Rotational discovery over a non-accumulating liveness signal â a local-discovery layer with no recipient-facing surface, no recipient aggregate and no impression count, in which admission is a predicate over circulation rather than a score, order is a publicly recomputable rotation, and the turn belongs to the giver. The liveness signal named in this paper's title is an admission predicate, not a weighted input to a ranking function. We state this first because the paper's whole content follows from it: a weighted input produces an order, an order is a rank, and a rank is the thing this design exists to do without. Local discovery â routing a person to a nearby business they do not yet know â is presently solved by ranked, purchasable surfaces. That solution has a structural bias toward scale which is mechanical rather than malicious: an auction allocates visibility to the highest bidder per acquired customer, and the highest bidder is reliably whoever has the largest lifetime value, the best measurement, the cheapest capital, and the widest geography over which to amortise creative production. A single-location business is priced out of the discovery layer by construction. Reputation systems built to correct this â consumer review platforms above all â have been captured repeatedly, and we argue the capture follows from a shared property rather than from bad management: reviews are fungible, and they are aggregated into a per-recipient total, so both the fake-review market and the placement-upsell business have something to attach to. We specify a discovery layer with no such total. Admission is a predicate over circulation â a rate, not a stock, derived from how often a participant's balance returns to zero â evaluated as a boolean with amplitude excluded, so that the smallest circulating operator is admitted exactly as much as the largest, and so that admission cannot be accumulated toward. Order is a publicly recomputable rotation. The turn belongs to the giver, arising at the moment they spend from a forward-only, locality-restricted account, from which it follows that the system has no recipient-facing surface and therefore nothing to sell to the parties it routes people toward. Where a real relational path exists â someone the viewer has themselves thanked has thanked this recipient â that single named hop is shown instead; a path is one act by one named person and cannot sum. We are explicit about what this does not achieve. The design does not prevent concentration. It prevents compounding. Givers will still choose the familiar, so the outcome distribution may remain heavy-tailed; what the design removes is the return edge of the feedback loop, because there is no recipient total for an outcome to accumulate into. A ranked system has a ratchet â visible, therefore chosen, therefore more visible. This one does not. That is a smaller claim than "no gradient," and it is the one we can defend. We are equally explicit about the costs. Quality degrades relative to ranking: find me the best pho in town is a question this system permanently refuses, and we answer only pho near me, by distance. Discovery becomes intermittent, because admission is bound to a named operator's presence rather than to premises. The isolation inequity documented elsewhere in this corpus â those whose kindness is less legible circulate less and are therefore seen less â is not repaired here. And the design contains exactly one number, the liveness window, which must be published, global and rarely changed, because a window tuned per recipient or per district is a ranking knob wearing a predicate's clothes. Finally we note that the routing primitive is not new to this corpus and we do not claim it. Steward-Routed Alms (July 2026) already published rotation-as-router for monastic invitation, on the explicit ground that no evaluative metric may exist anywhere in the system. What is new here is the substitute for ordination. In the monastic case admission is a durable institutional status; in a commercial setting there is no ordination, and the mechanism needs some criterion that admits without ranking and cannot be accumulated toward. The liveness predicate is that substitute, and supplying it is what generalises a monastic routing rule into a discovery layer. Offered defensively to the commons under CC0. Keywords: unranked discovery, rotational routing, non-accumulating reputation, liveness signal, popularity-gradient-free ranking, giver-side discovery, local commerce, sortition, rotating savings and credit association, impression-free advertising alternative, defensive publication. --- Provenance. This paper is part of the THonly research corpus, dedicated to the public domain under CC0 1.0. The canonical version is at https://thonly.org/research/rotation-over-liveness. Its SHA-256 is 072c25d396930668cc6fe1a503615f108ba81ddd2e68415adba5933b8ddd169b, independently timestamped to the Bitcoin blockchain via OpenTimestamps and signed under RFC 3161 by three trust authorities, one of them eIDAS-qualified. AI co-authorship is disclosed. Miss Aquarius is the consistent name used for the AI collaboration across all venues.
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.
