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Advanced ANNEM Modeling

Deep dive into the Agentic Neural Network Economic Model architecture

ANNEM Architecture Overview

The Agentic Neural Network Economic Model (ANNEM) represents a sophisticated framework that combines:

Agent Types and Strategies

Neural Momentum Agents

Trend-following agents that use neural networks to identify and exploit momentum patterns. They enhance traditional momentum strategies with attention mechanisms and adaptive learning.

# Create neural momentum agent
agent <- create_annem_agent("momentum_001", "neural_momentum", 1000000)

# The agent applies momentum strategy with neural enhancement
# decision = base_neural_decision * 0.7 + tanh(momentum * 10) * 0.3
Contrarian AI Agents

Mean-reversion specialists that identify oversold/overbought conditions using AI signals. They trade against prevailing trends when conditions are extreme.

# Contrarian agents reverse momentum signals
# decision = base_neural_decision * 0.6 - tanh(mean_return * 10) * 0.4
Fundamentalist ML Agents

Technical analysis experts that combine traditional indicators with machine learning. They use moving averages, price deviations, and ML-enhanced signals.

# Fundamentalist strategy with ML enhancement
# Uses SMA-20 and price deviations
# decision = base_neural_decision * 0.8 + tanh(-deviation * 5) * 0.2
Social Network Agents

Herding behavior specialists that heavily weight peer influence and network signals. They represent the social dimension of market behavior.

# Social influence weighting
social_influence <- 0.3
# decision = base_decision * (1 - social_influence) + network_signals * social_influence
Meta Learning Agents

Adaptive strategy agents inspired by Model-Agnostic Meta-Learning (MAML). They adjust their confidence and strategy based on recent performance.

# Meta-learning with performance-based confidence
# confidence = max(0.1, min(0.9, 0.5 + recent_performance * 2))
# decision = base_decision * confidence
Adaptive Noise Agents

Random strategy agents that add stochastic elements to the market. They represent unpredictable market participants and help test system robustness.

# Simple noise addition
# noise = rnorm(1, 0, 0.1)
# decision = base_decision + noise

Neural Network Architecture

Each agent employs a 3-layer neural network for decision making:

# Neural network structure
nn_weights <- list(
  W1 = matrix(rnorm(50 * 128, 0, 0.1), 50, 128),  # Input to hidden (50->128)
  b1 = rnorm(128, 0, 0.1),                         # Hidden layer 1 bias
  W2 = matrix(rnorm(128 * 64, 0, 0.1), 128, 64),  # Hidden to hidden (128->64)
  b2 = rnorm(64, 0, 0.1),                          # Hidden layer 2 bias
  W3 = matrix(rnorm(64 * 1, 0, 0.1), 64, 1),      # Hidden to output (64->1)
  b3 = rnorm(1, 0, 0.1)                            # Output bias
)

# Forward propagation with ReLU activation
h1 <- pmax(0, state_vector %*% nn_weights$W1 + nn_weights$b1)
h2 <- pmax(0, h1 %*% nn_weights$W2 + nn_weights$b2)
output <- tanh(h2 %*% nn_weights$W3 + nn_weights$b3)  # Output in (-1, 1)

State Vector Construction

The 50-dimensional input state vector combines multiple market signals:

# State vector components (total: 50 dimensions)
state_vector <- c(
  scale(prices)[1:10],        # Normalized prices (10 dims)
  returns[1:10],              # Returns (10 dims)
  volatility[1:5],            # Volatility measures (5 dims)
  network_signals,            # Network connectivity (1 dim)
  sentiment,                  # Market sentiment (1 dim)
  risk_tolerance,             # Agent risk tolerance (1 dim)
  rep(0, 22)                  # Padding to reach 50 dimensions
)

Dynamic Network Evolution

Networks evolve based on agent decision similarity:

# Network evolution algorithm
similarity_matrix <- outer(agent_decisions, agent_decisions, 
                          function(x, y) 1 - abs(x - y))

# Add connections for similar agents
if (similarity_matrix[i, j] > similarity_threshold && runif(1) < evolution_rate) {
  adj_matrix[i, j] <- adj_matrix[j, i] <- 1
}

# Remove connections for dissimilar agents
if (similarity_matrix[i, j] < (1 - similarity_threshold) && runif(1) < evolution_rate/2) {
  adj_matrix[i, j] <- adj_matrix[j, i] <- 0
}

Learning and Adaptation

Agents learn from their performance and adapt their neural networks:

# Performance-based learning
performance_gradient <- tail(performance_history, 1)
learning_rate <- 0.001

# Update neural network weights (simplified gradient descent)
nn_weights$W3 <- nn_weights$W3 + learning_rate * performance_gradient * 
                 rnorm(length(nn_weights$W3), 0, 0.01)
nn_weights$b3 <- nn_weights$b3 + learning_rate * performance_gradient * 
                 rnorm(length(nn_weights$b3), 0, 0.01)

Custom Agent Distributions

You can customize the proportion of each agent type:

# Custom agent distribution
custom_dist <- list(
  neural_momentum = 0.30,      # 30% momentum agents
  contrarian_ai = 0.25,        # 25% contrarian agents
  fundamentalist_ml = 0.20,    # 20% fundamentalist agents
  social_network = 0.15,       # 15% social agents
  meta_learning = 0.10,        # 10% meta-learning agents
  adaptive_noise = 0.00        # 0% noise agents (exclude)
)

# Run analysis with custom distribution
results <- run_annem_analysis(
  symbols = c("AAPL", "MSFT", "GOOGL"),
  n_agents = 1000,
  agent_distribution = custom_dist
)

Performance Validation

ANNEM includes comprehensive validation against mathematical framework:

# Mathematical framework validation
validation <- validate_annem_framework(market, simulation_results, agent_performance)

# Check specific validation metrics
validation[validation$Metric == "Agent Distribution Error", ]
validation[validation$Metric == "Network Density", ]
validation[validation$Metric == "Wealth Gini Coefficient", ]

Next Steps