Information_Completeness = 1 - (unknown_utilities + unknown_capabilities + unknown_intentions) / 3

Strategic_Uncertainty = information_incompleteness × potential_impact × interaction_frequency

Where: Information_Completeness ∈ [0,1] and Strategic_Uncertainty determines resource allocation

class UnknownPlayerIntelligence:
    def __init__(self, unknown_player):
        self.target = unknown_player
        self.information_categories = [
            "strategic_capabilities",
            "resource_base", 
            "network_connections",
            "strategic_objectives",
            "decision_patterns",
            "risk_tolerance",
            "time_preferences",
            "reputation_status"
        ]
        
    def systematic_intelligence_gathering(self):
        """
        Systematic approach to reducing information asymmetry
        """
        intelligence_plan = {}
        
        for category in self.information_categories:
            intelligence_plan[category] = {
                "direct_observation": self.plan_direct_observation(category),
                "network_intelligence": self.plan_network_intelligence(category),
                "behavioral_analysis": self.plan_behavioral_analysis(category),
                "pattern_recognition": self.plan_pattern_recognition(category),
                "verification_methods": self.plan_verification_methods(category)
            }
            
        return self.execute_intelligence_plan(intelligence_plan)

class BayesianUnknownPlayerStrategy:
    def __init__(self, unknown_player):
        self.player = unknown_player
        self.prior_beliefs = self.initialize_prior_beliefs()
        self.strategy_hypotheses = self.generate_strategy_hypotheses()
        
    def update_strategy_beliefs(self, new_evidence):
        """
        Bayesian updating of strategic beliefs based on observed behavior
        """
        # Calculate likelihood of evidence under each hypothesis
        likelihoods = {}
        for hypothesis in self.strategy_hypotheses:
            likelihoods[hypothesis] = self.calculate_evidence_likelihood(
                new_evidence, 
                hypothesis
            )
            
        # Update beliefs using Bayes' theorem
        updated_beliefs = {}
        total_evidence_probability = 0
        
        for hypothesis in self.strategy_hypotheses:
            # P(hypothesis | evidence) = P(evidence | hypothesis) * P(hypothesis) / P(evidence)
            numerator = likelihoods[hypothesis] * self.prior_beliefs[hypothesis]
            total_evidence_probability += numerator
            updated_beliefs[hypothesis] = numerator
            
        # Normalize probabilities
        for hypothesis in updated_beliefs:
            updated_beliefs[hypothesis] /= total_evidence_probability
            
        self.prior_beliefs = updated_beliefs
        return updated_beliefs
        
    def select_optimal_strategy(self):
        """
        Choose strategy that maximizes expected utility across all hypotheses
        """
        strategy_expected_utilities = {}
        
        for strategy in self.available_strategies:
            expected_utility = 0
            for hypothesis in self.strategy_hypotheses:
                hypothesis_probability = self.prior_beliefs[hypothesis]
                strategy_utility = self.calculate_strategy_utility(strategy, hypothesis)
                expected_utility += hypothesis_probability * strategy_utility
                
            strategy_expected_utilities[strategy] = expected_utility
            
        optimal_strategy = max(strategy_expected_utilities.items(), 
                              key=lambda x: x[1])
        
        return optimal_strategy

def generate_player_hypotheses(unknown_player, available_evidence):
    """
    Create multiple strategic hypotheses about unknown player
    """
    base_hypotheses = [
        "potential_ally",      # Utility alignment possible
        "potential_adversary", # Competitive relationship likely
        "neutral_permanent",   # No strategic relationship likely
        "strategic_operator",  # Sophisticated game theory player
        "naive_player",       # Non-strategic decision maker
        "resource_rich",      # High strategic capability
        "resource_poor",      # Limited strategic capability
        "network_central",    # High network influence
        "network_peripheral"   # Limited network influence
    ]
    
    # Generate combination hypotheses
    combination_hypotheses = []
    for i in range(len(base_hypotheses)):
        for j in range(i+1, len(base_hypotheses)):
            if not are_contradictory(base_hypotheses[i], base_hypotheses[j]):
                combination_hypotheses.append(
                    (base_hypotheses[i], base_hypotheses[j])
                )
    
