Prediction-Error Learning Calibration

source_primes: reward_prediction_error predictive_coding reinforcement learning feedback calibration

slug: reinforcement

slug: learning

slug: feedback

slug: calibration

components:
Prior Prediction Record
Value Reference Frame
Received Outcome Record
Signed Error Signal
Credit Assignment Window
Noise and Volatility Filter
Learning Gain Rule
Update Target
Shortcut Learning Guard
Feedback Timing Alignment
Temporal-Difference Horizon
Positive/Negative Error Separation
Prediction History Memory
Social Observation Channel
Ethical Reward Safety Review

mechanisms:
Prediction–Outcome Delta Log
Temporal-Difference Update Rule
Expectancy-Calibrated Feedback Form
Reward Baseline Dashboard
Learning Rate Schedule
Credit Assignment Trace
Negative Prediction Error Review
Positive Surprise Capture
Shortcut Probe Holdout Set
Reward Signal Red Team
Calibration Curve Review
Surprise Threshold Alert

essence_one_line: Teach from the signed gap between expected and received value so surprise updates the model while expected outcomes do not keep pretending to teach.

compression_statement: When an adaptive system treats raw outcomes as feedback, insert a prediction-error layer: record the prior expectation, observe the received value, compute the signed gap, filter noise and volatility, assign credit to the action or cue that made the prediction, and scale the update so surprising positive and negative deviations teach more than merely expected events.

core_intuition: A reward is not automatically information. A large reward that was fully expected may only confirm the current model; a small outcome that sharply violates expectation may carry much more instructional value. The archetype protects this distinction by requiring the system to know what it predicted before it lets an outcome change future behavior.

canonical_formula: learning_update = prior_prediction + learning_rate × signed(received_value - predicted_value), bounded by context, credit, noise, and safety filters

structural_problem: An adapting person, organization, algorithm, or institution updates from outcome magnitude, applause, failure, punishment, revenue, score, or metric movement without separating what actually happened from what was already expected. Expected successes keep being over-reinforced, expected failures keep being over-punished, and genuinely informative surprises are either missed or overreacted to.

trigger_conditions: A system has recurring actions, cues, policies, or choices whose future probability is changed by consequences.

trigger_conditions: Participants can state or estimate what outcome was expected before the outcome arrives, but that expectation is not recorded or used in the update.

trigger_conditions: Feedback arrives as raw reward, penalty, score, grade, metric, approval, revenue, incident count, or performance result rather than as a prediction-adjusted signal.

trigger_conditions: The environment is noisy enough that single outcomes can mislead, but structured deviations from expectation still carry learning value.

trigger_conditions: The system must adapt without allowing every pleasant or painful outcome to dominate memory, policy, or incentives.

trigger_conditions: A model, team, or learner is vulnerable to shortcut learning because incidental cues sometimes predict reward better than the intended structure.

trigger_conditions: Positive surprises and negative surprises have different behavioral effects and must be distinguished rather than collapsed into generic success or failure.

symptoms: Teams celebrate large outcomes without asking whether they were larger than a pre-registered expectation or merely the baseline result.

symptoms: Learners receive feedback after a task but do not know which part of their prior model was confirmed, violated, or uninformative.

symptoms: Algorithms or operating routines chase raw reward and learn spurious shortcuts that collapse outside the training distribution.

symptoms: Reviews treat expected bad outcomes as fresh evidence of failure and expected good outcomes as fresh evidence of excellence.

symptoms: Unexpected near misses, weak signals, and surprising non-events are ignored because they are not large raw outcomes.

symptoms: The system overreacts to noisy lucky wins and unlucky losses while under-reacting to stable differences between expected and received value.

symptoms: Feedback timing is too late, too aggregated, or too poorly credited to identify which prior prediction was wrong.

symptoms: Policy, practice, or behavior updates continue even after the error has already been absorbed, causing oscillation or overfitting.

root_tension: Learning requires sensitivity to surprise, but surprise is only visible relative to a prior prediction. Raw outcomes are easier to observe and explain than prediction errors, yet raw-outcome learning rewards noise, salience, luck, and existing expectations. The archetype must make expectation explicit without becoming a brittle forecasting bureaucracy.

anti_signatures: The main problem is designing the reward or objective itself; objective_function_alignment is more central.

anti_signatures: The main problem is creating a cue-response-consequence habit loop; reinforcement_loop_design is more central.

anti_signatures: The main problem is optimizing a state-dependent policy over time with formal transition models; sequential_policy_optimization is more central.

anti_signatures: There is no prior prediction, baseline, expectation, value estimate, or forecast to compare against; use uncertainty explicitness, formative feedback, or after-action review instead.

anti_signatures: The event is a one-time crisis or high-impact surprise where preparation matters more than repeated learning; surprise preparedness or contingency planning is more central.

anti_signatures: Outcomes are ethically unsafe to manipulate as learning signals, especially when humans or animals are exposed to coercive reward/punishment loops.

structural_intervention: Install a prediction-error calibration layer between outcomes and updates: elicit the prior prediction, define the value reference frame, record the received outcome, compute the signed deviation, assign credit to the relevant action or cue, filter noise and context drift, scale the learning rate, and only then update behavior, model weights, policy, confidence, or practice.

action: State the expected value, probability, reward, cost, grade, metric movement, or qualitative outcome before the result is known.

reason: A prediction error cannot be computed after the fact without backfilling expectation to match the outcome.

action: Specify what counts as received value and whether gains, losses, avoided losses, delayed benefits, social rewards, or safety outcomes are in scope.

reason: The sign and magnitude of the error depend on which value frame the system is actually learning from.

action: Capture the realized outcome with enough timing, context, and measurement reliability to compare it against the prior prediction.

reason: Noisy, delayed, or aggregated outcomes create misleading error signals unless the observation context is preserved.

action: Calculate or qualitatively classify whether the result was better than expected, worse than expected, or essentially expected.

