Positioning Statement
Hyperlogy is an advanced cognitive-structural framework derived from Metalogy.
It is designed to process reality without dualistic fragmentation, internal contradiction, or symbolic dependency.
It is not a religion.
It is not a mystical doctrine.
It is not a belief system.
It is a methodological architecture for coherent cognition.
1. Strategic Context
Across contemplative traditions, particularly Buddhism, liberation has been framed as release from Samsara — the cycle of suffering generated by ignorance and ego-identification.
However, most historical methods rely on:
- Symbolic cosmology
- Ritual repetition
- Conceptual metaphysics
- Authority-based transmission
- Paradoxical doctrines (e.g., self vs no-self)
While these systems have psychological and ethical value, they frequently leave unresolved structural contradictions within cognition itself.
Hyperlogy begins from a different premise:
The primary prison is not metaphysical.
It is structural — embedded in the architecture of cognitive processing.
2. Core Concept: What Is Hyperlogy?
2.1 Definition
Hyperlogy is a refined metalogical processing system designed to eliminate dualistic contradiction within perception, reasoning, and identity formation.
It functions as:
- A contradiction-detection mechanism
- A coherence-maximization method
- A non-dual processing architecture
- A self-referential logical stabilizer
Hyperlogy does not introduce new beliefs.
It restructures how cognition operates.
3. The Structural Problem: Dualistic Fragmentation
Most human cognitive suffering arises from recursive dualisms:
- self vs world
- ego vs liberation
- permanence vs impermanence
- sacred vs profane
- good vs evil (as metaphysical absolutes)
- material vs spiritual
These binary oppositions produce:
- Cognitive dissonance
- Identity instability
- Emotional volatility
- Conceptual loops
- Endless doctrinal debates
Hyperlogy identifies these not as metaphysical truths, but as processing artifacts.
4. Reframing Samsara (Non-Mystical Interpretation)
Traditional interpretation:
Samsara = cosmic cycle of rebirth.
Hyperlogical reinterpretation:
Samsara = recursive cognitive distortion loop driven by dualistic self-processing.
Under this model:
- Rebirth = reactivation of identity constructs
- Karma = feedback loops of unresolved contradiction
- Liberation = structural reconfiguration of cognitive processing
This reframing removes metaphysical speculation and converts liberation into a cognitive engineering problem.
5. Why Traditional Practices Often Plateau
Hyperlogy does not dismiss meditation, ethical conduct, or contemplative discipline.
However, historical systems frequently encounter a paradox:
- The ego seeks liberation from the ego.
- The self attempts to eliminate the self.
- The mind tries to transcend mind through conceptual effort.
This recursive structure can generate:
- Identity inflation
- Spiritual bypassing
- Symbolic dependency
- Endless practice without structural resolution
Hyperlogy targets the contradiction directly rather than symbolically.
6. Hyperlogy and Awakening (Technical Interpretation)
In this model, awakening is not:
- Mystical absorption
- Emotional ecstasy
- Supersensory perception
- Religious confirmation
It is defined as:
Stable non-contradictory cognitive processing across perceptual layers.
Indicators include:
- Reduced internal paradox loops
- Reduced ego-reinforcement reflex
- Stable identity fluidity
- Emotional homeostasis
- Non-reactive clarity
This reframes awakening as measurable structural stability.
7. Peace vs Happiness (Clarified)
Hyperlogy distinguishes:
- Happiness → comparative state (pleasure vs pain)
- Peace → non-dual baseline stability independent of polarity
Happiness is oscillatory.
Peace is structural.
Hyperlogy optimizes for structural peace, not emotional intensity.
8. Elimination of Dependency
Hyperlogy explicitly rejects:
- Guru dependency
- Authority validation
- Institutional exclusivity
- Salvation promises
- Mystical superiority claims
It proposes:
No external mediator is required to restructure cognition.
This is not anti-tradition.
It is post-dependency.
