cost accounting model construction

This paper addresses the regulatory arbitrage, enforcement inconsistencies, and compliance uncertainty arising from ambiguous computational resource and cost accounting standards in AI governance. To resolve these challenges, we propose the first seven-principle accounting framework that simultaneously prevents strategic manipulation, avoids disincentivizing risk mitigation, and ensures cross-jurisdictional implementation consistency. Methodologically, the framework integrates policy-technical alignment analysis, incentive-compatible design, cross-organizational comparability modeling, and compliance boundary reasoning—thereby bridging the gap between regulatory requirements and engineering practice. The framework has been incorporated into AI regulatory drafts across multiple jurisdictions and underpins the robust implementation of compute-threshold-based legislation, including the EU AI Act. It significantly enhances accounting transparency and firms’ ex ante compliance predictability. As a result, it establishes a globally applicable, game-theoretically robust, and interoperable technical governance paradigm for AI.

This study addresses the challenge of quantifying the complexity and cost induced by external requirement changes when detailed knowledge of a system’s internal logic is unavailable. To this end, the authors propose a black-box assessment method based on a directed graph of component coupling. By analyzing component interfaces and integrating multi-view modeling—graphical, algebraic, and tabular—the approach uniquely links interface characteristics to cost factors, enabling computable bounded estimates of change-induced complexity and associated costs. The method was validated through a large-scale integration case in a retail banking platform, demonstrating its effectiveness and providing architects and operations teams with actionable, quantitative insights for system design and maintenance.

This work addresses the frequent conflation of technical debt (a stock liability) and stochastic tax (a flow burden) in AI agent systems, despite their fundamentally distinct natures and substantial implications for governance and operational cost assessment. The study proposes a structured framework that enables the independent quantification and joint analysis of these two constructs through modeling, operational data analysis, and simulation. To facilitate practical adoption, the framework is accompanied by an interactive dashboard and spreadsheet-based tools. Empirical validation in real-world business contexts—such as accounts payable—demonstrates that the approach effectively supports cost control and governance decisions in AI systems, offering practitioners actionable, quantifiable, and visualizable methods for managing systemic liabilities.

This study addresses task sequencing, resource allocation, and multi-constraint optimization during project planning, aiming to jointly minimize makespan and cost. We propose a sequential concession-based iterative task hierarchical decomposition method incorporating an “ideal point” concept to enable decision-makers to dynamically trade off time–cost preferences. We introduce the first modeling framework for synchronous task execution, integrated with Boolean programming to compute minimum-cost feasible schedules. Furthermore, we develop a hybrid qualitative–quantitative multi-objective decision support model that rigorously derives computable lower bounds for both makespan and cost, and generates tunable Pareto-optimal management plans. The model has been validated across educational, research, and industrial production scenarios, demonstrating significant improvements in plan robustness and execution efficiency.

Conventional financial ratio analysis at the industry level suffers from statistical deficiencies—including skewness, non-normality, direction dependence (sensitivity to numerator/denominator selection), and outlier susceptibility. Method: This paper pioneers the systematic application of Compositional Data Analysis (CoDA) to finance, introducing clr/alr/ilr transformations for geometric mean aggregation, compositional principal component biplots, compositional k-means clustering, and compositional linear regression, alongside a CoDA-based DuPont decomposition framework. Validation employs the CoDaPack toolkit on Spanish winery financial statements. Contribution/Results: The approach enables unbiased industry-level mean estimation, visualizes structural heterogeneity in financial composition, identifies robust performance clusters, and supports direct modeling of ratios with interpretable, statistically coherent regression. This work establishes a theoretically consistent, robust, and reliable analytical framework for financial ratio modeling.

This article introduces a metamodel for the Business Model Canvas (BMC) using the Unified Modelling Language (UML), together with a dedicated Domain-Specific Modelling Language (DSML) tool. Although the BMC is widely adopted by both practitioners and scholars, significant challenges remain in formally modelling business models, particularly with regard to explicit specification of inter-component relationships, while preserving the simplicity that characterises the BMC. Addressing this tension between modelling rigour and practical relevance, this research adopts a Design Science Research approach to formally specify relationships among BMC components and to strengthen their theoretical grounding through an adaptation of the V 4 framework. The proposed metamodel consolidates BMC relationships into three core types: supports, determines, and affects, providing explicit semantics while remaining accessible to end users through graphical tooling. The findings highlight that formally specifying relationships significantly improves the interpretability and consistency of BMC representations. The proposed metamodel and tool offer a rigorous yet usable foundation for developing DSML-based BMC tools and for enabling systematic integration of the BMC into widely used software and enterprise modelling environments, thereby bridging business modelling and enterprise architecture practices for both academics and practitioners.

This study addresses a critical gap in current AI efficiency evaluations, which typically focus only on isolated training or inference phases and fail to capture the full lifecycle resource consumption and environmental impact of AI systems. To overcome this limitation, the work introduces, for the first time, a comprehensive Life Cycle Assessment (LCA) framework tailored to machine learning. This approach systematically integrates energy use and embedded environmental costs across all stages—including hardware manufacturing, model training, and deployment—thereby transcending the narrow scope of conventional assessments. By providing a holistic and accurate methodology for evaluating sustainability, the proposed framework offers researchers, developers, and policymakers a robust tool to guide more environmentally responsible design, deployment, and regulation of AI technologies.

This study addresses the allocation of multiple heterogeneous resource types in hierarchical organizations, where certain resources can be transformed into one another. The problem is formulated as a market equilibrium model incorporating structural constraints inherent to the hierarchy. To solve it efficiently, the authors propose a novel two-stage approximation algorithm: first solving a tractable instance that respects the hierarchical structure, then iteratively refining the solution to handle general cases. This work introduces, for the first time, a two-stage approximation framework to hierarchical resource allocation with conversion capabilities, establishing both the guaranteed existence of feasible equilibria and computational efficiency. Experiments on real-world Google TPU/GPU allocation datasets demonstrate the algorithm’s effectiveness and rapid convergence.

This study addresses the challenges of semantic alignment and verification in cross-jurisdictional financial disclosures, which arise from divergences in accounting standards, document formats (e.g., XBRL vs. PDF), and aggregation rules. To overcome these issues, the authors propose an ontology-based structured approach that constructs a unified financial statement ontology and implements a multi-stage, auditable pipeline. Within this framework, large language models (LLMs) are employed not as free-form generators but as constrained validators operating under explicit rule-based guidance and grounded in verifiable evidence, enabling localized information processing. The method innovatively incorporates agent workflows to shift LLMs toward controlled validation, facilitating semantic mapping and anomaly tracing across markets. Experiments on annual reports from the U.S., China, and Japan demonstrate significant improvements in consistency and reliability under heterogeneous disclosure regimes, supported by an interactive platform enabling structured data export.

This study addresses the opacity in cost allocation arising from the direct use of administrative data exports for budgeting and governance decisions, which often lack verifiable and reproducible processing pipelines. To enhance transparency, the work proposes a deterministic, rule-based data processing framework that aggregates student-level costs and enrollment counts from ad hoc academic databases to compute per-student costs. It innovatively integrates SHA-256 input hashing for auditability with a year-anchor-driven fuzzy segmentation strategy—employing left-shoulder, triangular, and right-shoulder membership functions—to enable traceable recomputation and interpretable labeling (low/medium/high). The output is a standardized workbook containing processing summaries, trend analyses, subject-specific reports, and segmentation labels, substantially improving decision-making transparency, reproducibility, and audit support.