docs/IMPLEMENTATION_EXAMPLE.md

# Recursive Implementation Example

This document provides a detailed example of how the recursive cognitive architecture works in Multi-Agent Debate. We'll walk through the complete lifecycle of a market decision, from data ingestion to trade execution, highlighting the recursive patterns and interpretability mechanisms at each stage.

## Overview

<div align="center">
   <img src="assets/images/recursive_flow_detailed.png" alt="Recursive Flow Detailed" width="800"/>
</div>

In this example, we'll follow a complete decision cycle focused on analyzing Tesla (TSLA) stock, showing how multiple philosophical agents evaluate the same data through different lenses, form consensus, and generate a final decision with full attribution.

## 1. Data Ingestion

The process begins with market data ingestion from Yahoo Finance:

```python
from multi_agent_debate.market.environment import MarketEnvironment

# Initialize market environment
market = MarketEnvironment(data_source="yahoo", tickers=["TSLA"])

# Get current market data
market_data = market.get_current_market_data()
```

The market data includes:
- Price history
- Volume data
- Fundamental metrics
- Recent news sentiment
- Technical indicators

## 2. Agent-Specific Processing

Each philosophical agent processes this data through its unique cognitive lens. Let's look at three agents:

### Graham (Value) Agent

```python
# Graham Agent processing
graham_agent = GrahamAgent(reasoning_depth=3)
graham_processed = graham_agent.process_market_data(market_data)
```

The Graham agent focuses on intrinsic value calculation:

```python
# Internal implementation of Graham's intrinsic value calculation
def _calculate_intrinsic_value(self, fundamentals, ticker_data):
    eps = fundamentals.get('eps', 0)
    book_value = fundamentals.get('book_value_per_share', 0)
    growth_rate = fundamentals.get('growth_rate', 0)
    
    # Graham's formula: IV = EPS * (8.5 + 2g) * 4.4 / Y
    bond_yield = ticker_data.get('economic_indicators', {}).get('aaa_bond_yield', 0.045)
    bond_factor = 4.4 / max(bond_yield, 0.01)
    
    growth_adjusted_pe = 8.5 + (2 * growth_rate)
    earnings_value = eps * growth_adjusted_pe * bond_factor if eps > 0 else 0
    
    # Calculate book value with margin
    book_value_margin = book_value * 1.5
    
    # Use the lower of the two values for conservatism
    if earnings_value > 0 and book_value_margin > 0:
        intrinsic_value = min(earnings_value, book_value_margin)
    else:
        intrinsic_value = earnings_value if earnings_value > 0 else book_value_margin
    
    return max(intrinsic_value, 0)
```

The agent calculates a margin of safety:

```
Ticker: TSLA
Current Price: $242.15
Intrinsic Value: $180.32
Margin of Safety: -34.3% (negative margin indicates overvaluation)
Analysis: TSLA appears overvalued compared to traditional value metrics.
Recommendation: SELL
Confidence: 0.78
```

### Wood (Innovation) Agent

```python
# Wood Agent processing
wood_agent = WoodAgent(reasoning_depth=4)
wood_processed = wood_agent.process_market_data(market_data)
```

The Wood agent focuses on disruptive innovation and growth potential:

```python
# Internal implementation of growth potential analysis
def _analyze_growth_potential(self, ticker_data, market_context):
    # Analyze innovation factors
    innovation_score = self._calculate_innovation_score(ticker_data)
    
    # Analyze addressable market
    tam = self._calculate_total_addressable_market(ticker_data, market_context)
    
    # Project future growth
    growth_projection = self._project_exponential_growth(
        ticker_data, innovation_score, tam
    )
    
    return {
        "innovation_score": innovation_score,
        "total_addressable_market": tam,
        "growth_projection": growth_projection,
    }
```

The agent's analysis shows:

```
Ticker: TSLA
Innovation Score: 0.87
Total Addressable Market: $4.2T
5-Year CAGR Projection: 28.3%
Analysis: TSLA is well-positioned in multiple disruptive fields including EVs, energy storage, AI, and robotics.
Recommendation: BUY
Confidence: 0.82
```

### Dalio (Macro) Agent

```python
# Dalio Agent processing
dalio_agent = DalioAgent(reasoning_depth=3)
dalio_processed = dalio_agent.process_market_data(market_data)
```

