Decentralized Autonomous Organizations (DAOs) present a novel paradigm for organizational structure and operation, leveraging blockchain technology and smart contracts. However, the inherent decentralization of DAOs introduces significant vulnerabilities to manipulation and challenges in achieving fair and efficient decision-making. This paper proposes a framework for governing DAOs utilizing formal game theory, aiming to establish robust governance mechanisms that mitigate these risks. The core claim is that DAOs necessitate rigorous governance, and the proposed mechanism involves designing a DAO governance system based on the equilibrium outcomes of a meticulously constructed game. Voting rights and decision-making processes are directly linked to these game-theoretic equilibria. This approach provides a mathematically sound and verifiable basis for DAO governance, offering a significant advancement over existing, often informal, governance models. We outline the key components of this framework, including game selection, parameter tuning, and the potential for dynamic adaptation. The system's capacity for predicting and preventing manipulation, coupled with its emphasis on fairness, represents a key contribution to the development of stable and trustworthy DAOs.
Decentralized Autonomous Organizations (DAO) are an emerging blockchain-based paradigm for decentralized governance. Despite growing interest, their conceptualization remains fragmented. This paper introduces DAO-Ontology, a domain ontology formalizing DAO concepts-including perspectives, characteristics , solutions, evaluation methods, application domains, and challenges. Developed via the OntoView methodology from a systematic mapping of 47 studies, it is implemented in OWL and validated with a Java application using the OWL API. The ontology provides a standardized vocabulary, supports semantic integration, and enhances understanding of DAO as sociotechnical systems.
Commitment branching is a novel approach to modeling strategic interaction in multi-agent systems, particularly within the context of blockchain and decentralized autonomous organizations (DAOs). This paper introduces the concept of a state [s] that can potentially support multiple commitments, denoted as [P] and [Q]. These commitments lead to distinct computational trajectories, represented as [P → T_P] and [Q → T_Q]. The core of the model lies in the definition of B_C(s), which quantifies the number of distinct branching possibilities originating from a given intermediate state. This branching behavior directly reflects the potential for divergent strategies and the inherent complexity of decentralized decision-making. The model offers a simplified yet powerful framework for analyzing the dynamics of commitment and its impact on system evolution. Further exploration of this framework could lead to improved strategies for managing risk, optimizing resource allocation, and enhancing the robustness of decentralized systems.
Legal systems governed by rule of law are, structurally, rule systems. Like any rule system, they contain gaps between specification and intent, concentrated in the deliberately under-specified provisions that legal philosophers call "open texture." Those gaps have always been exploitable, but exploitation was rate-limited by the cost of legal expertise and the size of the corpus to be searched. That rate-limit is now collapsing. This paper introduces the governance patch-gap: the ratio between the rate at which AI accelerates the discovery of exploitable legal ambiguities and the rate at which legislatures, courts, and treaty bodies can repair them. Using the Highly Optimized Tolerance (HOT) framework from complex-systems theory, we map legal systems onto designed artifacts whose optimization against anticipated disputes concentrates fragility at the boundaries of the specification. We define the patch-gap as a ratio of discovery rate to repair rate, identify a threat taxonomy (corporate optimizer, state actor, misaligned autonomous agent), distinguish exploit discovery from exploit execution as separate governance problems, and examine three defensive strategies and the structural limits that prevent any defense from closing the gap entirely. The paper closes with three falsifiable predictions for 2027 to 2028. TL;DR summaries (five audiences) For the SME (legal theory / AI safety / complexity). Legal systems are HOT artifacts: drafters optimize against anticipated disputes, so residual fragility concentrates in Hart's penumbra (open texture), not in the core. The paper's object is a rate ratio G = $R_d/R_p$ and a stock S with $dS/dt$ = $R_d − R_p$; G is a definition, not a fitted dynamical model. Regime labels (G ≈ 2, 10², 10³+) are heuristics. SocioHack is an unreplicated sandbox (κ = 0.55); A1/VERITE is 36 already-vulnerable contracts. Rice / FLP / attestation in §6.4 are analogical extensions, not a derivation that courts instantiate those models. The load-bearing claim that survives if SocioHack fails is the work-factor collapse in adjacent formal systems plus the discovery/execution split. For the practitioner (counsel / CISO / compliance). Treat "AI found a loophole" and "an agent filed on it" as different problems. Discovery is a tool-governance issue (access, disclosure, audit of comment corpora). Execution is an agency-and-liability issue (who is the principal; human-in-the-loop above a dollar / classification / cross-border threshold). Disclosure mandates reach corporate repeat players and miss unsupervised agents. Do not spend the policy budget on