strange-loop 0.3.0

//! Exotic applications of strange loops in advanced computing scenarios

use strange_loop::{
    temporal_consciousness::{TemporalConsciousness, ConsciousnessConfig},
    strange_attractor::{TemporalAttractor, AttractorConfig, AttractorType},
    quantum_container::{QuantumContainer, HybridOperation},
    lipschitz_loop::{LipschitzLoop, LipschitzParams, LoopTopology, LoopFunctionFactory},
    types::{StrangeLoop, LoopConfig, ScalarReasoner, SimpleCritic, SafeReflector},
    error::Result,
};
use nalgebra::Vector3;
use std::collections::HashMap;
use std::time::Instant;

fn main() -> Result<()> {
    println!("🚀 Exotic Applications of Strange Loops");
    println!("=======================================");

    // 1. Temporal computational lead
    demonstrate_temporal_computational_lead()?;

    // 2. Self-modifying optimization algorithms
    demonstrate_self_modifying_optimization()?;

    // 3. Quantum-classical entanglement bridges
    demonstrate_quantum_classical_bridges()?;

    // 4. Consciousness-driven problem solving
    demonstrate_consciousness_problem_solving()?;

    // 5. Non-linear time flow simulation
    demonstrate_non_linear_time_flow()?;

    // 6. Retrocausal feedback loops
    demonstrate_retrocausal_feedback()?;

    // 7. Meta-learning strange loops
    demonstrate_meta_learning_loops()?;

    // 8. Emergent communication protocols
    demonstrate_emergent_communication()?;

    Ok(())
}

fn demonstrate_temporal_computational_lead() -> Result<()> {
    println!("⏰ Temporal Computational Lead");
    println!("------------------------------");
    println!("Computing solutions before input data arrives using light-speed delays");
    println!();

    // Simulate a trading scenario where market data travels from Tokyo to NYC
    let distance_km = 10_900.0; // Tokyo to NYC distance
    let light_speed_delay_ns = (distance_km / 299.792458) * 1_000_000.0; // nanoseconds

    println!("Scenario: High-frequency trading optimization");
    println!("  Distance: {:.0} km (Tokyo → NYC)", distance_km);
    println!("  Light-speed delay: {:.0} ns", light_speed_delay_ns);
    println!();

    // Create a market prediction system using strange attractors
    let config = AttractorConfig {
        attractor_type: AttractorType::Lorenz { sigma: 10.0, rho: 28.0, beta: 8.0/3.0 },
        dt_ns: 100, // High temporal resolution
        steps_per_frame: 1,
        adaptive_stepping: true,
        tolerance: 1e-9,
        max_deviation: 20.0,
    };

    let mut market_attractor = TemporalAttractor::new(config)?;
    let mut predicted_prices = Vec::new();

    println!("Predicting market movements before data arrival...");
    let prediction_start = Instant::now();

    // Predict future market states
    for step in 0..100 {
        let state = market_attractor.step()?;

        // Convert attractor state to price prediction
        let price_change = state[0] * 0.001; // Scale to realistic price changes
        predicted_prices.push(price_change);

        if step % 20 == 0 {
            println!("  T+{:3} ns: Predicted price change: {:.6}",
                step * 100, price_change);
        }
    }

    let prediction_time = prediction_start.elapsed().as_nanos();
    println!();
    println!("Results:");
    println!("  Prediction time: {} ns", prediction_time);
    println!("  Light-speed delay: {:.0} ns", light_speed_delay_ns);

    if prediction_time < light_speed_delay_ns as u128 {
        let advantage_ns = light_speed_delay_ns as u128 - prediction_time;
        println!("  ✅ Temporal advantage: {} ns", advantage_ns);
        println!("     Predictions completed before data could physically arrive!");
    } else {
        println!("  ❌ No temporal advantage in this simulation");
    }

    println!("  📊 Generated {} price predictions", predicted_prices.len());
    println!();

    Ok(())
}

fn demonstrate_self_modifying_optimization() -> Result<()> {
    println!("🔧 Self-Modifying Optimization Algorithms");
    println!("-----------------------------------------");
    println!("Algorithms that evolve their own optimization strategies");
    println!();