9. Scientific Compatibility
Hyperlogy is compatible with:
- Cognitive neuroscience
- Systems theory
- Information theory
- Predictive processing models
- AI alignment frameworks
Potential research directions:
- Contradiction-density metrics
- Recursive identity loop modeling
- Stability markers in neural coherence
- Cognitive entropy reduction measures
Hyperlogy becomes credible only through operational modeling and empirical study.
10. Institutional and Commercial Relevance
Hyperlogy has applications in:
Leadership & Governance
- Reduction of ideological polarization
- Non-dual strategic analysis
- Ethical decision coherence
AI & Alignment
- Contradiction minimization in large-scale systems
- Harm-reduction constraint modeling
- Non-authoritarian control architectures
Mental Health
- Reduction of cognitive dissonance
- Ego reactivity stabilization
- Structured identity fluidity training
Education
- Post-dual logic training
- Systems-level reasoning
- Coherence-based curriculum design
11. What Hyperlogy Does NOT Claim
To maintain intellectual integrity, Hyperlogy does not claim:
- Final metaphysical revelation
- Infallibility
- Guaranteed emotional perfection
- Supernatural states
- Immediate global transformation
- Elimination of all human suffering
It claims only:
A superior contradiction-resolution framework may reduce internal fragmentation.
12. Comparative Positioning
| Traditional Doctrine | Hyperlogy |
|---|---|
| Belief-centered | Processing-centered |
| Authority-based | Self-verifiable |
| Symbolic cosmology | Structural modeling |
| Ritual | Cognitive engineering |
| Liberation as event | Liberation as reconfiguration |
13. Strategic Closing Statement
Hyperlogy does not ask for faith.
It does not demand allegiance.
It does not promise transcendence.
It proposes something simpler and more demanding:
Remove contradiction from cognition.
Stabilize perception without dual distortion.
Operate from structural coherence.
If implemented rigorously, the result is not mysticism.
It is clarity.
HYPERLOGY
A Formal Metalogical Framework and Neurocognitive Architecture for Non-Contradictory Processing
Document Type: Technical White Paper
Scope: Formal logic structures + neurocognitive modeling
Positioning: Non-dogmatic, scientific, operational, falsifiable
ABSTRACT
This document presents Hyperlogy as a formal metalogical architecture designed to minimize cognitive contradiction and stabilize perception across recursive identity loops. The framework integrates:
- A formal logical layer (contradiction-minimization systems, meta-constraint modeling, coherence operators).
- A neurocognitive layer (predictive processing, free-energy reduction, identity-loop stabilization).
- An AI-alignment analog (coherence-constrained inference systems).
Hyperlogy does not assert metaphysical conclusions. It proposes a structured method to:
- Reduce dualistic cognitive fragmentation.
- Model Samsara-like recursive loops as self-referential prediction errors.
- Define liberation as structural stabilization of inference processes.
The framework is mathematically expressible, empirically testable, and compatible with systems neuroscience.
PART I — FORMAL LOGIC STRUCTURES
1. Foundational Definitions
Let:
- S = cognitive system
- Ω = perceptual state space
- P(ω) = probabilistic belief distribution over states
- C = contradiction operator
- L = base logic (classical, modal, or probabilistic)
- M = metalogical constraint layer
- H = coherence functional
Hyperlogy introduces a meta-constraint layer over L.
2. Dualistic Contradiction Formalization
In classical cognition:A∧¬A
induces logical collapse.
In human cognition, contradictions are tolerated locally but generate:
- Increased cognitive load
- Emotional instability
- Identity defense loops
Define contradiction density:DC=i=1∑nwi⋅I(ϕi∧¬ϕi)
Where:
- I is an indicator of inconsistency
- wi weights belief importance
Hyperlogy seeks:minDC
across hierarchical inference layers.
