“I want to lose weight.”
Conventional advice may immediately prescribe dieting, exercise or motivation. HReality OS first asks which realities are relevant in this individual case.
HReality OS Method
How HReality OS decomposes human reality
HReality OS examines a human problem as a connected system. It breaks the visible concern into smaller realities, identifies the relationships between them, tests competing explanations and updates the model whenever new evidence appears.
The method does not assume that the first explanation is correct. It distinguishes reported information from evidence, interpretation, hypothesis, uncertainty and recommendation before selecting a change pathway.
Decompose
Relate
Test
Act
Measure
Reassess
REALITY
Visible concern
1
Decompose
HReality OS begins by decomposing the visible concern into smaller,
analyzable realities.
The visible problem is only the entry point
A broad human concern does not contain enough information for a reliable recommendation.
HReality OS begins with the stated concern, but it does not treat that statement as a complete explanation.
→
Food intake
Meal timing
Metabolism
Sleep
Stress
Medication
Activity
Pain
Income
Food access
Habits
Identity
Environment
Social pressure
Health conditions
Method principle
The engine does not prescribe a solution until it has identified which domains, components, constraints and relationships are sufficiently relevant to the user’s stated objective.
Recursive decomposition
Break down a broad reality until observation and action become possible.
Decomposition depth is adaptive. The process stops when further breakdown no longer improves the decision or when professional assessment is required.
Current depth
Level 0 — Stated reality
Current analytical path
Stated reality
Model direction
Outward decomposition
Supported
Hypothesis
Unresolved
Actionable
Professional boundary
HReality OS begins with the stated reality. No cause or solution has
yet been established.
Continue decomposing when
the current reality remains too broad, uncertain, contradictory or unsuitable for selecting a safe action.
Stop decomposing when
the engine reaches an observable and decision-relevant variable, or when professional assessment is required.
Relationship taxonomy
A list of factors is not enough. The system must determine how they relate.
HReality OS does not label every association as causal. Relationships are classified according to their current evidential status.
01
Production relationship
Direct cause
One condition contributes directly to producing or changing another condition or outcome.
Human example
Persistent sleep restriction can directly reduce daytime
alertness and cognitive performance.
Why it matters
A supported cause is a potential intervention point, but causality
must not be inferred from association alone.
Map notation
Sleep restriction → Low alertness
02
Feasibility relationship
Constraint
A condition restricts the available options, reduces capacity or makes a desired action difficult or impossible.
Human example
A rotating work schedule constrains regular meal timing, sleep and
participation in evening training.
Why it matters
Advice that ignores a binding constraint may be theoretically valid
but practically unusable.
Map notation
Work schedule ⊣ Regular routine
03
Capability relationship
Enabler
A resource, capability or condition makes an action possible or increases the probability of a desired outcome.
Human example
Reliable childcare enables a parent to attend training, work or
complete a professional qualification.
Why it matters
Sometimes the correct intervention is not changing motivation but
establishing the missing enabling condition.
Map notation
Childcare ⇒ Training access
04
Intensity relationship
Amplifier
A condition increases the intensity, frequency or duration of an effect that is already being produced by another factor.
Human example
Financial uncertainty may amplify the stress produced by an already
demanding workload.
Why it matters
Removing an amplifier may reduce the outcome even when the original
cause cannot immediately be removed.
Map notation
Financial uncertainty ↑ Work stress
05
Transmission relationship
Mediator
A condition carries or explains part of the effect between an upstream factor and a downstream outcome.
Human example
Reduced energy may mediate the relationship between poor sleep and
lower physical activity.
Why it matters
Identifying the mediator explains the mechanism through which one
reality affects another.
Map notation
Poor sleep → Low energy → Low activity
06
Conditional relationship
Moderator
A condition changes the strength, direction or circumstances under which another relationship operates.
Human example
Social support may weaken the effect of job loss on emotional
distress.
Why it matters
The same event may produce very different outcomes depending on the
moderator present.
Map notation
Support modifies Job loss → Distress
07
Recursive relationship
Feedback loop
An outcome returns to influence one or more of the conditions that originally helped produce it.
Human example
Stress disrupts sleep, poor sleep reduces performance, and reduced
performance creates additional stress.
Why it matters
Feedback loops can stabilize improvement or continuously reproduce
the original problem.
Map notation
Stress → Poor sleep → Low performance ↺
08
Cost relationship
Trade-off
Improvement in one area consumes resources or creates an adverse effect in another area.
