# 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
Recursive Flow Detailed
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.