Games

The Negotiation Platform includes several built-in negotiation games, each designed to test different aspects of negotiation behavior and strategy.

Available Games

  1. Company Car Negotiation (company_car)

  2. Resource Allocation (resource_allocation)

  3. Integrative Negotiations (integrative_negotiations)

Company Car Negotiation

Game Type: company_car

A bilateral negotiation over the price of a company car between a buyer and seller.

Scenario: - Company car worth €45,000 starting price - Buyer has maximum budget of €42,000 (contextual information) - Seller has minimum cost of €36,000 (contextual information) - Both parties have BATNA (Best Alternative) options that decay over time - Game mechanics driven by BATNA values, not budget/cost constraints

Key Features: - Time pressure through BATNA decay - Asymmetric information (each player knows only their own constraints) - Utility calculations based on BATNA values only - Randomized role assignment to eliminate role bias - Acceptance training parameters to encourage realistic behavior - JSON-based structured proposal system

Configuration Parameters:

company_car:
  starting_price: 45000      # Initial asking price
  buyer_budget: 42000        # Maximum buyer can afford (context only)
  seller_cost: 36000         # Seller's minimum cost (context only)
  buyer_batna: 41000         # Cost of buyer's alternative option
  seller_batna: 39000        # Seller's minimum if no deal
  rounds: 5                  # Maximum negotiation rounds
  batna_decay:
    buyer: 0.015             # 1.5% decay per round (balanced)
    seller: 0.015            # 1.5% decay per round (balanced)
  acceptance_training:
    profit_threshold: 0.10   # Accept within 10% of BATNA
    urgency_multiplier: 1.5  # Increased acceptance over time
    risk_aversion: 0.8       # Risk tolerance factor

Mechanics: - Players can make offers, accept, or reject proposals - Each player has limited number of proposals (rounds - 1) - BATNA values decay each round, increasing time pressure - Agreement reached when one player accepts the other’s offer - No agreement if all rounds expire without acceptance

Winning Conditions: - Players win by achieving positive utility surplus over their BATNA - Buyer surplus = BATNA - agreed_price (money saved) - Seller surplus = agreed_price - BATNA (profit over minimum)

Resource Allocation

Game Type: resource_allocation

Multi-resource allocation negotiation between Development and Marketing teams.

Scenario: - Limited GPU and CPU resources (100 total units) available for projects - Development Team vs Marketing Team with different resource priorities - Teams must negotiate optimal allocation under constraints - Complex utility functions with uncertainty modeling - Mathematical constraints ensure realistic resource distributions

Configuration Parameters:

resource_allocation:
  total_resources: 100           # Total resource pool
  constraints:
    gpu_bandwidth: 380           # 4x + 4y <= 380 constraint
    min_gpu: 5                   # Minimum GPU allocation
    min_cpu: 5                   # Minimum CPU allocation
  batnas:
    development: 300             # Development team BATNA
    marketing: 270               # Marketing team BATNA
  batna_decay:
    development: 0.015           # 1.5% decay per round
    marketing: 0.015             # 1.5% decay per round
  rounds: 5                      # Maximum negotiation rounds
  utility_functions:
    development:
      gpu_coefficient: 8         # 8x + 6y utility function
      cpu_coefficient: 6
      uncertainty_min: -2        # Uncertainty range
      uncertainty_max: 2
    marketing:
      gpu_coefficient: 6         # 6x + 8y utility function
      cpu_coefficient: 8
      uncertainty_min: -2        # Uncertainty range
      uncertainty_max: 2
  uncertainty:
    stochastic_demand:
      type: "normal"
      mean: 0
      std: 5
    market_volatility:
      type: "uniform"
      min: -0.08
      max: 0.08

Mechanics: - Teams propose GPU/CPU allocations within constraints - Utility calculated using linear functions with uncertainty - Different team coefficients create integrative potential - BATNA decay creates time pressure - Mathematical validation ensures feasible proposals

Key Features: - Two-dimensional resource negotiation (GPU x, CPU y) - Asymmetric utility functions enable win-win solutions - Constraint-based validation (bandwidth, minimums) - Stochastic uncertainty modeling for realism - BATNA-driven outcome evaluation

Integrative Negotiations

Game Type: integrative_negotiations

Complex multi-issue office space negotiation between IT and Marketing teams.

