This Online Appendix provides forty supplementary sections supporting the main manuscript. The principal contributions are: (i) a complete multi-input atomicity proof for the UTXO Consistency Result under crash-fault semantics, with six-case exhaustive analysis and sourcecode correspondence; (ii) a formal liveness theorem establishing bounded recovery from Validator crashes during the compensating-unspend protocol; (iii) three alternative scalingefficiency models (η(M): logarithmic, power-law, Amdahl) with cross-validation, bootstrap prediction intervals, and architectural justification for model selection; and (iv) an SPV verification analysis demonstrating that Simplified Payment Verification remains feasible at 10 11-TPS fleet scale without requiring archival nodes. Additional sections provide the full G/G/c throughput-ceiling derivation, 60-point derating calibration methodology, NVMe-spill and mixed-workload production estimates with uncertainty propagation, Aerospike strongconsistency formal model, and responses to all reviewer concerns including a mathematical foundation audit.
When an end-to-end automated driving system neither forms nor retains a recoverable, decision-specific semantic record, investigators cannot reconstruct the internal path from perception to control. This study develops condition verification to produce contestable causal findings from the records that remain. The study combines information-structure analysis; comparative mapping of five verification questions to regulated and manufacturer-held evidence in the United States, European Union, United Nations vehicle-regulation system, and China; and scenario analysis of two constructed Level 3 crashes. Evidence sources span regulated recorders, fleet incident and exposure records, and manufacturer training, validation, and boundary-management materials. Where no decision-specific semantic record is formed and retained, the internal account is eliminated rather than merely opaque; additional logging and interpretability tools cannot restore it. Each question maps to records either mandated where Level 3 systems are approved or held by manufacturers; the sole new duty is a training-coverage disclosure. Across two contrasting Level 3 crashes, the same instrument set establishes event-level system contribution in one and negates it in the other, alongside three typed systemic findings resolved as established, negated, or indeterminate. For incomplete Level 2 records, an indirect path permits a rebuttable inference only when event anchors, a matched fleet propensity, no sufficient alternative account, and operator-controlled record gaps subject to retention duties coincide. Where burden-shifting instruments reallocate a proof object that neither party can establish, condition verification changes the object itself: from the unrecorded internal path to verifiable external conditions. Its indirect path gives the malfunction doctrine a version-indexed, exposure-normalized form for continuously updated fleets. Investigators can apply the protocol as a checklist linked to identified records and custodians. Insurers can incorporate its typed findings into claims analysis. Courts and regulators receive structured predicates for defect, negligence, causation, and apportionment, not final determinations.
We study social choice functions that take preferences over sets as input, when voters evaluate sets according to conditional expected utility obtained from Bayesian updating of a common prior.While under a uniform prior both dictatorial and bidictatorial rules satisfy strategy-proofness and unanimity, we show that this multiplicity is a knife-edge case: for every non-uniform prior, dictatorship is the unique rule satisfying these properties. The argument links set-valued rules to random dictatorship and shows that the probability ratios imposed by non-uniform updating are incompatible with combining the top choices of multiple voters. We also characterize the admissible rules when voters hold heterogeneous subjective priors.
On May 7, 2024, R. J. Mathar contributed to OEIS A186185 a conjectured order-6 Precursive recurrence, with a matching asymptotic conjectured the same day by V. Kotesovec. We give a short proof, deriving an explicit cubic algebraic equation for the generating function from its defining functional equation and extracting the recurrence via the Bostan-Chyzak-Salvy algebraic-to-holonomic correspondence.
This chapter explores how regulatory contract theory might inform and reshape American employment law, in particular its distinct employment at-will doctrine that permits termination without cause. That principle, and its freedom-of-contract ethos, has long constrained and distorted efforts to regulate employment contracts. Substantive statutory intervention has been piecemeal and reactive. Labor laws that might have supported a collective contract model of regulation have been too weak to sustain a culture of union affiliation or successful bargaining. And individual employment contract law has developed in ways that enhance managerial power and stymie application of traditional policing doctrines. Accepting the futility of efforts to defeat employment-at-will, the chapter imagines how regulatory contract theory might blunt its edges. It advances a common law reform agenda that preserves the core of employment-at-will – employers' substantive discretion to terminate – but imposes non-waivable procedural and relational obligations, including reasonable notice of termination and good-faith limitations on changes to employment terms. The chapter concludes that a reorientation of employment-at-will doctrine, grounded in regulatory contract theory, can enhance workplace fairness while still preserving the basic structure of American employment law.
