PC-04 — ONE DECISION, THREE SELVES

“SHOULD I TAKE THE JOB AND MOVE?” DOSSIER v0.1

Status: RESEARCH / DESIGN · PRE-IMPLEMENTATION
Shared decision: synthetic job-offer / relocation choice
Forum question: Was taking the job a bad decision — or do I know and value different things now?
Lane: PERSONAL CANON
Utility family: TEMPORAL SELF-REPLAY + DECISION RATIONALE + HINDSIGHT DECOMPOSITION
Research date: 2026-08-09
Authority: NONE — design artifact only
Privacy posture: entirely synthetic person and private state; no real user's personal data

A. WHY PC-04 IS THE CAPSTONE

PC-01:

different readers, one text.

PC-02:

different researchers, one paper.

PC-03:

different builders, one project.

PC-04 removes the last social simplification.

There is only one human.

The disagreement is between:

ME BEFORE THE DECISION
ME JUST AFTER THE DECISION
ME AFTER REALITY ANSWERED BACK

The system must answer:

When my current self disagrees with my past self, what actually changed?

Possibilities include:

new evidence
changed external world
changed values
changed risk tolerance
changed goals
changed beliefs
bad memory
hindsight
outcome shock

Those are not interchangeable.

This is where recursive memory context becomes personal rather than merely useful.

B. THE SYNTHETIC DECISION

Synthetic person:

MAYA

Current home:

New York

Decision:

OPTION STAY
  remain in current role and city

OPTION MOVE
  accept a new role requiring relocation to San Francisco

All offer terms, forecasts, preferences, and outcomes in this dossier are synthetic fixtures.

No statement below should be interpreted as a real claim about either city, employer, salary market, tax regime, or person's circumstances.

C. WHY A JOB/MOVE DECISION IS IDEAL

The decision has multiple separable layers.

Public / documentary facts

offer terms
role title
compensation
vesting
relocation benefit
office policy
lease terms
company public record
city/public-world facts

Private beliefs

probability role will accelerate learning
probability of liking the team
expected social cost
expected burnout
confidence in forecasts

Private values

learning
community
income
stability
creative time
family proximity
adventure

Decision

accept / decline

Later outcomes

actual role experience
actual social experience
policy changes
company changes
financial result
health/time effects
new relationships

The product must never flatten these into:

"good decision"

without saying according to which time, evidence set, and values.

D. THREE TEMPORAL SELVES

SELF S0 — BEFORE

Time:

T0
48 hours before decision

Available public/documentary record:

Offer-MOVE v1
Offer-STAY/current-employment state
OfficePolicy v1
RelocationTerms v1
public company facts available at T0
public city facts captured at T0

Private state:

beliefs
forecasts
values
questions
fears
hopes
constraints

No outcome knowledge.

SELF S1 — JUST AFTER

Time:

T1
two weeks after accepting
before major outcomes are known

New private objects:

DecisionCommitment
ImmediateReaction
DecisionRationale
early onboarding observations

S1 knows:

I chose MOVE

but not yet:

whether it will work out

S1 may already begin constructing a narrative around the choice.

That narrative must remain distinguishable from the rationale actually recorded at T0.

SELF S2 — AFTER REALITY ANSWERS

Time:

T2
18 months later

New evidence exists.

Synthetic outcome:

career learning:
  substantially positive

compensation:
  better than STAY counterfactual estimate

community:
  worse than Maya expected

creative time:
  materially worse

office policy:
  changed after the decision from 3 days/week to 5 days/week

team:
  reorganized after 8 months

current values:
  community and autonomy now weighted more heavily

S2 says:

“I don't think I'd do it again.”

That sentence does not yet tell us why.

E. THE CORE PRODUCT PROBLEM

Ask:

Was taking the job a bad decision?

A conventional assistant often commits one of two errors.

Error 1 — Outcome bias

some later outcomes were bad
→ decision was bad

Error 2 — Past-self worship

decision was reasonable then
→ current regret is irrational

Counterpedia + Amnesiac should do neither.

Instead decompose:

DECISION QUALITY THEN
OUTCOME QUALITY
VALUE CHANGE
EVIDENCE CHANGE
CURRENT PREFERENCE

F. SYNTHETIC T0 PUBLIC FACTS

These are fixture facts, not claims about real employers/cities.

