Working prototype
BehaviorStream Simulator
A behavior-analytic simulator. The core contingencies — per-class motivating operations, reinforcement schedules, latency-decay on consequence strength, response variability — are exposed as parameters; behavior emerges from their interaction. Procedures like functional analysis, prompt fading, token economy, and behavioral momentum are authored on top. Every session produces a replayable record.
How it runs
Two surfaces and a recording
The simulator has two surfaces. In the Designer, the analyst authors a Lesson — selecting procedures from the library, defining operational definitions, configuring the scene, tuning the engine's parameters. In the Participant view, the analyst runs the session live: the engine emits learner behavior on variable schedules conditioned on motivating operations and the active condition, and the operator responds with the RBT actions a session calls for — present SD, present EO, deliver reinforcement, attend, take data, advance the prompt level.
This is a modeler of contingencies, not a model of any individual learner. The engine doesn't learn from response history; it expresses operant relations parametrically, so behavior in the simulator is the consequence of contingencies the analyst sets.
Every session produces a SessionRecording: a structured artifact with per-condition response rates, latencies, IOA paired observations, prompt fading trajectories, and the procedure-specific analytics each Lesson configures — DR fidelity, demand elasticity, FA condition rates, equivalence relation accuracy. The recording exports to markdown and is replayable end-to-end.
What runs
Procedures the lesson can author
- Functional analysis — five canonical conditions plus custom, with fidelity rules and ground-truth diagnosis.
- Prompt hierarchies and prompt fading.
- Task analyses, forward and backward chaining.
- Token economy with thinning plan.
- Behavioral momentum — high-p / low-p demand chains.
- Behavioral Skills Training scaffold.
- Hanley PFA, SBT cascade, FCT replacement-response analytics.
- Stimulus equivalence and matching-to-sample.
- Differential reinforcement — DRA, DRI, DRO, DRL, DRH — with fidelity scoring.
- Generalization and maintenance probes.
- Behavioral economics demand curves.
- Pairing exercises and rapport-gated demands.
Mechanisms underneath
What the engine expresses
- Per-class motivating operations — edible, tangible, social, escape, sensory, activity — each with deprivation-recovery and satiation-on-delivery curves.
- Latency-decay reinforcement: strength as a function of time from emission.
- Real schedules — CRF, FR, VR, FI, VI, multiple, concurrent.
- Extinction with burst-then-decay patterning.
- Response variability and behavioral momentum mass.
- Continuous coordinates with 3D-ready orientation (yaw, pitch, roll).
- Motion primitives composed into named topographies via authored operational definitions.
- Characters with sight cones, reach radii, height tiers, and personality axes for non-learner roles.
- Append-only event log, replayable action stream, IOA capture, markdown export.
Status
Where the work is
Working prototype in active build. Designer and Participant surfaces both function; the scene-engine, scene-modes, and scene-renderer packages back them. No live demo is linked from this page yet — a layout pass on the Designer is queued before the Participant view goes public.
Parameter values are placeholder scaffolds. Calibration against recorded sessions is the next architectural step; the Lesson and SessionRecording contracts are shaped to make calibration a parameter-fit problem rather than a re-architecture.