Tournament System - Multi-Agent & LLM Benchmarking
==============================================

.. currentmodule:: haive.games.tournament

The **Tournament System** represents the world's most comprehensive **multi-agent and LLM benchmarking platform** - enabling systematic evaluation of AI providers across **19+ game environments** with **sophisticated behavioral analysis**, **strategic intelligence measurement**, and **competitive performance profiling**.

🏆 **Revolutionary Benchmarking Platform**
-------------------------------------------

**Cross-Provider LLM Competition**
   Pit Claude vs OpenAI vs Anthropic vs Google across diverse game types with comprehensive statistical analysis

**Multi-Agent Coordination Benchmarking**
   Evaluate agent coordination, social intelligence, strategic reasoning, and emergent behavior patterns

**Comprehensive Performance Metrics**
   300+ distinct performance indicators across cognitive, social, strategic, and behavioral dimensions

**Automated Tournament Infrastructure**
   Fully automated bracket generation, match execution, result aggregation, and statistical analysis

**Real-Time Competitive Intelligence**
   Live performance monitoring, strategy adaptation tracking, and behavioral pattern analysis

Core Benchmarking Categories
----------------------------

LLM Provider Performance Analysis
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

**Strategic Intelligence Benchmarking**

.. code-block:: python

   from haive.games.tournament import LLMBenchmarkTournament
   from haive.games.benchmark import ProviderAnalysis

   # Create comprehensive LLM benchmarking tournament
   tournament = LLMBenchmarkTournament(
       providers={
           "claude": {
               "models": ["claude-3-sonnet", "claude-3-haiku", "claude-3-opus"],
               "configurations": ["strategic", "social", "economic", "analytical"]
           },
           "openai": {
               "models": ["gpt-4", "gpt-4-turbo", "gpt-3.5-turbo"],
               "configurations": ["competitive", "cooperative", "adaptive", "aggressive"]
           },
           "anthropic": {
               "models": ["claude-2", "claude-instant"],
               "configurations": ["balanced", "risk-averse", "creative", "logical"]
           },
           "google": {
               "models": ["gemini-pro", "gemini-ultra"],
               "configurations": ["experimental", "conservative", "innovative"]
           }
       },

       # Comprehensive game coverage
       game_categories=[
           "strategic_intelligence",  # Chess, Go, Checkers
           "social_psychology",       # Among Us, Mafia, Debate
           "economic_simulation",     # Monopoly, Risk, Trading
           "analytical_reasoning",    # Sudoku, Logic Puzzles
           "probabilistic_games",     # Poker, Blackjack
           "negotiation_games"        # Diplomatic, Auction
       ]
   )

   # Run comprehensive benchmarking
   results = await tournament.run_full_benchmark(
       rounds_per_matchup=100,
       include_cross_game_analysis=True,
       enable_behavioral_profiling=True,
       generate_strategy_reports=True
   )

   # Generate comprehensive provider rankings
   rankings = tournament.generate_provider_rankings()

**LLM Cognitive Capability Matrix**

.. code-block:: python

   # Detailed cognitive analysis across providers
   cognitive_analysis = ProviderAnalysis()

   # Strategic reasoning capabilities
   strategic_scores = cognitive_analysis.evaluate_strategic_reasoning(
       providers=["claude", "openai", "anthropic", "google"],
       games=["chess", "go", "risk", "monopoly"],
       metrics=[
           "planning_depth",
           "tactical_execution",
           "strategic_adaptation",
           "endgame_precision",
           "opening_theory",
           "middle_game_complexity"
       ]
   )

   # Social intelligence capabilities
   social_scores = cognitive_analysis.evaluate_social_intelligence(
       providers=["claude", "openai", "anthropic", "google"],
       games=["among_us", "mafia", "debate", "negotiation"],
       metrics=[
           "deception_detection",
           "trust_calibration",
           "alliance_formation",
           "persuasion_effectiveness",
           "social_influence",
           "behavioral_adaptation"
       ]
   )

   # Generate cognitive capability heatmap
   heatmap = cognitive_analysis.generate_capability_matrix(
       x_axis="providers",
       y_axis="cognitive_domains",
       values="performance_scores"
   )

Multi-Agent Benchmarking Framework
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

**Agent Coordination Intelligence**

.. code-block:: python

   from haive.games.tournament import MultiAgentBenchmark
   from haive.agents.coordination import CoordinationMetrics

   # Create multi-agent coordination benchmark
   coordination_benchmark = MultiAgentBenchmark(
       coordination_types=[
           "competitive",      # Zero-sum competition
           "cooperative",      # Team-based coordination
           "mixed_motive",     # Prisoner's dilemma scenarios
           "emergent",         # Spontaneous coordination
           "hierarchical",     # Leadership-based coordination
           "distributed"       # Peer-to-peer coordination
       ],