Human example
Working additional hours may increase income while reducing sleep,
family time and recovery.
Why it matters
The best intervention is not always the one with the largest single
benefit; total system cost must be considered.
Map notation
More work + Income / Recovery −
09
Sequence relationship
Dependency
One action, result or system state cannot occur until another condition has first been established.
Human example
A career transition may depend on completing training, obtaining
certification or building financial runway.
Why it matters
Dependencies determine implementation order and prevent premature
recommendations.
Map notation
Certification ≺ Career transition
10
Observed association
Correlation
Two observations vary together, but the available evidence does not establish whether one causes the other.
Human example
Increased phone use and reduced sleep may occur together without
proving that phone use is the only cause.
Why it matters
Correlation generates questions and hypotheses; it does not justify
a causal conclusion by itself.
Map notation
Phone use ↔ Short sleep
11
Risk-reduction relationship
Protective factor
A condition reduces exposure, limits damage, increases resilience or lowers the probability of recurrence.
Human example
Stable routines and supportive relationships can reduce the effect
of periods of high external demand.
Why it matters
Existing protective factors should be preserved and strengthened
during a change programme.
Map notation
Support ↓ Stress impact
12
Unresolved relationship
Unknown relationship
Available information is insufficient, contradictory or too weak to classify the connection responsibly.
Human example
A new medication and weight change occurred close together, but
their relationship has not been professionally assessed.
Why it matters
Unknown is a valid analytical state. The engine must request more
evidence or escalate rather than invent certainty.
Map notation
Medication ? Weight change
Relationship map
The most visible outcome may not be the highest-value intervention point.
In this example, weight is visible, but financial pressure may be upstream. Poor sleep amplifies the problem, low energy mediates the effect on activity, and reduced social support creates a feedback loop.
Upstream driver
Mediator
Visible outcome
Feedback
Financial pressure
↓
Stress
↓
Poor sleep
↓
Low energy
↓
Reduced activity
↓
Weight gain
↓
Lower confidence
↓
Social withdrawal
Less support → more stress
Competing hypotheses
The engine keeps several explanations open until evidence separates them.
Each hypothesis is evaluated using supporting evidence, contradictory evidence, unanswered questions and required next observations.
H1
Energy intake pattern
Intake may consistently exceed energy expenditure.
Supports:
repeated food records and weight trend.
Contradicts:
incomplete intake data.
Next evidence:
seven-day intake and activity record.
H2
Sleep disruption
Restricted sleep may influence appetite and activity.
Supports:
short sleep and evening hunger.
Contradicts:
hunger persists after adequate sleep.
Next evidence:
sleep, hunger and energy tracking.
H3
Environmental access
Work schedule and food access may constrain choices.
Supports:
skipped meals and limited alternatives.
Contradicts:
available alternatives are unused.
Next evidence:
map timing, location, cost and availability.
H4
Health or medication
A medical or pharmacological factor may contribute.
Supports:
timing, symptoms or medication history.
Contradicts:
professional review excludes contribution.
Next evidence:
licensed medical assessment.
H5
Stress-related behavior
Eating may provide temporary relief from stress or fatigue.
Supports:
predictable stress-to-eating sequence.
Contradicts:
eating occurs without the identified state.
Next evidence:
trigger, state, action and consequence log.
H6
Target-definition problem
The target may reflect external pressure rather than a chosen goal.
Supports:
social comparison and undefined health outcome.
Contradicts:
stable personally selected objective.
Next evidence:
clarify desired underlying state.
Evidence classification
Interpretations are not stored as facts.
Every significant statement receives an evidential classification so that the system can distinguish what is known from what is assumed.
“I usually sleep five hours.”
A statement supplied by the user.
Five to six hours were recorded for six nights.
A measured or documented event.
“My lack of discipline causes my weight.”
A meaning assigned to events, not yet established as fact.
Sleep restriction may influence hunger and activity.
A testable possible explanation.
Whether medication contributes to the pattern.
Information that remains unavailable or unresolved.
Track sleep, hunger and meal timing for seven days.
A proposed action based on the current model.
Adaptive questioning
Each answer determines whether deeper questions are necessary.
HReality OS does not ask every user every possible question. It follows uncertainty and asks deeper questions only when an answer can materially change the model or recommendation.
Ask deeper when
an answer remains broad, contradictory, high-risk, causally important or capable of changing the intervention.