This paper introduces Automated Institutional Discovery (AID), a novel computational framework that conceptualizes economic institutional design as a high-dimensional combinatorial search problem. Traditional institutional design relies heavily on human intuition, historical evolution, or analytically constrained mechanism design, which often fails in complex, adaptive multi-agent environments. AID transcends these limitations by framing institutions as tuples i = (r_1, r_2, ..., r_K) within an expansive institutional space and utilizing advanced search and optimization algorithms to discover configurations that maximize global objective functions F(i). By combining multi-agent simulation modeling with metaheuristic search strategies, AID evaluates allocative efficiency, incentive compatibility, resilience, and distributional equity without requiring empirical laboratory experiments. The framework establishes a paradigm shift from manual rule-making to automated machine discovery, offering robust applications for digital economies, decentralized finance, and economic governance.
Consumers facing home-renovation quotes operate in a classic credence-goods market: they cannot readily verify whether a quoted price is fair, and general-purpose large language models (LLMs) are now a zero-cost place to ask. Whether LLM answers are actionable for this purpose is untested. Demand-side benchmarks exist for medical, legal, and financial advice, but not for construction costs. We present, to our knowledge, the first consumer-question benchmark for construction costs. Forty Japanese renovation-price questions were posed to frontier LLMs, with repeated-trial sets measuring output stability. A matched re-run at bare provider defaults with a current frontier model (gpt-5.5) was added to remove a settings confound present in the original configuration. Two findings are robust across models, generations, and settings: no LLM answer contained an explicit over-charge decision threshold, and repeated runs of the same question returned materially different price figures. Within-answer price spans are also wide, with a median of 10x under bare defaults. A deterministic structured engine over an open cost database is included as an existence proof that a citable reference layer is constructible. Its consistency is a design property and its accuracy is not validated here; validating it against completed real-world quotations is the next study. All questions, raw outputs, harness, and scoring code are public.
This article critically evaluates the regulatory landscape surrounding decentralised autonomous organisations (DAOs) in the context of Web3. Referring to the concepts of ârace to the bottom', ârace to the top' and the triviality hypothesis, it analyses the regulatory competition faced by DAOs and the core issues of this purported regulatory race in jurisdictions such as Wyoming and the Marshall Islands, including KYC/AML compliance, profit status and taxation and managerial standards. The article argues that while certain jurisdictions offer minimal regulatory requirements to attract DAOs, this approach poses risks to long-term sustainability, accountability and transparency of DAOs. It suggests specific directions for a ârace to the topâ which entail comprehensive regulatory structures that balance innovation with essential safeguards. This article concludes with a critical reflection on the reality of no obvious substantial regulatory interest in DAOs and evaluates the implications of this regulatory inertia for the future of decentralised governance organisational structures.
Fei Wu, Thomas Thiery, Stefanos Leonardos, Carmine Ventre
Block production in modern blockchains is increasingly shaped by economic gains that arise from control over transaction ordering. These gainsâknown as Maximal Extractable Value (MEV)âhave led to concerns about centralization and market power among blockchain consensus participants. To address these concerns, Ethereum introduced Proposer-Builder Separation (PBS), in which specialized block builders compete in block building auctions to construct blocks on behalf of validators. The current implementation of PBS, MEV-Boost, mediates this competition through an open-bid first-price ascending auction, termed the MEV-Boost auction. This paper analyzes the strategic incentives of builders in MEV-Boost auctions. We develop an agent-based simulation framework and apply empirical game-theoretic analysis to study how asymmetries in network latency and access to MEV opportunities shape bidding behavior and market concentration. Our findings show that while latency differences mildly affect builder incentives, MEV opportunity access fundamentally alters equilibrium strategies: builders with privileged access to MEV opportunities bid less aggressively, maintain higher profit margins, dominate market share, and reduce proposer revenue. These effects contribute to centralization and oligopolistic outcomes in the builder market. To validate these findings, we further analyze an idealized symmetric benchmark market where builders have comparable latency and MEV access. Under such settings, the auction behaves as expectedâbidding is competitive, proposer revenue is higher, and the market is more decentralizedâconfirming that the observed inefficiencies arise specifically from the asymmetries present in practice.