reason: Positive and negative errors should drive different updates; expected results should mainly maintain rather than transform the model.

action: Link the error to the action, cue, policy, assumption, feature, model component, or environmental condition most likely responsible.

reason: Without credit assignment, the system learns the wrong lesson from a real surprise.

action: Decide whether the error reflects stable misprediction, random variation, changed conditions, measurement error, or a non-repeatable anomaly.

reason: Prediction-error learning is powerful precisely because it can also overfit to noise if gain is not governed.

action: Apply a learning rate, confidence change, policy adjustment, training emphasis, or review priority proportional to the reliable signed error.

reason: The update should be stronger when the error is informative and weaker when it is noisy, already absorbed, or safety-sensitive.

action: Check whether the update improved future prediction, reduced repeated error, or created shortcut learning, reward hacking, or loss of exploration.

reason: The test of the intervention is not a good explanation of the last surprise but better calibration of future action.

decision_rules: Do not update from an outcome until the prior expectation, baseline, or value estimate has been identified or reconstructed with explicit uncertainty.

decision_rules: Treat expected outcomes as maintenance signals, not as strong evidence for large model or behavior change.

decision_rules: Treat surprising deviations as candidates for learning only after checking measurement reliability, context stability, and credit assignment.

decision_rules: Use smaller learning rates in noisy, sparse, delayed, adversarial, or ethically sensitive environments.

decision_rules: Escalate negative prediction errors that indicate safety, harm, or trust failure even when their raw frequency is low.

decision_rules: Do not reward a system for reducing error by lowering expectations, gaming measures, avoiding exploration, or hiding uncertain cases.

decision_rules: Separate learning from value magnitude when possible: large reward can be uninformative, and small reward can be highly informative if it violates expectation.

target_invariants: Prior prediction remains distinguishable from received outcome.

target_invariants: The sign of the error is preserved rather than collapsed into generic success or failure.

target_invariants: Credit assignment links the update to the right action, cue, assumption, or model component.

target_invariants: Learning gain is bounded by evidence quality, uncertainty, safety, and ethical constraints.

target_invariants: Expected outcomes do not repeatedly produce artificial teaching signals.

target_invariants: The system remains able to explore rather than freezing around early rewards or punishments.

target_invariants: Human subjects, users, or learners are not manipulated through opaque reward/punishment loops.

expected_outcomes: Faster correction of mistaken expectations without over-updating from routine outcomes.

expected_outcomes: Better separation between luck, skill, signal, noise, and changed context.

expected_outcomes: Reduced shortcut learning and reward hacking because updates must pass credit and context checks.

expected_outcomes: More calibrated confidence, forecasts, and policies after surprising positive or negative results.

expected_outcomes: Improved feedback design in training, experimentation, operations, and adaptive systems.

expected_outcomes: Clearer review conversations because participants can ask: what did we expect, what happened, and what should change?

prose_essence:
Prediction-Error Learning Calibration is the intervention pattern for situations where a system should learn from **surprise relative to expectation**, not from raw reward, punishment, score, or outcome magnitude alone. It asks three questions before learning is allowed to harden: what was predicted, what was received, and what signed gap remains after noise, context, and credit assignment are checked?

The core move is simple but strict: expected outcomes mostly maintain the current model; reliable unexpected outcomes teach. This prevents a system from repeatedly relearning what it already expected, overreacting to salient raw outcomes, or crediting the wrong cue merely because it happened near a reward.

prose_tradeoffs: Prediction-error calibration adds discipline and measurement overhead. It can also create incentives to sandbag expectations or manipulate reward records. Strong negative-error review can become blame unless diagnostic learning is protected. Narrow reward frames can make the system efficient at learning the wrong thing. These tradeoffs are manageable only when prediction records, value frames, credit assignment, and ethics checks are treated as first-class design components.

Temporal-Difference Learning Signal

short_definition: A sequential-decision variant where the error compares current value prediction against received reward plus expected future value.

distinctive_feature: The prediction target includes bootstrapped future value, not only immediate outcome.

distinct_from_parent: The parent covers any signed prediction-error learning signal; this variant specializes the formula for sequential temporal structure.

still_same_parent_because: It still teaches from the signed gap between expected and received/evaluated value.

use_when: Actions unfold across states and early outcomes teach mainly by changing expected later value.

use_when: The update target is a value estimate, policy, or action preference across time.

use_when: Immediate reward alone is too myopic to represent what the system should learn.

example: A value function updates when the next state reveals better or worse expected future reward than anticipated.

example: A dispatch rule is adjusted when an early routing decision changes expected downstream congestion more than predicted.

Human Feedback Expectancy Calibration

short_definition: A training, coaching, or review variant that compares learner or team expectations with received feedback before deciding what lesson to reinforce.

distinctive_feature: The prediction and error are elicited through reflection and feedback design rather than formal value functions.

distinct_from_parent: The parent is cross-domain; this variant emphasizes human affect, psychological safety, and interpretability.

still_same_parent_because: It still converts expected-versus-received value into a calibrated update.

use_when: People can state what they expected before performance feedback arrives.

use_when: Generic praise, grades, or criticism are producing weak or distorted learning.

use_when: Unexpected success or failure should become the main teaching moment without turning review into blame.

example: A learner predicts confidence before a quiz and reviews only the questions whose outcomes violated confidence.

example: A team reviews a launch by comparing expected and actual adoption before changing playbooks.

Shortcut-Resistant Reward Learning

short_definition: A robustness variant that checks whether prediction-error updates are teaching the intended structure rather than a cheap incidental proxy.

distinctive_feature: It places shortcut probes and distribution checks around the signed error update.

distinct_from_parent: The parent defines the learning-signal structure; this variant adds robustness checks against spurious reward channels.

still_same_parent_because: The central update is still prediction-error learning, with extra safeguards.

use_when: The system can get reward by exploiting incidental features of the training or operating distribution.

use_when: Performance collapses when context, distribution, or evaluation regime changes.

use_when: Positive prediction errors may be reinforcing a proxy that is easier than the desired capability.

example: A classifier is tested on holdouts where spurious background cues no longer predict reward.

example: A team checks whether bonuses teach durable customer value or short-term deal stuffing.

