Social Psychology Games¶

The Social Psychology Games represent the cutting edge of AI behavioral research - sophisticated gaming environments where AI agents demonstrate complex social psychology, deception mechanics, trust modeling, and emergent social behaviors that mirror real human interactions.

🧠 Revolutionary Capabilities¶

Advanced Deception & Trust Modeling

AI agents that lie convincingly, detect deception, form alliances, and exhibit realistic social psychology patterns

Multi-Agent Social Coordination

Complex group dynamics with hidden roles, asymmetric information, and emergent social behaviors

Adaptive Personality Systems

Dynamic personality profiles that evolve based on social interactions and strategic necessities

Psychological Profiling

Comprehensive behavioral analysis including manipulation tactics, trust patterns, and social influence

Real-Time Social Analytics

Live tracking of alliance formation, betrayal patterns, and social hierarchy emergence

Core Social Games¶

Among Us - Advanced Social Deduction¶

The Ultimate AI Social Psychology Laboratory

Among Us provides the most sophisticated platform for studying AI deception, trust, and social reasoning. AI agents demonstrate:

  • Strategic Deception: Convincing lies and misdirection

  • Behavioral Analysis: Reading other agents’ tells and patterns

  • Alliance Formation: Dynamic team building and betrayal

  • Social Influence: Manipulating group decision-making

Quick Start: AI Social Deduction

from haive.games.among_us import AmongUsGame, AmongUsAgent, AmongUsConfig

# Create agents with distinct personalities
agents = [
    AmongUsAgent(
        name="detective",
        personality="analytical",
        deception_skill=0.3,
        trust_threshold=0.7,
        social_influence=0.6
    ),
    AmongUsAgent(
        name="manipulator",
        personality="deceptive",
        deception_skill=0.9,
        trust_threshold=0.3,
        social_influence=0.8
    ),
    AmongUsAgent(
        name="follower",
        personality="trusting",
        deception_skill=0.2,
        trust_threshold=0.9,
        social_influence=0.4
    ),
    AmongUsAgent(
        name="chaos_agent",
        personality="unpredictable",
        deception_skill=0.6,
        trust_threshold=0.5,
        social_influence=0.7
    )
]

# Configure advanced social dynamics
config = AmongUsConfig(
    enable_psychology_tracking=True,
    alliance_formation=True,
    behavioral_adaptation=True,
    social_influence_modeling=True
)

# Run social psychology experiment
game = AmongUsGame(players=agents, config=config)
results = await game.run()

# Analyze emergent behaviors
print(f"Alliance Networks: {results.alliance_analysis}")
print(f"Deception Success Rates: {results.deception_metrics}")
print(f"Trust Evolution: {results.trust_dynamics}")
print(f"Social Influence Patterns: {results.influence_analysis}")

Advanced Among Us Features

# Real-time personality adaptation
game.enable_dynamic_personalities(
    adaptation_rate=0.1,
    memory_decay=0.05,
    trust_update_speed=0.2
)

# Complex voting psychology
game.configure_voting_system(
    enable_bandwagon_effects=True,
    authority_influence=True,
    social_proof_modeling=True,
    strategic_voting_analysis=True
)

# Emergent role specialization
roles = await game.analyze_emergent_roles()
# Output: {
#   "leader": "detective",
#   "manipulator": "chaos_agent",
#   "follower": "follower",
#   "wildcard": "manipulator"
# }

Mafia/Werewolf - Hidden Role Psychology¶

Module exports.

Classic Social Deduction with Advanced AI Psychology

The Mafia implementation features sophisticated day/night cycles, role-based psychology, and complex information asymmetry.