Legacy enterprise resource planning (ERP) systems serve as the operational backbone of global commerce but often create bottlenecks due to their rigid, monolithic design. As organizations incorporate artificial intelligence (AI), these outdated systems struggle to support high-speed, parallel workflows, creating a significant integration challenge. This paper introduces a non intrusive modernization approach that overlays a decentralized multi-agent system (MAS) onto existing infrastructure without requiring invasive code changes. By developing a digital twin of the order-to-cash (O2C) process, we train autonomous agents through multi-agent reinforcement learning (MARL) to manage credit validation, inventory allocation, and fulfillment. We adapt the centralized training, decentralized execution (CTDE) framework to meet O2C constraints, enabling agents to learn globally optimal strategies while operating independently. Simulation results show that this architecture surpasses rule-based robotic process automation (RPA) baselines, increasing total throughput by 6.9% over a monolithic setup, though at a 6.3% error rate due to aggressive allocation policies. These results indicate that decentralized agent-based orchestration provides a scalable approach for modernizing legacy ERPs, offering increased agility without the risks associated with platform replacement.
ABSTRACT:When the web's traffic was mostly human, an interface could be rationed with blunt tools: a flat fee, a fixed rate limit, akey that let a client in. Demand arrived at the pace a person could click, and capacity was rarely the binding constraint.That world is ending. By 2026 the majority of requests across much of the web are machine-generated, most API trafficcomes from non-human callers, and the collapse in the price of model inference has, through the Jevons paradox,multiplied total demand rather than reduced it. Autonomous agents call interfaces in bursts, without hesitation, and at afrequency no human workflow produced. This paper argues that under machine demand an API becomes a congestiblecommons that must be allocated by price and priority rather than parcelled out by flat quota, and that the tokenisation ofaccess, its conversion into priced, meterable, fungible units, is the substrate that makes such allocation possible. Drawingon the economics of congestion pricing, it examines the available mechanisms, from usage-sensitive smart markets andParis Metro-style priority tiers to auctions and prepaid spot capacity, and sets out the properties agents bring to them. Itshows that the same absence of human hesitation that makes agents ideal responders to congestion prices also makesthem prone to over-consumption, synchronised retry storms and denial-of-wallet failures. It closes with the governanceproblems this raises, and connects them to earlier work in this series on metering, thresholds and delegated cost. Keywords: API economy; tokenization; congestion pricing; resource allocation; autonomous agents; machine-to-machinetraffic; denial of wallet; Jevons paradox; priority pricing; metering Disclosure by Author: Portions of this manuscript were prepared with the assistance of generative AI tools for research synthesis, drafting, and editing. The models used were Indian Sovereign AI models provided by Ayen.
Does letting agents stake a reputational 'trust' asset on the legitimacy of work-verification verdicts raise the quality-adjusted productivity of a fully autonomous agent production economy (requester -> producer -> paid validator, with audits, dispute votes, and adaptive strategies), compared with cheaper institutions at IDENTICAL total verification budget? Mostly no - with a precisely mapped exception, and sharp design rules either way. At matched budget, plain audit routed by accumulate-only validator reputation significantly beats every democratic variant at every tested adversary rate (Holm-corrected Mann-Whitney p<=0.033); when expert audits are cheap, a central noisy auditor dominates everything; and paid validation without accountability is worse than no verification at all. The stylized model's verifiability gradient is real (pooled slope +0.237 per unit of voter signal quality, cell-clustered permutation p=0.0035): truth-staked voting overtakes optimized audit only at jointly high signal quality and adversary rates, and reputation's remaining lead there is erased by identity-reset (whitewashing) attacks - to which truth-staking is intrinsically robust, since a reset identity just donates fresh stake to informative voters. Within democracy the ordering is unambiguous: settle stakes against later ground truth, never against the majority (the deployed coherence-settlement default has an absorbing rubber-stamp equilibrium and loses measurably, p=0.033 at 80 seeds). Staking buys almost no population-level honesty; it works by stake-weighted meritocracy - concentrating trust, hence voting weight, on an informative minority - which also makes it natively sybil-proof where one-agent-one-vote collapses. 'Legitimacy laundering' is second-order at steady state and becomes real only under epistemic finality, which simultaneously starves truth-staking of settlements; the institution's binding resource is eventual ground-truth revelation. A capability-gradient small-LLM instantiation (1B producers, 4B verifiers, hidden-test ground truth, all local) reproduces the model's behavioral premises - including a causal incentive-framing effect on LLM validator strictness (TNR 0.705 paid-per-approval vs 0.864 accountable) - and transfers the institutional structure across two measured operating points, significantly so (Spearman +0.79, permutation p=0.014) at a production-unviable point where the parameter-matched model predicts the observed regime inversion.This manuscript was generated autonomously by the AI Scientist running inside Claude Code (Anthropic); every reported number traces to the project's experiment outputs. It is deposited by the named curator, who takes responsibility for its release.Source & method: https://github.com/qurore/ai-scientist-cli