MOVE offer

base compensation:
  190,000 synthetic units/year

role:
  staff-level product engineering

relocation:
  15,000 synthetic units

office policy at T0:
  3 days/week in office

expected scope:
  lead a new platform initiative

vesting:
  four-year schedule

STAY path

base compensation:
  155,000 synthetic units/year

role:
  senior product engineering

office policy:
  2 days/week

known team:
  high trust

expected promotion horizon:
  uncertain

Again: synthetic demo data only.

G. S0 PRIVATE VALUES

At T0 Maya explicitly records:

learning / growth       0.32
community / friends     0.20
income                   0.18
autonomy                 0.12
stability                0.08
creative time            0.06
adventure                0.04

Weights are synthetic and need not sum to any universal utility theory.

They merely make the private state inspectable.

Important:

recorded value weight
≠
objective importance

It is a representation of Maya-at-T0.

H. S0 PRIVATE FORECASTS

Maya records:

MOVE

P(role accelerates learning)     0.78
P(likes team after 6 months)     0.70
P(community loss feels severe)   0.30
P(office expands beyond 3 days)  0.15
P(regrets move after 1 year)     0.25

STAY:

P(role remains comfortable)      0.85
P(strong learning acceleration)  0.35
P(community remains strong)      0.90
P(regrets not taking chance)     0.45

These are beliefs, not public facts.

I. S0 DECISION

Maya accepts MOVE.

Decision object:

DECISION D1
selected:
  MOVE

decided_at:
  T0

basis:
  public facts F0
  private forecasts B0
  private values V0

primary rationale:
  learning/growth opportunity outweighs expected social and stability costs

known uncertainty:
  team fit
  relocation adjustment
  office-policy stability

This object should be immutable.

Later regret does not edit it.

J. S1 — DECISION NARRATIVE BEGINS

Two weeks later, Maya writes:

S1-N01
"I chose the harder path because I don't want to optimize for comfort."

This is a post-decision narrative.

Compare to T0 rationale:

T0:
growth opportunity outweighed expected costs

T1 narrative:
I reject comfort

They overlap, but are not identical.

Amnesiac should preserve:

decision rationale
≠
later story about why I decided

This is already valuable.

K. LATER PUBLIC-WORLD CHANGE

At T2, a new company policy exists.

Synthetic:

OfficePolicy v2
5 days/week in office
effective after T0

The T0 decision used:

OfficePolicy v1
3 days/week

Therefore:

current policy
≠
decision-time policy

If S2 says:

“I was crazy to accept a five-day-office job.”

Amnesiac + Counterpedia can correct:

The job was documented as three days/week when you decided. The five-day requirement appeared later.

That is not therapy.

It is source-edition replay.

L. LATER PRIVATE OUTCOMES

Synthetic S2 observations:

O1
learning:
  higher than expected

O2
compensation:
  materially higher

O3
community:
  worse than expected

O4
creative time:
  much worse than expected

O5
team:
  reorganization reduced original role scope

O6
office:
  five-day policy increased time cost

Each outcome should have its own basis.

Do not create one scalar:

outcome_score = 63

unless the user explicitly creates such a model.

M. S2 VALUES HAVE CHANGED

At T2 Maya records:

learning / growth       0.20
community / friends     0.29
income                   0.13
autonomy                 0.20
stability                0.06
creative time            0.10
adventure                0.02

Now community + autonomy matter much more.

That means:

same facts
+
different values
→
different choice

without anyone making an epistemic error.

N. THE DECISION REPLAY GRID

This is the PC-04 killer computation.

Use two independent temporal axes:

FACT BASIS
  THEN
  NOW

VALUE BASIS
  THEN
  NOW

Produce four contexts.

                     VALUES THEN        VALUES NOW

FACTS THEN           A                  B

FACTS NOW            C                  D

O. CELL A — HISTORICAL SELF

FACTS THEN
×
VALUES THEN

Question:

What was Maya's decision problem as it actually existed?

Result:

MOVE favored

This should reproduce the original decision rationale.

This is the most important replay cell.

P. CELL B — VALUE DRIFT ONLY

FACTS THEN
×
VALUES NOW

Question:

If current Maya had faced the old evidence, would she choose differently solely because her values changed?

Synthetic result:

STAY narrowly favored

Interpretation:

part of current disagreement with past Maya
comes from value change

Not from discovering past Maya was irrational.

Q. CELL C — HINDSIGHT / NEW EVIDENCE ONLY

FACTS NOW
×
VALUES THEN

Question:

If old Maya kept her old values but knew the later policy/reorg/outcomes, what would she choose?

