix W_ack and q before Stage 2. Sensitivity analysis should then repeat the full protocol with 0.90 and 0.99 quantiles for theta_S, theta_U, and theta_A, with alternative burden caps and candidate grids for theta_J, and with small discrete alternatives for W_ack and q. This procedure yields one explicit default configuration while preserving transparent robustness checks. [3] [2] [1]

8. Failure cases, conflicting priorities, and limits of generalization

The proposal has several foreseeable failure modes. Metadata incompleteness may corrupt the virtual-sensor neighborhoods used to compute V_i(t), because the BRICK-based method depends on accurate equipment-sensor and equipment-zone relationships. If the graph is incomplete or incorrectly mapped, anomaly scores may be misleading. The direct source also notes that metadata quality, rather than computation time, is the main practical bottleneck for scale. In such conditions, the appropriate consequence may be to disable the affected anomaly channel rather than to trust its residuals. [2]

A second failure mode concerns the evidence boundary itself. The direct operational findings rely mainly on simulated data and on temperature-bias faults. Real buildings would include communication dropouts, undocumented manual interventions, occupancy disturbances, and gradual degradation processes not fully represented in the cited simulation studies. Mild biases can also blur isolation because neighboring thermal sensors may appear more anomalous than the biased point. For that reason, threshold transfer from simulation to practice should be treated as unvalidated until site-specific calibration and review are performed. [2]

A third boundary concerns the operation datasets. Their stochastic controller broadens setpoint distributions and state-action coverage, which is useful for stress-testing decision logic, but those trajectories are intentionally exploratory and artificial within bounds. Success under such trajectories would not, by itself, establish that operators in ordinary facility practice will find the same prompts intelligible or acceptable. The dataset release is also bounded by the specific building models, climates, variables, and supervisory strategies included in that pipeline. [3]

Conflicting priorities are intrinsic to the problem. The optimization study demonstrates that energy, carbon, and comfort can be traded off explicitly rather than assumed to align. In override governance, these conflicts appear as policy choices over weights and thresholds. A high lambda_r may generate frequent reviews to guard comfort, whereas a high lambda_e may tolerate conditions that staff regard as unacceptable. The framework does not eliminate such disagreement. It only places the disagreement in declared parameters where it can be inspected, challenged, and revised. [4]

Organizational limits also matter. The adoption studies support the narrower claim that leadership support, technological competence, organizational readiness, and barrier mitigation shape whether a digital process is maintained. In the present context, organizational approval should therefore mean a documented authorization defining operator roles, escalation contacts, training completion requirements, configuration ownership, and acceptable fallback behavior. Without those artifacts, even technically coherent override logic could fail procedurally. [5] [6]

9. Organizational implementation requirements

Because override is an authority transition, it should be represented as policy as well as code. Before any pilot beyond offline replay, the organization would need to specify who may acknowledge review prompts, who may authorize a return from FALLBACK or LOCKED_SAFE to AUTO, who may place the system into MANUAL state, and who is responsible for metadata and schedule maintenance. Reason categories should also be standardized prospectively, such as suspected sensor fault, comfort complaint, equipment protection, schedule exception, metadata inconsistency, or data-quality issue.

A cautious implementation sequence would therefore remain staged. Advisory mode could be followed, if evidence later supports it, by monitored autonomy in which the controller acts but operators retain rapid cancellation rights. Automatic authority transfer through fallback or locked-safe transitions in live service should remain contingent on later empirical studies and explicit organizational approval. This modal language is deliberate: the present manuscript proposes an architecture and protocol, but it does not establish deployment readiness.

10. Conclusion

This article develops a bounded conceptual framework for human overrides in autonomous HVAC management. Three premises support the design. First, direct HVAC evidence indicates that closed-loop control can mask sensor bias at the faulty point and redistribute abnormality across correlated sensors and actuators, which justifies cross-sensor residual checks and actuator-discrepancy checks as override inputs. Second, reproducible multi-building operation datasets generated under rule-based and exploratory supervision provide a practical substrate for replay-based and simulation-based testing. Third, probabilistic prediction, explicit objective trade-offs, and organizational implementation conditions can be combined into a coherent authority-allocation design. [2] [3] [1] [4] [5] [6]

What has been established here is conceptual coherence, not field effectiveness. The framework has not yet shown calibrated thresholds, acceptable burden for operators, robustness to incomplete metadata, or generalizability across buildings and organizations. Those matters remain untested and would require staged empirical evaluation beginning with offline replay and closed-loop simulation, followed only later by approved advisory field studies and any subsequent live-authority trials.

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