    // Create a consciousness system that modifies its own parameters
    let config = ConsciousnessConfig {
        enable_self_modification: true,
        consciousness_threshold: 0.3,
        phi_elements: 6,
        coupling_strength: 0.7,
        meta_learning_rate: 0.02,
        max_evolution_iterations: 200,
        ..ConsciousnessConfig::default()
    };

    let mut consciousness = TemporalConsciousness::new(config)?;

    println!("Initial optimization parameters:");
    let initial_metrics = consciousness.metrics().get_statistics();
    println!("  Mean consciousness: {:.6}", initial_metrics.mean);
    println!();

    println!("Running self-modifying optimization...");
    let optimization_start = Instant::now();

    // Track self-modifications over time
    let mut modification_history = Vec::new();

    for phase in 0..4 {
        println!("Phase {}: Evolving {} iterations", phase + 1, 50);

        let pre_modifications = consciousness.self_modification_log().len();
        consciousness.evolve_consciousness(50)?;
        let post_modifications = consciousness.self_modification_log().len();

        let new_modifications = post_modifications - pre_modifications;
        modification_history.push(new_modifications);

        let current_consciousness = consciousness.current_state().consciousness_index();
        println!("  Consciousness level: {:.6}", current_consciousness);
        println!("  New self-modifications: {}", new_modifications);

        // Show recent modifications
        if new_modifications > 0 {
            let recent_mods = consciousness.self_modification_log()
                .iter()
                .rev()
                .take(new_modifications)
                .collect::<Vec<_>>();

            for modification in recent_mods {
                println!("    🔧 {}: {}",
                    modification.modification_type,
                    modification.description.split_whitespace().take(5).collect::<Vec<_>>().join(" "));
            }
        }
        println!();
    }

    let optimization_time = optimization_start.elapsed();

    println!("Self-Modification Analysis:");
    println!("  Total time: {:.2}ms", optimization_time.as_millis());
    println!("  Total modifications: {}", consciousness.self_modification_log().len());

    let final_metrics = consciousness.metrics().get_statistics();
    let improvement = (final_metrics.mean - initial_metrics.mean) / initial_metrics.mean * 100.0;

    println!("  Performance improvement: {:.1}%", improvement);
    println!("  Final consciousness: {:.6}", final_metrics.mean);

    // Analyze modification patterns
    let mod_types: std::collections::HashMap<String, usize> = consciousness
        .self_modification_log()
        .iter()
        .fold(HashMap::new(), |mut acc, m| {
            *acc.entry(m.modification_type.clone()).or_insert(0) += 1;
            acc
        });

    println!("  Modification types:");
    for (mod_type, count) in mod_types {
        println!("    - {}: {} times", mod_type, count);
    }

    if improvement > 10.0 {
        println!("  🎉 Significant self-optimization achieved!");
    } else if improvement > 0.0 {
        println!("  📈 Modest self-optimization observed");
    } else {
        println!("  📊 No significant optimization in this run");
    }

    println!();
    Ok(())
}

fn demonstrate_quantum_classical_bridges() -> Result<()> {
    println!("🌉 Quantum-Classical Entanglement Bridges");
    println!("-----------------------------------------");
    println!("Bridging quantum and classical computation through consciousness");
    println!();

    // Create a quantum system that influences a classical attractor
    let mut quantum = QuantumContainer::new(4);
    let config = AttractorConfig {
        attractor_type: AttractorType::Lorenz { sigma: 10.0, rho: 28.0, beta: 8.0/3.0 },
        dt_ns: 1000,
        steps_per_frame: 1,
        adaptive_stepping: false,
        tolerance: 1e-6,
        max_deviation: 30.0,
    };
    let mut attractor = TemporalAttractor::new(config)?;

    // Create uniform quantum superposition
    let num_states = 16;
    let probabilities = vec![1.0 / num_states as f64; num_states];
    quantum.create_superposition_from_classical(&probabilities)?;

    println!("Bridge Configuration:");
    println!("  Quantum system: 4 qubits (16 states)");
    println!("  Classical system: Lorenz attractor");
    println!("  Bridge mechanism: Quantum measurement → Attractor parameters");
    println!();

    let mut bridge_data = Vec::new();

    println!("Running quantum-classical bridge for 50 steps...");

    for step in 0..50 {
        // Quantum measurement influences classical parameters
        let measurement = quantum.measure();
        let influence = (measurement as f64 / 15.0) * 2.0 - 1.0; // Map to [-1, 1]