The Dalio agent examines macroeconomic factors:

```python
# Internal implementation of macroeconomic analysis
def _analyze_macro_environment(self, ticker_data, economic_indicators):
    # Analyze interest rate impact
    interest_impact = self._calculate_interest_sensitivity(ticker_data, economic_indicators)
    
    # Analyze inflation impact
    inflation_impact = self._calculate_inflation_impact(ticker_data, economic_indicators)
    
    # Analyze growth cycle position
    cycle_position = self._determine_economic_cycle_position(economic_indicators)
    
    # Assess geopolitical risks
    geopolitical_risk = self._assess_geopolitical_risk(economic_indicators)
    
    return {
        "interest_impact": interest_impact,
        "inflation_impact": inflation_impact,
        "cycle_position": cycle_position,
        "geopolitical_risk": geopolitical_risk,
    }
```

The agent's analysis shows:

```
Ticker: TSLA
Interest Rate Sensitivity: -0.65 (high negative sensitivity)
Inflation Impact: -0.32 (moderate negative impact)
Economic Cycle Position: Late Expansion
Analysis: TSLA will face headwinds from high interest rates and potential economic slowdown.
Recommendation: HOLD
Confidence: 0.65
```

## 3. Reasoning Graph Execution

Each agent's reasoning process is executed via a LangGraph reasoning structure. Here's a simplified view of the Wood agent's reasoning graph:

```python
def _configure_reasoning_graph(self) -> None:
    """Configure the reasoning graph for disruptive innovation analysis."""
    # Add custom reasoning nodes
    self.reasoning_graph.add_node(
        "innovation_analysis",
        self._innovation_analysis
    )
    
    self.reasoning_graph.add_node(
        "growth_projection",
        self._growth_projection
    )
    
    self.reasoning_graph.add_node(
        "competition_analysis",
        self._competition_analysis
    )
    
    self.reasoning_graph.add_node(
        "valuation_adjustment",
        self._valuation_adjustment
    )
    
    # Configure reasoning flow
    self.reasoning_graph.set_entry_point("innovation_analysis")
    self.reasoning_graph.add_edge("innovation_analysis", "growth_projection")
    self.reasoning_graph.add_edge("growth_projection", "competition_analysis")
    self.reasoning_graph.add_edge("competition_analysis", "valuation_adjustment")
```

Each reasoning node executes and passes state to the next node, building up a complete reasoning trace:

```
Step 1: Innovation Analysis
- Assessed disruptive potential in key markets
- Analyzed R&D pipeline and technological moats
- Identified 4 significant innovation vectors

Step 2: Growth Projection
- Projected TAM expansion in core markets
- Calculated penetration rates and growth curves
- Estimated revenue CAGR of 28.3% over 5 years

Step 3: Competition Analysis
- Assessed competitive positioning in EV market
- Analyzed first-mover advantages in energy storage
- Identified emerging threats in autonomous driving

Step 4: Valuation Adjustment
- Applied growth-adjusted valuation metrics
- Discounted future cash flows with risk adjustment
- Compared valuation to traditional metrics
```

## 4. Signal Generation

Each agent generates investment signals based on its reasoning:

```python
# Generate signals from each agent
graham_signals = graham_agent.generate_signals(graham_processed)
wood_signals = wood_agent.generate_signals(wood_processed)
dalio_signals = dalio_agent.generate_signals(dalio_processed)
```

Each signal includes:
- Action recommendation (buy/sell/hold)
- Confidence level
- Quantity recommendation
- Complete reasoning chain
- Value basis (philosophical foundation)
- Attribution trace (causal links to evidence)

Example of Wood agent's signal:

```json
{
  "ticker": "TSLA",
  "action": "buy",
  "confidence": 0.82,
  "quantity": 41,
  "reasoning": "Tesla shows strong innovation potential across multiple verticals including EVs, energy storage, AI, and robotics. Their R&D pipeline demonstrates continued technological leadership with high growth potential in the coming decade.",
  "intent": "Capitalize on long-term disruptive innovation growth",
  "value_basis": "Disruptive innovation creates exponential growth and market expansion that traditional metrics fail to capture",
  "attribution_trace": {
    "innovation_score": 0.35,
    "growth_projection": 0.25,
    "competition_analysis": 0.20,
    "valuation_adjustment": 0.20
  },
  "drift_signature": {
    "interest_rates": -0.05,
    "regulation": -0.03,
    "competition": -0.02
  }
}
```

