History-Dependent Dynamics (HDD)

Abstract

This deposit contains the complete documentation for History-Dependent Dynamics (HDD) , a falsifiable methodological framework for disentangling five inferential claims that are frequently conflated across disciplines: predictive history dependence, causal trajectory dependence, recurrent implementation, functional self-reference, and self-modeling. The framework proposes that evidence for one of these constructs should not be automatically interpreted as evidence for the next. Each construct requires its own experimental criteria, falsification conditions, and identifiability analysis. HDD treats these as diagnostic constructs rather than developmental stages, and classifies systems according to a profile D = (d₁, d₂, d₃, d₄, d₅) relative to the quadruple (S, O, I, M): the system, the observation model, the intervention set, and the mechanistic model class. Contents: Main Article: History-Dependent Dynamics (HDD) — the full methodological framework, including the five constructs, evidential structure, null hypotheses, statistical protocol, candidate empirical domains, and revision/falsification criteria. Appendix A: Computational Benchmark — Construct I — full protocol, extended results, and complete source code for the synthetic benchmark validating Construct I (Does history improve out-of-sample prediction?). The benchmark achieves 100% accuracy against ground truth across four synthetic systems (Markov, Hidden State, Delay Line, Recurrent), with robustness across five random seeds and four noise levels. Includes capacity controls (placebo history), state reconstruction diagnostics, and history dependence profiles. Key Results (Construct I): System HDD-I Relative Improvement (τ=10) Markov − -0.012% Hidden State + +9.60% Delay Line + +31.94% Recurrent + +6.75% Accuracy: 100% (4/4 systems correctly classified). No false positives for Markov across all seeds and noise levels. Scope and Limitations: This deposit validates Construct I only. Constructs II–V (causal trajectory dependence, recurrent implementation, functional self-reference, and self-modeling) are proposed as extensions requiring independent experimental validation. HDD is not a theory of consciousness. Consciousness is treated only as a possible downstream application. The framework is substrate-general and can be applied to biochemical networks, neural circuits, animal behavior, and artificial agents.