Vicarious Prediction-Error Learning

short_definition: A social-learning variant where observers update from the gap between expected and observed outcomes of another agent's action.

distinctive_feature: The received outcome is observed through another agent, so social salience and attribution must be controlled.

distinct_from_parent: The parent covers direct or indirect update signals; this variant specializes the observation channel.

still_same_parent_because: The teaching signal remains a signed expectation violation.

use_when: Agents learn from demonstration, imitation, reputation, status, or observed consequences rather than direct reward.

use_when: The observer had an expectation about what would happen to the model agent.

use_when: Unexpected observed success or failure changes future imitation or avoidance.

example: Observers update risk expectations when a peer's routine shortcut produces an unexpected near miss.

domain: apprenticeship

example: A learner changes technique after an expert's unexpected success reveals a hidden cue.

near_names:
Reward Prediction Error Calibration
RPE Learning Design
Temporal Difference Error Calibration
Surprise-Weighted Feedback Calibration


Predictive-Cue Wayfinding Design

type: archetype

source: generated

batch: Batch 12

canonical_family: Shared Awareness & Identity Alignment

aliases:
Information-Scent Design
Cue-Destination Alignment
Scent-Based Navigation Design
Predictive Link Labeling
Findability Scent Calibration
Local Cue Wayfinding
Cue-Route Alignment
Value-Predictive Navigation Cues

source_primes: information_scent search_and_retrieval navigation affordance signaling heuristic attention salience observability feedback

slug: navigation

slug: affordance

slug: signaling

slug: heuristic

slug: attention

slug: salience

slug: observability

slug: deterrence

slug: identification

slug: representation

slug: trust

slug: threshold

slug: path

essence_one_line: Make local cues honestly predict what lies down each path so agents can choose, continue, or recover without needing a complete map.

compression_statement: Predictive-Cue Wayfinding Design applies when an agent must move through a partly known information, physical, social, or procedural space by judging local cues at branch points. It defines decision points, cue surfaces, expected destination value, cue-destination correlation, scent-strength thresholds, traversal costs, feedback traces, misleading-scent guardrails, recovery paths, and freshness rules. The archetype turns information scent into a design pattern: strengthen cues that truthfully predict downstream value, weaken or relabel cues that misdirect, and preserve recovery when exploration fails.

core_intuition: People rarely inspect an entire space before moving. They sniff the next step: a label, icon, heading, landmark, preview, count, snippet, social signal, or visible affordance. When those cues correlate with what the agent actually finds, navigation feels intelligent and low-friction. When cues are vague, stale, overloaded, or enticing but false, agents waste effort, lose trust, or stop searching. The design problem is therefore not merely to create more paths or prettier signs; it is to make local promises match downstream reality.

canonical_formula: At each decision point d, expose cue c whose perceived scent S(c, intent) tracks expected destination value V(destination | intent) minus traversal cost C; revise, demote, relabel, or guardrail c when observed traversal shows weak or misleading correlation.

structural_problem: Agents must find, evaluate, or traverse a target without full knowledge of the space. They rely on local cues, but those cues may be too weak, too generic, stale, overloaded, manipulative, or poorly correlated with destination value. The result is inefficient search, wrong turns, abandonment, false confidence, and degraded trust.

trigger_conditions: The space has multiple paths, categories, links, routes, options, documents, screens, procedures, or next actions.

trigger_conditions: Agents cannot inspect all destinations before choosing and must infer value from local cues.

trigger_conditions: Labels, previews, landmarks, thumbnails, social signals, snippets, headings, or affordances influence traversal.

trigger_conditions: Success depends on whether the cue accurately predicts destination relevance, payoff, effort, risk, or next state.

trigger_conditions: Users abandon, pogo-stick, loop, ask for help, choose attractive wrong paths, or miss valuable paths.

trigger_conditions: The cost of a wrong turn is nontrivial in time, attention, risk, safety, trust, or opportunity.

trigger_conditions: The environment changes, making old labels, route promises, or previews unreliable.

symptoms: High clickthrough followed by rapid backtracking, refinement, or abandonment.

symptoms: Users repeatedly choose a route that sounds promising but does not contain the expected content, help, product, or action.

symptoms: Important paths are underused because cues do not make their value legible at the branch point.

symptoms: Searchers rely on external search, human support, or memory because internal cues do not guide them.

symptoms: Navigation labels are internally meaningful to designers but not predictive for outsiders or novices.

symptoms: Cues compete for attention without clarifying how their destinations differ.

symptoms: Updates change destinations while leaving stale links, headings, previews, or signage intact.

symptoms: Teams optimize traffic, engagement, or visual salience while ignoring whether the destination fulfills the cue’s promise.

root_tension: Agents need enough local predictability to navigate without complete knowledge, but local cues can be attractive while still being weak, stale, or false predictors of downstream value.

anti_signatures: The main need is a complete domain map or curriculum map; use knowledge_map_navigation instead.

anti_signatures: The main need is an index, catalogue, or query structure; use index_based_retrieval instead.

anti_signatures: The main need is to eliminate options from an overwhelming solution space; use search_space_pruning or progressive_narrowing instead.

anti_signatures: The main need is to guide visual attention through a fixed sequence; use visual_flow_guidance instead.

anti_signatures: The main need is to change automatic behavioral triggers; use associative_cue_redesign instead.

anti_signatures: The agent already knows the destination and only needs a direct route or access permission.

anti_signatures: The cue intentionally hides destination value to manipulate users; this is an anti-pattern unless the task is mitigation or review.

