Capability Matrix

Verified Capabilities of BehaviorSim 1.0.1

Every feature listed below is verified against the official behaviorsim==1.0.1 PyPI package release. BehaviorSim delivers dependable, mathematically grounded behavioral simulation tools.

Core Engine Capabilities

Comprehensive tooling for constructing, executing, and exporting sequential behavioral datasets.

Sequential Behavioral Trajectories

Generates ordered temporal interaction sequences indexed by sequence_id and interaction_id rather than independent rows.

Discrete State Machine

Define custom discrete states with stationary Markov transition matrices, absorbing states, and custom priors.

8 Parametric Emission Distributions

Supports Normal, Lognormal, Exponential, Poisson, Uniform, Uniform Discrete, Bernoulli, and Categorical distributions.

Dynamic Condition Rules

Modulate state transitions using threshold expressions evaluated on historical aggregations (last, sum, max, mean, min).

4 Domain Presets

Ready-to-use domain generators for finance, healthcare, education, and mobile application behavioral modeling.

Multi-Profile Heterogeneity

Assign multiple agent archetypes with custom mixture weights to model diverse population segments in a single run.

Deterministic Seed Control

Guarantees exact, reproducible sequence and feature outputs across all operating systems and environments.

Native Pandas DataFrame Export

Returns rich, tabular data structures ready for immediate analysis, visualization, or machine learning training.

Command-Line Interface (CLI)

Standardized behaviorsim CLI with validate and run subcommands exporting to CSV, JSON, or Apache Parquet formats.

Declarative YAML / JSON Configs

Version-control simulation parameters outside of Python source code with schema-validated YAML and JSON files.

Zero Heavy ML Runtime

Lightweight NumPy and Pandas core dependencies ensure fast installation and zero GPU hardware requirements.

REST Cloud API Synchronization

Integrates with the BehaviorSim cloud simulation API for remote execution, quota tracking, and team collaboration.

Declarative Workflows: CLI & Configuration

Run simulations directly from the shell or orchestrate complex configurations with version-controlled YAML files.

Standardized Command-Line Interface

terminal

# Validate a YAML simulation configuration
behaviorsim validate simulation.yaml

# Execute a simulation and export directly to Parquet or CSV
behaviorsim run simulation.yaml \
  --output ./traces.parquet \
  --seed 42 \
  --interactions 100 \
  --sequences 25

The CLI supports output formatting in CSV, JSON, and Apache Parquet with optional seed and interaction overrides.

Declarative YAML Specification

simulation.yaml

# simulation.yaml - Declarative Configuration
seed: 42
states:
  - name: "Active"
    description: "Engaged session state"
  - name: "Idle"
    description: "Passive browsing state"
distributions:
  - state: "Active"
    feature: "action_count"
    distribution: "poisson"
    params: { lam: 8.5 }
  - state: "Active"
    feature: "session_duration"
    distribution: "lognormal"
    params: { mean: 4.2, sigma: 0.8 }

Define state graphs, parametric distributions, and transition conditions without writing repetitive Python boilerplate.

Scope & Functional Boundaries

Clear delineation of what BehaviorSim does and does not do, ensuring scientific honesty and integrity.

Explicit Non-Claims & Scope Demarcation

What BehaviorSim IS:

A stochastic sequential behavioral trajectory synthesizer.
A reproducible testing and machine learning benchmarking utility.
A parameterizable Markov state engine with configurable feature emissions.
A lightweight Python library and CLI tool.

What BehaviorSim IS NOT:

Not a causal inference or counterfactual reasoning engine.
Not a pre-trained predictive AI model for real-world individuals.
Not clinically or financially certified decision software.
Not a privacy-preserving differential privacy synthesizer.

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