## 5. Meta-Agent Arbitration

The portfolio meta-agent receives signals from all philosophical agents:

```python
# Create portfolio manager (meta-agent)
portfolio = PortfolioManager(
    agents=[graham_agent, wood_agent, dalio_agent],
    arbitration_depth=2,
    show_trace=True
)

# Process market data through meta-agent
meta_result = portfolio.process_market_data(market_data)
```

### Consensus Formation

The meta-agent first attempts to find consensus on non-conflicting signals:

```python
def _consensus_formation(self, state) -> Dict[str, Any]:
    """Form consensus from agent signals."""
    # Extract signals by ticker
    ticker_signals = state.context.get("ticker_signals", {})
    
    # Form consensus for each ticker
    consensus_decisions = []
    
    for ticker, signals in ticker_signals.items():
        # Collect buy/sell/hold signals
        buy_signals = []
        sell_signals = []
        hold_signals = []
        
        for item in signals:
            signal = item.get("signal", {})
            action = signal.action.lower()
            
            if action == "buy":
                buy_signals.append((item, signal))
            elif action == "sell":
                sell_signals.append((item, signal))
            elif action == "hold":
                hold_signals.append((item, signal))
        
        # Skip if conflicting signals (handle in conflict resolution)
        if (buy_signals and sell_signals) or (not buy_signals and not sell_signals and not hold_signals):
            continue
        
        # Form consensus for non-conflicting signals
        if buy_signals:
            # Form buy consensus
            consensus = self._form_action_consensus(ticker, "buy", buy_signals)
            if consensus:
                consensus_decisions.append(consensus)
        
        elif sell_signals:
            # Form sell consensus
            consensus = self._form_action_consensus(ticker, "sell", sell_signals)
            if consensus:
                consensus_decisions.append(consensus)
        
    return {
        "context": {
            **state.context,
            "consensus_decisions": consensus_decisions,
            "consensus_tickers": [decision.get("ticker") for decision in consensus_decisions],
        },
        "output": {
            "consensus_decisions": consensus_decisions,
        }
    }
```

### Conflict Resolution

For TSLA, we have a conflict: Graham (SELL) vs. Wood (BUY) vs. Dalio (HOLD). The meta-agent resolves this conflict:

```python
def _resolve_ticker_conflict(self, ticker: str, action_signals: Dict[str, List[Tuple[Dict[str, Any], Any]]]) -> Optional[Dict[str, Any]]:
    """Resolve conflict for a specific ticker."""
    # Calculate total weight for each action
    action_weights = {}
    action_confidences = {}
    
    for action, signals in action_signals.items():
        total_weight = 0.0
        weighted_confidence = for action, signals in action_signals.items():
        total_weight = 0.0
        weighted_confidence = 0.0
        
        for item, signal in signals:
            agent_id = item.get("agent_id", "")
            
            # Skip if missing agent ID
            if not agent_id:
                continue
            
            # Get agent weight
            agent_weight = self.agent_weights.get(agent_id, 0)
            
            # Add to weighted confidence
            weighted_confidence += signal.confidence * agent_weight
            total_weight += agent_weight
        
        # Store action weight and confidence
        if total_weight > 0:
            action_weights[action] = total_weight
            action_confidences[action] = weighted_confidence / total_weight
    
    # Choose action with highest weight
    if not action_weights:
        return None
        
    best_action = max(action_weights.items(), key=lambda x: x[1])[0]
    
    # Check confidence threshold
    if action_confidences.get(best_action, 0) < self.consensus_threshold:
        return None
    
    # Get signals for best action
    best_signals = action_signals.get(best_action, [])
    
    # Form consensus for best action
    return self._form_action_consensus(ticker, best_action, best_signals)
```

In our case, after attributing current agent weights (based on historical performance):
- Graham agent: 0.25 (weight) × 0.78 (confidence) = 0.195 (weighted confidence)
- Wood agent: 0.40 (weight) × 0.82 (confidence) = 0.328 (weighted confidence)
- Dalio agent: 0.35 (weight) × 0.65 (confidence) = 0.228 (weighted confidence)

The Wood agent's BUY signal has the highest weighted confidence, so the meta-agent forms consensus around it.