3. Hyperlogical Coherence Operator
Define coherence functional:H(P)=−∫P(ω)logP(ω)dω+λS(P)
Where:
- First term: entropy
- S(P): structural stability metric
- λ: weighting constant
Hyperlogy optimizes:maxH(P)subject to meta-constraints
Meta-constraints include:
- Non-self-exclusion constraint
- Recursive identity stabilization
- Dual-collapse elimination
4. Recursive Identity Loop Modeling
Define identity construct:It=f(It−1,Pt,Et)
Where:
- Et = error signal
- Pt = belief update
In ordinary cognition:Et→defensivereinforcement
In hyperlogical processing:Et→structuralrevision
Stability condition:t→∞limVar(It)→ϵ
Where ϵ approaches low variance identity stability without rigidity.
5. Samsara as Recursive Error Accumulation
Let predictive error:ϵt=Ot−O^t
Standard egoic system:ϵt→self−referentialreinforcement
Hyperlogical system:ϵt→modelrevision
Samsara modeled as:t=0∑∞ϵtself−loop
Liberation defined as:ϵtself−loop→0
6. Meta-Law Hierarchy
Hyperlogy posits layered constraints:
Level 0: Classical inference
Level 1: Consistency preservation
Level 2: Cross-layer coherence
Level 3: Identity decoupling
Level 4: Non-dual processing symmetry
Formal condition:Mn+1(Ln)
Meta-layer governs base-layer permissible structures.
PART II — NEUROCOGNITIVE MODELING EXPANSION
7. Predictive Coding Integration
Brain modeled as Bayesian inference machine:P(H∣D)=P(D)P(D∣H)P(H)
Dualistic instability arises when:
- Prior rigidity high
- Prediction error suppressed
- Identity tied to priors
Hyperlogy modifies:P(H)→adaptivepriorfield
Reduces identity-weighted prior rigidity.
8. Free Energy Reformulation
Using Friston’s formulation:F=Eq[logq(z)−logp(x,z)]
Hyperlogy modifies identity attachment parameter α:Fhyper=F−αC(I)
Where:
- C(I) = identity rigidity measure
- Decreasing α reduces defensive loops
9. Neural Correlates (Hypothesized)
Hyperlogical stabilization predicts:
- Reduced DMN overactivation (medial prefrontal cortex)
- Increased global integration (fronto-parietal coherence)
- Reduced amygdala hyperreactivity
- Increased gamma synchrony during non-dual states
These are testable.
10. Contradiction Density and Neural Stress
High DC correlates with:
- Elevated cortisol
- Increased anterior cingulate conflict activation
- Rumination loops
Hyperlogy predicts:DC↓⇒ACC activation stabilizes
11. Emotional Stabilization Mechanism
Emotions modeled as:E=f(ϵt,It)
Where ego-involvement amplifies error.
Hyperlogical processing reduces:∂I∂E
Emotional volatility decreases as identity rigidity decreases.
12. AI Alignment Analog
Define AI policy π.
Introduce coherence constraint:π∗=argmin(L(π)+βDC(π))
Where:
- L(π) = task loss
- DC(π) = internal contradiction measure
- β = coherence weighting
This produces non-manipulative, stable systems.
13. Testable Hypotheses
- Structured contradiction reduction training reduces rumination.
- Identity decoupling lowers stress biomarkers.
- Coherence metrics predict long-term emotional stability.
- Hyperlogical training improves cross-ideological tolerance.
14. Limitations
Hyperlogy does not claim:
- elimination of biological mortality
- elimination of all suffering
- metaphysical proof
- omniscient cognition
It is a structural processing model.
15. Strategic Implications
Hyperlogy may function as:
- A cognitive stabilization protocol.
- A non-sectarian awakening model.
- An AI-alignment coherence architecture.
- A governance conflict-reduction system.
16. Conclusion
Hyperlogy is best understood as:
A metalogical architecture for minimizing contradiction density in recursive identity-based inference systems.