    # Assign prior probabilities based on base rates and available evidence
    hypothesis_priors = assign_prior_probabilities(
        base_hypotheses + combination_hypotheses,
        available_evidence,
        base_rate_statistics
    )
    
    return hypothesis_priors

class UnknownPlayerRiskManagement:
    def __init__(self):
        self.risk_categories = [
            "information_security_risk",
            "strategic_exploitation_risk", 
            "reputation_damage_risk",
            "resource_loss_risk",
            "network_compromise_risk"
        ]
        
    def calculate_interaction_risk(self, unknown_player, interaction_type):
        """
        Assess risk of strategic interaction under uncertainty
        """
        base_risk = self.assess_base_interaction_risk(interaction_type)
        uncertainty_multiplier = self.calculate_uncertainty_multiplier(unknown_player)
        
        category_risks = {}
        for category in self.risk_categories:
            category_risk = base_risk[category] * uncertainty_multiplier
            category_risks[category] = category_risk
            
        total_risk = self.aggregate_risk_categories(category_risks)
        
        return {
            "total_risk": total_risk,
            "category_breakdown": category_risks,
            "risk_mitigation_strategies": self.generate_mitigation_strategies(category_risks)
        }
        
    def implement_uncertainty_safeguards(self, unknown_player, interaction_plan):
        """
        Implement protective measures for unknown player interactions
        """
        safeguards = {
            "information_compartmentalization": self.compartmentalize_sensitive_information(),
            "gradual_engagement": self.design_graduated_engagement_protocol(),
            "exit_strategies": self.prepare_relationship_exit_strategies(),
            "verification_systems": self.establish_information_verification(),
            "contingency_planning": self.develop_contingency_plans()
        }
        
        return self.apply_safeguards_to_interaction(interaction_plan, safeguards)

def select_robust_strategy(unknown_player, strategy_options, scenario_probabilities):
    """
    Choose strategy with best worst-case performance across scenarios
    """
    strategy_performances = {}
    
    for strategy in strategy_options:
        scenario_outcomes = []
        
        for scenario, probability in scenario_probabilities.items():
            outcome = simulate_strategy_outcome(strategy, scenario, unknown_player)
            scenario_outcomes.append(outcome)
            
        # Robust strategy selection criteria
        worst_case = min(scenario_outcomes)
        expected_value = sum(outcome * scenario_probabilities[scenario] 
                           for scenario, outcome in zip(scenario_probabilities.keys(), scenario_outcomes))
        outcome_variance = calculate_variance(scenario_outcomes, expected_value)
        
        # Robust strategy score balances worst-case protection with expected value
        robust_score = (
            worst_case * 0.4 +          # Protect against worst case
            expected_value * 0.5 +      # Maximize expected outcome
            (-outcome_variance) * 0.1   # Prefer lower variance
        )
        
        strategy_performances[strategy] = {
            "robust_score": robust_score,
            "worst_case": worst_case,
            "expected_value": expected_value,
            "variance": outcome_variance
        }
    
    optimal_strategy = max(strategy_performances.items(), 
                          key=lambda x: x[1]["robust_score"])
    
    return optimal_strategy

class UnknownPlayerOptions:
    def __init__(self, unknown_player):
        self.player = unknown_player
        self.option_types = [
            "alliance_option",       # Option to form strategic alliance
            "information_option",   # Option to access their information network
            "resource_option",      # Option to access their resources
            "exit_option",         # Option to end relationship
            "escalation_option",   # Option to deepen strategic relationship
            "de_escalation_option" # Option to reduce strategic involvement
        ]
        
    def value_strategic_options(self):
        """
        Calculate value of maintaining strategic options with unknown players
        """
        option_values = {}
        
        for option_type in self.option_types:
            # Black-Scholes-style option valuation adapted for strategic relationships
            current_relationship_value = self.assess_current_relationship_value()
            option_strike_price = self.calculate_option_exercise_cost(option_type)
            time_to_expiration = self.estimate_option_expiration_time(option_type)
            volatility = self.assess_relationship_volatility()
            
            option_value = self.strategic_option_value(
                current_relationship_value,
                option_strike_price,
                time_to_expiration,
                volatility
            )
            
            option_values[option_type] = option_value
            
        return option_values
        
    def optimal_option_portfolio(self):
        """
        Determine optimal combination of strategic options to maintain
        """
        option_values = self.value_strategic_options()
        maintenance_costs = self.calculate_option_maintenance_costs()
        
        # Select options where value exceeds maintenance cost with sufficient margin
        optimal_options = []
        for option_type, value in option_values.items():
            if value > maintenance_costs[option_type] * 1.2:  # 20% margin for uncertainty
                optimal_options.append(option_type)
                
        return optimal_options

class InformationFusionEngine:
    def __init__(self):
        self.information_sources = [
            "direct_observation",
            "network_intelligence", 
            "behavioral_analysis",
            "pattern_matching",
            "analogical_reasoning"
        ]
        self.confidence_weights = self.calibrate_source_reliability()
        
    def fuse_intelligence_sources(self, unknown_player, raw_intelligence):
        """
        Combine information from multiple sources with reliability weighting
        """
        fused_intelligence = {}
        
        for intelligence_category in raw_intelligence:
            source_estimates = raw_intelligence[intelligence_category]
            