### Position Sizing

The meta-agent determines position size based on confidence and attribution:

```python
def _calculate_position_size(self, ticker: str, action: str, confidence: float,
                          attribution: Dict[str, float], portfolio_value: float) -> float:
    """Calculate position size based on confidence and attribution."""
    # Base position size as percentage of portfolio
    base_size = self.min_position_size + (confidence * (self.max_position_size - self.min_position_size))
    
    # Calculate attribution-weighted size
    if attribution:
        # Calculate agent performance scores
        performance_scores = {}
        for agent_id, weight in attribution.items():
            # Find agent
            agent = None
            for a in self.agents:
                if a.id == agent_id:
                    agent = a
                    break
            
            if agent:
                # Use consistency score as proxy for performance
                performance_score = agent.state.consistency_score
                performance_scores[agent_id] = performance_score
        
        # Calculate weighted performance score
        weighted_score = 0
        total_weight = 0
        
        for agent_id, weight in attribution.items():
            if agent_id in performance_scores:
                weighted_score += performance_scores[agent_id] * weight
                total_weight += weight
        
        # Adjust base size by performance
        if total_weight > 0:
            performance_factor = weighted_score / total_weight
            base_size *= (0.5 + (0.5 * performance_factor))
    
    # Calculate currency amount
    target_size = portfolio_value * base_size
    
    return target_size
```

For TSLA:
- Base position size: 0.01 + (0.82 × (0.20 - 0.01)) = 0.165 (16.5% of portfolio)
- Adjusted for agent performance: 16.5% × 1.1 = 18.2% of portfolio
- For a $1,000,000 portfolio: $182,000 position size
- At current price of $242.15: 751 shares

### Meta Reflection

The meta-agent performs a final reflection on its decision process:

```python
def _meta_reflection(self, state) -> Dict[str, Any]:
    """Perform meta-reflection on decision process."""
    # Extract decisions
    sized_decisions = state.context.get("sized_decisions", [])
    
    # Update meta state with arbitration record
    arbitration_record = {
        "id": str(uuid.uuid4()),
        "decisions": sized_decisions,
        "timestamp": datetime.datetime.now().isoformat(),
    }
    
    self.meta_state["arbitration_history"].append(arbitration_record)
    
    # Update agent weights based on performance
    self._update_agent_weights()
    
    # Calculate meta-confidence
    meta_confidence = sum(decision.get("confidence", 0) for decision in sized_decisions) / len(sized_decisions) if sized_decisions else 0.5
    
    # Return final output
    return {
        "output": {
            "consensus_decisions": sized_decisions,
            "meta_confidence": meta_confidence,
            "agent_weights": self.agent_weights,
            "timestamp": datetime.datetime.now().isoformat(),
        },
        "confidence": meta_confidence,
    }
```

The meta-agent final reflection includes:
- Consensus tracking
- Agent weight adjustment
- Meta-confidence calculation
- Temporal memory update

## 6. Trade Execution

The final step is trade execution:

```python
# Execute trades based on consensus decisions
consensus_decisions = meta_result.get("meta_agent", {}).get("consensus_decisions", [])
execution_results = portfolio.execute_trades(consensus_decisions)
```

The execution includes:
- Position sizing
- Order placement
- Confirmation handling
- Portfolio state update

```json
{
  "trades": [
    {
      "ticker": "TSLA",
      "action": "buy",
      "quantity": 751,
      "price": 242.15,
      "cost": 181853.65,
      "timestamp": "2024-04-17T14:23:45.123456"
    }
  ],
  "errors": [],
  "portfolio_update": {
    "timestamp": "2024-04-17T14:23:45.654321",
    "portfolio_value": 1000000.00,
    "cash": 818146.35,
    "positions": {
      "TSLA": {
        "ticker": "TSLA",
        "quantity": 751,
        "entry_price": 242.15,
        "current_price": 242.15,
        "market_value": 181853.65,
        "allocation": 0.182,
        "unrealized_gain": 0.0,
        "entry_date": "2024-04-17T14:23:45.123456"
      }
    },
    "returns": {
      "total_return": 0.0,
      "daily_return": 0.0
    },
    "allocation": {
      "cash": 0.818,
      "TSLA": 0.182
    }
  }
}
```

## 7. Attribution Tracing

Throughout this process, complete attribution tracing is maintained:

```python
# Generate attribution report
attribution_report = portfolio.tracer.generate_attribution_report(meta_result.get("meta_agent", {}).get("consensus_decisions", []))
```