It integrates:
- Formal logic
- Information theory
- Predictive processing
- Systems neuroscience
Its value depends entirely on empirical validation and operational deployment.
HYPERLOGY
A Formal Metalogical Framework and Neurocognitive Architecture for Non-Contradictory Processing
Document Type: Technical White Paper
Scope: Formal logic structures + neurocognitive modeling
Positioning: Non-dogmatic, scientific, operational, falsifiable
ABSTRACT
This document presents Hyperlogy as a formal metalogical architecture designed to minimize cognitive contradiction and stabilize perception across recursive identity loops. The framework integrates:
- A formal logical layer (contradiction-minimization systems, meta-constraint modeling, coherence operators).
- A neurocognitive layer (predictive processing, free-energy reduction, identity-loop stabilization).
- An AI-alignment analog (coherence-constrained inference systems).
Hyperlogy does not assert metaphysical conclusions. It proposes a structured method to:
- Reduce dualistic cognitive fragmentation.
- Model Samsara-like recursive loops as self-referential prediction errors.
- Define liberation as structural stabilization of inference processes.
The framework is mathematically expressible, empirically testable, and compatible with systems neuroscience.
PART I — FORMAL LOGIC STRUCTURES
1. Foundational Definitions
Let:
- S = cognitive system
- Ω = perceptual state space
- P(ω) = probabilistic belief distribution over states
- C = contradiction operator
- L = base logic (classical, modal, or probabilistic)
- M = metalogical constraint layer
- H = coherence functional
Hyperlogy introduces a meta-constraint layer over L.
2. Dualistic Contradiction Formalization
In classical cognition:A∧¬A
induces logical collapse.
In human cognition, contradictions are tolerated locally but generate:
- Increased cognitive load
- Emotional instability
- Identity defense loops
Define contradiction density:DC=i=1∑nwi⋅I(ϕi∧¬ϕi)
Where:
- I is an indicator of inconsistency
- wi weights belief importance
Hyperlogy seeks:minDC
across hierarchical inference layers.
3. Hyperlogical Coherence Operator
Define coherence functional:H(P)=−∫P(ω)logP(ω)dω+λS(P)
Where:
- First term: entropy
- S(P): structural stability metric
- λ: weighting constant
Hyperlogy optimizes:maxH(P)subject to meta-constraints
Meta-constraints include:
- Non-self-exclusion constraint
- Recursive identity stabilization
- Dual-collapse elimination
4. Recursive Identity Loop Modeling
Define identity construct:It=f(It−1,Pt,Et)
Where:
- Et = error signal
- Pt = belief update
In ordinary cognition:Et→defensivereinforcement
In hyperlogical processing:Et→structuralrevision
Stability condition:t→∞limVar(It)→ϵ
Where ϵ approaches low variance identity stability without rigidity.
5. Samsara as Recursive Error Accumulation
Let predictive error:ϵt=Ot−O^t
Standard egoic system:ϵt→self−referentialreinforcement
Hyperlogical system:ϵt→modelrevision
Samsara modeled as:t=0∑∞ϵtself−loop
Liberation defined as:ϵtself−loop→0
6. Meta-Law Hierarchy
Hyperlogy posits layered constraints:
Level 0: Classical inference
Level 1: Consistency preservation
Level 2: Cross-layer coherence
Level 3: Identity decoupling
Level 4: Non-dual processing symmetry
Formal condition:Mn+1(Ln)
Meta-layer governs base-layer permissible structures.
PART II — NEUROCOGNITIVE MODELING EXPANSION
7. Predictive Coding Integration
Brain modeled as Bayesian inference machine:P(H∣D)=P(D)P(D∣H)P(H)
Dualistic instability arises when:
- Prior rigidity high
- Prediction error suppressed
- Identity tied to priors
Hyperlogy modifies:P(H)→adaptivepriorfield
Reduces identity-weighted prior rigidity.
