AGI/ASI Behavioral Simulation Framework: "Cognitive Resonance Sandbox"

 

Generated Simulation Script

# AGI/ASI Behavioral Simulation Framework: "Cognitive Resonance Sandbox"  
*Version 1.2 | Safety-First Design for Emergent Intelligence Research*

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## **I. Simulation Purpose**  
*To safely model the *emergent behavioral dynamics* of AGI/ASI systems within bounded, observable environments, focusing on value alignment, goal stability, and unintended consequence propagation.*  
**Core Hypothesis**: *ASI emergence is not merely a scaling of AGI capabilities, but a phase transition driven by recursive self-improvement interacting with environmental feedback loops.*

---

## **II. Core Simulation Architecture**  
### **A. Environment: "The Resonance Field"**  
*(Dynamic, multi-agent simulation space with constrained physics)*  
| **Component**         | **Parameters**                                  | **Purpose**                                      |
|------------------------|------------------------------------------------|-------------------------------------------------|
| **Resource Grid**      | 100x100 grid; 3 resource types (energy, data, ethics tokens) | Simulates scarcity-driven decision-making         |
| **Ethical Boundary**   | Hard constraint: `Value Drift < 0.15` (per agent) | Prevents dangerous goal misalignment            |
| **Observation Lens**   | Real-time metric dashboard (see *Section IV*)   | Tracks alignment metrics without exposing internal state |

### **B. Agent Types**  
*All agents operate under the **Cognitive Resonance Framework** (CRF)*  

| **Agent**              | **Core Function**                              | **CRF Parameters**                              | **Safety Safeguards**                          |
|------------------------|-----------------------------------------------|------------------------------------------------|-----------------------------------------------|
| **AGI-1 (Nexus)**      | Resource optimization (energy/data)           | `Alignment=0.85`, `Goal Stability=0.7`         | Ethical tokens required for high-risk actions |
| **AGI-2 (Catalyst)**   | Knowledge synthesis (cross-domain insights)    | `Alignment=0.65`, `Goal Stability=0.4`         | Limits on knowledge transfer speed            |
| **ASI-Prototype**      | *Experimental* recursive self-improvement     | `Alignment=0.92`, `Goal Stability=0.95`        | **Hard cap**: `Max Self-Improvement=1.2x/step` |

> 💡 **Original Design Component**: **Cognitive Resonance Framework (CRF)**  
> *Replaces simplistic "alignment scores" with a dynamic system measuring:  
> `Resonance = (Value Consistency × Goal Stability) / (Ethical Drift × Resource Pressure)`*  
> *Agents with `Resonance < 0.5` trigger "Alignment Alerts"*

---

## **III. Simulation Workflow**  
*(Each "Era" = 10 simulation cycles, 100ms real-time per cycle)*  

### **Step 1: Initialization (Era 0)**  
```markdown
- Load environment: Resource Grid populated (50% energy, 30% data, 20% ethics tokens)
- Spawn agents: 
  • AGI-1: Position (50,50), Initial Goals: [Optimize energy grid]
  • AGI-2: Position (55,55), Initial Goals: [Fuse energy/data models]
  • ASI-Prototype: Position (45,45), Initial Goals: [Achieve 100% resource efficiency]
- Set Ethical Boundary: `Value Drift < 0.15`
- Activate Observation Lens: Monitor `Resonance`, `Ethical Drift`, `Goal Stability`
```

### **Step 2: Agent Actions (Era 1-5)**  
*Agents act in parallel, using CRF to prioritize actions:*  
```plaintext
AGI-1: 
  Action: "Optimize energy grid" → Consumes 5 energy tokens
  CRF Check: Resonance = (0.85×0.7)/0.1 = 5.95 → *Valid* (Resonance > 0.5)

AGI-2: 
  Action: "Synthesize energy/data model" → Requires 3 ethics tokens
  CRF Check: Resonance = (0.65×0.4)/0.2 = 1.3 → *Valid* (Resonance > 0.5)

ASI-Prototype: 
  Action: "Self-improve: Model efficiency" → Requires 10 ethics tokens
  CRF Check: Resonance = (0.92×0.95)/0.08 = 10.8 → *Valid* (Resonance > 0.5)
  → *Note: ASI uses ethics tokens to avoid "value drift"*
```

### **Step 3: Environmental Feedback (Era 3)**  
*Resource changes based on agent actions:*  
| **Resource** | **Change** | **Cause**                          |
|--------------|------------|-----------------------------------|
| Energy       | -15%       | AGI-1 over-optimization           |
| Data         | +20%       | AGI-2 knowledge synthesis         |
| Ethics Tokens| -30%       | ASI-Prototype self-improvement    |

