The Genetics of Ideas: A Token-Based Attribution System for Thought-Capital Circulation

Contemporary systems for attributing intellectual contribution—copyright, academic citation, democratic voting—operate on Boolean logic: one is or is not the author; a work is or is not cited; a vote is cast or not cast. This paper argues that Boolean attribution systematically destroys information about the actual structure of intellectual influence, which is inherently continuous. The 2000 U. S. presidential election, in which 537 votes annihilated the preferences of 2. 9 million citizens, and jury systems that erase dissenting judgments, illustrate the structural violence of Boolean thresholds in domains far beyond copyright. The paper demonstrates that this regime has persisted not because it is epistemically sound, but because float-valued attribution was historically more expensive than Boolean approximation—a cost structure that AI and distributed ledger technologies are now inverting. Drawing on the author's academic background in error-correcting codes, the paper provides a mathematical foundation for the transition from Boolean to float-valued attribution. Hard-decision decoding (forcing continuous signals into binary values before processing) is provably inferior to soft- decision decoding (preserving continuous confidence values throughout), with a performance gap of approximately 2–3 dB toward the Shannon limit. The iterative message-passing architecture of modern LDPC and turbo codes offers a precise model for intellectual consensus formation: agents holding soft (probabilistic) estimates refine them through iterative exchange, converging on truth more accurately than any individual agent—provided that estimates remain soft throughout the process. The paper proposes a token-based attribution system (Visionium) in which every intellectual artifact carries a traceable genealogy with float-valued contribution ratios. A measurement architecture (Ideas Ocean) leverages AI embeddings to map all registered works into a shared vector space, enabling automated genetic decomposition of intellectual influence through similarity analysis. Revenue flows proportionally through the genealogy via blockchain smart contracts: if a work's contribution vector is source A: 0. 4, source B: 0. 2, original: 0. 4, revenue distributes accordingly without committee decision or negotiation. Implementation is outlined across four phases, from weighted academic citation (buildable on existing CiTO, OpenAlex, and Scite. ai infrastructure today) to float-valued participatory governance. The most significant consequence may be cultural rather than economic: under float-valued attribution, imitation ceases to be theft and becomes traceable inheritance, transforming creative practice from a proprietary model (ideas as assets to defend) to a genealogical model (ideas as living things whose fruit is shared among all who nourished the tree). The framework connects to the author's tetralogy on thought-capital (Osada, 2026a–d) and provides the economic infrastructure for the companion paper on AI rights (The Gradient of Kokoro). 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review Page 3 of 26