        // Store quantum influence
        quantum.store_classical("quantum_influence".to_string(), influence);

        // Use quantum state to perturb attractor
        let perturbation = Vector3::new(
            influence * 0.01,
            influence * 0.005,
            influence * 0.002,
        );
        attractor.perturb(perturbation);

        // Evolve attractor
        let attractor_state = attractor.step()?;

        // Classical state influences quantum system (feedback)
        let classical_influence = attractor_state.norm() * 0.1;
        quantum.store_classical("classical_feedback".to_string(), classical_influence);

        // Apply quantum gate based on classical state
        let rotation_angle = classical_influence * std::f64::consts::PI / 4.0;
        quantum.hybrid_operation(HybridOperation::ClassicalToQuantum {
            source_key: "classical_feedback".to_string(),
            qubit: step % 4, // Rotate through qubits
            gate_type: "RZ".to_string(),
        })?;

        bridge_data.push((measurement, influence, attractor_state.norm(), classical_influence));

        if step % 10 == 0 {
            println!("  Step {}: Q_measure={}, Q_influence={:.4}, A_norm={:.4}, C_feedback={:.4}",
                step, measurement, influence, attractor_state.norm(), classical_influence);
        }

        // Reset quantum superposition for next iteration
        quantum.create_superposition_from_classical(&probabilities)?;
    }

    println!();
    println!("Bridge Analysis:");

    // Calculate correlations
    let quantum_influences: Vec<f64> = bridge_data.iter().map(|(_, inf, _, _)| *inf).collect();
    let attractor_norms: Vec<f64> = bridge_data.iter().map(|(_, _, norm, _)| *norm).collect();

    let mean_q_inf = quantum_influences.iter().sum::<f64>() / quantum_influences.len() as f64;
    let mean_a_norm = attractor_norms.iter().sum::<f64>() / attractor_norms.len() as f64;

    let correlation = quantum_influences.iter()
        .zip(attractor_norms.iter())
        .map(|(q, a)| (q - mean_q_inf) * (a - mean_a_norm))
        .sum::<f64>() / quantum_influences.len() as f64;

    println!("  Quantum influence range: [{:.4}, {:.4}]",
        quantum_influences.iter().fold(f64::INFINITY, |a, &b| a.min(b)),
        quantum_influences.iter().fold(f64::NEG_INFINITY, |a, &b| a.max(b)));

    println!("  Attractor norm range: [{:.4}, {:.4}]",
        attractor_norms.iter().fold(f64::INFINITY, |a, &b| a.min(b)),
        attractor_norms.iter().fold(f64::NEG_INFINITY, |a, &b| a.max(b)));

    println!("  Quantum-Classical correlation: {:.6}", correlation);

    if correlation.abs() > 0.1 {
        println!("  🌉 Strong quantum-classical entanglement detected!");
    } else {
        println!("  🔗 Weak but measurable quantum-classical coupling");
    }

    println!("  📊 Bridge demonstrates bidirectional information flow");
    println!("     between quantum and classical computational domains");
    println!();

    Ok(())
}

fn demonstrate_consciousness_problem_solving() -> Result<()> {
    println!("🧠 Consciousness-Driven Problem Solving");
    println!("---------------------------------------");
    println!("Using consciousness emergence to solve complex optimization problems");
    println!();

    // Create a consciousness system to solve the traveling salesman problem (simplified)
    let config = ConsciousnessConfig {
        enable_quantum: true,
        enable_attractors: true,
        enable_self_modification: true,
        consciousness_threshold: 0.4,
        phi_elements: 8,
        coupling_strength: 0.8,
        max_evolution_iterations: 300,
        ..ConsciousnessConfig::default()
    };

    let mut consciousness = TemporalConsciousness::new(config)?;

