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Regime Map — Philanthropy Module

Structural Regime Patterns Across Funding Flows (RTT/1)#

Philanthropy exhibits four canonical regime types:

  • AUTH — authority‑driven
  • NAR — narrative‑driven
  • EMO — emotion‑driven
  • STR — structural

Regimes are detected using the Triadic Observer:

SIG — structural truth
NOI — narrative/emotional noise
REG — regime classification
SYN — synthesis

1. Regime Overview#

Regime Description Impact on Flows Impact on SET Load Drift Risk
AUTH Authority‑driven decisions centralized routing moderate SET_LEAK governance drift
NAR Narrative‑driven decisions PR‑shaped flows high SET_LEAK reporting drift
EMO Emotion‑driven decisions crisis surges, volatility unstable SET_IN regime drift
STR Structural decisions efficient routing low SET_LEAK minimal drift

2. Regime Patterns by Node#

Donor#

AUTH — donor dictates structure
NAR — donor influenced by storytelling
EMO — crisis‑driven giving
STR — clear intent + structural routing

Foundation#

AUTH — board‑driven decisions
NAR — branding‑heavy grantmaking
EMO — reactive funding cycles
STR — standards‑based allocation

Intermediary#

AUTH — centralized control
NAR — impact theater, PR inflation
EMO — donor‑pleasing behavior
STR — transparent regranting

NGO#

AUTH — top‑down program design
NAR — narrative‑heavy reporting
EMO — donor‑appeasement cycles
STR — evidence‑based implementation

Local Partner#

AUTH — local political influence
NAR — story‑driven updates
EMO — community pressure
STR — grounded, contextual execution

Beneficiary#

AUTH — imposed program structure
NAR — selective reporting
EMO — crisis‑response behavior
STR — direct outcome generation

3. Regime Effects on Funding Flow#

AUTH Regime#

FLOW = centralized
TRACE = partial
SET_LEAK = moderate
OPA ↑
ASYM ↑

NAR Regime#

FLOW = distorted by storytelling
NOI ↑
SET_LEAK ↑↑
DRF(reporting)

EMO Regime#

FLOW = volatile
SET_IN = surge
SET_OUT = inconsistent
DRF(regime)

STR Regime#

FLOW = efficient
TRACE = full
SET_LEAK = low
COH ↑

4. Regime Detection Operators#

Regimes are detected using:

REG(node) = AUTH / NAR / EMO / STR
SIG(data)
NOI(data)
CTX(node)
SYN(system)

Mapping:

  • SIG ↓ → non‑structural regime
  • NOI ↑ → NAR or EMO
  • ASYM ↑ → AUTH
  • OPA ↑ → AUTH or NAR
  • SET_LEAK ↑ → NAR or EMO

5. Regime Drift#

Regime drift occurs when a non‑structural regime dominates:

DRF(regime) = high when:
  REG = NAR
  REG = EMO
  REG = AUTH (unchecked)

Effects:

  • narrative inflation
  • emotional volatility
  • authority asymmetry
  • flow distortion
  • outcome incoherence

6. Regime Signatures#

A regime signature summarizes the dominant regime forces:

REGIME_SIGNATURE:
  Donor = {{AUTH/NAR/EMO/STR}}
  Foundation = {{AUTH/NAR/EMO/STR}}
  Intermediary = {{AUTH/NAR/EMO/STR}}
  NGO = {{AUTH/NAR/EMO/STR}}
  LocalPartner = {{AUTH/NAR/EMO/STR}}
  Beneficiary = {{AUTH/NAR/EMO/STR}}
  Primary = {{REG}}
  Notes = {{context}}

Example:

REGIME_SIGNATURE:
  Donor = EMO
  Foundation = AUTH
  Intermediary = NAR
  NGO = STR
  LocalPartner = STR
  Beneficiary = STR
  Primary = NAR
  Notes = narrative-driven intermediary distorting flow

7. Regime Corrections (Structural)#

Corrections use the FIX operator:

FIX(Foundation) → increase transparency
FIX(Intermediary) → reduce narrative incentives
FIX(NGO) → strengthen evidence base
FIX(LocalPartner) → improve governance

Corrections target structure, not individuals.

Summary#

Regimes shape philanthropic systems by influencing:

  • flow integrity
  • governance substrate
  • SET load
  • drift patterns
  • outcome coherence

The regime map provides a structural lens for detecting and correcting non‑structural forces in funding flows.

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