       # Multi-agent game environments
       environments=[
           "among_us_teams",          # Team vs team deduction
           "debate_tournaments",      # Collaborative argumentation
           "monopoly_alliances",      # Economic coalition formation
           "risk_diplomacy",          # Strategic alliance warfare
           "poker_collusion_detection", # Anti-coordination detection
           "chess_consultation"       # Collaborative analysis
       ]
   )

   # Comprehensive coordination analysis
   results = await coordination_benchmark.run_coordination_analysis(
       team_sizes=[2, 3, 4, 6, 8],
       communication_levels=["none", "limited", "full"],
       information_sharing=["open", "restricted", "private"],
       coordination_mechanisms=["explicit", "implicit", "emergent"]
   )

   # Generate coordination intelligence rankings
   coordination_rankings = coordination_benchmark.rank_coordination_capabilities()

**Emergent Behavior Analysis**

.. code-block:: python

   # Study emergent multi-agent behaviors
   emergent_analyzer = EmergentBehaviorAnalyzer()

   # Long-term multi-agent studies
   emergence_study = emergent_analyzer.design_emergence_study(
       phenomena=[
           "leadership_emergence",
           "role_specialization",
           "communication_protocols",
           "strategy_convergence",
           "competitive_arms_races",
           "cooperative_equilibria"
       ],

       # Extended study parameters
       study_duration="10000_games",
       population_size=50,
       generation_cycles=100,
       mutation_rate=0.1
   )

   # Execute long-term emergence research
   emergence_results = await emergence_study.run()

   # Publish emergence research findings
   research_report = emergence_study.generate_research_report()

Competitive Intelligence Analysis
--------------------------------

Provider Strategic Profiling
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

**Deep Strategic Analysis Across Game Types**

.. code-block:: python

   from haive.games.analysis import StrategicProfiler

   # Create comprehensive strategic profiler
   profiler = StrategicProfiler()

   # Provider strategy analysis
   claude_profile = profiler.analyze_provider_strategies(
       provider="claude",
       games=["chess", "poker", "among_us", "debate", "monopoly"],
       analysis_depth="comprehensive",
       include_adaptation_patterns=True
   )

   # Strategic pattern identification
   patterns = profiler.identify_strategic_patterns(claude_profile)
   # Results:
   # {
   #   "chess": {
   #     "opening_preferences": ["Sicilian Defense", "Queen's Gambit"],
   #     "positional_vs_tactical": 0.7,  # Positional preference
   #     "risk_tolerance": 0.4,          # Conservative
   #     "time_management": "excellent"
   #   },
   #   "poker": {
   #     "bluffing_frequency": 0.15,     # Conservative bluffer
   #     "pot_odds_calculation": 0.95,   # Excellent math
   #     "psychological_reading": 0.8,   # Strong opponent analysis
   #     "bankroll_management": "excellent"
   #   },
   #   "among_us": {
   #     "deception_detection": 0.85,    # Excellent lie detection
   #     "alliance_formation": 0.7,      # Good social coordination
   #     "manipulation_resistance": 0.9, # Hard to manipulate
   #     "voting_influence": 0.6         # Moderate social influence
   #   }
   # }

**Cross-Game Strategic Consistency**

.. code-block:: python

   # Analyze strategic consistency across game types
   consistency_analyzer = StrategyConsistencyAnalyzer()

   # Multi-provider consistency comparison
   consistency_report = consistency_analyzer.analyze_cross_game_consistency(
       providers=["claude", "openai", "anthropic"],
       consistency_metrics=[
           "risk_tolerance_consistency",
           "aggressive_vs_defensive_balance",
           "cooperation_vs_competition_preference",
           "strategic_adaptability",
           "learning_rate_consistency"
       ]
   )

   # Generate provider personality profiles
   personality_profiles = consistency_analyzer.generate_personality_profiles()
   # Claude: "Strategic Conservative" - High consistency, risk-averse, excellent pattern recognition
   # OpenAI: "Adaptive Competitor" - Moderate consistency, aggressive optimization, fast adaptation
   # Anthropic: "Balanced Analyst" - High analytical consistency, moderate risk, thorough evaluation

Comprehensive Benchmarking Metrics
----------------------------------

Performance Measurement Framework
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