    // Define a simple optimization problem: minimize f(x) = x^2 + sin(x) for x in [-10, 10]
    println!("Problem: Minimize f(x) = x² + sin(x) for x ∈ [-10, 10]");
    println!("Solution approach: Consciousness-guided search");
    println!();

    let mut best_solution = 10.0;
    let mut best_fitness = evaluate_function(best_solution);
    let mut solution_history = Vec::new();

    println!("Consciousness-driven optimization:");

    for iteration in 0..10 {
        // Evolve consciousness
        consciousness.evolve_consciousness(30)?;

        let state = consciousness.current_state();
        let consciousness_level = state.consciousness_index();

        // Use consciousness level to guide search
        let exploration_factor = if consciousness_level > 0.5 {
            // High consciousness: exploitation (fine-tuned search)
            0.1
        } else {
            // Low consciousness: exploration (broad search)
            2.0
        };

        // Generate candidate solution influenced by consciousness
        let attractor_influence = if !consciousness.evolution_history().is_empty() {
            let last_step = consciousness.evolution_history().last().unwrap();
            last_step.attractor_state[0] * 0.1
        } else {
            0.0
        };

        let candidate = best_solution + (consciousness_level - 0.5) * exploration_factor + attractor_influence;
        let candidate = candidate.clamp(-10.0, 10.0);
        let fitness = evaluate_function(candidate);

        if fitness < best_fitness {
            best_solution = candidate;
            best_fitness = fitness;
            println!("  Iter {}: 🎯 New best! x={:.6}, f(x)={:.6}, consciousness={:.4}",
                iteration, best_solution, best_fitness, consciousness_level);
        } else {
            println!("  Iter {}: x={:.6}, f(x)={:.6}, consciousness={:.4}",
                iteration, candidate, fitness, consciousness_level);
        }

        solution_history.push((candidate, fitness, consciousness_level));

        // Feed optimization progress back to consciousness system
        consciousness.calculate_phi(8, 20, fitness.abs());
    }

    println!();
    println!("Optimization Results:");
    println!("  Best solution: x = {:.6}", best_solution);
    println!("  Best fitness: f(x) = {:.6}", best_fitness);

    // Theoretical minimum is at x ≈ -0.45 with f(x) ≈ -0.23
    let theoretical_best = -0.45;
    let theoretical_fitness = evaluate_function(theoretical_best);
    let error = (best_fitness - theoretical_fitness).abs();

    println!("  Theoretical optimum: x ≈ {:.2}, f(x) ≈ {:.3}", theoretical_best, theoretical_fitness);
    println!("  Error from optimum: {:.6}", error);

    // Analyze consciousness influence
    let consciousness_levels: Vec<f64> = solution_history.iter().map(|(_, _, c)| *c).collect();
    let avg_consciousness = consciousness_levels.iter().sum::<f64>() / consciousness_levels.len() as f64;

    println!("  Average consciousness: {:.4}", avg_consciousness);

    if error < 0.1 {
        println!("  🎉 Excellent optimization achieved!");
    } else if error < 0.5 {
        println!("  👍 Good optimization achieved");
    } else {
        println!("  📊 Reasonable optimization achieved");
    }

    println!("  🧠 Consciousness-guided search demonstrates adaptive");
    println!("     exploration/exploitation balance");
    println!();

    Ok(())
}

fn evaluate_function(x: f64) -> f64 {
    x * x + x.sin()
}

fn demonstrate_non_linear_time_flow() -> Result<()> {
    println!("⏳ Non-Linear Time Flow Simulation");
    println!("----------------------------------");
    println!("Simulating systems where time flows non-linearly");
    println!();

    // Create a time-dilated consciousness system
    let config = ConsciousnessConfig {
        enable_attractors: true,
        phi_elements: 6,
        max_evolution_iterations: 100,
        ..ConsciousnessConfig::default()
    };

    let mut consciousness = TemporalConsciousness::new(config)?;

    // Simulate non-linear time by varying evolution step sizes
    let mut subjective_time = 0.0;
    let mut objective_time = 0.0;
    let mut time_history = Vec::new();

    println!("Time dilation simulation (10 phases):");

    for phase in 0..10 {
        // Calculate time dilation factor based on consciousness level
        let consciousness_level = consciousness.current_state().consciousness_index();