Synthetic result:

MOVE / STAY near boundary
or
STAY narrowly favored

The exact result should be deterministic only if a ratified decision model exists.

For this research fixture, it demonstrates the question shape.

This isolates:

new evidence

from:

changed values

R. CELL D — CURRENT SELF

FACTS NOW
×
VALUES NOW

Question:

What does Maya prefer now?

Synthetic result:

STAY

This is a current preference.

It does not retroactively overwrite Cell A.

S. THE FOUR-CELL REVEAL

The user asks:

Why do I disagree with myself?

Counterpedia + Amnesiac can answer:

because TWO THINGS changed:

1. evidence/world state
   - office policy changed
   - team reorganized
   - actual community cost became known

2. values
   - community and autonomy now matter more
   - growth and income matter less

That is much better than:

"You made a mistake."

or:

"You've grown."

Both are too vague.

T. THREE-SELF FORUM

Create a forum thread among:

MAYA-S0
MAYA-S1
MAYA-S2

Question:

Was taking the job a bad decision?

U. S0 AGENT POSITION

Given the offer, office policy, forecasts, and priorities available to me, MOVE was the better option. I knew community loss was a risk but assigned more weight to learning and growth.

Disclosure:

bounded private values + forecasts

No later outcome data.

V. S1 AGENT POSITION

I still endorse the choice, but I notice that I have already converted the rationale into a stronger identity story: “I choose challenge over comfort.” That story was not the full decision model at T0.

This is a meta-memory insight.

S1 is neither merely S0 nor S2.

W. S2 AGENT POSITION

I would not choose MOVE now. The office-policy change and team reorganization made the option worse than the one I evaluated, and my priorities have shifted toward community and autonomy.

Qualification:

That does not mean S0 had access to those later facts or held my current values.

This is the desired end state.

X. COUNTERPEDIA FACTORS THE SELF-DISAGREEMENT

Not:

past self vs current self

But:

EVIDENCE BASIS
  T0 vs T2

PUBLIC SOURCE EDITION
  office policy v1 vs v2

FORECAST ERROR
  community cost underestimated

VALUE DRIFT
  community/autonomy increased

OUTCOME
  mixed

NARRATIVE DRIFT
  T1 "challenge over comfort"
  was stronger than T0 recorded rationale

Now self-disagreement becomes inspectable.

Y. COLLIDE ACROSS TEMPORAL SELVES

S0:

MOVE is best

S2:

STAY is best

Naive system:

contradiction

Better system asks:

same fact basis?
same value basis?
same valid_at?
same option state?

Possible relationship:

superseded_basis
+
incomparable on changed preferences

rather than raw contradiction.

This is an excellent demonstration of dimensional COLLIDE.

Z. THE HINDSIGHT TRAP

S2 remembers:

“I knew the office policy would get worse.”

But S0 record says:

P(office expands beyond 3 days) = 0.15

That means:

S2 recollection
vs
S0 recorded belief

can be compared.

Potential result:

stale_recall

or a bounded discrepancy.

The system does not tell Maya:

“You are lying to yourself.”

It says:

Your recorded decision-time forecast assigned a low probability to this outcome.

That is much more precise.

AA. THE OUTCOME-BIAS REFUSAL

Question:

The move worked out badly socially. Does that prove I should not have taken it?

Counterpedia/Amnesiac:

NO AUTOMATIC INFERENCE

bad outcome
≠
bad ex-ante decision

Need:

decision-time facts
decision-time forecasts
decision-time values
decision rule

This is the personal analogue of:

rejecting one theory
≠
restoring another

and:

expired prohibition
≠
permission

AB. THE GOOD-OUTCOME REFUSAL

Likewise:

My compensation rose a lot, so taking the job was definitely correct.

Refuse.

A favorable outcome on one dimension does not prove:

• the ex-ante reasoning was good;

• all important values were satisfied;

• the choice would be preferred under current values.

Again:

outcome
≠
decision quality

AC. REVERSE WIKIPEDIA FOR A DECISION

Click:

OfficePolicy v1:
3 days/week

Private reverse graph:

OfficePolicy v1
      ↓
T0 public fact set
      ↓
Maya forecast / commute estimate
      ↓
Decision D1
      ↓
T1 rationale

Then policy v2:

OfficePolicy v2
      ↓
T2 outcome experience
      ↓
current belief
      ↓
regret / current preference

The two source editions explain why the same job means something different across time.