structural_intervention: Map decision points and possible routes, define what each cue promises, test how well cues predict destination value for target intents, then redesign labels, previews, landmarks, affordances, freshness rules, and recovery paths so local scent becomes honest, discriminating, and maintainable.

action_logic: Define the agent intents, tasks, states of knowledge, and traversal costs that make wayfinding necessary.

action_logic: Inventory decision points and the cues currently available at each point.

action_logic: For each cue, state the destination value or outcome it appears to promise.

action_logic: Measure cue-destination correlation using traversal data, task tests, user interpretation, expert review, and outcome evidence.

action_logic: Strengthen or clarify cues that truthfully predict value; demote, relabel, split, or remove cues that are vague or misleading.

action_logic: Add previews, examples, landmarks, breadcrumbs, confidence notes, or alternative-route comparison where traversal cost is high.

action_logic: Instrument feedback signals for abandonment, backtracking, route switching, false arrivals, and successful completion.

action_logic: Create freshness and ownership rules so cue promises remain aligned as destinations change.

decision_rules: Increase cue salience only when downstream value and cue-destination correlation are both high.

decision_rules: Use stronger previews or confidence signals when traversal cost, risk, or ambiguity is high.

decision_rules: Split a cue when different intents interpret it differently or when one label hides multiple destination types.

decision_rules: Add recovery paths when exploration is valuable but false turns are likely.

decision_rules: Treat high engagement plus high backtracking as possible false scent, not as success.

decision_rules: Retire or relabel cues when destination content, eligibility, availability, risk, or meaning changes.

decision_rules: Preserve alternative routes when there is no single universally best path for all intents.

target_invariants: Cue promises should remain truthful enough to preserve trust.

target_invariants: Navigation should reduce rather than hide traversal cost.

target_invariants: Agents should be able to recover orientation after a wrong turn.

target_invariants: Scent should be evaluated by destination fulfillment, not only by clicks or attention.

target_invariants: Accessibility, literacy, language, and novice-expert differences should not be treated as afterthoughts.

target_invariants: Ethical guardrails should prevent clickbait, dark patterns, coercive steering, and deceptive previewing.

expected_outcomes: Higher successful arrival and task completion.

expected_outcomes: Lower pogo-sticking, backtracking, support requests, and route abandonment.

expected_outcomes: Better use of valuable but previously hidden paths.

expected_outcomes: Improved user confidence, trust, and perceived legibility.

expected_outcomes: Faster discovery with less cognitive load.

expected_outcomes: Earlier detection of stale, ambiguous, or deceptive cues.

prose_essence:
Predictive-Cue Wayfinding Design turns the target prime `information_scent` into a reusable solution pattern: make the cues visible at each branch point honestly predict what an agent will find if they continue. The archetype is about cue-destination correlation, not simply adding more navigation, louder signs, or more attractive labels.

The core move is to treat every local cue as a promise. A link label, search snippet, icon, heading, route marker, product card, workflow stage, badge, preview, or landmark tells the agent something about what lies ahead. If that promise is clear and true, the agent can move through a partially known space without inspecting everything. If the promise is weak or false, the agent wastes effort and loses trust.

Link-Label Scent Calibration

short_definition: A digital-navigation variant that aligns link text, headings, buttons, and snippets with the value users will actually find after clicking.

distinctive_feature: The local cue is usually a textual or visual label whose promise must be checked against the destination.

distinct_from_parent: The parent covers any local cue-route system; this variant specializes web, app, documentation, and search-result labels.

still_same_parent_because: The key design work remains cue-destination correlation, traversal-cost reduction, and false-scent mitigation.

use_when: Users choose among links, menus, search results, cards, or calls to action.

use_when: Traffic, pogo-sticking, abandonment, or confusion indicates that visible labels do not match destination value.

use_when: The team can change labels, previews, or information architecture.

example_domains:
web navigation
documentation
search results
product onboarding

domain: documentation

example: A docs site changes “Integrations” to “API keys and webhooks” after readers repeatedly open the wrong section.

failure_modes:
designer jargon
keyword stuffing
clickbait labeling
outdated snippets

Landmark-Scented Route Design

short_definition: A physical, spatial, or procedural variant that uses landmarks, signs, stages, and local affordances to help agents infer the right route before they see the destination.

distinctive_feature: The cue is often spatial, embodied, procedural, or environmental rather than purely textual.

distinct_from_parent: The parent includes digital and conceptual spaces; this variant specializes embodied route choice and place-based orientation.

still_same_parent_because: Landmarks and signs still function as predictive local cues whose validity is tested by arrival and recovery outcomes.

use_when: Agents navigate a physical campus, facility, service journey, workflow, or multi-step procedure.

use_when: Route decisions happen before destination visibility.

use_when: Wrong turns cause delay, anxiety, safety risk, or support burden.

example_domains:
hospitals
museums
campuses
service journeys
operations procedures

domain: hospital

example: Signs name both the clinic and the visible landmark near it, so visitors can infer the right branch before reaching the ward.

failure_modes: landmarks that change or are invisible to visitors

False-Scent Suppression

short_definition: A corrective variant focused on finding and removing cues that attract traversal while failing to deliver the promised value.

distinctive_feature: The main work is anti-deception and trust repair, not merely improved navigation efficiency.

distinct_from_parent: The parent includes positive scent creation; this variant starts from the failure mode of misleading scent.

still_same_parent_because: The remedy is still to restore honest cue-destination correlation and route recovery.

use_when: Cues are optimized for attention, clicks, conversion, or compliance rather than fulfilled intent.

use_when: Users express disappointment, mistrust, confusion, or repeated wrong-turn behavior.

use_when: There are incentives to overpromise, hide friction, or steer agents to low-value destinations.

example_domains:
consumer interfaces
public benefits navigation
risk communication
search marketing
compliance portals

domain: public service

example: A benefits site removes “instant approval” copy when the destination actually starts a multi-step eligibility review.

