AgentsMCP: Agentic Neural Network Economic Model with MCP Communication

Source: R/AgentsMCP-package.R

A comprehensive implementation of the Agentic Neural Network Economic Model (ANNEM), featuring heterogeneous AI agents with neural decision-making capabilities, Model Context Protocol (MCP) communication, dynamic network formation, and empirical validation using daily stock market data.

Details

The AgentsMCP package provides tools for agent-based economic modeling, financial network analysis, and performance comparison with traditional econometric models including DSGE and VAR frameworks.

Key Features

Agent Types

The package implements six heterogeneous agent types:

  • Neural Momentum: Trend-following agents with neural network enhancement

  • Contrarian AI: Mean-reversion agents with AI-based signals

  • Fundamentalist ML: Technical analysis agents with machine learning

  • Adaptive Noise: Random strategy agents with adaptive learning

  • Social Network: Peer influence and herding behavior agents

  • Meta Learning: MAML-inspired strategy adaptation agents

Core Functionality

  • Agent-based market simulation with neural decision making

  • Dynamic network evolution and MCP communication protocols

  • Real-time market data integration from Yahoo Finance

  • Comprehensive performance analysis and benchmarking

  • Advanced visualization suite with interactive networks

  • Mathematical framework validation and empirical testing

Mathematical Framework

The implementation is based on the mathematical framework described in: "ANNEM: A Mathematical Framework for AI-MCP-Network Economic Hybrids"

Key mathematical components include:

  • Agent space definition with heterogeneous types

  • Neural policy networks with attention mechanisms

  • MCP communication protocol for inter-agent messaging

  • Dynamic network formation based on decision similarity

  • Market dynamics with price formation and spillover effects

Quick Start

# Load the package
library(AgentsMCP)

# Run a basic ANNEM analysis
results <- run_annem_analysis(
  symbols = c("AAPL", "MSFT", "GOOGL"),
  n_agents = 500,
  n_steps = 100
)

# Generate visualizations
plots <- generate_annem_report(results)

# View summary
annem_summary(results)

Advanced Usage

# Create custom market environment
market <- create_annem_market(
  n_agents = 1000,
  symbols = c("AAPL", "MSFT", "GOOGL", "TSLA", "NVDA")
)

# Run simulation with custom parameters
simulation_results <- market$run_simulation(n_steps = 250, verbose = TRUE)

# Analyze performance
agent_performance <- market$analyze_agent_performance()
network_metrics <- market$analyze_network_evolution()
benchmark_comparison <- market$compare_with_benchmarks()

# Create specific visualizations
perf_plots <- plot_agent_performance(agent_performance)
network_plots <- plot_network_evolution(network_metrics)
wealth_plots <- plot_wealth_dynamics(simulation_results)

Data Requirements

The package automatically downloads financial data using the quantmod package. For optimal results, ensure:

  • Internet connection for data retrieval

  • Valid stock symbols (Yahoo Finance format)

  • Sufficient memory for large simulations (8GB+ recommended for 1000 agents)

Performance Considerations

For large-scale simulations:

  • Use set_annem_seed() for reproducible results

  • Consider reducing n_agents or n_steps for testing

  • Monitor system resources during execution

  • Use save_results = TRUE to preserve results

References

  • Farmer, J. D., & Foley, D. (2009). The economy needs agent-based modelling. Nature, 460(7256), 685-686.

  • Jackson, M. O. (2008). Social and economic networks. Princeton university press.

  • Billio, M., Getmansky, M., Lo, A. W., & Pelizzon, L. (2012). Econometric measures of connectedness and systemic risk in the finance and insurance sectors. Journal of financial economics, 104(3), 535-559.

See also

Author

Avishek Bhandari bavisek@gmail.com