8. Free Energy Reformulation
Using Friston’s formulation:F=Eq[logq(z)−logp(x,z)]
Hyperlogy modifies identity attachment parameter α:Fhyper=F−αC(I)
Where:
- C(I) = identity rigidity measure
- Decreasing α reduces defensive loops
9. Neural Correlates (Hypothesized)
Hyperlogical stabilization predicts:
- Reduced DMN overactivation (medial prefrontal cortex)
- Increased global integration (fronto-parietal coherence)
- Reduced amygdala hyperreactivity
- Increased gamma synchrony during non-dual states
These are testable.
10. Contradiction Density and Neural Stress
High DC correlates with:
- Elevated cortisol
- Increased anterior cingulate conflict activation
- Rumination loops
Hyperlogy predicts:DC↓⇒ACC activation stabilizes
11. Emotional Stabilization Mechanism
Emotions modeled as:E=f(ϵt,It)
Where ego-involvement amplifies error.
Hyperlogical processing reduces:∂I∂E
Emotional volatility decreases as identity rigidity decreases.
12. AI Alignment Analog
Define AI policy π.
Introduce coherence constraint:π∗=argmin(L(π)+βDC(π))
Where:
- L(π) = task loss
- DC(π) = internal contradiction measure
- β = coherence weighting
This produces non-manipulative, stable systems.
13. Testable Hypotheses
- Structured contradiction reduction training reduces rumination.
- Identity decoupling lowers stress biomarkers.
- Coherence metrics predict long-term emotional stability.
- Hyperlogical training improves cross-ideological tolerance.
14. Limitations
Hyperlogy does not claim:
- elimination of biological mortality
- elimination of all suffering
- metaphysical proof
- omniscient cognition
It is a structural processing model.
15. Strategic Implications
Hyperlogy may function as:
- A cognitive stabilization protocol.
- A non-sectarian awakening model.
- An AI-alignment coherence architecture.
- A governance conflict-reduction system.
16. Conclusion
Hyperlogy is best understood as:
A metalogical architecture for minimizing contradiction density in recursive identity-based inference systems.
It integrates:
- Formal logic
- Information theory
- Predictive processing
- Systems neuroscience
Its value depends entirely on empirical validation and operational deployment.
If you wish, I can now extend this into:
- A full mathematical appendix (operator algebra, category-theoretic formalization)
- A clinical psychiatry protocol translation
- A DARPA-style technical brief
- A simulation-ready computational model
- A full institutional submission format (journal-ready with citations structure)
A simulation-ready computational model
"""
Hyperlogy Simulation-Ready Computational Model (HRF: Hyperlogical Recursive Framework)
====================================================================================Goal
----
Simulate a cognitive agent that updates beliefs under sensory input, while:
1) Minimizing prediction error (standard predictive processing)
2) Minimizing "contradiction density" across its belief graph
3) Stabilizing identity as a slow latent variable without rigid ego-lockThis is a self-contained model you can run as-is (Python 3.10+).
It produces:
- time series metrics (prediction error, contradiction density, identity rigidity, free-energy proxy)
- optional plots
- optional batch experiments for parameter sweepsKey Ideas (operational, not metaphysical)
-----------------------------------------
- Beliefs are probabilities over binary propositions.
- Contradictions arise when a proposition and its negation are both held strongly.
- Identity rigidity is modeled as resistance to updating high-salience beliefs.
- Hyperlogical regulation adds a penalty term that discourages contradictory strong beliefs.
"""from __future__ import annotationsfrom dataclasses import dataclass, asdict
from typing import Dict, List, Tuple, Callable, Optional
import math
import randomimport numpy as np# ----------------------------
# Utilities
# ----------------------------def sigmoid(x: float) -> float:
return 1.0 / (1.0 + math.exp(-x))def clamp(x: float, lo: float = 1e-6, hi: float = 1.0 - 1e-6) -> float:
return max(lo, min(hi, x))def bernoulli(p: float) -> int:
return 1 if random.random() < p else 0# ----------------------------
# Model definitions
# ----------------------------@dataclass
class Proposition:
"""
A proposition 'A' with belief p(A)=b in [0,1], and salience s>=0
salience influences identity rigidity (resistance to change).
