“Westworld” simulation

Initializes a simulation environment with configurable agent populations, spatial boundaries, and environmental parameters. The system uses a declarative configuration approach to define agent types, counts, initial positions, and behavioral parameters, then instantiates the simulation world with these specifications. Supports heterogeneous agent types within a single environment and allows runtime parameter adjustment before simulation execution.

Executes the simulation by advancing time in discrete steps, where each step triggers perception, decision-making, and action phases for all agents in sequence or parallel. The execution engine manages the simulation loop, coordinates agent state updates, handles collision detection and interaction resolution, and maintains temporal consistency across the agent population. Supports configurable step duration and execution modes (synchronous or asynchronous).

Provides a class-based or prototype-based system for defining agent types with shared properties, behaviors, and state management. Agents can inherit from base classes or mixins to reuse common functionality, and custom agent types can override or extend inherited methods. The system supports multiple inheritance or composition patterns to combine behaviors from different agent archetypes.

Enables agents to communicate through an event or message-passing system where agents can emit events and subscribe to event types. The system maintains an event queue, delivers messages to subscribed agents, and ensures message ordering and delivery guarantees. Supports both direct agent-to-agent messaging and broadcast events that reach all interested agents.

Provides a framework for defining agent behaviors through policy functions that map perceived state to actions. Agents execute their assigned policies each simulation step, receiving a perception object containing local environmental state and returning action commands. The system supports behavior composition, where agents can switch between multiple policies based on conditions, and includes built-in support for common behavior patterns like movement, interaction, and state transitions.

Maintains a spatial representation of the environment (typically grid-based or continuous coordinate space) and provides efficient neighbor/proximity queries for agents. The system tracks agent positions, updates spatial indices as agents move, and allows agents to query nearby entities within a specified radius or grid neighborhood. Uses spatial partitioning (e.g., quadtrees, grid cells) to optimize query performance from O(n) to O(log n) or O(1) depending on implementation.

Detects when agents occupy the same or overlapping space and executes interaction logic to resolve collisions or trigger behaviors. The system identifies collision pairs using spatial queries, applies interaction rules (e.g., agents merge, repel, exchange resources), and updates agent state accordingly. Supports both hard constraints (agents cannot occupy same space) and soft interactions (agents influence each other without physical collision).

Records agent state changes across simulation steps, maintaining a history of agent attributes, positions, and interactions. The system captures snapshots of agent state at configurable intervals or on-demand, allowing post-simulation analysis and visualization of agent trajectories and behavior evolution. Supports filtering and querying historical data to extract specific agent properties or interaction sequences.

Provides visualization capabilities to display agent positions, movements, and interactions in real-time or from recorded history. The system renders agents as visual elements (circles, sprites, etc.) on a 2D canvas or 3D scene, updates positions each simulation step, and can overlay information like agent state, interaction networks, or heatmaps. Supports both live visualization during simulation execution and playback of recorded simulation data.

Enables running multiple simulation instances with different parameter configurations to study how system behavior varies with input parameters. The system generates parameter combinations (Cartesian product or custom sampling), executes simulations in parallel or sequence, and aggregates results for comparative analysis. Supports both exhaustive parameter sweeps and targeted sensitivity analysis.

Provides deterministic random number generation through explicit seed management, ensuring that simulations with identical parameters and seeds produce identical results. The system seeds the random number generator before each simulation run and allows specifying seeds at the global, agent, or behavior level. Supports both pseudo-random sequences (for reproducibility) and true randomness (for non-deterministic exploration).

Collects runtime metrics including simulation step duration, agent computation time, spatial query performance, and memory usage. The system instruments key operations (perception, decision-making, collision detection) to measure execution time and resource consumption, then aggregates metrics across simulation steps. Supports both real-time metric reporting and post-simulation analysis.