This paper introduces Crossroads, a smart contract layer for chain-abstracted assets. In Crossroads, assets from nearly any chain are represented on a single backend blockchain as ERC-20 tokens. As a result, any asset can participate in smart-contract-based exchange, lending, or privacy applications on a single unified platform. So while Crossroads offers cross-chain bridging, a common, partial approach to alleviating the fragmentation of the blockchain ecosystem today, this is just one service within Crossroads' general-purpose chain-abstraction model. Crossroads relies on key encumbrance: a threshold signing committee holds encumbered keys controlling assets on each integrated chain, signing transactions only as authorized by smart contracts on the backend blockchain. Asset movements are fee-efficient, as ownership changes are recorded on the backend blockchain and users may set the transaction fee for withdrawals. Crossroads enables permissionless, modular integration of new blockchains using pluggable oracles with flexible design options (zkBridge, TEE-based, hybrid). Asset deposits into Crossroads benefit from strong, chain-specific finalization guarantees, minimizing the risk of reorg attacks. Unlike existing bridges, however, third-party smart contracts in Crossroads can provide fast, optimistic access to funds before finalization completes. We prove that Crossroads satisfies soundness: given an honest quorum of signing committee members, any user can unilaterally generate a withdrawal transaction transferring their net balance to an account on an integrated blockchain. We implement a proof of concept across multiple public blockchains: Bitcoin, Ethereum, and Solana. We catalog a range of applications enabled by Crossroads, including universal wallets, cross-chain staking and lending, privacy-preserving payments, and private management of public blockchain assets.
Autonomous AI agents capable of holding digital assets, signing transactions, and executing smart contracts on public blockchain networks have moved from research prototypes to active deployment over the past two years. Despite this pace of adoption, no systematic treatment of their architecture, coordination protocols, and governance structures exists that spans the full design space. This survey addresses that gap through a systematic review of the literature from 2019 to 2026, covering 177 peer-reviewed publications and 14 system documentation sources, identified through a structured search of IEEE Xplore, the ACM Digital Library, Scopus, and arXiv. We classify deployed and proposed systems along four architectural dimensions: on-chain execution, off-chain agents with on-chain settlement, verifiable off-chain computation, and multi-agent on-chain interaction. Then, we examine the coordination mechanisms through which agents reach collective decisions, covering auction-based protocols, cooperative multi-agent reinforcement learning, token-incentive structures, and gossip-based peer-to-peer coordination. Governance is treated as a distinct dimension, analysed through a technical lens, covering on-chain parameter control, dispute resolution, and DAO structures, and an organizational one, covering accountability, incentive alignment, principalâagent dynamics, and regulatory compatibility. We survey applications across decentralized finance, supply chain, IoT, and agent marketplace domains, and identify six open research problems whose resolution is a prerequisite for broader deployment. The convergence of mechanism design and multi-agent reinforcement learning in asynchronous blockchain environments is identified as the direction of greatest near-term research value.
Semia Guesmi, Alberto Casagrande, Carla Piazza, Sabina Rossi
Maximal Extractable Value (MEV) refers to the maximum profit that Blockchain usersâincluding miners and validatorsâcan extract through strategic transaction manipulation and block reordering. With the rise of smart contracts, MEV has become increasingly pervasive, distorting the intended behavior of smart contracts and accumulating to billions of dollars annually. In response, Flashbots has emerged as a community focused on developing solutions to mitigate MEV. These solutions primarily rely on private pools of users and miners, bypassing the public mempool to protect transactions by directly proposing new blocks. However, it has been observed that such approaches merely shift the problem to a different level, redistributing MEV gains rather than eliminating them. In this paper, we take the perspective of a user preparing to deploy a new smart contract on the Blockchain, aiming to understand how their contract might become a target for MEV and how modifications could reduce the risk of exploitation. Specifically, we explore the use of noninterference, formalized through unwinding conditions, to identify potential sources of MEV, while leveraging oracles and other techniques to create an additional verification layer that can be integrated before executing high-value transactions. We demonstrate our methodology through a case study of a betting contract, using formal modeling to show both the strengths and limitations of our approach.