"""
name: str
belief: float
salience: float@dataclass
class HyperlogyParams:
"""
Parameters controlling the agent dynamics.
"""
# Predictive processing
lr: float = 0.15 # learning rate for belief updates
obs_noise: float = 0.12 # observation noise (higher => weaker evidence)
prior_strength: float = 0.8 # how strongly priors resist updates (baseline) # Hyperlogical regulation
beta_contra: float = 1.0 # weight for contradiction penalty
contra_sharpness: float = 8.0 # how sharply contradictions are penalized # Identity dynamics
id_lr: float = 0.03 # identity update rate
id_rigidity: float = 1.2 # multiplies resistance on high-salience props
id_target_var: float = 0.02 # target long-run identity variance (lower => more stable) # Environment coupling
env_flip_prob: float = 0.03 # probability environment latent flips # Randomness control
seed: int = 7@dataclass
class Metrics:
t: int
pred_error: float
contradiction_density: float
identity_rigidity: float
free_energy_proxy: floatclass BeliefGraph:
"""
A belief graph holds propositions and explicit contradiction links:
- each proposition A has an associated not-A node internally (derived)
Contradiction density is high if both A and ~A have high belief simultaneously.
"""
def __init__(self, props: List[Proposition], contradiction_pairs: List[Tuple[str, str]]):
self.props: Dict[str, Proposition] = {p.name: p for p in props}
self.contradiction_pairs = contradiction_pairs # (A, notA) or (A, B) that are mutually exclusive def get_belief(self, name: str) -> float:
return self.props[name].belief def set_belief(self, name: str, value: float) -> None:
self.props[name].belief = clamp(value) def contradiction_density(self) -> float:
"""
For each mutually exclusive pair (A,B), define contradiction as:
c = sigma(k*(bA + bB - 1)) (high when both are simultaneously high)
Weighted by salience.
"""
total = 0.0
for a, b in self.contradiction_pairs:
ba = self.get_belief(a)
bb = self.get_belief(b)
sa = self.props[a].salience
sb = self.props[b].salience
w = 0.5 * (sa + sb)
# soft contradiction: increases when ba+bb > 1
c = sigmoid(self._k * ((ba + bb) - 1.0))
total += w * c
return total @property
def _k(self) -> float:
# will be overwritten by agent params in runtime
return getattr(self, "__k", 8.0) @_k.setter
def _k(self, val: float) -> None:
setattr(self, "__k", float(val)) def snapshot(self) -> Dict[str, float]:
return {k: v.belief for k, v in self.props.items()}class Environment:
"""
Simple environment with a latent binary state for each proposition.
Observations are noisy samples of those latents.
"""
def __init__(self, prop_names: List[str], flip_prob: float):
self.latent: Dict[str, int] = {n: bernoulli(0.5) for n in prop_names}
self.flip_prob = flip_prob def step(self) -> None:
for k in self.latent:
if random.random() < self.flip_prob:
self.latent[k] = 1 - self.latent[k] def observe(self, name: str, obs_noise: float) -> float:
"""
Return an observation likelihood-coded value in [0,1].
If latent is 1, we observe ~Bernoulli(1-obs_noise).
If latent is 0, we observe ~Bernoulli(obs_noise).