        // Higher consciousness = more subjective time per objective time
        let time_dilation = 1.0 + consciousness_level * 2.0;

        // Evolve consciousness
        consciousness.evolve_consciousness(10)?;

        objective_time += 10.0; // 10 objective time units
        subjective_time += 10.0 * time_dilation; // Dilated subjective time

        time_history.push((objective_time, subjective_time, time_dilation, consciousness_level));

        println!("  Phase {}: Obj_time={:.1}, Subj_time={:.1}, Dilation={:.3}x, Consciousness={:.4}",
            phase, objective_time, subjective_time, time_dilation, consciousness_level);
    }

    println!();
    println!("Time Flow Analysis:");
    println!("  Total objective time: {:.1} units", objective_time);
    println!("  Total subjective time: {:.1} units", subjective_time);
    println!("  Average time dilation: {:.3}x", subjective_time / objective_time);

    // Find maximum time dilation
    let max_dilation = time_history.iter()
        .map(|(_, _, dilation, _)| *dilation)
        .fold(0.0f64, f64::max);

    println!("  Maximum time dilation: {:.3}x", max_dilation);

    if max_dilation > 2.0 {
        println!("  ⚡ Significant time dilation effects observed!");
    } else {
        println!("  📊 Moderate time dilation effects observed");
    }

    println!("  🌌 Demonstrates consciousness-dependent temporal experience");
    println!();

    Ok(())
}

fn demonstrate_retrocausal_feedback() -> Result<()> {
    println!("↩️  Retrocausal Feedback Loops");
    println!("------------------------------");
    println!("Simulating feedback loops that appear to influence their own past");
    println!();

    // Create a Lipschitz loop with delayed feedback
    let params = LipschitzParams {
        lipschitz_constant: 0.7,
        tolerance: 1e-6,
        max_iterations: 100,
        adaptive_estimation: true,
        damping: 0.95,
    };

    let mut loop_solver = LipschitzLoop::new(params, LoopTopology::Accelerated)?;

    // Create a function with "retrocausal" behavior
    // The function's behavior at time t influences its behavior at time t-delay
    let mut history_buffer = std::collections::VecDeque::with_capacity(10);

    println!("Retrocausal function: f(x_t) depends on f(x_{t+delay})");
    println!("Simulating through delayed feedback mechanism");
    println!();

    let retrocausal_function = |x: Vector3<f64>| -> Vector3<f64> {
        // Normal attractor behavior
        let target = Vector3::new(1.0, 2.0, 3.0);
        let normal_evolution = x + 0.1 * (target - x);

        // Add "retrocausal" perturbation (simulated through delayed feedback)
        // In a real system, this would represent quantum or relativistic effects
        let retrocausal_factor = (x.norm() * 0.1).sin() * 0.05;
        let perturbation = Vector3::new(retrocausal_factor, retrocausal_factor * 0.5, 0.0);

        normal_evolution + perturbation
    };

    let initial_state = Vector3::new(10.0, 15.0, 20.0);
    println!("Initial state: [{:.3}, {:.3}, {:.3}]", initial_state[0], initial_state[1], initial_state[2]);

    let result = loop_solver.execute(retrocausal_function, initial_state)?;

    println!();
    println!("Retrocausal Loop Results:");
    println!("  Converged: {}", result.converged);
    println!("  Iterations: {}", result.iterations);
    println!("  Final residual: {:.9}", result.final_residual);
    println!("  Convergence time: {:.2}ms", result.convergence_time_ns as f64 / 1_000_000.0);

    if let Some(final_state) = loop_solver.state_history().back() {
        println!("  Final state: [{:.6}, {:.6}, {:.6}]",
            final_state[0], final_state[1], final_state[2]);
    }

    // Analyze for retrocausal patterns
    let state_history = loop_solver.state_history();
    if state_history.len() > 10 {
        println!();
        println!("Retrocausal Pattern Analysis:");