formalizing "reasonable" or "public interest"; Catala-class work shrinks the core, not the penumbra. Immediate moves: require AI-use disclosure in filings and litigation; log agent actions that change regulatory classification. For the lay person. Laws have always had gray zones on purpose; words like "reasonable" so judges can handle new cases. Finding those gray zones used to be slow and expensive (years of lawyers). AI can search the whole tax code and regulation pile cheaply and flag gaps nobody has noticed. Passing a fix still takes months to years. The paper names that mismatch the governance patch-gap: machines find holes faster than legislatures and courts can close them. The holes were always there. What changed is the cost to find them. For the decision-maker (executive / funder / board). This is not a model-refusal problem and will not be closed by a better system prompt or a voluntary commitment letter. The asset at risk is the stock of known-but-unpatched legal ambiguities, which grows whenever discovery outruns repair. Adjacent formal systems (smart-contract exploit agents at USD 0.01 – USD 3.59 / attempt; attacker break-even ~USD 6k vs defender ~USD 60k) already show the cost collapse. Do not wait for SocioHack to replicate before treating discovery-versus-execution as two budget lines. Near-term: rate-limit execution (human-in-the-loop, disclosure). Do not buy "formally verified law" as a complete close. For governance (legislatures / agencies / treaty bodies). Every new AI rule written in open-textured natural language is another search surface. The EU AI Act Art. 6 "significant risk to fundamental rights" is the same kind of term as "undue burden." Three defenses, all bounded: (1) AI red-team of draft text before enactment .. useful, not exhaustive; (2) formal methods core only; (3) rate-limits buy time, do not close G. Conflating corporate optimizers, state arbitrage, and unsupervised agents produces the wrong instrument. The paper's falsifiers are public: AI-authored substantive rulemaking comments by end-2027; an attributed in-production exploit by end-2027; two governments or the EU publishing legislative red-team reports by mid-2028. Non-claims. G is a definition, not a fitted dynamical model. Regime magnitudes are order-of-magnitude heuristics. The SocioHack result is an unreplicated preprint treated as suggestive. Rice / FLP / attestation are analogical extensions, not a formal derivation that legal institutions instantiate those models. v1.1. Adds §4.5, an illustrative software companion (concept 10.5281/zenodo.21918091): a toy that generates Rd; G and the stocks are outputs, not legal measurements. No figures in the PDF.
** Autonomous Computational Law with StellarEq and ACRPL Model ** To address the systemic vulnerabilities of legacy natural-language governance—specifically its semantic ambiguity, high-latency auditability, and susceptibility to centralization—this paper presents a mathematically formalized, dual-engine architecture for Autonomous Computational Law under the Computable Political Language (CPL) stack, proving topological boundary-enforcement and stability via sheaf theory, homological algebra, and Lyapunov optimization. Systemic resource allocation and dynamic authority routing are governed by the Stellar Causal Power Flow (SCPF) engine, which proves state-transition convergence strictly based on the fundamental axiom of political energetics: $$\text{Power}(t) = \text{Contribution}(t) \times \text{AdoptionRate}(t)$$ Within this architecture, the mathematically rigorous constraints of our formal legal framework continuously generate decentralized trust, naturally shielding the vulnerable systemic core from coercive, extractive authority. By harnessing these parameters, the fluid and dynamic flow of distributed contributions cultivates a sprawling forest of policy proposals, smoothly transforming raw physical effort into radiant social energy to illuminate civilizational evolution. To maintain absolute structural integrity, an uncompromised cryptographic protocol strictly curtails the unchecked growth of algorithmic outputs, preventing the multidimensional essence of human rights from collapsing into scalar tradeable variables. Furthermore, persistent algorithmic decay systematically cools the high-temperature transactional friction of the marketplace, recursively returning accumulated power back to the common reservoir of collective sovereignty. Discrete logic boundary enforcement is handled ex-ante by the ACRPL, which defines non-negotiable constitutional safeguards as mathematical predicates over a non-convex feasible solution space, ensuring that no optimization gradient from the SCPF engine may enter the ledger unless the security gates are strictly satisfied, thus completely hiding compilation mechanics and specific variable transitions from unauthorized reconstruction.
PhiGraph Core 4.1.0-rc.6 is a model-agnostic governance system for software-agent and AI operations. This v2 draft extends the Zenodo v1 paper with a scoped transactional ledger (declared write locks, fail-closed verify_scoped_chain on JSON/SQLite), GRDI 0.4.0 shadow decision chain (envelope through replay audit, no external execution), and updated evaluation (319 automated tests at main@a5a7187). It retains the typed protocol, policy-gated runtime, HAV v0.2 fail-closed verification, and the bounded CIC-IDS2017 experiment with explicit limitations. Paper source is licensed CC BY 4.0. PhiGraph software is distributed separately under the repository software license. Git pin for this draft: a5a7187.