Neutral_Player_Value = conversion_potential × conversion_cost⁻¹ × opportunity_cost

Where:
- conversion_potential ∈ [0,1] = probability of converting to allied status
- conversion_cost = strategic resources required for relationship development
- opportunity_cost = alternative uses of strategic resources

def optimize_neutral_player_strategy(neutral_players, available_resources):
    """
    Allocate strategic resources to neutral players for maximum expected value
    """
    # Calculate expected value for each neutral player
    expected_values = []
    for player in neutral_players:
        conversion_probability = assess_conversion_probability(player)
        potential_alliance_value = calculate_alliance_value(player)
        conversion_cost = estimate_conversion_cost(player)
        
        expected_value = conversion_probability * potential_alliance_value - conversion_cost
        expected_values.append((player, expected_value))
    
    # Sort by expected value (highest first)
    prioritized_players = sorted(expected_values, key=lambda x: x[1], reverse=True)
    
    # Allocate resources using knapsack optimization
    selected_players = knapsack_resource_allocation(
        prioritized_players,
        available_resources,
        conversion_cost_function
    )
    
    return selected_players

class NeutralPlayerAssessment:
    def __init__(self, target_player):
        self.target = target_player
        
    def strategic_profile_analysis(self):
        return {
            "capabilities": self.assess_strategic_capabilities(),
            "objectives": self.infer_strategic_objectives(), 
            "constraints": self.identify_strategic_constraints(),
            "network": self.map_strategic_network(),
            "reputation": self.evaluate_strategic_reputation(),
            "resources": self.assess_available_resources(),
            "decision_patterns": self.analyze_decision_patterns()
        }
    
    def conversion_feasibility_analysis(self):
        profile = self.strategic_profile_analysis()
        
        alignment_potential = calculate_objective_alignment(profile["objectives"], our_objectives)
        resource_complementarity = assess_resource_fit(profile["resources"], our_needs)
        network_synergy = evaluate_network_overlap(profile["network"], our_network)
        reputation_compatibility = check_reputation_alignment(profile["reputation"])
        
        feasibility_score = (
            alignment_potential * 0.35 +
            resource_complementarity * 0.25 +
            network_synergy * 0.25 +
            reputation_compatibility * 0.15
        )
        
        return feasibility_score

class TrustBuildingProtocol:
    def __init__(self, neutral_player):
        self.player = neutral_player
        self.trust_level = 0.1  # Start with minimal trust
        self.trust_threshold = 0.7  # Threshold for strategic alliance
        
    def execute_trust_building_sequence(self):
        stages = [
            {"name": "reliability_demonstration", "trust_gain": 0.1, "risk_level": "low"},
            {"name": "information_sharing", "trust_gain": 0.15, "risk_level": "low"},
            {"name": "small_collaboration", "trust_gain": 0.2, "risk_level": "medium"}, 
            {"name": "resource_sharing", "trust_gain": 0.25, "risk_level": "medium"},
            {"name": "strategic_vulnerability", "trust_gain": 0.3, "risk_level": "high"}
        ]
        
        for stage in stages:
            if self.trust_level >= self.trust_threshold:
                return "CONVERSION_READY"
                
            success = self.execute_trust_stage(stage)
            if success:
                self.trust_level += stage["trust_gain"]
            else:
                return f"CONVERSION_FAILED_AT_{stage['name']}"
                
        return "CONVERSION_COMPLETE" if self.trust_level >= self.trust_threshold else "INSUFFICIENT_TRUST"

def handle_conversion_failure(neutral_player, failure_stage, invested_resources):
    """
    Optimize exit strategy from failed neutral player conversion
    """
    # Assess relationship damage
    relationship_damage = assess_conversion_attempt_impact(failure_stage)
    
    if relationship_damage < "moderate":
        # Return to minimal engagement
        return set_interaction_level(neutral_player, "minimal_monitoring")
    elif relationship_damage < "severe":
        # Cooling off period then return to neutral
        return schedule_relationship_cooling_period(neutral_player, duration_months=6)
    else:
        # Relationship compromised, minimize future interaction
        return set_interaction_level(neutral_player, "avoidance_protocol")