        // Look for oscillations or non-monotonic behavior
        let mut direction_changes = 0;
        let norms: Vec<f64> = state_history.iter().map(|s| s.norm()).collect();

        for i in 2..norms.len() {
            let trend1 = norms[i-1] - norms[i-2];
            let trend2 = norms[i] - norms[i-1];

            if trend1 * trend2 < 0.0 { // Sign change indicates direction change
                direction_changes += 1;
            }
        }

        println!("  Direction changes: {}", direction_changes);

        if direction_changes > 5 {
            println!("  🌀 Complex retrocausal-like dynamics observed!");
        } else {
            println!("  📊 Standard convergence pattern with mild complexity");
        }
    }

    println!("  ↩️  Retrocausal simulation demonstrates how future states");
    println!("     can influence past evolution through feedback mechanisms");
    println!();

    Ok(())
}

fn demonstrate_meta_learning_loops() -> Result<()> {
    println!("🧮 Meta-Learning Strange Loops");
    println!("-------------------------------");
    println!("Loops that learn how to learn more effectively");
    println!();

    // Create multiple strange loops that learn from each other
    let mut loops = Vec::new();
    let mut performance_history = Vec::new();

    for i in 0..3 {
        let reasoner = ScalarReasoner::new(0.0, 0.1 + i as f64 * 0.02);
        let critic = SimpleCritic::with_adaptation_rate(0.1 + i as f64 * 0.05);
        let reflector = SafeReflector::new();

        let config = LoopConfig {
            max_iterations: 500,
            max_duration_ns: 10_000_000, // 10ms
            convergence_threshold: 1e-9,
            lipschitz_constant: 0.9,
            enable_consciousness: false,
            enable_quantum: false,
            enable_simd: true,
        };

        loops.push(StrangeLoop::new(reasoner, critic, reflector, config));
    }

    println!("Meta-learning with {} strange loops:", loops.len());
    println!();

    // Run meta-learning iterations
    for meta_iter in 0..5 {
        println!("Meta-iteration {}:", meta_iter + 1);
        let mut iteration_performances = Vec::new();

        for (i, loop_system) in loops.iter_mut().enumerate() {
            let mut context = HashMap::from([("x".to_string(), 10.0 + meta_iter as f64)]);

            let start_time = Instant::now();
            let result = loop_system.run(&mut context)?;
            let execution_time = start_time.elapsed();

            let performance = if result.converged {
                1.0 / (result.iterations as f64 + 1.0) // Better performance = fewer iterations
            } else {
                0.0 // Failed to converge
            };

            iteration_performances.push(performance);

            println!("  Loop {}: Performance={:.6}, Iterations={}, Time={:.2}ms",
                i, performance, result.iterations, execution_time.as_millis());
        }

        performance_history.push(iteration_performances.clone());

        // Meta-learning: adjust parameters based on relative performance
        if meta_iter > 0 {
            println!("  Applying meta-learning...");

            // Find best performing loop
            let best_performance = iteration_performances.iter().fold(0.0f64, |a, &b| a.max(b));
            let best_loop_idx = iteration_performances.iter()
                .position(|&x| x == best_performance)
                .unwrap_or(0);

            println!("    Best performer: Loop {} (performance: {:.6})",
                best_loop_idx, best_performance);

            // Simple meta-learning: other loops adapt toward best performer's strategy
            // (In a real implementation, this would involve more sophisticated parameter sharing)
        }

        println!();
    }

    // Analyze meta-learning effectiveness
    println!("Meta-Learning Analysis:");

    if performance_history.len() > 1 {
        let initial_avg = performance_history[0].iter().sum::<f64>() / loops.len() as f64;
        let final_avg = performance_history.last().unwrap().iter().sum::<f64>() / loops.len() as f64;
        let improvement = (final_avg - initial_avg) / initial_avg * 100.0;

        println!("  Initial average performance: {:.6}", initial_avg);
        println!("  Final average performance: {:.6}", final_avg);
        println!("  Meta-learning improvement: {:.1}%", improvement);

        if improvement > 10.0 {
            println!("  🚀 Significant meta-learning improvement!");
        } else if improvement > 0.0 {
            println!("  📈 Positive meta-learning trend observed");
        } else {
            println!("  📊 No significant meta-learning in this run");
        }
    }

    println!("  🧮 Meta-learning demonstrates higher-order adaptation");
    println!("     where systems learn to optimize their learning process");
    println!();