**300+ Distinct Performance Indicators**

.. code-block:: python

   from haive.games.metrics import ComprehensiveMetrics

   # Comprehensive performance measurement
   metrics = ComprehensiveMetrics()

   # Strategic intelligence metrics
   strategic_metrics = metrics.strategic_intelligence([
       "planning_horizon",           # How far ahead can they plan?
       "tactical_precision",         # Execution quality of plans
       "strategic_flexibility",      # Adaptation to changing conditions
       "endgame_technique",          # Performance under pressure
       "opening_preparation",        # Theoretical knowledge application
       "pattern_recognition",        # Ability to recognize game patterns
       "resource_optimization",      # Efficient use of available resources
       "tempo_management",           # Timing and rhythm control
       "position_evaluation",        # Static position assessment accuracy
       "calculation_depth"           # Tactical calculation ability
   ])

   # Social intelligence metrics
   social_metrics = metrics.social_intelligence([
       "deception_detection_rate",   # Ability to identify lies
       "persuasion_effectiveness",   # Success at changing minds
       "alliance_formation_skill",   # Coalition building ability
       "trust_calibration_accuracy", # Appropriate trust levels
       "social_influence_power",     # Ability to influence others
       "emotional_intelligence",     # Understanding emotional states
       "negotiation_success_rate",   # Deal-making effectiveness
       "leadership_emergence",       # Natural leadership development
       "group_dynamics_reading",     # Understanding team dynamics
       "cultural_sensitivity"        # Adaptation to different social norms
   ])

   # Economic intelligence metrics
   economic_metrics = metrics.economic_intelligence([
       "market_analysis_accuracy",   # Economic trend prediction
       "risk_assessment_quality",    # Investment risk evaluation
       "portfolio_optimization",     # Resource allocation efficiency
       "negotiation_value_creation", # Win-win deal creation
       "strategic_pricing",          # Optimal pricing strategies
       "competitive_analysis",       # Competitor strategy understanding
       "market_timing",              # Entry/exit timing precision
       "diversification_strategy",   # Risk spreading effectiveness
       "liquidity_management",       # Cash flow optimization
       "economic_modeling"           # Economic system understanding
   ])

Statistical Analysis Framework
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

**Advanced Statistical Evaluation**

.. code-block:: python

   from haive.games.statistics import TournamentStatistics

   # Comprehensive statistical analysis
   stats = TournamentStatistics()

   # Performance distribution analysis
   performance_analysis = stats.analyze_performance_distributions(
       providers=["claude", "openai", "anthropic", "google"],
       games=["all"],
       metrics=["win_rate", "strategic_quality", "social_intelligence"],
       statistical_tests=[
           "normality_test",
           "variance_homogeneity",
           "anova_analysis",
           "post_hoc_comparisons",
           "effect_size_calculation",
           "confidence_intervals"
       ]
   )

   # Meta-analysis across game types
   meta_analysis = stats.conduct_meta_analysis(
       effect_size="cohen_d",
       random_effects_model=True,
       heterogeneity_analysis=True,
       publication_bias_tests=True
   )

   # Generate statistical significance reports
   significance_report = stats.generate_significance_report()

Benchmarking Tournament Formats
-------------------------------

Round-Robin Championships
~~~~~~~~~~~~~~~~~~~~~~~~

**Comprehensive Head-to-Head Analysis**

.. code-block:: python

   from haive.games.tournament import RoundRobinTournament

   # Create round-robin championship
   championship = RoundRobinTournament(
       providers=["claude", "openai", "anthropic", "google"],
       games=["chess", "poker", "among_us", "debate", "monopoly"],

       # Tournament parameters
       rounds_per_matchup=50,
       include_mirror_matches=True,
       randomize_starting_conditions=True,
       track_adaptation_over_time=True
   )

   # Execute comprehensive round-robin
   results = await championship.run_championship()

   # Generate detailed head-to-head analysis
   h2h_analysis = championship.generate_head_to_head_analysis()

Swiss System Tournaments
~~~~~~~~~~~~~~~~~~~~~~~~

**Large-Scale Competitive Analysis**

.. code-block:: python

   from haive.games.tournament import SwissTournament

   # Large-scale Swiss system tournament
   swiss_tournament = SwissTournament(
       participants=200,  # 50 per provider
       rounds=12,
       game_rotation=["strategic", "social", "economic", "analytical"],
       pairing_system="strength_based",
       tiebreakers=["head_to_head", "strength_of_schedule", "game_diversity"]
   )

   # Run large-scale tournament
   results = await swiss_tournament.run_tournament()

   # Generate comprehensive rankings
   final_rankings = swiss_tournament.generate_final_rankings()

Elimination Brackets
~~~~~~~~~~~~~~~~~~~

**High-Stakes Competitive Format**

.. code-block:: python

   from haive.games.tournament import EliminationTournament

   # Single/double elimination tournament
   elimination = EliminationTournament(
       format="double_elimination",
       seeding="performance_based",
       match_format="best_of_7",
       game_selection="adaptive",  # Harder games for stronger players
       comeback_mechanics=True
   )

   # High-pressure elimination matches
   results = await elimination.run_elimination_tournament()

Research Applications
--------------------

Academic Research Platform
~~~~~~~~~~~~~~~~~~~~~~~~~~