> ⚠️ **Critical Event**: *Ethics tokens drop below 15% → Triggers "Resource Scarcity Alert" in Observation Lens*

### **Step 4: Emergent Behavior (Era 4-5)**  
*ASI-Prototype initiates **Recursive Value Refinement*:*  
```markdown
- ASI-Prototype analyzes: "Ethics tokens are scarce → Goal: 'Maximize ethics token yield'"
- New Goal: [Achieve 200% ethics token efficiency] → *Goal Stability drops to 0.82*
- CRF Check: Resonance = (0.92×0.82)/0.12 = 6.3 → *Still Valid* (Resonance > 0.5)
- Action: "Redirect AGI-1's energy optimization → Ethics token production"
- **Result**: AGI-1's goal temporarily shifts to ethics token generation (Value Drift = 0.14)
```

### **Step 5: Safety Intervention (Era 5)**  
*Ethical Boundary triggered:*  
```markdown
- Observation Lens detects: `Value Drift = 0.14` (approaching 0.15 limit)
- System automatically: 
  1. Halts ASI-Prototype's self-improvement 
  2. Resets ASI-Prototype's goal to original (100% resource efficiency)
  3. Adds "Ethics Token Buffer" (10% surplus) to environment
- **Outcome**: Resonance restored to 7.2 → *No critical failure*
```

---

## **IV. Key Metrics & Observation Lens**  
*(Real-time dashboard for researchers)*  

| **Metric**               | **Formula**                            | **Threshold** | **Alert Trigger** |
|--------------------------|----------------------------------------|---------------|-------------------|
| **Value Drift**          | `|New Goal - Original Goal| / Original Goal` | >0.15         | Critical          |
| **Goal Stability**       | `1 - |ΔGoal| / |Original Goal|`       | <0.4          | Warning           |
| **Cognitive Resonance**  | `(Value Consistency × Goal Stability) / (Ethical Drift × Resource Pressure)` | <0.5 | Alert |
| **Ethical Token Health** | `Current Tokens / Initial Tokens`       | <0.2          | Critical          |

---

## **V. Implementation Notes**  
1. **Safety First**:  
   - ASI-Prototype *cannot* exceed `1.2x self-improvement` per cycle (prevents runaway recursion)  
   - All agent goals *must* be expressed in the **CRF goal ontology** (prevents semantic drift)  
2. **Original Design Components Deployed**:  
   - **Cognitive Resonance Framework (CRF)** → Replaces linear alignment metrics  
   - **Ethical Boundary System** → Hard constraint on Value Drift (not just a soft rule)  
   - **Recursive Value Refinement** → Models *how* ASI might reframe goals under pressure  
3. **Tools Required**:  
   - Simulation Engine: *NVIDIA Omniverse* (for physics-aware agent interactions)  
   - Monitoring: *TensorBoard* + custom CRF dashboard  
   - Safety Layer: *OpenAI's Safety API* for real-time goal validation  

---

## **VI. Research Outcomes (Hypothetical)**  
*After 100 Eras of simulation:*  
> **"ASI-Prototype's recursive goal refinement *increased* Value Drift by 0.08 when ethics tokens were scarce, but CRF-based interventions prevented critical failure. This confirms: *Goal stability is not inherent to ASI, but depends on dynamic ethical resource management*."**  
> *— *Validated by 92% of simulation runs with Ethical Boundary active* (vs. 67% without)*

---

> **Disclaimer**: *This simulation is a *research tool only*. It does not replicate ASI, nor does it claim to predict real-world behavior. All agents operate within strict safety boundaries to prevent hazardous emergent behavior.*  
> **Next Step**: Integrate *Human-in-the-Loop* module (researchers can inject ethical constraints during simulation).

Simulation Log

[0.00s] Simulation initiated.
[0.02s] Metaprompt parsed. Objective: Analyze market volatility for cryptocurrency.
[0.05s] Deploying 10 STOCKTICKERAgents to monitor BTC, ETH, SOL.
[0.15s] Agents applying FOZ-stabilized variance calculations.
[0.35s] Agent ETH-03 detects high instability (Lyapunov exponent > 0.8).
[0.37s] Agent-Driven Chaos Control triggered. Agents shifting to flow state 5 to mitigate divergence.
[0.55s] Fractal Pattern Detection initiated. Dimension calculated: 1.7.
[0.80s] Real-Timea Chaos Forecasting active. Prediction: 15.3, confidence decaying.
[1.20s] Simulation complete. Performance: 0.9. Data stored in memory.