The attribution report shows the complete decision provenance:

```json
{
  "agent_name": "PortfolioMetaAgent",
  "timestamp": "2024-04-17T14:23:46.123456",
  "signals": 1,
  "attribution_summary": {
    "Wood": 0.45,
    "Dalio": 0.35,
    "Graham": 0.20
  },
  "confidence_summary": {
    "mean": 0.82,
    "median": 0.82,
    "min": 0.82,
    "max": 0.82
  },
  "top_factors": [
    {
      "source": "innovation_score",
      "weight": 0.35
    },
    {
      "source": "growth_projection",
      "weight": 0.25
    },
    {
      "source": "economic_cycle_position",
      "weight": 0.15
    },
    {
      "source": "competition_analysis",
      "weight": 0.10
    },
    {
      "source": "intrinsic_value_calculation",
      "weight": 0.10
    }
  ],
  "shell_patterns": [
    {
      "pattern": "v07 CIRCUIT-FRAGMENT",
      "count": 1,
      "frequency": 1.0
    }
  ]
}
```

## 8. Visualization

The system provides multiple visualization tools for interpretability:

### Consensus Graph

```python
# Generate consensus graph
consensus_graph = portfolio.visualize_consensus_graph()
```

The consensus graph shows the flow of influence between agents and decisions:

```json
{
  "nodes": [
    {
      "id": "meta",
      "label": "Portfolio Meta-Agent",
      "type": "meta",
      "size": 20
    },
    {
      "id": "agent-1",
      "label": "Graham Agent",
      "type": "agent",
      "philosophy": "Value investing focused on margin of safety",
      "size": 15,
      "weight": 0.20
    },
    {
      "id": "agent-2",
      "label": "Wood Agent",
      "type": "agent",
      "philosophy": "Disruptive innovation investing",
      "size": 15,
      "weight": 0.45
    },
    {
      "id": "agent-3",
      "label": "Dalio Agent",
      "type": "agent",
      "philosophy": "Macroeconomic-based investing",
      "size": 15,
      "weight": 0.35
    },
    {
      "id": "position-TSLA",
      "label": "TSLA",
      "type": "position",
      "size": 10,
      "value": 181853.65
    }
  ],
  "links": [
    {
      "source": "agent-1",
      "target": "meta",
      "value": 0.20,
      "type": "influence"
    },
    {
      "source": "agent-2",
      "target": "meta",
      "value": 0.45,
      "type": "influence"
    },
    {
      "source": "agent-3",
      "target": "meta",
      "value": 0.35,
      "type": "influence"
    },
    {
      "source": "meta",
      "target": "position-TSLA",
      "value": 1.0,
      "type": "allocation"
    },
    {
      "source": "agent-2",
      "target": "position-TSLA",
      "value": 0.45,
      "type": "attribution"
    },
    {
      "source": "agent-3",
      "target": "position-TSLA",
      "value": 0.35,
      "type": "attribution"
    },
    {
      "source": "agent-1",
      "target": "position-TSLA",
      "value": 0.20,
      "type": "attribution"
    }
  ],
  "timestamp": "2024-04-17T14:23:46.987654"
}
```

### Agent Conflict Map

```python
# Generate agent conflict map
conflict_map = portfolio.visualize_agent_conflict_map()
```

The conflict map visualizes the specific disagreements between agents:

```json
{
  "nodes": [
    {
      "id": "agent-1",
      "label": "Graham Agent",
      "type": "agent",
      "philosophy": "Value investing focused on margin of safety",
      "size": 15
    },
    {
      "id": "agent-2",
      "label": "Wood Agent",
      "type": "agent",
      "philosophy": "Disruptive innovation investing",
      "size": 15
    },
    {
      "id": "agent-3",
      "label": "Dalio Agent",
      "type": "agent",
      "philosophy": "Macroeconomic-based investing",
      "size": 15
    },
    {
      "id": "position-TSLA",
      "label": "TSLA",
      "type": "position",
      "size": 10
    }
  ],
  "links": [
    {
      "source": "agent-1",
      "target": "agent-2",
      "value": 1.0,
      "type": "conflict",
      "ticker": "TSLA"
    },
    {
      "source": "agent-2",
      "target": "agent-3",
      "value": 1.0,
      "type": "conflict",
      "ticker": "TSLA"
    },
    {
      "source": "agent-1",
      "target": "agent-3",
      "value": 1.0,
      "type": "conflict",
      "ticker": "TSLA"
    }
  ],
  "conflict_zones": [
    {
      "id": "conflict-1",
      "ticker": "TSLA",
      "agents": ["agent-1", "agent-2", "agent-3"],
      "resolution": "resolved",
      "timestamp": "2024-04-17T14:23:44.567890"
    }
  ],
  "timestamp": "2024-04-17T14:23:47.654321"
}
```