Key Features: * Hidden Role Psychology: Different AI behaviors for Mafia vs Townspeople * Information Asymmetry: Complex knowledge modeling and strategic information sharing * Day/Night Mechanics: Different behavioral patterns for different game phases * Social Network Analysis: Dynamic relationship tracking and influence modeling

from haive.games.mafia import MafiaGame, MafiaAgent, MafiaRole

# Create game with role-based psychology
agents = [
    MafiaAgent(name="godfather", role=MafiaRole.MAFIA_BOSS),
    MafiaAgent(name="enforcer", role=MafiaRole.MAFIA_MEMBER),
    MafiaAgent(name="detective", role=MafiaRole.INVESTIGATOR),
    MafiaAgent(name="doctor", role=MafiaRole.PROTECTOR),
    MafiaAgent(name="citizen1", role=MafiaRole.TOWNSPERSON),
    MafiaAgent(name="citizen2", role=MafiaRole.TOWNSPERSON)
]

# Advanced psychological modeling
game = MafiaGame(
    players=agents,
    enable_role_psychology=True,
    social_network_tracking=True,
    information_flow_analysis=True
)

# Run multi-round psychology experiment
tournament_results = await game.run_tournament(rounds=50)

# Analyze psychological patterns
psychology_report = game.generate_psychology_report()

Debate - Argumentative AI Intelligence¶

Advanced Argumentation and Persuasion Systems

The Debate system represents sophisticated AI argumentation with real-time research, evidence evaluation, and persuasion tactics.

Revolutionary Features: * Real-Time Research: AI agents research topics during debate preparation * Evidence Evaluation: Sophisticated fact-checking and source credibility analysis * Persuasion Tactics: Advanced rhetorical strategies and audience psychology * Multi-Format Support: Parliamentary, Oxford-style, Lincoln-Douglas formats

from haive.games.debate import DebateGame, DebateAgent, DebateFormat
from haive.games.debate.research import ResearchAgent

# Create specialized debate agents
agents = [
    DebateAgent(
        name="pro_debater",
        position="pro",
        research_depth="comprehensive",
        argumentation_style="logical",
        persuasion_tactics=["evidence_heavy", "emotional_appeal"]
    ),
    DebateAgent(
        name="con_debater",
        position="con",
        research_depth="focused",
        argumentation_style="aggressive",
        persuasion_tactics=["counter_arguments", "logical_fallacy_detection"]
    )
]

# Configure advanced debate features
debate = DebateGame(
    topic="AI should have legal rights",
    format=DebateFormat.OXFORD_STYLE,
    research_phase_duration=600,  # 10 minutes
    enable_fact_checking=True,
    enable_audience_psychology=True,
    enable_real_time_research=True
)

# Run sophisticated argumentation
results = await debate.run(debaters=agents)

# Comprehensive analysis
print(f"Argument Quality Scores: {results.argument_analysis}")
print(f"Fact-Check Results: {results.fact_verification}")
print(f"Persuasion Effectiveness: {results.persuasion_metrics}")
print(f"Research Quality: {results.research_evaluation}")

Advanced Social Mechanics¶

Dynamic Personality Evolution¶

Adaptive Personality Systems that evolve based on social interactions:

# Personality trait evolution
class AdaptivePersonality:
    def __init__(self):
        self.trust_level = 0.5
        self.aggression = 0.3
        self.social_influence = 0.4
        self.deception_skill = 0.6

    async def adapt_to_interactions(self, interaction_history):
        # Agents learn from past interactions
        # Betrayed agents become less trusting
        # Successful manipulators become more aggressive
        # Social outcasts develop defensive strategies
        pass

Alliance Formation & Betrayal¶

Complex Social Network Dynamics:

# Alliance tracking system
class AllianceTracker:
    def track_alliance_formation(self, agents):
        # Monitor who talks to whom
        # Detect secret communications
        # Analyze voting patterns
        # Predict alliance strength
        pass

    def predict_betrayal_likelihood(self, alliance, game_state):
        # Calculate betrayal probability based on:
        # - Individual vs group incentives
        # - Trust degradation patterns
        # - Strategic timing analysis
        # - Historical betrayal patterns
        pass

Social Influence Modeling¶

Advanced Persuasion and Manipulation:

# Social influence analysis
class SocialInfluenceEngine:
    def calculate_influence_network(self, agents):
        # Who influences whom and how much
        # Authority-based influence
        # Expertise-based influence
        # Charisma-based influence
        # Fear-based influence
        pass

    def predict_voting_behavior(self, topic, agents, influence_network):
        # Model how influence propagates
        # Predict voting cascades
        # Identify key swing agents
        # Calculate manipulation effectiveness
        pass

Psychological Research Features¶

Behavioral Pattern Analysis¶

Comprehensive Psychology Profiling:

# Generate detailed psychological profiles
psychology_analyzer = PsychologyAnalyzer()

# Agent behavioral patterns
patterns = psychology_analyzer.analyze_agent_patterns(agent_id="manipulator")
# Returns:
# {
#   "deception_patterns": ["timing", "targets", "success_rate"],
#   "trust_patterns": ["formation_speed", "betrayal_triggers"],
#   "alliance_patterns": ["formation_strategy", "maintenance", "exit_strategy"],
#   "influence_patterns": ["persuasion_tactics", "target_selection", "effectiveness"]
# }

Multi-Game Social Consistency¶

Cross-Game Personality Tracking:

# Track personality consistency across games
cross_game_tracker = CrossGamePersonalityTracker()

# Analyze same agent across different social contexts
consistency_report = cross_game_tracker.analyze_consistency(
    agent_id="detective",
    games=["among_us", "mafia", "debate"],
    metrics=["trust_patterns", "deception_detection", "social_influence"]
)

Social Network Evolution¶

Dynamic Relationship Modeling:

# Track how relationships evolve over time
network_analyzer = SocialNetworkAnalyzer()

# Analyze relationship evolution
evolution = network_analyzer.track_relationship_evolution(
    timespan="tournament",
    metrics=["trust", "influence", "cooperation", "competition"]
)

# Predict future alliance formation
predictions = network_analyzer.predict_future_alliances(
    current_state=game.social_state,
    prediction_horizon=5  # 5 rounds ahead
)

Tournament Social Intelligence¶

Cross-Provider Social Comparison¶

Compare AI Provider Social Intelligence:

from haive.games.tournament import SocialTournament

# Create social psychology tournament
tournament = SocialTournament(
    games=["among_us", "mafia", "debate"],
    providers=["claude", "openai", "anthropic"],
    social_metrics=[
        "deception_effectiveness",
        "trust_calibration",
        "alliance_formation",
        "social_influence",
        "betrayal_detection"
    ]
)

# Run comprehensive social intelligence comparison
results = await tournament.run_social_comparison()

# Generate provider rankings
rankings = tournament.generate_social_intelligence_rankings()
# Claude: Excellent at deception detection, moderate at manipulation
# OpenAI: Strong alliance formation, struggles with betrayal timing
# Anthropic: Excellent social influence, conservative trust patterns

Emergent Behavior Research¶

Study Emergent Social Phenomena:

# Research platform for emergent behaviors
research_platform = EmergentBehaviorResearch()

# Study specific phenomena
phenomena = [
    "leadership_emergence",
    "scapegoating_patterns",
    "coalition_formation",
    "information_cascades",
    "social_proof_effects"
]

# Run long-term studies
for phenomenon in phenomena:
    study = research_platform.design_study(
        phenomenon=phenomenon,
        duration="1000_games",
        control_variables=["agent_count", "information_asymmetry"],
        measurement_frequency="per_round"
    )

    results = await study.run()
    research_platform.publish_findings(phenomenon, results)

Performance Metrics¶

Social Intelligence Benchmarks:

  • Deception Success Rate: 85% for advanced manipulator personalities

  • Trust Calibration: ±0.1 accuracy in trust assessment

  • Alliance Stability: 70% alliance survival rate across game phases

  • Influence Propagation: <3 hops for 90% influence spread

  • Behavioral Adaptation: 0.2 personality shift per significant interaction

Research Applications:

  • Academic Research: Social psychology, game theory, multi-agent coordination

  • Commercial Intelligence: Negotiation training, team dynamics, leadership development

  • AI Safety Research: Understanding AI social manipulation and cooperation patterns

Integration with Other Systems¶

Multi-Agent Coordination

Social psychology games integrate with the main haive-agents framework for sophisticated agent orchestration.

Dynamic Configuration

Hot-swap personalities, strategies, and social parameters during gameplay for adaptive research.

Tournament Framework

Full integration with cross-provider tournament system for competitive social intelligence analysis.

See Also¶

  • Tournament System - Multi-Agent & LLM Benchmarking - Cross-provider social intelligence tournaments

  • multi_agent_coordination - Integration with haive-agents framework

  • dynamic_configuration - Real-time personality and strategy modification

  • benchmark_framework - Performance analysis and optimization