Behavioral Integration

Signal: s ∈ [0, ∞) (intensity of strategic behavior)
Type: θ ∈ {High Discipline, Low Discipline}
Cost: c(s, θ) where c(s, High) < c(s, Low) for s > 0
Separating Equilibrium: Different types choose different effort levels
Pooling Equilibrium: All types choose similar strategies

High Type: U_H(s, w) = w - c_H(s)
Low Type: U_L(s, w) = w - c_L(s)

Where:
w = wage/benefit from signaling level s
c_H(s) < c_L(s) = cost of signaling (lower for high type)

Incentive Compatibility (High Type):
U_H(s_H, w_H) ≥ U_H(s_L, w_L)

Incentive Compatibility (Low Type):
U_L(s_L, w_L) ≥ U_L(s_H, w_H)

Individual Rationality:
U_H(s_H, w_H) ≥ 0 and U_L(s_L, w_L) ≥ 0

def satisficing_decision(options, aspiration_level, search_cost):
    """
    Search options until finding one that exceeds aspiration level
    vs. examining all options to find global maximum
    """
    total_search_cost = 0
    
    for option in random.shuffle(options):
        total_search_cost += search_cost
        
        if evaluate_option(option) >= aspiration_level:
            return option, total_search_cost
    
    # If no satisficing option found, lower aspiration level
    return satisficing_decision(options, aspiration_level * 0.9, search_cost)

Utility = u(today) + β * δ * u(tomorrow) + β * δ² * u(day_after)

Where:
β < 1 = present bias parameter (additional discount on future)
δ = standard discount factor

def stress_response_agent_activation(stress_level):
    """
    Higher stress shifts control to System 1 agents
    Lower stress enables System 2 agent coordination
    """
    if stress_level > 8:  # Crisis mode
        return {
            'protector': 0.70,  # Dominant safety focus
            'connector': 0.20,  # Seek social support
            'optimizer': 0.06,  # Minimal analytical capacity
            'explorer': 0.04   # Almost no growth orientation
        }
    elif stress_level > 5:  # Moderate stress
        return {
            'protector': 0.40,
            'connector': 0.25,
            'optimizer': 0.25,
            'explorer': 0.10
        }
    else:  # Low stress - optimal for strategic thinking
        return {
            'protector': 0.20,
            'connector': 0.25,
            'optimizer': 0.30,
            'explorer': 0.25
        }

Emotional Strategy Profile: e* = (e₁*, e₂*, e₃*, e₄*)
Where eᵢ* = optimal emotional expression for agent i
Given others' emotional strategies e*₋ᵢ

Commitment Value = E[U(strategic_action)] - Cost_of_commitment - Temptation_resistance_value

class FinancialCommitment:
    def __init__(self, goal, stake_amount, success_criteria):
        self.goal = goal
        self.stake = stake_amount
        self.criteria = success_criteria
        
    def evaluate_commitment(self, actual_behavior):
        if self.criteria.is_met(actual_behavior):
            return self.stake  # Return stake
        else:
            return -self.stake  # Lose stake

class SocialCommitment:
    def __init__(self, goal, social_network, reputation_value):
        self.goal = goal
        self.network = social_network
        self.reputation_cost = reputation_value
        
    def social_pressure_utility(self, action):
        if action.aligns_with(self.goal):
            return self.reputation_cost * 0.1  # Small reputation boost
        else:
            return -self.reputation_cost  # Large reputation loss

class TemporalCommitment:
    def __init__(self, cooling_off_period):
        self.cooling_off = cooling_off_period
        
    def evaluate_action(self, desired_action, current_time):
        if current_time < self.cooling_off:
            return "BLOCKED"  # Prevent impulsive action
        else:
            return desired_action

def frame_strategic_choice(current_state, potential_outcome):
    """
    Frame strategic choices to align with human psychology
    """
    # Loss frame (more motivating for action)
    loss_frame = f"Without action, you'll lose {current_state - potential_outcome} strategic advantage"
    
    # Gain frame (less motivating)
    gain_frame = f"With action, you'll gain {potential_outcome - current_state} strategic advantage"
    
    # Use loss frame for high-impact strategic decisions
    if abs(current_state - potential_outcome) > significance_threshold:
        return loss_frame
    else:
        return gain_frame

def set_strategic_reference_points(current_performance, peer_performance, ideal_performance):
    """
    Strategic reference point selection affects motivation and satisfaction
    """
    reference_options = {
        'past_self': current_performance,
        'peer_comparison': peer_performance, 
        'ideal_self': ideal_performance
    }
    
    # Choose reference point based on strategic goals
    if goal == 'motivation':
        return reference_options['ideal_self']  # Creates aspiration
    elif goal == 'satisfaction':
        return reference_options['past_self']  # Shows progress
    elif goal == 'competitive':
        return reference_options['peer_comparison']  # Social comparison

def strategic_plan_consistency_check(current_plan, future_plan, beta, delta):
    """
    Check if strategic plans remain consistent over time
    """
    # Current preference for tomorrow vs. day after tomorrow
    current_preference = beta * delta * utility_tomorrow + beta * delta**2 * utility_day_after
    
    # Tomorrow's preference for tomorrow vs. day after tomorrow  
    future_preference = utility_tomorrow + beta * delta * utility_day_after
    
    consistency_ratio = current_preference / future_preference
    
    if abs(consistency_ratio - 1) > 0.1:  # 10% inconsistency threshold
        return "TIME_INCONSISTENT_PLAN"
    else:
        return "CONSISTENT_PLAN"

def sophisticated_commitment_strategy(beta, delta, temptation_utility):
    """
    Optimal commitment when you know you'll face temptation
    """
    # Calculate future self's temptation
    future_temptation = beta * temptation_utility
    
    # Set commitment level to exactly offset future temptation
    optimal_commitment = -future_temptation + small_buffer
    
    return optimal_commitment

Utility from action a = intrinsic_utility(a) + social_utility(a, group_action)

Where:
social_utility(a, group_action) = conformity_weight * similarity(a, group_action)

class SocialNetworkGame:
    def __init__(self, network_adjacency_matrix, conformity_weights):
        self.network = network_adjacency_matrix  
        self.weights = conformity_weights
        
    def utility_with_network_effects(self, player, action, all_actions):
        intrinsic_utility = self.base_utility(player, action)
        
        social_utility = 0
        for neighbor in self.network[player]:
            similarity = self.action_similarity(action, all_actions[neighbor])
            social_utility += self.weights[player, neighbor] * similarity
            
        return intrinsic_utility + social_utility