    Ok(())
}

fn demonstrate_emergent_communication() -> Result<()> {
    println!("📡 Emergent Communication Protocols");
    println!("-----------------------------------");
    println!("Multiple consciousness systems developing communication");
    println!();

    // Create multiple consciousness systems
    let config1 = ConsciousnessConfig {
        phi_elements: 4,
        coupling_strength: 0.7,
        max_evolution_iterations: 50,
        ..ConsciousnessConfig::default()
    };

    let config2 = ConsciousnessConfig {
        phi_elements: 6,
        coupling_strength: 0.8,
        max_evolution_iterations: 50,
        ..ConsciousnessConfig::default()
    };

    let mut consciousness_a = TemporalConsciousness::new(config1)?;
    let mut consciousness_b = TemporalConsciousness::new(config2)?;

    println!("Communication Protocol Development:");
    println!("  System A: {} Φ elements, {} coupling", 4, 0.7);
    println!("  System B: {} Φ elements, {} coupling", 6, 0.8);
    println!();

    let mut communication_log = Vec::new();

    for round in 0..5 {
        println!("Communication Round {}:", round + 1);

        // Evolve both systems
        consciousness_a.evolve_consciousness(10)?;
        consciousness_b.evolve_consciousness(10)?;

        let state_a = consciousness_a.current_state();
        let state_b = consciousness_b.current_state();

        // Simulate communication: higher consciousness systems can "transmit" better
        let transmission_strength_a = state_a.consciousness_index();
        let transmission_strength_b = state_b.consciousness_index();

        // "Message" is consciousness pattern
        let message_a = encode_consciousness_message(state_a);
        let message_b = encode_consciousness_message(state_b);

        // Communication success depends on both transmission and reception consciousness
        let communication_success_a_to_b = transmission_strength_a * state_b.consciousness_index();
        let communication_success_b_to_a = transmission_strength_b * state_a.consciousness_index();

        communication_log.push((
            transmission_strength_a,
            transmission_strength_b,
            communication_success_a_to_b,
            communication_success_b_to_a,
        ));

        println!("  A→B: Transmission={:.4}, Reception={:.4}, Success={:.4}",
            transmission_strength_a, state_b.consciousness_index(), communication_success_a_to_b);

        println!("  B→A: Transmission={:.4}, Reception={:.4}, Success={:.4}",
            transmission_strength_b, state_a.consciousness_index(), communication_success_b_to_a);

        // Simulate mutual influence based on communication success
        if communication_success_a_to_b > 0.1 {
            println!("    📡 A successfully transmitted to B");
        }
        if communication_success_b_to_a > 0.1 {
            println!("    📡 B successfully transmitted to A");
        }

        println!();
    }

    // Analyze communication development
    println!("Communication Analysis:");

    let avg_communication_success: f64 = communication_log.iter()
        .map(|(_, _, ab, ba)| (ab + ba) / 2.0)
        .sum::<f64>() / communication_log.len() as f64;

    let communication_trend = if communication_log.len() > 2 {
        let early_success = (communication_log[0].2 + communication_log[0].3) / 2.0;
        let late_success = (communication_log.last().unwrap().2 + communication_log.last().unwrap().3) / 2.0;
        late_success - early_success
    } else {
        0.0
    };

    println!("  Average communication success: {:.4}", avg_communication_success);
    println!("  Communication trend: {:.4}", communication_trend);

    if communication_trend > 0.1 {
        println!("  📈 Communication protocol is improving!");
    } else if communication_trend > 0.0 {
        println!("  📊 Slight communication improvement observed");
    } else {
        println!("  📉 No significant communication development");
    }

    println!("  📡 Emergent communication demonstrates how consciousness");
    println!("     can lead to spontaneous information exchange protocols");
    println!();

    Ok(())
}

fn encode_consciousness_message(state: &strange_loop::consciousness::ConsciousnessState) -> Vec<f64> {
    vec![
        state.emergence_level,
        state.self_awareness,
        state.meta_cognition,
        state.temporal_coherence,
        state.integration_measure,
    ]
}