Santiago went from about 100 electric buses in 2018 to nearly 4,400 by early 2026, the largest electric-bus fleet outside China. This paper analyzes the contract design behind the transition. Successive tenders unbundled service operation from fleet ownership and charging infrastructure. They turned buses and terminals into concession-dedicated assets that transfer to the next operator. They also paid fleet financing through fixed monthly installments, administered centrally and owed regardless of which operator runs the route. This design removed the two risks that make lenders charge a premium on transit assets: stranding at contract end and operator default. It made electric capital financeable without operators bearing it. A permanent subsidy, created in 2009, made the long payment horizons credible. Competition widened accordingly: participation rose from 16 offers in 2019 to 84 in 2023, and the monthly fleet installment per electric bus fell by 10 to 25 percent. Using payment and tender records, I show that electrification did not materially change the cost per kilometer of service and did not require a new funding source. The 2019 bids priced both technologies under the same contracts. They imply that renewing the fleet with new diesel buses would not have been cheaper per kilometer. I conclude with policy lessons and the conditions under which the model transfers to other cities.
This paper proposes a polar-coordinate framework for Bitcoin prices around the halving cycle, where each revolution is one halving epoch and radial distance represents log price. This alignment reveals recurring patterns conventional time-series charts obscure. We develop five statistics, spanning seasonality amplitude, phase concentration, intercycle repeatability, radial growth, and bull-bear asymmetry, each paired with an explicit null hypothesis and, given the small number of completed cycles, block-bootstrap or randomization inference. To test whether the structure is specific to Bitcoin's supply schedule, we apply the identical clock to Ethereum and the S&P 500 as placebo assets. Bitcoin shows significant halving-phase seasonality (p
Cryptocurrencies generate no contractual cash flows, so the discounted-cash-flow models used to value equities and bonds cannot be applied to Bitcoin, leaving its pricing without a conventional economic anchor. We address this gap with a flexible hybrid framework that combines a fundamental component, monetary policy, liquidity, and inflation expectations, with a speculative component built from autoregressive return dynamics, both estimated over rolling windows so their exposures can evolve as Bitcoin's market has evolved. Using daily data from 2014-2026, the selected rolling ARX(1), 56-day specification produces a sequence of one-step-ahead return projections whose accumulated path closely mimics Bitcoin's realized cumulative return across three boom-bust cycles (Figure 1). Building on that fit, the same rolling coefficients generate a forward-looking Bitcoin price distribution over the next six months (Figure 2). The paper's central contribution is this two-step link: a framework validated by how closely it reproduces Bitcoin's past return path is then used to project its future one, conditional on macroeconomic conditions remaining unchanged.
Three years after the public release of ChatGPT, the economic literature on artificial intelligence's effect on employment, wages, and productivity has grown rapidly but has not converged. This review synthesises that literature, organising it around the task-based framework developed by Autor, Levy, and Murnane (2003) and extended by Acemoglu and Restrepo's (2019) distinction between the displacement and reinstatement effects of automating technologies, and situates the evidence against India's services-led economy as a case study of concentrated exposure. On employment, the review finds the aggregate evidence genuinely contested: administrative payroll data analysed by Brynjolfsson, Chandar, and Chen (2025a) documents sizable relative employment declines in the most AI-exposed occupations following ChatGPT's release, while Hartley, Jolevski, Melo, and Moore's (2026) large-scale US worker survey finds small positive wage effects and no statistically significant decline in job openings or employment despite 35.9 percent of workers having adopted generative AI tools by December 2025 — a divergence this review argues partly reflects Iscenko and Millet's (2026) finding that AI-exposed occupations are disproportionately concentrated in interest-rate-sensitive sectors, confounding technology effects with macroeconomic conditions. Where the evidence is more consistent is at the margin: employment among 22-to-25-year-old workers in AI-exposed US technology occupations fell roughly 6 percent between late 2022 and mid-2025 even as older workers in the same occupations gained roughly 9 percent, a pattern echoed in India's own entry-level information-technology hiring, which EY estimates fell 20 to 25 percent over the same period. On wages, the review highlights Brynjolfsson, Li, and Raymond's (2025) Quarterly Journal of Economics finding that generative AI assistance raised customer-service agents' productivity by up to 34 percent for novice workers while leaving experienced workers' productivity essentially unchanged, a skill-levelling pattern in some tension with the skill-biased technical change literature that dominated the pre-generative-AI automation debate. On productivity, the review reads firm- and task-level gains against Brynjolfsson, Rock, and Syverson's (2019) productivity J-curve framework, arguing that the continued absence of a clear acceleration in aggregate productivity statistics is consistent with, rather than contrary to, the historical pattern following prior general-purpose technologies. India's IT-BPM sector, examined as a concentrated case study, shows early, tangible effects: the country's three largest IT services firms recorded a combined net headcount decline of approximately 65,000 in 2024, the first such combined decline in two decades. The review closes with policy recommendations addressing the specific evidentiary and distributional gaps this synthesis identifies.