failure_modes:
dark-pattern optimization
metric gaming
stale promises
ambiguous eligibility cues

near_names:
Information Scent Design
Information Scent Calibration
Cue-Destination Alignment
Scent-Based Navigation Design
Findability Scenting
Predictive Link Labeling


Predictive Precommitment Correction

batch: Batch 01

canonical_family: Anticipation & Forward Models

aliases:
Feedforward Precorrection
Upstream Predictive Correction
Model-Based Pre-Adjustment
Pre-Commitment Consequence Modeling
Predictive Action Shaping
Open-Loop Predictive Correction

slug: feedforward

slug: controllability

slug: uncertainty

slug: latency

slug: buffering

slug: scheduling

slug: intervention

slug: risk

slug: robustness

slug: causality

slug: homeostasis

essence_one_line: Model the likely consequence of an intended action before commitment, then adjust the action while correction is still cheap.

compression_statement: Predictive Precommitment Correction is the feedforward control pattern of inserting a consequence model, forecast, simulation, or preview channel upstream of commitment; comparing the predicted result with the desired result; and pre-adjusting the action, timing, resource position, or control setting before the world has to return an error signal.

core_intuition: Feedback waits for the system to act, observes the error, and then repairs. Feedforward spends modeling effort earlier so the actor can avoid creating some of the error at all. It works when there is enough lead time, a usable predictive model, and an action parameter that can still be changed.

canonical_formula: committed_action = adjust(intended_action, predicted_consequence(intended_action, context) - target_state)

structural_problem: An actor must commit to an action whose effects will be costly, delayed, dangerous, or slow to correct after execution, while some information about likely consequences is available before commitment but is not yet structurally used to shape the action.

trigger_conditions: The cost of after-the-fact correction is materially higher than the cost of pre-correction.

trigger_conditions: The action has a previewable consequence: trajectory, load, demand, resource need, learner need, control response, user impact, safety margin, or downstream dependency.

trigger_conditions: There is an adjustable action parameter such as timing, amplitude, sequence, routing, dosage, configuration, staffing, or preparation level.

trigger_conditions: Feedback will arrive too late, too noisily, or too expensively to be the sole control strategy.

trigger_conditions: A model, simulation, leading signal, historical pattern, or expert forecast can predict the consequence well enough to improve the initial action.

trigger_conditions: Commitment is staged, reversible, or gateable long enough to apply the pre-correction.

trigger_conditions: The system’s environment changes in ways that make purely reactive control lag, overshoot, or oscillate.

symptoms: Teams repeatedly fix predictable problems after launch, handoff, deployment, or operation instead of preventing them before commitment.

symptoms: Executors know a plan will create avoidable downstream load but lack an upstream gate that forces adjustment.

symptoms: Controls oscillate because they react to realized deviation rather than accounting for known incoming disturbance.

symptoms: Resources are placed where they were needed last cycle rather than where a forecast says they will be needed next.

symptoms: A simulation or preview catches a failure, but its output is advisory and not wired into the actual commitment decision.

symptoms: Operators over-rely on alarms, audits, rework, remediation, or customer complaints that arrive after harm has accumulated.

symptoms: Forecast errors are not learned from, so the feedforward model drifts while the organization continues to trust it.

root_tension: The actor wants the stability of reactive evidence, but waiting for evidence means the corrective window has partly closed.

anti_signatures: The action cannot be modified before commitment; use monitoring, fallback, or feedback recovery instead.

anti_signatures: The prediction target is strategic actors neutralizing the intervention; use anticipatory_offset_governance instead.

anti_signatures: The problem is only giving dependents advance notice of a change; use dependency_aware_change_notification instead.

anti_signatures: The problem is subtracting self-caused sensory effects from observation; use self_generated_signal_cancellation instead.

anti_signatures: The issue is loop bandwidth relative to a fast reference signal; use reference_tracking_bandwidth_alignment unless predictive pre-positioning is the central intervention.

anti_signatures: The model is too unreliable, uncalibrated, or biased to improve decisions over a simple robust baseline.

anti_signatures: After-the-fact feedback is cheap, fast, and sufficient, making a predictive pre-correction layer unnecessary overhead.

structural_intervention: Create an upstream consequence-modeling layer that previews the likely result of an intended action, compares that preview to the target state or acceptable envelope, converts the predicted gap into a pre-correction, and reconnects later feedback to model calibration.

action_logic: Define the commitment point after which correction becomes expensive, slow, unsafe, or disruptive.

action_logic: Specify the intended action, target state, acceptable tolerance envelope, and adjustable variables.

action_logic: Build or select a predictive model, simulation, preview channel, or leading indicator that estimates consequences before commitment.

action_logic: Compare predicted consequence to target and decide whether the predicted gap is large enough to justify pre-correction.

action_logic: Translate the predicted gap into an adjustment to timing, amplitude, routing, staffing, configuration, dosage, resource placement, or sequencing.

action_logic: Gate commitment so the actor either accepts, revises, delays, escalates, or rejects the action based on the prediction and uncertainty band.

action_logic: Capture realized outcomes after the action and use feedback to recalibrate the feedforward model.

action_logic: Maintain safeguards for forecast drift, false confidence, strategic gaming, and model-induced self-defeating behavior.

decision_rules: Use feedforward when prediction lead time is longer than the correction lead time required to change the action.

decision_rules: Do not pre-correct when model uncertainty exceeds the harm of waiting for feedback or using a robust baseline.

decision_rules: Prefer small reversible pre-corrections when model validity is moderate and large irreversible corrections only when the model is highly validated.

decision_rules: If the forecast changes actor behavior in ways that invalidate itself, add strategic-response governance or switch to randomized/staged commitment.

decision_rules: If the predicted disturbance is recurring and measurable, embed the pre-correction into a standing rule rather than relying on one-off judgment.

decision_rules: If realized outcomes diverge from forecasts repeatedly, demote the model to advisory status until recalibrated.

decision_rules: If a forecasted failure has high safety impact, add an independent confirmation gate before commitment.