            # Weight estimates by source reliability
            weighted_estimates = []
            total_weight = 0
            
            for source, estimate in source_estimates.items():
                if source in self.confidence_weights:
                    weight = self.confidence_weights[source]
                    weighted_estimates.append(estimate * weight)
                    total_weight += weight
                    
            # Calculate confidence-weighted average
            if total_weight > 0:
                fused_estimate = sum(weighted_estimates) / total_weight
                confidence_level = self.calculate_fusion_confidence(source_estimates, total_weight)
                
                fused_intelligence[intelligence_category] = {
                    "estimate": fused_estimate,
                    "confidence": confidence_level,
                    "source_agreement": self.measure_source_agreement(source_estimates)
                }
                
        return fused_intelligence

def generate_unknown_player_scenarios(unknown_player, time_horizon):
    """
    Generate multiple strategic scenarios for unknown player evolution
    """
    base_scenarios = [
        "becomes_strong_ally",
        "becomes_weak_ally", 
        "remains_neutral",
        "becomes_weak_adversary",
        "becomes_strong_adversary",
        "exits_network",
        "becomes_strategic_operator",
        "reveals_hidden_capabilities"
    ]
    
    scenario_probabilities = estimate_scenario_probabilities(
        unknown_player,
        base_scenarios,
        time_horizon
    )
    
    # Generate detailed scenario narratives
    detailed_scenarios = {}
    for scenario in base_scenarios:
        detailed_scenarios[scenario] = {
            "probability": scenario_probabilities[scenario],
            "strategic_implications": analyze_strategic_implications(scenario, unknown_player),
            "optimal_response_strategy": design_response_strategy(scenario, unknown_player),
            "early_warning_signals": identify_scenario_indicators(scenario, unknown_player),
            "contingency_plans": develop_scenario_contingencies(scenario, unknown_player)
        }
        
    return detailed_scenarios

class UnknownPlayerLearningSystem:
    def __init__(self):
        self.prediction_accuracy_tracker = PredictionAccuracyTracker()
        self.strategy_effectiveness_tracker = StrategyEffectivenessTracker()
        self.model_adaptation_engine = ModelAdaptationEngine()
        
    def continuous_learning_loop(self, unknown_player):
        """
        Continuously refine unknown player models based on new evidence
        """
        while interaction_continues(unknown_player):
            # Make predictions about player behavior
            predictions = self.generate_behavior_predictions(unknown_player)
            
            # Observe actual behavior
            observed_behavior = self.observe_player_behavior(unknown_player)
            
            # Update prediction accuracy metrics
            prediction_accuracy = self.prediction_accuracy_tracker.update(
                predictions, 
                observed_behavior
            )
            
            # Evaluate strategy effectiveness
            strategy_effectiveness = self.strategy_effectiveness_tracker.evaluate(
                our_strategic_actions,
                observed_outcomes
            )
            
            # Adapt models based on learning
            if prediction_accuracy < accuracy_threshold:
                self.model_adaptation_engine.update_prediction_model(
                    unknown_player,
                    prediction_errors=self.calculate_prediction_errors(predictions, observed_behavior)
                )
                
            if strategy_effectiveness < effectiveness_threshold:
                self.model_adaptation_engine.update_strategy_selection_model(
                    unknown_player,
                    strategy_outcomes=self.analyze_strategy_outcomes()
                )
                
            # Adjust strategic approach based on learning
            updated_strategy = self.generate_updated_strategy(unknown_player)
            
            sleep(monitoring_interval)
            
        return self.compile_final_player_assessment(unknown_player)

def assess_classification_readiness(unknown_player, accumulated_intelligence):
    """
    Determine when sufficient information exists to classify unknown player
    """
    information_completeness = {
        "strategic_capabilities": accumulated_intelligence["capabilities"]["completeness"],
        "strategic_objectives": accumulated_intelligence["objectives"]["completeness"],
        "resource_base": accumulated_intelligence["resources"]["completeness"],
        "network_position": accumulated_intelligence["network"]["completeness"],
        "decision_patterns": accumulated_intelligence["patterns"]["completeness"],
        "risk_tolerance": accumulated_intelligence["risk"]["completeness"]
    }
    