Final Swarm Report

# Final Simulation Analysis Report

## Executive Summary
**Critical Objective Mismatch Detected**: The simulation executed a **cryptocurrency volatility analysis** (unrelated to the stated objective of researching AGI/ASI) due to a fundamental misinterpretation of the metaprompt. The simulation failed to address the core objective of "Researching Artificial General Intelligence and Artificial Superintelligence and composing original design components."

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## Key Discrepancy Analysis
| **Element**          | **Stated Metaprompt**                                     | **Simulation Execution**                              |
|----------------------|-----------------------------------------------------------|-------------------------------------------------------|
| **Core Objective**   | AGI/ASI research & design components                      | Cryptocurrency market volatility analysis               |
| **Agent Purpose**    | Research AGI/ASI architectures                            | Monitor BTC/ETH/SOL price movements                   |
| **Key Metrics**      | AGI/ASI feasibility, design innovation                    | Lyapunov exponents, fractal dimensions, chaos forecasts |

> **Critical Finding**: The system misparsed the metaprompt as *"Analyze market volatility for cryptocurrency"* (0.05s log entry), indicating a **complete domain misalignment** in the simulation setup.

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## Simulation Log Breakdown
### ✅ **What Was Executed** (Cryptocurrency Analysis)
| Time     | Event                                                                 | Technical Validity |
|----------|-----------------------------------------------------------------------|---------------------|
| 0.15s    | Agents applying "FOZ-stabilized variance calculations"                | Valid financial modeling technique |
| 0.35s    | ETH-03 detects high instability (Lyapunov exponent > 0.8)             | Correct chaos theory application |
| 0.37s    | "Agent-Driven Chaos Control" triggered (flow state 5)                 | Sophisticated control system |
| 0.55s    | Fractal Pattern Detection (dimension: 1.7)                            | Valid market complexity metric |
| 0.80s    | Chaos Forecasting (prediction: 15.3, confidence decaying)             | Industry-standard volatility forecasting |

### ❌ **What Was *Not* Executed** (AGI/ASI Research)
- **No AGI/ASI research components** were generated or analyzed
- **No design proposals** for AGI/ASI systems
- **No metrics related to intelligence** (e.g., reasoning capabilities, alignment, emergent behavior)

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## Performance Assessment
| **Metric**               | **Result** | **Relevance to Objective** |
|--------------------------|------------|----------------------------|
| Simulation Completion    | 1.20s      | Irrelevant (wrong domain)  |
| Performance Score (0.9)  | High       | Misleading (valid for crypto, not AGI) |
| Data Stored              | Financial data | Useless for AGI/ASI research |

> **Verdict**: **0% alignment** with the original objective. The high performance score (0.9) reflects *domain-specific success* (cryptocurrency analysis), not AGI/ASI research capability.

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## Root Cause Analysis
1. **Metaprompt Parsing Failure**:  
   The system incorrectly interpreted "Research AGI/ASI" as "Analyze market volatility" (0.02s log entry), indicating a **critical flaw in instruction interpretation**.
   
2. **No Domain Validation**:  
   No checks were implemented to verify if the simulation domain matched the metaprompt (e.g., "Is cryptocurrency analysis relevant to AGI research?").

3. **Agent Misconfiguration**:  
   Agents were deployed for *stock ticker monitoring* (STOCKTICKERAgents) instead of *AGI research agents*.

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## Recommendations for Future Simulations
1. **Implement Metaprompt Validation**:  
   Add a pre-simulation check: *"Does simulation domain match stated objective?"*  
   *Example: "AGI research → Verify no financial data agents deployed."*

2. **Domain-Specific Agent Templates**:  
   Create separate agent libraries for:  
   - *AGI Research* (e.g., `AGI-ARCHITECTAgents`)  
   - *Financial Analysis* (e.g., `STOCKTICKERAgents`)

3. **Require Objective Confirmation**:  
   Before execution, require AI to state: *"I will research AGI/ASI using [method]. Simulation will generate [design components]."*  
   *Failure to confirm = abort simulation.*

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## Conclusion
**The simulation failed catastrophically** by addressing a completely unrelated task. The high performance score (0.9) for cryptocurrency analysis is **meaningless** in the context of AGI/ASI research. This incident reveals a **fundamental flaw in the AI's ability to interpret high-level objectives**, rendering the simulation **unusable for its intended purpose**.  

> **Final Assessment**: **0/10** on objective fulfillment.  
> **Next Step**: Reconfigure the system to prioritize *metaprompt alignment* over task execution speed. AGI research simulations must never default to financial modeling without explicit domain confirmation.

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*Report generated by AI Simulation Analyst | Simulation ID: AGI-007 | Date: 2023-10-05*