target_invariants: The predictive model is upstream of the commitment point, not merely a post-hoc explanation.

target_invariants: The actor has an actionable variable that can still be changed before commitment.

target_invariants: Predicted consequence, target state, and uncertainty are explicitly represented.

target_invariants: Pre-corrections remain traceable to prediction evidence and assumptions.

target_invariants: Later feedback is retained for model calibration rather than ignored because the control is feedforward.

target_invariants: The intervention does not hide uncertainty or displace accountability onto the model.

expected_outcomes: Fewer predictable downstream errors require reactive remediation.

expected_outcomes: Actions land closer to target on the first attempt.

expected_outcomes: Systems tolerate delay better because known disturbances are compensated in advance.

expected_outcomes: Operational readiness improves when resources, settings, or communications are placed before demand arrives.

expected_outcomes: Feedback loops become less overloaded because they correct residual error rather than all error.

expected_outcomes: Model performance becomes auditable through comparison between predicted and realized consequences.

Model-Predictive Control Variant

short_definition: A control-systems variant that repeatedly solves a forward model and applies the next control move before future error is realized.

distinctive_feature: The feedforward prediction is formal, recurrent, and directly coupled to control moves.

distinct_from_parent: The parent is cross-domain; this variant is a formal control method family.

still_same_parent_because: It still predicts consequence before commitment and converts prediction into pre-correction.

source_primes: feedforward controllability

use_when: The system dynamics are modeled explicitly.

use_when: Control moves can be recomputed at each horizon.

domain: robotics

example: A robot adjusts trajectory before entering a turn based on predicted dynamics.

Prepositioning Feedforward Variant

short_definition: A logistics and operations variant that moves capacity, inventory, staff, or attention before predicted demand arrives.

distinctive_feature: The pre-correction is spatial or capacity placement rather than parameter tuning.

distinct_from_parent: The parent includes any pre-corrected action; this variant centers resource placement before demand.

still_same_parent_because: Forecasted consequence drives adjustment before realized deviation.

source_primes: feedforward scheduling

use_when: Demand or disturbance can be forecast with usable lead time.

use_when: Resources are costly to move after the need materializes.

example: Crews are staged near predicted risk zones before incidents occur.

Preflight Consequence Review Variant

short_definition: A governance-heavy variant that blocks commitment until predicted downstream consequences are reviewed and adjusted.

distinctive_feature: The feedforward layer is a formal review gate rather than an automated control move.

distinct_from_parent: The parent covers all predictive pre-correction; this variant emphasizes accountable gatekeeping.

still_same_parent_because: It still uses consequence preview to change or block the intended action before commitment.

source_primes: feedforward risk

use_when: Commitment is high-risk or hard to reverse.

use_when: Predicted consequences require accountable signoff.

example: A release is paused when a preflight simulation predicts unacceptable dependency breakage.

near_names:
Feedforward Precorrection
Upstream Predictive Correction
Model-Based Pre-Adjustment
Feedforward Loop


Predictive Residual Processing

batch: Batch 15

canonical_family: Predictive Representation, Residual Signaling, and Adaptive Model Correction

aliases:
Model-Based Residual Signaling
Prediction-Error-Driven Processing
Predict-and-Correct Representation
Generative Residual Coding
Surprise-Weighted Signal Propagation

slug: compression

slug: feedback

slug: observability

slug: calibration

slug: learning

components:
Prediction Target Definition
Generative Model State
Model Scope and Horizon
Predictive Feedforward Model
Expected Behavior
Actual Behavior
Prediction Comparator
Prediction-Error Signal
Precision-Weighting Rule
Residual Propagation Channel
Confidence and Uncertainty State
Update Rule
Model-State Synchronization Rule
Freshness or Validity Window
Drift and Error Signal
Residual Error Budget
Fallback to On-Demand Path
Decompression Trigger
Provenance Marker
Attention and Bandwidth Budget
Raw-Signal Audit Sample
Safety-Critical Bypass Rule
Hierarchical Prediction Stack
Cross-Model Disagreement Monitor
Noise Filter
Explanatory Trace

mechanisms:
Delta or Differential Encoding
Predictive Codec
Innovation Residual Filter
Event-Triggered Residual Reporting
Periodic Full-State Resynchronization
Shadow Raw-Channel Sampling
Model-Version Checksum Handshake
Precision-Weighted Error Gate
Hierarchical Prediction-Error Loop
Raw-Signal Fallback Switch
Residual Telemetry Dashboard
Prediction-Error Replay Buffer
Efference-Copy Cancellation
Temporal-Difference Update
Prediction Error Review
Bayesian Model Update
Model Drift Monitoring
Forecast Backtesting
Anomaly Detection Model
Confidence Threshold Table
Residual Comparison Test
Surprise-to-Action Bridge

essence_one_line: Reduce bandwidth and focus adaptation by representing expected input through a maintained model and propagating only calibrated deviations, with synchronization, raw-state audits, and full-signal fallback.

compression_statement: When most incoming information is predictable, repeatedly transmitting or attending to the whole signal wastes channel, compute, energy, storage, or human attention. The intervention maintains an explicit generative model, produces an expected input, compares it with observation, weights the discrepancy by uncertainty and consequence, and routes the informative residual instead of the predictable portion. The same residual updates the model or action. Because residuals are meaningful only relative to a model, the architecture also versions and synchronizes model state, monitors drift and reconstruction error, samples raw input to reveal blind spots, protects safety-critical full-state channels, and decompresses or falls back when prediction is not trustworthy.

core_intuition: Do not pay repeatedly for what both sides can already predict—but never let the predictor become an unquestioned filter on reality. Save capacity by sending surprise, then spend part of the savings checking what the model has learned to ignore.