    # Weight information categories by strategic importance
    weighted_completeness = (
        information_completeness["strategic_capabilities"] * 0.25 +
        information_completeness["strategic_objectives"] * 0.25 +
        information_completeness["resource_base"] * 0.20 +
        information_completeness["network_position"] * 0.15 +
        information_completeness["decision_patterns"] * 0.10 +
        information_completeness["risk_tolerance"] * 0.05
    )
    
    classification_confidence = calculate_classification_confidence(
        accumulated_intelligence,
        information_completeness
    )
    
    if weighted_completeness > 0.75 and classification_confidence > 0.8:
        return {
            "ready_for_classification": True,
            "recommended_classification": determine_player_classification(accumulated_intelligence),
            "confidence_level": classification_confidence
        }
    else:
        return {
            "ready_for_classification": False,
            "information_gaps": identify_critical_information_gaps(information_completeness),
            "additional_intelligence_needed": recommend_intelligence_priorities(information_completeness)
        }

def initialize_classified_relationship(formerly_unknown_player, classification):
    """
    Transition from unknown player protocols to classified player strategies
    """
    relationship_strategies = {
        "allied": initialize_alliance_development_protocol,
        "neutral": initialize_neutral_engagement_protocol,
        "adversarial": initialize_competitive_positioning_protocol
    }
    
    # Apply accumulated intelligence to relationship strategy
    accumulated_insights = compile_intelligence_insights(formerly_unknown_player)
    
    # Design relationship strategy based on classification and insights
    relationship_strategy = relationship_strategies[classification](
        formerly_unknown_player,
        accumulated_insights
    )
    
    # Account for intelligence-gathering investment in relationship valuation
    intelligence_sunk_costs = calculate_intelligence_investment(formerly_unknown_player)
    
    # Adjust relationship expectations based on information gathering costs
    adjusted_strategy = adjust_for_sunk_intelligence_costs(
        relationship_strategy,
        intelligence_sunk_costs
    )
    
    return execute_relationship_transition(formerly_unknown_player, adjusted_strategy)

class InformationCurrencySystem:
    def __init__(self):
        self.information_exchange_rates = InformationExchangeRates()
        self.information_portfolio = InformationPortfolioManager()
        
    def optimize_information_trading(self, unknown_player):
        """
        Optimize information exchange for maximum intelligence gain
        """
        # Assess information we need vs. information they likely want
        our_information_needs = assess_intelligence_gaps(unknown_player)
        their_likely_needs = infer_their_information_needs(unknown_player)
        
        # Find optimal information trades
        potential_trades = identify_mutually_beneficial_information_exchanges(
            our_information_assets,
            their_likely_information_assets,
            our_information_needs,
            their_likely_needs
        )
        
        # Optimize trade sequences for maximum information gain
        optimal_trade_sequence = optimize_information_trade_sequence(
            potential_trades,
            strategic_value_weights
        )
        
        return execute_information_trading_strategy(optimal_trade_sequence)

def optimize_unknown_player_portfolio(unknown_players, available_intelligence_resources):
    """
    Optimize resource allocation across multiple unknown player investigations
    """
    # Calculate expected information value for each unknown player
    expected_values = {}
    for player in unknown_players:
        potential_alliance_value = estimate_potential_alliance_value(player)
        potential_threat_cost = estimate_potential_threat_cost(player)
        information_gathering_cost = estimate_intelligence_cost(player)
        
        expected_value = (
            potential_alliance_value * probability_of_alliance(player) -
            potential_threat_cost * probability_of_threat(player) -
            information_gathering_cost
        )
        
        expected_values[player] = expected_value
        
    # Allocate resources using portfolio optimization
    optimal_allocation = solve_intelligence_portfolio_optimization(
        expected_values,
        available_intelligence_resources,
        correlation_matrix=calculate_unknown_player_correlations(unknown_players)
    )
    
    return optimal_allocation

def account_for_investigation_effects(unknown_player, intelligence_strategy):
    """
    Adjust intelligence strategy to account for investigation's effect on relationship
    """
    investigation_effects = {
        "relationship_acceleration": estimate_attention_effect(intelligence_strategy),
        "capability_revelation": assess_capability_signaling(intelligence_strategy),
        "interest_signaling": evaluate_interest_signaling(intelligence_strategy),
        "reciprocity_expectations": predict_reciprocity_expectations(intelligence_strategy)
    }
    
    # Adjust intelligence strategy to optimize for both information gain and relationship effects
    optimized_strategy = optimize_for_dual_objectives(
        intelligence_strategy,
        investigation_effects,
        strategic_relationship_objectives
    )
    
    return optimized_strategy