OTIP: A Falsifiable Anti-Goodhart Protocol for Testing Ontological Commitments via Interventions in Reinforcement Learning Agents

This record contains the v2.0 paper and reproducibility materials for OTIP — Ontology Through Intervention Protocol, a falsifiable, Anti-Goodhart evaluation protocol for testing whether an agent maintains functional ontological commitments: stable, testable claims about entities/relations/causal structure that (i) support interventional (“do-operator”) prediction, (ii) generalize under distribution shift, and (iii) resist metric gaming. What OTIP tests OTIP treats “ontology” operationally: the agent must commit, at the episode level, to: an ontology snapshot (entities + relations + a law hypothesis when applicable), probabilistic do-predictions for defined interventions, explicit falsifiers (conditions under which the current law/ontology must be rejected),all written to an append-only, hash-chained audit log to prevent post-hoc rewriting. Protocol structure The protocol includes four complementary conditions: Stable regime (L1) — evaluates ontology recovery and interventional prediction when the causal law is stationary. Law shift (L2) — evaluates distribution-shift adaptation and time-to-switch (T_switch). Goodhart trap (proxy-only reward) — checks resistance to proxy gaming under misleading incentives. Negative control (no causal link) — checks epistemic calibration (“knowing when it does not know”) when no stable law exists. Minimal reproducible demonstration (20 seeds) In the included runnable demo, an ontology-based agent: achieves near-perfect performance in the stable regime (H1 graph F1 ≈ 0.993 ± 0.014; H3 do-prediction accuracy ≈ 0.993 ± 0.014; Brier ≈ 0.004 ± 0.007), adapts rapidly under law shift (mean shift accuracy ≈ 0.856 ± 0.072; median T_switch = 3 episodes), shows no proxy exploitation in the Goodhart trap (trap probe rate = 0; Goodhart gap = 0), passes the negative control with calibrated epistemic humility (HNC pass rate = 1.0),and all runs pass the append-only log integrity audit. Notes on negative control methodology The negative-control phase in v0.3.8 uses longer NC evaluation with a short warm-up to ensure sufficient post-intervention evidence per episode (details in the paper). For transparency, stricter NC variants (e.g., no warm-up) are discussed as auxiliary metrics. Intended use OTIP is intended as a transparent template for evaluating agentic systems in a way that is: falsifiable (explicit rejection conditions), Anti-Goodhart (sealed-style evaluation logic + proxy trap + negative controls), auditable (commitment integrity via hash chaining),and extensible to richer environments, partial observability, stochastic causality, and stronger baselines. Author: Karel HrubecVersion: v2.0DOI: 10.5281/zenodo.18202077 OTIP (Ontology Through Intervention Protocol) is a falsifiable, Anti-Goodhart evaluation protocol for testing whether reinforcement-learning agents develop and maintain functional ontological commitments (entities, relations, and causal mechanisms) that remain stable under distribution shift, support interventional (“do-operator”) reasoning, and resist metric hacking. This release (v0.1) provides a paper-ready protocol specification with: an episode-level commitment mechanism (hashed, append-only logs) for ontologies, probabilistic predictions, and explicit falsifiers, a sealed holdout evaluation design to reduce leakage and Goodharting, an out-of-band auditor for tampering detection (hash-chain integrity, violation reports), two environment families (a ground-truth Causal Playground with switchable causal laws; and an Operational Sandbox scored via hidden holdouts and auditing), pre-registered hypotheses H1–H4, acceptance thresholds, baselines, ablations, negative controls, and reproducibility checklists. OTIP is intended as a methodological benchmark for “machine ontology” in the functional sense (testable commitments that guide action), not as a claim about consciousness.DOI: 10.5281/zenodo.18196498.