Optimal_Neutral_Engagement = f(
    available_social_energy,
    neutral_player_tier_distribution,
    alliance_development_opportunities,
    competitive_threat_management_requirements
)

class NeutralPlayerManagement:
    def __init__(self):
        self.neutral_database = NeutralPlayerDatabase()
        self.interaction_scheduler = AutomatedInteractionScheduler()
        self.value_delivery_system = StrategicValueDeliverySystem()
        
    def automated_relationship_maintenance(self):
        for player in self.neutral_database.get_active_neutrals():
            interaction_due = self.interaction_scheduler.check_interaction_timing(player)
            
            if interaction_due:
                optimal_interaction = self.calculate_optimal_interaction(player)
                self.value_delivery_system.execute_interaction(player, optimal_interaction)
                self.interaction_scheduler.update_next_interaction(player)

class LongTermConversionMonitoring:
    def __init__(self):
        self.trigger_detection_system = ConversionTriggerDetection()
        self.neutral_player_lifecycle_tracker = LifecycleTracker()
        
    def monitor_conversion_triggers(self, neutral_player):
        current_status = self.neutral_player_lifecycle_tracker.get_status(neutral_player)
        
        trigger_probability = self.trigger_detection_system.assess_triggers([
            "career_transition", 
            "relationship_change",
            "geographic_move",
            "strategic_challenge",
            "resource_availability_change",
            "network_disruption"
        ], current_status)
        
        if trigger_probability > 0.6:
            return "CONVERSION_WINDOW_OPENING"
        elif trigger_probability > 0.3:
            return "MONITORING_INTENSIFY"
        else:
            return "CONTINUE_MINIMAL_ENGAGEMENT"

def prevent_neutral_to_adversarial_conversion(neutral_player):
    """
    Monitor and prevent negative relationship evolution
    """
    risk_factors = {
        "resource_competition": assess_resource_overlap(neutral_player),
        "network_conflicts": check_network_relationship_conflicts(neutral_player),
        "information_asymmetry": measure_information_imbalance(neutral_player),
        "reputation_risk": monitor_reputation_channels(neutral_player),
        "strategic_interference": analyze_strategy_impact_on_player(neutral_player)
    }
    
    total_risk_score = sum(risk_factors.values())
    
    if total_risk_score > 0.7:
        return execute_relationship_repair_protocol(neutral_player, risk_factors)
    elif total_risk_score > 0.4:
        return implement_preventive_measures(neutral_player, risk_factors)
    else:
        return "RELATIONSHIP_STATUS_STABLE"

class NeutralInformationSecurity:
    def __init__(self):
        self.information_classification = {
            "public": 0,
            "general": 1, 
            "strategic": 2,
            "sensitive": 3,
            "confidential": 4
        }
        
    def authorize_information_sharing(self, information, neutral_player):
        info_level = self.classify_information(information)
        player_trust = self.assess_neutral_trust_level(neutral_player)
        
        # Neutrals get limited access compared to allies
        max_neutral_access = min(2, player_trust)  # Cap at "strategic" level
        
        return "AUTHORIZED" if info_level <= max_neutral_access else "DENIED"

def balance_immediate_vs_longterm_neutral_strategy(current_resources, longterm_potential):
    """
    Optimize between immediate strategic focus and long-term neutral player investment
    """
    immediate_optimization_value = calculate_current_strategic_opportunities(current_resources)
    longterm_neutral_value = estimate_neutral_conversion_value(longterm_potential)
    
    # Discount future value based on uncertainty and time preference
    discounted_longterm_value = longterm_neutral_value * discount_factor * certainty_adjustment
    
    if immediate_optimization_value > discounted_longterm_value * 1.5:  # 50% premium for certainty
        return "FOCUS_IMMEDIATE_OPTIMIZATION"
    else:
        return "INVEST_NEUTRAL_CONVERSION"