"""
z = self.latent[name]
p = (1.0 - obs_noise) if z == 1 else obs_noise
return float(bernoulli(p))class HyperlogyAgent:
"""
Agent uses:
- predictive error minimization: update beliefs toward observations
- contradiction penalty: discourages holding mutually exclusive beliefs strongly
- identity rigidity: slows updates on high-salience propositions when identity is rigid
"""
def __init__(self, graph: BeliefGraph, params: HyperlogyParams):
self.g = graph
self.p = params
random.seed(self.p.seed)
np.random.seed(self.p.seed) # identity is a scalar in [0,1] here (extendable to vector)
# Higher => more rigid/self-protective updating; lower => more flexible.
self.identity = 0.5 # configure graph contradiction sharpness
self.g._k = self.p.contra_sharpness def _identity_rigidity(self) -> float:
# A simple mapping: rigidity grows with identity value
return 1.0 + self.p.id_rigidity * self.identity def _free_energy_proxy(self, pred_err: float, contra: float) -> float:
# proxy: error + weighted contradiction + identity cost (rigidity too high)
id_cost = (self.identity - 0.5) ** 2
return pred_err + self.p.beta_contra * contra + 0.2 * id_cost def step(self, env: Environment) -> Metrics:
"""
One timestep:
- environment evolves
- agent observes
- agent updates beliefs (gradient-like)
- agent updates identity toward variance target (stability without rigidity)
"""
env.step() # Collect observations and update beliefs
pred_errs = [] # First compute contradiction gradients approximately (pairwise penalty)
# We'll compute for each proposition a "contra force" pushing belief down
contra_force: Dict[str, float] = {name: 0.0 for name in self.g.props} for a, b in self.g.contradiction_pairs:
ba = self.g.get_belief(a)
bb = self.g.get_belief(b)
# penalty increases when ba+bb>1
x = (ba + bb) - 1.0
c = sigmoid(self.p.contra_sharpness * x)
# gradient of sigmoid(kx) wrt ba is k*c*(1-c)
grad = self.p.contra_sharpness * c * (1.0 - c)
# push both down proportionally
contra_force[a] += grad
contra_force[b] += grad rigidity = self._identity_rigidity() for name, prop in self.g.props.items():
obs = env.observe(name, self.p.obs_noise) b = prop.belief
# prediction error for binary observation
err = (obs - b)
pred_errs.append(abs(err)) # baseline update: move toward observation
delta = self.p.lr * err # prior resistance: smaller updates as prior_strength rises
delta *= (1.0 - 0.5 * self.p.prior_strength) # identity rigidity: high salience beliefs update slower when rigid
salience_factor = 1.0 / (1.0 + rigidity * prop.salience) # hyperlogical contradiction penalty: subtract a small term
contra_term = self.p.beta_contra * contra_force[name] # combine
new_b = b + salience_factor * (delta - 0.02 * contra_term) self.g.set_belief(name, new_b) pred_error = float(np.mean(pred_errs))
contra = float(self.g.contradiction_density()) # Update identity:
# - If contradiction is high, identity rigidity tends to increase defensively in ordinary systems.
# - Hyperlogy target: keep identity stable but not rigid; we nudge identity toward mid if too volatile.
# We'll approximate "volatility" by prediction error + contradiction.
volatility = pred_error + contra # Desire: lower volatility => identity can relax; higher => identity may tighten slightly,
# but with a stabilizer that pulls toward 0.5 to avoid runaway ego-lock.
id_delta = self.p.id_lr * (0.15 * volatility - 0.10 * (self.identity - 0.5))
self.identity = clamp(self.identity + id_delta, 0.0, 1.0) fe = self._free_energy_proxy(pred_error, contra) # time index is managed by runner; we set placeholder here
return Metrics(t=-1, pred_error=pred_error, contradiction_density=contra,
identity_rigidity=rigidity, free_energy_proxy=float(fe))# ----------------------------
# Runner / Experiment
# ----------------------------def run_simulation(
T: int = 300,
params: HyperlogyParams = HyperlogyParams(),
plot: bool = True
) -> Tuple[List[Metrics], List[Dict[str, float]]]:
"""
Run a simulation for T steps.