The implementation of the Goods and Services Tax (GST) on July 1, 2017, represented a watershed moment in India's fiscal history, consolidating a fragmented indirect tax regime into a single unified framework. Nevertheless, for the micro, small and medium enterprises (MSMEs) constituting the industrial sinews of Ludhiana-often heralded as the 'Manchester of North India' and the global epicenter of bicycle manufacturing-this structural transition has proven to be less an emancipation from bureaucratic entanglement and more an intricate maze of procedural obligations. The present investigation critically scrutinizes the tax-related adversities encountered by indigenous MSMEs in Ludhiana, encompassing escalating compliance expenditures, blocked input tax credit (ITC) refunds, inverted duty structures, digital literacy deficits, and acute working capital strangulation. Drawing upon primary data from 150 MSME units (stratified as 50 micro, 50 small, and 50 medium enterprises) and supplemented by semistructured interviews with 30 proprietors operating across the hosiery, bicycle parts, and metal fabrication clusters, the empirical findings reveal that GST compliance costs are markedly regressive, disproportionately eroding the already slender profit margins of micro-enterprises. Furthermore, procedural complexities surrounding the reconciliation and refund of accumulated ITC have precipitated severe liquidity constraints, compelling numerous firms to rely on highinterest informal credit channels to sustain operations. Notwithstanding these operational
Blockchain ventures exhibit failure rates markedly higher than those of conventional startups, yet the technological drivers of their survival remain underexplored. This study examines how innovation and security risk shape the long-term survival of blockchain ventures. Drawing on detailed source code data from blockchain ventures' GitHub repositories, together with corresponding survival and market data, I employ two code-based metrics, code originality and code vulnerability, and estimate their effects on venture survival using Cox proportional hazards and Weibull accelerated failure time models. The results reveal a two-sided dynamic: higher code originality decreases failure risk through an appropriability channel, whereas greater code vulnerability increases failure risk through a legitimacy channel. Further analyses reveal that these effects are heterogeneous across venture development profiles. Originality reduces failure risk for pioneering ventures but increases it for imitative ventures, while vulnerability raises failure risk for both groups, with a substantially stronger effect among imitative ventures. Overall, this study links code-level technological characteristics to venture survival and highlights that effective survival strategies should align with a venture's development profile.
Blockchain is a revolutionary technology transforming the digital economy. This research paper provides a comprehensive primer on blockchain technology, covering its architecture, core principles, and applications. We explore the transition from centralized to decentralized systems, the cryptographic foundations of security (SHA-256, Merkle Trees), and the evolution of smart contracts. Additionally, we analyze the impact of blockchain across various industries and its role in the emerging Web3 infrastructure.
Blockchain has evolved beyond speculative cryptocurrency use cases into a technology supporting real world enterprise infrastructure. This report analyzes 50 enterprise blockchain deployments across finance, supply chains, healthcare, energy, and government to identify the factors that influence successful implementation. The analysis finds that narrowly defined use cases with clear operational benefits consistently outperform broad industry transformation initiatives. Governance and coordination among multiple stakeholders remain the primary barriers to large scale adoption, while institutional investment in tokenized real world assets demonstrates sustained momentum. The findings suggest that blockchain adoption is increasingly driven by measurable business value, interoperability, and regulatory alignment rather than technological novelty. This report provides practical insights for organizations evaluating blockchain as enterprise infrastructure.
This dissertation presents the design, implementation and evaluation of a blockchain-based e-voting prototype which supports ballot confidentiality, verifiability and auditability by combining homomorphic encryption, zero-knowledge proofs and Merkle inclusion proofs. The key innovation of this work lies in a unified on-chain protocol that cryptographically binds each ballot's Poseidon commitment to its ElGamal ciphertext through a shared binding hash, enabling Groth16-verified tallying and anonymous receipt-based audit without any trusted middleware. In the voting phase, the voter encrypts a one-hot ballot, computes a commitment and a ciphertext-binding hash, and submits them together with a zero-knowledge proof that the ballot is well formed without revealing the selected candidate. During the tallying stage, all encrypted ballots are off-chain aggregated, the final results are accompanied by a proof of correct decryption. Voters can confirm their ballots are included by using locally stored receipts and an audit bundle provided by the administrator. The prototype is implemented with Solidity for the smart contracts, Circom and snarkjs for zero-knowledge circuits, JavaScript for frontend interaction and Python for local serving and cryptographic validation. It was tested locally with Hardhat and then deployed to the Sepolia testnet for more realistic on-chain execution and gas measurement. The results show that the system functions as a research prototype, although further hardening is still needed before any production use.