### Shell Failure Map

```python
# Create shell diagnostics
shell_diagnostics = ShellDiagnostics(
    agent_id="portfolio",
    agent_name="Portfolio",
    tracing_tools=TracingTools(
        agent_id="portfolio",
        agent_name="Portfolio",
        tracing_mode=TracingMode.DETAILED,
    )
)

# Create shell failure map
failure_map = ShellFailureMap()

# Analyze each agent's state for shell failures
for agent in [graham_agent, wood_agent, dalio_agent]:
    agent_state = agent.get_state_report()
    
    # Simulate shell failures based on agent state
    for shell_pattern in [ShellPattern.CIRCUIT_FRAGMENT, ShellPattern.META_FAILURE]:
        failure_data = shell_diagnostics.simulate_shell_failure(
            shell_pattern=shell_pattern,
            context=agent_state,
        )
        
        # Add to failure map
        failure_map.add_failure(
            agent_id=agent.id,
            agent_name=agent.name,
            shell_pattern=shell_pattern,
            failure_data=failure_data,
        )

# Generate visualization
shell_failure_viz = failure_map.generate_failure_map_visualization()
```

The shell failure map visualizes interpretability patterns detected in the agents:

```json
{
  "nodes": [
    {
      "id": "agent-1",
      "label": "Graham Agent",
      "type": "agent",
      "size": 15,
      "failure_count": 1
    },
    {
      "id": "agent-2",
      "label": "Wood Agent",
      "type": "agent",
      "size": 15,
      "failure_count": 2
    },
    {
      "id": "agent-3",
      "label": "Dalio Agent",
      "type": "agent",
      "size": 15,
      "failure_count": 1
    },
    {
      "id": "v07 CIRCUIT-FRAGMENT",
      "label": "CIRCUIT-FRAGMENT",
      "type": "pattern",
      "size": 10,
      "failure_count": 3
    },
    {
      "id": "v10 META-FAILURE",
      "label": "META-FAILURE",
      "type": "pattern",
      "size": 10,
      "failure_count": 1
    },
    {
      "id": "failure-1",
      "label": "Failure 3f4a9c",
      "type": "failure",
      "size": 5,
      "timestamp": "2024-04-17T14:23:48.123456"
    },
    {
      "id": "failure-2",
      "label": "Failure b7d5e2",
      "type": "failure",
      "size": 5,
      "timestamp": "2024-04-17T14:23:48.234567"
    },
    {
      "id": "failure-3",
      "label": "Failure 9c6f1a",
      "type": "failure",
      "size": 5,
      "timestamp": "2024-04-17T14:23:48.345678"
    },
    {
      "id": "failure-4",
      "label": "Failure 2e8d7f",
      "type": "failure",
      "size": 5,
      "timestamp": "2024-04-17T14:23:48.456789"
    }
  ],
  "links": [
    {
      "source": "agent-1",
      "target": "failure-1",
      "type": "agent_failure"
    },
    {
      "source": "v07 CIRCUIT-FRAGMENT",
      "target": "failure-1",
      "type": "pattern_failure"
    },
    {
      "source": "agent-2",
      "target": "failure-2",
      "type": "agent_failure"
    },
    {
      "source": "v07 CIRCUIT-FRAGMENT",
      "target": "failure-2",
      "type": "pattern_failure"
    },
    {
      "source": "agent-2",
      "target": "failure-3",
      "type": "agent_failure"
    },
    {
      "source": "v10 META-FAILURE",
      "target": "failure-3",
      "type": "pattern_failure"
    },
    {
      "source": "agent-3",
      "target": "failure-4",
      "type": "agent_failure"
    },
    {
      "source": "v07 CIRCUIT-FRAGMENT",
      "target": "failure-4",
      "type": "pattern_failure"
    }
  ],
  "timestamp": "2024-04-17T14:23:49.000000"
}
```