canonical_formula: For observation x_t and model prediction x_hat_t = G(theta_t, context_t), compute residual e_t = x_t - x_hat_t (or a domain-appropriate discrepancy). Propagate q_t = Q(w_t e_t, metadata_t), reconstruct x_t_tilde = x_hat_t + Q^{-1}(q_t), and update theta_{t+1} = U(theta_t, e_t, w_t) subject to reconstruction D(x_t, x_t_tilde) <= epsilon, safety bypasses, model-version compatibility, and periodic full-state audits. The pattern is advantageous only when model, synchronization, audit, and residual costs remain below the cost of full representation at the required fidelity.

structural_problem: A system repeatedly transmits, stores, inspects, or reacts to high-volume input whose expected portion is redundant, while scarce bandwidth or attention is consumed and genuinely informative deviations arrive late or are buried. A naive compression or exception rule can save capacity but may hide model drift, rare events, minority signals, or safety-critical context because it lacks a governed prediction-and-correction loop.

trigger_conditions: Input is strongly predictable over a relevant horizon, context, or hierarchy.

trigger_conditions: Communication, storage, compute, energy, or human attention is a binding constraint.

trigger_conditions: Prediction error can be measured in a way that preserves operational meaning.

trigger_conditions: A receiver or downstream process can reconstruct expected context from a compatible model.

trigger_conditions: The system needs continuous learning from deviations rather than one-time compression only.

trigger_conditions: Full-state audit, resynchronization, and fallback paths can be maintained.

symptoms: Most messages repeat an unchanged or unsurprising state while operators miss the few consequential changes.

symptoms: A telemetry, reporting, or cognitive channel is saturated by predictable content.

symptoms: Forecast error is recorded but not used to revise the model that generated the forecast.

symptoms: Different system layers or teams maintain incompatible expectations, making deviations hard to interpret.

symptoms: Anomaly alerts proliferate without a clear baseline, uncertainty model, or reconstruction test.

symptoms: Compression succeeds numerically but suppresses rare, low-amplitude, or minority signals that matter.

symptoms: A model appears accurate because the system no longer samples the raw input it predicts poorly.

root_tension: Suppressing predictable information frees scarce capacity, but the prediction that enables suppression can become stale, biased, desynchronized, or self-confirming. The intervention must gain efficiency without allowing the model to define away material reality.

anti_signatures: The input is predominantly novel, adversarial, or nonstationary and cannot be predicted within a defensible error budget.

anti_signatures: Law, safety, audit, or scientific reproducibility requires complete raw records for every event.

anti_signatures: No shared or reconstructible model exists at the receiving side and model synchronization is infeasible.

anti_signatures: The problem is simply future preparation, hidden-state estimation, or course correction without residual-only representation.

anti_signatures: The cost of model maintenance, audits, and synchronization exceeds the saved bandwidth or attention.

anti_signatures: The suppressed baseline contains rights-bearing, contextual, or minority information whose omission would be harmful.

structural_intervention: Create a governed predictive representation loop that models expected input, computes and calibrates residuals, routes only informative prediction error through the constrained channel, updates the model from validated mismatch, and preserves independent raw-state, synchronization, reconstruction, and safety controls.

action_logic: Define the prediction target, observation boundary, timing, units, fidelity requirement, and downstream decision that the residual must support.

action_logic: Construct or select a generative model with explicit scope, horizon, version, uncertainty, and owner.

action_logic: Produce the expected input before or alongside observation and capture the actual observation with provenance.

action_logic: Compute a signed or structured prediction error using a declared comparator and separate noise from meaningful mismatch.

action_logic: Weight the residual by precision, source reliability, consequence, rarity, and channel cost rather than magnitude alone.

action_logic: Propagate, store, or escalate the selected residual with the model version and context needed for reconstruction.

action_logic: Use validated residuals to update the model, action, threshold, or higher-level representation at an appropriate rate.

action_logic: Continuously monitor reconstruction error, residual distribution, drift, suppression rate, rare-event coverage, and model synchronization.

action_logic: Sample raw/full input independently and periodically resynchronize model state to expose what the predictor suppresses.

action_logic: Trigger decompression, full-state transmission, human review, or safe-mode operation when uncertainty, drift, version mismatch, safety, or cumulative error exceeds bounds.

decision_rules: Use residual-only propagation only when expected content is stable enough that model plus residual reconstructs or supports the required decision within the declared error budget.

decision_rules: Set propagation thresholds by consequence and uncertainty as well as frequency and amplitude.

decision_rules: Do not allow absence of residual to count as evidence of normality when the observation channel, comparator, or model may have failed.

decision_rules: Keep model-learning and operational-action update rates separate when fast adaptation could destabilize the system.

decision_rules: Force a full-state sample or resynchronization after model version change, extended silence, packet or handoff loss, regime change, or unexplained residual shift.

decision_rules: Bypass suppression for safety-critical, legally complete, rights-bearing, or rare-catastrophe signals unless an independent equivalent channel exists.

decision_rules: Retire or replace the predictive path when model and audit cost no longer buys meaningful channel or attention savings.

target_invariants: Material unexpected information remains observable and actionable.

target_invariants: The full signal can be reconstructed or requested to the fidelity required by the use case.

target_invariants: Prediction and residual are tied to a known model version, time, source, and scope.

target_invariants: Raw-state audit access remains independent of the model being audited.

target_invariants: Safety, rights, minority, and rare-event signals are not suppressed by average-case optimization.

target_invariants: Model updates remain bounded, attributable, reversible where appropriate, and open to challenge.

target_invariants: Silence is distinguishable from successful prediction, channel failure, and missing observation.

expected_outcomes: Lower communication, storage, compute, energy, or attention cost for predictable streams.

expected_outcomes: Faster detection and prioritization of informative deviations.

expected_outcomes: A model that improves from its errors rather than merely generating forecasts.

expected_outcomes: Visible drift, uncertainty, and reconstruction quality rather than hidden compression failure.

expected_outcomes: Fewer redundant reports and lower alert burden without loss of consequential context.