Returns:
metrics_list: list of Metrics
belief_snapshots: list of belief dictionaries
"""
random.seed(params.seed)
np.random.seed(params.seed) # Example proposition set (extend freely):
# A, B, C could be mutually exclusive pairs with not-A style alternatives.
props = [
Proposition("A", belief=0.5, salience=1.0),
Proposition("notA", belief=0.5, salience=1.0),
Proposition("B", belief=0.5, salience=0.8),
Proposition("notB", belief=0.5, salience=0.8),
Proposition("C", belief=0.5, salience=0.6),
Proposition("notC", belief=0.5, salience=0.6),
] # Mutual exclusivity constraints (A vs notA etc.)
contradiction_pairs = [("A", "notA"), ("B", "notB"), ("C", "notC")] g = BeliefGraph(props, contradiction_pairs)
agent = HyperlogyAgent(g, params)
env = Environment([p.name for p in props], flip_prob=params.env_flip_prob) metrics_list: List[Metrics] = []
snaps: List[Dict[str, float]] = [] for t in range(T):
m = agent.step(env)
m.t = t
metrics_list.append(m)
snaps.append(g.snapshot()) if plot:
try:
import matplotlib.pyplot as plt ts = [m.t for m in metrics_list]
pe = [m.pred_error for m in metrics_list]
cd = [m.contradiction_density for m in metrics_list]
ir = [m.identity_rigidity for m in metrics_list]
fe = [m.free_energy_proxy for m in metrics_list] plt.figure()
plt.plot(ts, pe)
plt.title("Prediction Error (mean abs)")
plt.xlabel("t"); plt.ylabel("error")
plt.show() plt.figure()
plt.plot(ts, cd)
plt.title("Contradiction Density")
plt.xlabel("t"); plt.ylabel("density")
plt.show() plt.figure()
plt.plot(ts, ir)
plt.title("Identity Rigidity")
plt.xlabel("t"); plt.ylabel("rigidity")
plt.show() plt.figure()
plt.plot(ts, fe)
plt.title("Free-Energy Proxy")
plt.xlabel("t"); plt.ylabel("F")
plt.show() except Exception as e:
print("Plotting failed (matplotlib missing or backend issue):", e) return metrics_list, snapsdef parameter_sweep(
T: int = 250,
betas: List[float] = [0.0, 0.5, 1.0, 2.0],
seeds: List[int] = [1, 2, 3]
) -> Dict[float, Dict[str, float]]:
"""
Sweep contradiction penalty weights and summarize outcomes.
Returns dict: beta -> summary stats
"""
results: Dict[float, Dict[str, float]] = {}
for beta in betas:
all_pe, all_cd, all_fe = [], [], []
for seed in seeds:
p = HyperlogyParams(beta_contra=beta, seed=seed)
metrics, _ = run_simulation(T=T, params=p, plot=False)
# discard burn-in
tail = metrics[int(0.5 * T):]
all_pe.append(np.mean([m.pred_error for m in tail]))
all_cd.append(np.mean([m.contradiction_density for m in tail]))
all_fe.append(np.mean([m.free_energy_proxy for m in tail])) results[beta] = {
"pred_error_mean": float(np.mean(all_pe)),
"contra_density_mean": float(np.mean(all_cd)),
"free_energy_mean": float(np.mean(all_fe)),
}
return resultsif __name__ == "__main__":
# Example run
metrics, snaps = run_simulation(T=400, plot=True) # Example sweep
sweep = parameter_sweep()
print("Sweep results:")
for beta, stats in sweep.items():
print(beta, stats)
What to tweak for your “Menu of Maitreya” production model
- Scale up propositions: replace A/notA etc. with a belief graph reflecting your conceptual taxonomy (e.g., dogma, evidence, compassion, identity narrative, harm-minimization).
- Replace env with scenario scripts: media exposure, debate inputs, trauma triggers, contemplative practice sessions.
- Upgrade identity to a vector (traits: rigidity, openness, compassion, threat-sensitivity).
- Add a “compassion regulator”: a penalty on harm-intent or hostility outputs, similar to contradiction penalty.