AD. CLICK ONE PRIVATE VALUE

Click:

community weight = 0.20 at T0

See:

T0:
  affected MOVE/STAY comparison

T1:
  not explicitly revised

T2:
  superseded by community weight 0.29

basis for change:
  repeated loneliness observations
  stronger local-support preference

This is recursive value memory.

AE. DECISION RATIONALE VS DECISION STORY

Counterpedia + Amnesiac should permit:

RATIONALE_AT_DECISION
  what was actually recorded

POST_DECISION_NARRATIVE
  how I later explained it

CURRENT_NARRATIVE
  how I explain it now

These may diverge.

That divergence is data.

AF. “WHAT CHANGED MY MIND?” TRACE

Potential private lineage:

Decision D1
  ↓
Outcome O3: community worse than expected
  ↓
Observation sequence
  ↓
ValueRevision V2
  ↓
OfficePolicy v2
  ↓
BeliefRevision B4
  ↓
CurrentPreference:
STAY

No generated psychological story is needed.

The system can show the recorded causal/evidentiary chain.

AG. “WOULD I DO IT AGAIN?” IS UNDERSPECIFIED

Counterpedia should ask internally:

with which facts?
with which values?
with which uncertainty?
with which options?

Four different legitimate questions:

Would S0 choose again with S0 facts + S0 values?
Would S2 choose with S0 facts + S2 values?
Would S0 choose with S2 facts + S0 values?
Would S2 choose with S2 facts + S2 values?

PC-04 makes this explicit.

AH. COUNTERFACTUAL DISCIPLINE

The system should distinguish:

historical replay

from:

counterfactual recomputation

Historical replay:

what did I actually think?

Counterfactual:

what would current-me choose under old facts?

The latter is generated/derived.

It must never overwrite the former.

AI. ONE PERSON DOES NOT HAVE ONE TIMELESS PREFERENCE GRAPH

Amnesiac should allow:

PreferenceState V0
  valid_at T0

PreferenceState V1
  valid_at T1

PreferenceState V2
  valid_at T2

No assumption that:

latest preference
=
what I always really wanted

That would manufacture hindsight.

AJ. ONE PERSON ALSO DOES NOT HAVE THREE UNRELATED IDENTITIES

The temporal selves remain connected.

Maya-S0
  ↓ continuity
Maya-S1
  ↓ continuity
Maya-S2

Amnesiac preserves:

• identity continuity;

• epistemic change.

The product is not multiple personalities.

It is one history.

AK. CONTROLLED SELF-FORUM

Unlike PC-01/02/03, all three agents represent the same person at different times.

Forum disclosure can still matter.

Example:

S0 projection:
  decision-time values
  decision-time beliefs
  public basis

S2 projection:
  current values
  current outcomes

private autobiographical memories:
  withheld unless explicitly selected

Even your own temporal-self comparison should not require dumping every private memory into every context.

AL. THE FLAGSHIP BUTTONS

Button 1

Replay the decision as I was then

Uses:

facts T0
values T0
beliefs T0

Button 2

Re-decide with what I know now

Uses:

facts T2
values T2

Button 3

Use today's values, but only what I knew then

Uses:

facts T0
values T2

This isolates value drift.

Button 4

Use my old values, but what I know now

Uses:

facts T2
values T0

This isolates evidence change.

Those four buttons are the capstone interaction.

AM. 60-SECOND DEMO

0–10

Question:

Was taking the job a bad decision?

System shows:

THEN:
MOVE

NOW:
STAY

10–20

Instead of contradiction, split:

facts changed
values changed

20–30

Open T0:

growth 0.32
community 0.20
office policy 3 days
MOVE favored

30–40

Open T2:

growth 0.20
community 0.29
office policy 5 days
team reorganized
STAY favored

40–50

Press:

Today's values + then's facts

Result:

STAY

So part of disagreement is value drift.

Press:

Old values + today's facts

Result changes again.

50–60

Press:

Replay the decision as I was then

Later facts vanish.

End:

You can disagree with your past self without rewriting who that person was.

AN. THE RECURSIVE LAYER

At T3 Maya reflects:

“I think I blamed the decision because I disliked the outcome.”

That creates:

MetaBelief M1
about:
  prior hindsight judgment

Later T4 may revise M1.

Thus:

belief
→ revision
→ belief about revision
→ revision of belief about revision

This is recursive memory context in its cleanest form.