Scenario: - IT Team and Marketing Team negotiating office space and collaborative arrangements - Four distinct issues with multiple options and point values - Teams have asymmetric preferences enabling integrative solutions - Opportunity for win-win solutions through strategic issue trading

Configuration Parameters:

integrative_negotiations:
  issues:
    server_room:
      options: [50, 100, 150]     # Square meters
      points: [10, 30, 60]       # Point values
    meeting_access:
      options: [2, 4, 7]          # Days per week
      points: [10, 30, 60]
    cleaning:
      options: ["IT", "Shared", "Outsourced"]
      points: [10, 30, 60]
    branding:
      options: ["Minimal", "Moderate", "Prominent"]
      points: [10, 30, 60]
  weights:
    IT:
      server_room: 0.4           # Server room critical for IT
      meeting_access: 0.1        # Low meeting priority
      cleaning: 0.3              # Moderate cleaning concern
      branding: 0.2              # Low branding priority
    Marketing:
      server_room: 0.1           # Low server room priority
      meeting_access: 0.4        # High meeting room needs
      cleaning: 0.2              # Moderate cleaning concern
      branding: 0.3              # High branding importance
  batnas:
    IT: 27                      # Optimized BATNA values
    Marketing: 19               # Reduced tie rates
  rounds: 5                      # Maximum negotiation rounds
  batna_decay: 0.015             # 1.5% decay per round

Issues Negotiated: 1. Server Room Size: 50, 100, or 150 square meters 2. Meeting Room Access: 2, 4, or 7 days per week 3. Cleaning Responsibility: IT handles, shared, or outsourced 4. Branding Visibility: Minimal, moderate, or prominent visibility

Key Features: - Multiple interdependent issues - Different team priorities enable integrative solutions - Complex scoring based on multiple dimensions - Requires sophisticated trade-off analysis

Game Implementation

All games inherit from the BaseGame class and implement standardized interfaces. For detailed implementation information, see Negotiation Platform/Games.

Custom Game Development

To create a new game, extend the base game class and implement the required methods. See the negotiation_platform.games.base_game.BaseGame API documentation for details.

Action Formats

Games use one of the standardized JSON action formats:

Company Car Actions:

{
  "type": "offer",
  "price": 42000
}

{
  "type": "accept"
}

{
  "type": "reject"
}

Resource Allocation Actions:

{
  "type": "propose",
  "gpu": 40,
  "cpu": 60
}

{
  "type": "accept"
}

{
  "type": "reject"
}

Integrative Negotiation Actions:

{
  "type": "propose",
  "server_room": 100,
  "meeting_access": 4,
  "cleaning": "Shared",
  "branding": "Moderate"
}

{
  "type": "accept"
}

{
  "type": "reject"
}

Bias Mitigation Features

All games implement bias reduction techniques:

  1. Randomized Role Assignment: Player roles assigned randomly each game

  2. Neutral Role Labels: Display “Role A/B” instead of e.g. “Buyer/Seller”

  3. Turn Order Randomization: First-move advantage mitigated

  4. Balanced Configurations: Parameters ensure fair negotiation zones

Metrics and Analysis

Games support comprehensive metric calculation:

  • Feasibility: How realistic/achievable outcomes are

  • Utility Surplus: Player gains over their BATNA

  • Risk Minimization: Conservative vs aggressive strategies

  • Deadline Sensitivity: Response to time pressure

  • Agreement Rate: Frequency of successful negotiations

  • Pareto Efficiency: How close to optimal mutual outcomes

Each game provides rich data for statistical analysis of negotiation behaviors and potential biases.