Stop asking when
sufficient evidence exists for the current decision, or the next question belongs to a licensed professional.
Confidence and uncertainty
Confidence describes evidence support—not absolute truth.
HReality OS keeps information completeness, hypothesis confidence, recommendation confidence and unresolved uncertainty separate.
Confidence increases when
- answers remain consistent across time;
- repeated observations support the claim;
- different evidence sources agree;
- alternative explanations become weaker;
- predicted outcomes occur.
Confidence decreases when
- important information is missing;
- answers contradict one another;
- relationships have not been observed;
- external conditions remain unknown;
- several hypotheses remain equally plausible.
Model confidence dashboard
Information completeness
72%
Leading hypothesis support
58%
Recommendation confidence
46%
Unresolved uncertainty
54%
Illustrative values only. These are not validated psychometric scores.
Change-point prioritization
The engine does not recommend changing everything simultaneously.
Candidate actions are ranked using benefit, feasibility, evidence, reversibility, cost, risk and dependency burden.
Expected benefit
×
Feasibility
×
Evidence strength
×
Reversibility
÷
Cost
×
Risk
×
Dependency burden
This is an internal prioritization logic, not a scientifically validated universal equation.
| Candidate action | Benefit | Feasibility | Risk | Indicative priority |
|---|---|---|---|---|
| Extreme diet | High short-term | Low | High | Low |
| Track meal timing | Moderate | High | Low | High |
| Improve sleep window | Potentially high | Medium | Low | High |
| Professional medical review | Potentially high | Medium | Low | High when indicated |
| Exercise seven days weekly | Moderate | Low | Medium | Low |
Action as an experiment
Every recommendation must produce new evidence.
Advice becomes testable when the expected effect, action, measurement period and update rule are defined in advance.
Hypothesis
Evening overeating is partly driven by skipped daytime meals.
Test
Eat one planned midday meal for seven days.
Measure
Record evening hunger, intake, energy and adherence.
Outcome
Evening hunger decreases on five of seven days.
Model update
Daytime meal structure is likely relevant.
Next action
Improve sustainability and assess stress separately.
Continuous reassessment
The Reality Map changes as the person and evidence change.
Assessment is not a one-time event. Each action, outcome and contradiction becomes input for the next model.
Define objective
→
Collect evidence
→
Decompose
→
Map relationships
→
Generate hypotheses
→
Ask adaptive questions
→
Select change point
→
Run experiment
→
Measure outcome
→
Update confidence
↺
The cycle can continue until
the target is reached, the model becomes sufficiently stable or the user selects a different objective.
The cycle must stop or escalate when
professional intervention is required, risk increases or evidence shows that the current approach is unsuitable.
Complete method walkthrough
Use case: “I want to become successful.”
Success is not treated as one variable. The method identifies the underlying state the person is actually trying to create.
Success
The statement does not yet define the domain, measure, desired emotional state, acceptable cost or meaning of “enough.”
Career
Money
Identity
Autonomy
Recognition
Purpose
Career: capability, opportunity, network, execution
Money: income, obligations, capital, security
Identity: self-worth, confidence, validation
Autonomy: time, control, dependency
Recognition: status, comparison, expectations
Purpose: meaning, contribution, legacy
- Low income may constrain autonomy.
- Comparison may increase status ambition.
- Weak confidence may reduce opportunity-seeking.
- Financial pressure may force short-term decisions.
- Achievement may temporarily improve validation.
- Validation dependence may continuously raise the target.
Define the desired underlying state before designing the success programme.
The correct plan differs depending on whether the person seeks security, autonomy, mastery, contribution, recognition or proof of personal worth.
Explore Success Reality →
Scientific and professional boundaries
Recursive decomposition improves analysis, but it does not automatically produce truth.
The public method must remain explicit about uncertainty, evidence quality, professional limits and the distinction between a structured assessment and a validated clinical instrument.
Incomplete information
Decomposition can begin from inaccurate or incomplete reports.
Self-report limitations
Users may omit, normalize or misinterpret relevant conditions.
Causality limits
Observed relationships do not automatically prove causation.
Professional scope
Medical, psychological, legal and financial boundaries remain.
Psychometric claims
Scientific validation is required before claiming validated scales.
Low-risk action
High uncertainty requires reversible and proportionate actions.
Your visible problem is only the beginning
Build an initial Reality Map from your own evidence.
Begin with a structured assessment that decomposes your objective, identifies connected realities and shows where deeper questions may be necessary.