## 9. Agent Memory & Learning

After each trading cycle, agents update their internal state:

```python
# Update agent states based on market feedback
market_feedback = {
    'portfolio_value': execution_results['portfolio_update']['portfolio_value'],
    'performance': {'TSLA': 0.02},  # Example: 2% return
    'decisions': consensus_decisions,
    'avg_confidence': 0.82,
}

# Update each agent's state
for agent in [graham_agent, wood_agent, dalio_agent]:
    agent.update_state(market_feedback)
```

Each agent processes the feedback differently based on its philosophy:

### Wood Agent Memory Update

```python
def _update_beliefs(self, market_feedback: Dict[str, Any]) -> None:
    """Update agent's belief state based on market feedback."""
    # Extract relevant signals
    if 'performance' in market_feedback:
        performance = market_feedback['performance']
        
        # Record decision outcomes
        if 'decisions' in market_feedback:
            for decision in market_feedback['decisions']:
                self.state.decision_history.append({
                    'decision': decision,
                    'outcome': performance.get(decision.get('ticker'), 0),
                    'timestamp': datetime.datetime.now()
                })
                
                # For Wood Agent, reinforce innovation beliefs on positive outcomes
                if performance.get(decision.get('ticker'), 0) > 0:
                    ticker = decision.get('ticker')
                    # Strengthen innovation belief
                    current_belief = self.state.belief_state.get(f"{ticker}_innovation", 0.5)
                    self.state.belief_state[f"{ticker}_innovation"] = min(1.0, current_belief + 0.05)
                    
                    # Update industry trend belief
                    industry = self._get_ticker_industry(ticker)
                    if industry:
                        industry_belief = self.state.belief_state.get(f"{industry}_trend", 0.5)
                        self.state.belief_state[f"{industry}_trend"] = min(1.0, industry_belief + 0.03)
        
        # Update general belief state based on performance
        for ticker, perf in performance.items():
            general_belief_key = f"{ticker}_general"
            current_belief = self.state.belief_state.get(general_belief_key, 0.5)
            
            # Wood Agent weights positive outcomes more heavily for innovative companies
            if self._is_innovative_company(ticker):
                update_weight = 0.3  # Higher weight for innovative companies
            else:
                update_weight = 0.1  # Lower weight for traditional companies
            
            # Update belief
            updated_belief = current_belief * (1 - update_weight) + np.tanh(perf) * update_weight
            self.state.belief_state[general_belief_key] = updated_belief
            
            # Track belief drift
            if general_belief_key in self.state.belief_state:
                drift = updated_belief - current_belief
                self.state.drift_vector[general_belief_key] = drift
                
                # Wood Agent's drift pattern analysis
                self._analyze_drift_pattern(ticker, drift)
```

## 10. Command Interface

Throughout the system, the symbolic command interface enables deeper introspection:

```python
# Get a reflection trace from the Graham agent
reflection_trace = graham_agent.execute_command(
    command="reflect.trace",
    depth=3
)

# Generate signals from alternative sources
alt_signals = wood_agent.execute_command(
    command="fork.signal",
    source="beliefs"
)

# Check for decision collapse
collapse_check = dalio_agent.execute_command(
    command="collapse.detect",
    threshold=0.7,
    reason="consistency"
)

# Attribute weight to a justification
attribution = portfolio.execute_command(
    command="attribute.weight",
    justification="Tesla's innovation in AI and robotics represents a paradigm shift that traditional valuation metrics fail to capture."
)

# Track belief drift
drift_observation = wood_agent.execute_command(
    command="drift.observe",
    vector={"TSLA_innovation": 0.05, "AI_trend": 0.03, "EV_market": 0.02},
    bias=0.01
)
```

These commands form the foundation of the system's interpretability architecture, enabling detailed tracing and analysis of decision processes.

## Conclusion

This example demonstrates the recursive cognitive architecture of Multi-Agent Debate in action. From market data ingestion to trade execution, the system maintains complete transparency and interpretability through:

1. Agent-specific cognitive lenses
2. Recursive reasoning graphs
3. Attribution tracing
4. Meta-agent arbitration
5. Position sizing
6. Trade execution
7. Memory and learning

Each component is designed to enable deeper introspection into the decision-making process, creating a truly transparent and interpretable multi-agent market cognition system.

The symbolic command interface and visualization tools provide multiple ways to understand and analyze the system's behavior, making it both effective and explainable.