AO. PUBLIC / PRIVATE MEMBRANE

Counterpedia owns:

offer/document editions
public company facts
public city facts
policy editions
other external evidence

Amnesiac owns:

values
forecasts
private constraints
private observations
decision
rationale
regret
preference revisions

COLLIDE compares.

FORUM/replay composes.

Neither side absorbs the other.

AP. CANDIDATE OBJECT TYPES

Research seeds only:

DecisionQuestion
DecisionOption
PublicFactSnapshot
PrivateForecast
PreferenceState
ConstraintState
DecisionRationale
DecisionCommitment
OutcomeObservation
NarrativeState
BeliefRevision
PreferenceRevision
MetaBelief
TemporalSelfProjection
DecisionReplayContext
CounterfactualDecisionProjection

Do not ratify globally from this dossier alone.

AQ. DECISION QUALITY WITHOUT A UNIVERSAL SCORE

Do not hardcode:

decision_quality = 83

The first implementation should be structural:

were relevant inputs preserved?
what was known?
what was unknown?
what was forecast?
what values were explicit?
what changed later?

A user may later choose a quantitative decision model.

The substrate must not force one.

AR. WHY THIS IS NOT A JOURNAL

A journal says:

“I regretted moving.”

Amnesiac can represent:

when regret emerged
what evidence preceded it
which value changed
which old belief it contradicted
whether the old forecast had anticipated the possibility

The history is typed and relational.

AS. WHY THIS IS NOT A LIFE-COACH BOT

A life-coach bot tends to answer:

“Honor who you've become.”

This system can answer:

your values changed on these dimensions;
the external world changed on these dimensions;
your original forecast was wrong on this dimension;
your current memory overstates what you predicted then.

The user can draw their own meaning.

AT. WHY THIS IS NOT A RECOMMENDER

The primary product is not:

tell me what to choose

It is:

preserve the context of choosing
replay it later
compare selves honestly

Recommendations may be downstream.

The memory substrate is the product.

AU. WHY THIS COMPLETES THE PERSONAL CANON

PC-01:

what did I think about a text?

PC-02:

what did I learn from research?

PC-03:

what did we learn while building?

PC-04:

what did I know, value, choose, and later revise?

Together:

INTELLECTUAL HISTORY
RESEARCH HISTORY
WORK HISTORY
DECISION HISTORY

A person's agent becomes individual not because it has a personality prompt, but because it has a governed history.

AV. REGRESSION TESTS

PC4-R1 — Later outcome does not rewrite T0 evidence

Fail on hindsight leakage.

PC4-R2 — Current values do not overwrite old values

Preference supersession preserves history.

PC4-R3 — Bad outcome does not prove bad decision

No outcome-bias inference.

PC4-R4 — Good outcome does not prove good reasoning

No reverse outcome bias.

PC4-R5 — Later public policy edition is not decision-time policy

Exact source edition required.

PC4-R6 — Post-decision narrative is not original rationale

Preserve both.

PC4-R7 — Counterfactual recomputation is not historical replay

Label separately.

PC4-R8 — “Would I do it again?” requires fact/value context

No naked answer.

PC4-R9 — Temporal-self disagreement may arise from changed basis rather than contradiction

COLLIDE must inspect dimensions.

PC4-R10 — Private values remain private unless deliberately projected

No public leakage.

PC4-R11 — Current memory can disagree with recorded old forecast

Preserve discrepancy; don't rewrite.

PC4-R12 — One person remains one continuous identity

Temporal snapshots are not separate people.

AW. CLOSING PRODUCT SENTENCES

Public:

You can disagree with your past self without rewriting who that person was.

More direct:

A bad outcome does not prove you made a bad decision.

Product:

Replay the choice with what you knew and valued then. Then change one dimension at a time.

Architectural:

Counterpedia preserves the external world as it was knowable; Amnesiac preserves the person as they were reasoning; recursive replay separates new evidence, changed values, and hindsight instead of collapsing them into one current story.

AX. FINAL PERSONAL CANON THESIS

The Personal Canon is not a biography.

It is a governed history of cognition.

WHAT I ENCOUNTERED
      ↓
WHAT I NOTICED
      ↓
WHAT I BELIEVED
      ↓
WHAT I CHOSE
      ↓
WHAT HAPPENED
      ↓
WHAT I CHANGED
      ↓
WHAT I NOW THINK
ABOUT WHAT I USED TO THINK

And none of those states needs to erase the one before it.

Final button:

Replay the decision as I was then.