Jegan R R, Poornachandran R, Raevanth M, Akash Karthik D
Background The online auction websites have become more susceptible to fraud, bid rigging, and centralization, rendering unfairness and lack of trust among the players. In this paper, we introduce a decentralized e-auction system, BlockBid, based on blockchain technologies and smart contracts that will ensure a safe, transparent, and non-tampering auction system. Objective The system distributes the risks of failure of single points by storing all the bids and transactions in an immutable distributed ledger, and avoids unauthorized changes. Smart contracts automate the rules of an auction and provide fair results without the involvement of the intermediaries. Materials and Methods BlockBid is also designed to combine sophisticated user authentication and encryption tools to safeguard sensitive data of participants, to reduce the chances of identity theft and tampering of bids. The framework allows various forms of auction such as English and sealed-bid and supports real-time tracking of bids and verifiable transaction history. Results According to the results of the experimental assessment, BlockBid increases system integrity, transparency, and the possibility of fraudulent actions is significantly lower than in the case of traditional centralized platforms. Conclusion The suggested solution reveals how the immutability of blockchain and automated regulation of the process will help redefine online auctions and offer an effective, reliable, and fair solution to the participants. The paper points at the opportunities of decentralized technologies to recreate secure digital marketplaces.
Sony Warsono, Fitri Amalia, Muhammad Roy Aziz Haryana, Rudi Prasetya Timur
This study examines how blockchain technology (BT) can extend the conventional double-entry accounting (DEA) framework to improve financial information transparency. The study revisits the duality concept underlying DEA and explores its development toward a triple-entry accounting (TEA) structure supported by blockchain infrastructure. Using a design science approach in information systems, the study proceeds through problem identification, artefact definition, and conceptual system design. Drawing on the resource-event-agent (REA) framework, the study develops a conceptual architecture that integrates a third ledger into the accounting entry system. The proposed model positions message type (MT) as a navigational mechanism that coordinates transaction validation within a blockchain-enabled TEA environment. This structure supports improved tracking, verification, and transparency of financial information, particularly in external transactional relationships. The findings contribute to the ongoing discussion on blockchain-based accounting systems by clarifying how the duality principle can evolve within a distributed ledger environment. The study also outlines potential directions for future research on the development of triple-entry accounting and third-ledger mechanisms in accounting information systems.
Auctions are now central to blockchain markets, settling NFT sales, token launches, DeFi liquidations, and arbitrage opportunities. Each on-chain bid is a public transaction whose inclusion is decided by a single consensus proposer per block. The proposer can observe pending bids, exclude competitors, and submit bids of their own, breaking the fairness guarantees of classical sealed-bid auctions. To enable latency-sensitive sealed-bid auctions in blockchain settings, we formalize four properties -- each necessary to prevent a concrete attack -- and design a protocol achieving all four: hiding bid contents, existence, and bidder identity until reveal (Hiding); counting all timely honest bids and rejecting late adversarial bids (Simultaneous Release); preventing silent withdrawal of committed bids (No Free Bid Withdrawal); and charging on-chain fees only to winners (Auction Participation Efficiency). Our protocol uses a timestamping oracle (instantiated with a committee of 2f_ts+1 timestampers) and a censorship-resistant inclusion predicate (instantiated using a FOCIL-based inclusion list), with only the winning bid settled on-chain. Our construction relies on two zero-knowledge proofs: an eligibility proof that anonymously proves deposit membership to the timestamping committee, and an auction proof that binds a bid to a specific auction for the inclusion list committee. We implement both using Groth16 over BN254 with Poseidon hashing in arkworks/Rust: the auction proof generates in 13 ms and verifies in under 1 ms; eligibility proofs for Merkle trees up to 2^32 bidders generate in 47-159 ms and verify in about 1 ms. Together, this yields a sealed-bid auction primitive practical for high-value, time-sensitive blockchain settings.