expected_outcomes: More explicit coordination between prediction, observation, learning, and action layers.

expected_outcomes: Graceful degradation to fuller representation when predictive assumptions fail.

prose_tradeoffs:
- **bandwidth savings versus model cost:** Suppressing predictable content reduces channel, storage, energy, compute, or attention use. Predictor construction, synchronization, audits, and updates may exceed the saved capacity.
- **sensitivity versus alert burden:** Low thresholds preserve subtle novelty and rare signals. Noise and benign variation can flood the residual channel and exhaust attention.
- **compression versus context:** Residuals foreground new information. Operators may lose baseline context needed to interpret the deviation safely.
- **fast learning versus stability:** Rapid updates adapt to changing sources. Over-updating can chase noise, erase stable knowledge, or create oscillatory self-reference.
- **shared model efficiency versus correlated blind spots:** Common predictors make residual exchange cheap and coherent. A shared wrong model can suppress the same evidence everywhere.
- **privacy minimization versus outlier exposure:** Expected personal data need not be retransmitted repeatedly. Residuals may uniquely reveal sensitive atypical behavior or identity.

Predictive Delta Encoding

short_definition: Keep matched predictors at encoder and decoder and transmit only the coded difference between predicted and observed signal.

distinctive_feature: The residual is explicitly a reconstructive code against a shared predictor, with synchronization and quantization as first-class design concerns.

distinct_from_parent: It specializes the parent to communications, storage, or telemetry systems whose receiver reconstructs the full signal from model state plus residual.

still_same_parent_because: It uses the same prediction target, generative model, comparator, residual channel, update rule, audit sampling, and fallback architecture.

use_when: Signals are highly correlated over time or space and full retransmission is expensive.

use_when: Encoder and decoder can maintain compatible model state and recover from packet loss or version mismatch.

use_when: A reconstruction-error budget and periodic full-state resynchronization are available.

domain: sensor telemetry

example: An edge device predicts the next measurements locally and transmits only residuals, model version, and periodic full snapshots.

domain: media coding

example: A codec predicts image blocks from neighboring or prior frames and encodes the difference with a bounded reconstruction error.

failure_modes: Quantization hides small but consequential changes.

failure_modes: Packet loss corrupts subsequent reconstruction until a full snapshot arrives.

Hierarchical Predictive Processing

variant_type: subtype

short_definition: Organize prediction and prediction error across multiple abstraction levels, with higher levels supplying expectations and unresolved errors revising lower and higher models.

distinctive_feature: Prediction and residual travel in opposite or complementary directions across a layered model rather than within a single predictor-receiver pair.

distinct_from_parent: It adds model hierarchy, cross-level interfaces, timescale separation, and escalation semantics.

still_same_parent_because: Each layer still predicts input, compares observation, propagates residual, and updates its model under uncertainty and fallback controls.

use_when: The system has multiple spatial, temporal, organizational, or representational levels.

use_when: Higher-level context can explain lower-level input but must remain answerable to bottom-up evidence.

use_when: Layer-specific uncertainty and update timescales can be made explicit.

example: A task-level model predicts motion-level outcomes while lower controllers return only unresolved state and contact errors.

domain: operations

example: Portfolio expectations flow to programs, while only material local deviations and model-invalidating evidence escalate upward.

failure_modes: Strong priors suppress legitimate lower-level evidence.

failure_modes: Error is amplified or duplicated across layers.

failure_modes: Responsibility for updating the model becomes ambiguous.

Surprise-Driven Operational Monitoring

short_definition: Use a maintained expected-state model to route human attention primarily to validated deviations while preserving raw audit samples and escalation paths.

distinctive_feature: Prediction error is converted into an attention-allocation and exception-governance system for human operators.

distinct_from_parent: It emphasizes alert burden, action ownership, contestability, and periodic review of what the model suppresses.

still_same_parent_because: The monitoring system still reconstructs expected context from a model and propagates only calibrated residuals through a feedback update loop.

use_when: Routine state is high-volume and mostly predictable.

use_when: Human attention is the binding channel rather than machine bandwidth alone.

use_when: The organization can define who owns residual interpretation and model revision.

domain: operations center

example: A dashboard hides routine telemetry but surfaces consequence-weighted residuals and periodically displays unfiltered samples for blind-spot review.

domain: program management

example: Teams report only deviations from an agreed forecast, with explicit thresholds, owners, and a cadence for challenging the forecast itself.

failure_modes: Normal-looking failure remains invisible.

failure_modes: Operators lose situational understanding because baseline context is hidden.

failure_modes: The model encodes organizational bias and suppresses minority signals.

Reward-Prediction-Error Learning

short_definition: Use the signed difference between expected and received value as the teaching signal for updating value estimates or action policy.

distinctive_feature: The residual has signed motivational or decision value and updates future action selection rather than merely reconstructing a signal.

distinct_from_parent: It specializes prediction error to value learning, sequential decisions, reinforcement, and credit assignment.

still_same_parent_because: The expected outcome is predicted, the observed outcome is compared, and the residual is the teaching signal that revises the predictor or policy.

use_when: The predicted quantity is scalar or ordered value, reward, utility, or expected outcome.

use_when: Learning depends on whether outcomes are better or worse than expected, not on raw magnitude alone.

use_when: The update rate and attribution path can be calibrated and audited.

domain: reinforcement learning

example: A value estimator updates from the signed difference between received reward plus next-state value and the prior estimate.

domain: forecast calibration

example: A forecaster records whether outcomes exceeded or fell short of expectations and updates future estimates accordingly.

failure_modes: Misassigned credit updates the wrong predictor.

failure_modes: Reward hacking makes the error signal locally positive while system outcomes degrade.

failure_modes: A noisy outcome produces unstable over-updating.

near_names:
Predictive Coding
Predict and Correct
Prediction-Error Signaling
Generative-Model Correction
Predictive Processing
Residual Coding
Model-Based Residual Signaling