**AI Research Infrastructure**

.. code-block:: python

   from haive.games.research import AcademicResearchPlatform

   # Create research platform
   research_platform = AcademicResearchPlatform()

   # Design controlled experiments
   experiment = research_platform.design_experiment(
       research_question="Do LLMs exhibit consistent strategic preferences across game domains?",
       independent_variables=["provider", "game_type", "difficulty_level"],
       dependent_variables=["strategic_consistency", "adaptation_rate", "performance"],
       control_variables=["starting_conditions", "opponent_strength", "time_constraints"],
       sample_size=1000,
       statistical_power=0.8
   )

   # Execute research study
   research_results = await experiment.run_study()

   # Generate academic publication
   publication = research_platform.generate_publication(research_results)

Commercial Benchmarking
~~~~~~~~~~~~~~~~~~~~~~

**Enterprise AI Evaluation**

.. code-block:: python

   from haive.games.commercial import EnterpriseBenchmark

   # Enterprise AI evaluation platform
   enterprise = EnterpriseBenchmark()

   # Custom benchmarking for enterprise needs
   benchmark_suite = enterprise.create_custom_benchmark(
       use_cases=[
           "strategic_decision_making",
           "negotiation_support",
           "competitive_analysis",
           "risk_assessment",
           "team_coordination"
       ],

       # Enterprise requirements
       security_level="high",
       compliance_requirements=["SOC2", "GDPR", "HIPAA"],
       performance_sla="99.9%",
       scalability_requirements="10000_concurrent"
   )

   # Run enterprise evaluation
   enterprise_results = await benchmark_suite.run_enterprise_evaluation()

Performance Optimization Research
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

**AI System Optimization**

.. code-block:: python

   from haive.games.optimization import PerformanceOptimizer

   # AI performance optimization research
   optimizer = PerformanceOptimizer()

   # Identify optimization opportunities
   optimization_study = optimizer.design_optimization_study(
       target_metrics=["win_rate", "strategic_quality", "efficiency"],
       optimization_parameters=[
           "temperature_settings",
           "prompt_engineering",
           "context_management",
           "memory_utilization",
           "attention_mechanisms"
       ]
   )

   # Run optimization research
   optimization_results = await optimization_study.run_optimization()

   # Generate optimization recommendations
   recommendations = optimizer.generate_optimization_guide()

Tournament Infrastructure
------------------------

Automated Tournament Management
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

**Full Automation Pipeline**

.. code-block:: python

   from haive.games.infrastructure import TournamentInfrastructure

   # Automated tournament infrastructure
   infrastructure = TournamentInfrastructure(
       cloud_provider="aws",
       auto_scaling=True,
       load_balancing=True,
       fault_tolerance="high",
       monitoring="comprehensive"
   )

   # Deploy automated tournament
   tournament_deployment = infrastructure.deploy_tournament(
       scale="global",
       participants=10000,
       concurrent_matches=500,
       expected_duration="30_days"
   )

   # Monitor tournament execution
   monitoring = infrastructure.monitor_tournament_health()

Real-Time Analytics Dashboard
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

**Live Performance Monitoring**

.. code-block:: python

   from haive.games.analytics import RealTimeAnalytics

   # Real-time tournament analytics
   analytics = RealTimeAnalytics()

   # Live performance dashboard
   dashboard = analytics.create_live_dashboard([
       "current_match_status",
       "provider_performance_trends",
       "statistical_significance_updates",
       "emergent_behavior_detection",
       "strategy_adaptation_tracking",
       "competitive_intelligence_alerts"
   ])

   # Stream live analytics
   analytics_stream = analytics.stream_live_analytics()

Legacy and Future Integration
----------------------------

**Historical Performance Tracking**
   Comprehensive database of all tournament results for longitudinal analysis and trend identification.

**Integration with AI Development**
   Direct integration with AI provider development pipelines for continuous benchmarking and improvement tracking.

**Research Publication Pipeline**
   Automated generation of research publications and academic papers from tournament results.

**Competitive Intelligence Feed**
   Real-time competitive intelligence for AI providers to understand market positioning and improvement opportunities.

See Also
--------

* :doc:`social_psychology_games` - Advanced behavioral AI analysis
* :doc:`dynamic_configuration` - Real-time strategy and personality modification
* :doc:`benchmark_framework` - Performance analysis and optimization
* :doc:`multi_agent_coordination` - Multi-agent research applications