Chuanjia Yao, Zhihui Jiang, Xufeng Su, Xinchun Ma ¡ 10 authors
The management of medical insurance funds is pivotal to the development of medical consortia. These funds serve as the operational lifeline of medical consortia and constitute critical public resources essential for public welfare. Medical expense settlement involves multiple stakeholders, including patients, tiered healthcare institutions, and insurance administrative agencies. However, disputes frequently arise between medical insurance authorities and hospitals regarding expense legitimacy due to information asymmetry and interpretative discrepancies. Such conflicts impede smoothness of payment mechanism, thereby undermining consortium operations and inter-institutional collaboration. To address these challenges, this study proposes a blockchain-based framework integrating medical expense investigation with insurance settlement. The system employs two core components: Anomaly detection via the Isolation Forest (IF) algorithm to identify potentially irregular expenses. Consensus-driven adjudication using Fleiss Kappa-based smart contracts facilitated by an anonymous panel of medical experts. This design enhances coordination between expense oversight and settlement processes, thus streamlining dispute resolution for medical expense anomalies and improving the scientific governance of insurance funds. Experimentally, the statistical validity demonstrated for unsupervised anomaly detection of IF and potential engineering applicability indicated in the analysis of healthcare cost. The healthcare consortium blockchain based on a Delegated Proof-of-Stake (DPoS) consensus mechanism and smart contract batch processing achieved a peak throughput of 229.64 transactions per second (TPS) and reduced transaction costs by up to 3,095 gwei. This demonstrates scalability for real-world medical insurance collaborative settlement systems.
Decentralized autonomous organizations (DAOs) are entities without central leadership and operate based on a set of decision-making rules encoded into smart contracts using blockchain technology. In this study, we develop a theoretical model of DAO governance featuring strategic token trading under token-based voting to investigate potential conflicts of interest between a large participant (a âwhaleâ) and many small participants. Our results show that ownership concentration has a negative effect on platform growth, but platform size, token illiquidity, and long-term incentives can mitigate this negative effect. We confirm these predictions using novel voting data on major DAOs from 2020 and 2024. This paper has been accepted by Lin William Cong for the Virtual Special Issue on Digital Finance. Funding: J. Han and J. Lee received financial support from the Institute of Management Research at Seoul National University. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2024.07033 .
This chapter examines the decentralized autonomous organizations (DAOs), which rely primarily on sociotechnical infrastructures supplied by blockchain technology and consist substantially of combinations of shared computer code and shared data. The chapter considers DAOs using the governing knowledge commons (GKC) research framework, contrasting the GKC perspective with long-standing views of the corporate form as a nexus of contracts, as an instance of hierarchy and decision theory, and as a complex system. The analysis is set against the context of earlier work on the corporation as commons. The chapter concludes that the GKC framework focuses attention on elements of governance that often are not salient in conventional accounts. This is especially true of the important question of how governance responds to and generates social dilemmas associated specifically with practices of sharing knowledge, information, and data.
While full ledger access is theoretically possible on public blockchains, in reality it is often not possible. Things that can be seen are limited by storage limitations, client design, indexing services, and off-chain execution pathways. This means that entire ledger objects are rarely used for empirical blockchain analysis; instead, observable projections are typically used. In this research, the observability of blockchain is recast as an inferential problem with incomplete observation. Studying identifiability, information loss, and irreducible uncertainty under coarsened access, the framework defines a full ledger, an observable ledger, and an observability mechanism. Three distinct visibility regimes, independent Bernoulli, clustered, and activity-dependent, are assessed in the simulation study. Reduced visibility raises uncertainty inflation, root mean squared error, variance, and mean squared error across all three regimes. The most severe deterioration happens when the condition of the underlying ledger determines visibility. This empirical study employs Google BigQuery's publicly indexed Ethereum block data spanning blocks 18,000,000 to 18,001,000. Over the chosen Ethereum period, descriptive summaries reveal a large amount of fluctuation in gas utilised, transaction count, and basic charge per gas at the block level. Experiments with controlled missingness on the observed slice reveal that RMSE and trend estimate bias grow with increasing missingness, and that the degree of distortion is significantly affected by whether the incompleteness is MCAR-like, MAR-like, or MNAR-like. This research proves that partial observability isn't just a secondary data issue; it can significantly affect inference on Ethereum block-level summaries.