tco analysis

Cyber threat intelligence (CTI) investments face justification challenges within conventional cost-benefit frameworks due to the “negative evidence problem”—the difficulty of quantifying value derived from prevented, rather than observed, incidents. Method: This paper proposes a data-driven CTI return-on-investment (ROI) quantification framework that integrates an extended Gordon-Loeb model with the Factor Analysis of Information Risk (FAIR) methodology. It introduces the Threat Intelligence Effectiveness Index (TIEI)—a weighted geometric mean of quality, enrichment level, integration maturity, and operational impact—thereby systematically transforming negative evidence into interpretable ROI metrics. The framework further incorporates empirical parameters—including mean time to detect (MTTD), mean time to respond (MTTR), and attacker dwell time—to enable multidimensional assessment across financial loss reduction, adversary coverage, and business enablement. Results: Validated across financial services, healthcare, and retail sectors, the framework supports CTI’s strategic repositioning from a cost center to a value-generating investment, demonstrating cross-industry replicability and capacity for continuous refinement.

Traditional quantitative investment systems typically optimize a single metric—such as the information ratio—and thus struggle to meet professional investors’ multifaceted objectives, including pure alpha generation, style control, drawdown resilience, and turnover and capacity constraints. This work proposes an Objective-Oriented Quantitative Investment (OOQI) framework that formally encodes investment intent as strategy specifications and compiles them into composable, constraint-satisfying strategy assemblies. Key innovations include establishing a dual lattice structure between specifications and assemblies, designing a satisfaction-driven synthesis mechanism, and introducing rolling recertification via e-process-based validation. Empirical results demonstrate that the specification-driven approach satisfies 100% of target constraints across 32 strategies, at the cost of only a 5.5% reduction in information ratio, whereas conventional outcome-oriented methods—despite higher in-sample information ratios—fulfill merely 25% of the specified requirements.

Organizations face significant challenges in AI investment decision-making: conventional ROI models fail to simultaneously capture AI’s cost-reduction and efficiency-gain benefits and its novel risk exposures—including algorithmic failure, bias-related litigation, model drift, and regulatory noncompliance. This paper introduces the first risk-adjusted financial evaluation framework explicitly aligned with regulatory standards such as ISO/IEC 42001 and the EU AI Act. Methodologically, it innovatively incorporates control effectiveness, failure contingency reserves, and ongoing operational costs into benefit quantification, and employs annualized loss expectancy analysis, Monte Carlo simulation, and risk exposure gap modeling for rigorous risk-adjusted valuation. The framework enables precise calculation of AI project net benefits, thereby supporting evidence-based capital allocation and investment decisions. It further fulfills dual objectives: upholding fiduciary duty and ensuring regulatory compliance.

Rising energy price volatility significantly increases the total cost of ownership (TCO) of high-performance computing (HPC) systems. Method: This paper proposes an energy budget management framework that integrates variable-capacity scheduling with a refined TCO model—specifically, the first to embed dynamic compute capacity adjustment into an HPC TCO model, enabling quantitative economic trade-off analysis among hardware utilization, energy-saving benefits, and idle-resource risk under time-varying electricity pricing. The approach leverages a parametric cost model calibrated with real operational data from a university-scale HPC cluster. Contribution/Results: Empirical simulation demonstrates that dynamic response to time-of-use electricity tariffs reduces energy expenditure by up to 18.7%; however, imposing a minimum load threshold is essential to mitigate resource idleness. The framework provides a practical, decision-support tool for jointly optimizing energy efficiency and economic performance in green-energy–integrated HPC infrastructures.

本文针对AI商业模式下的企业估值不确定性问题，提出了一种基于里程碑的实物期权框架方法来解决。

This study addresses the challenge enterprises face in evaluating the true returns of AI initiatives due to uncertain feasibility, a context where traditional ROI methods often fail. To overcome this limitation, the authors propose an expected Return on Investment (eROI) framework that decouples AI project assessment into three independently evaluable business dimensions: value upon success, probability of success, and required investment. This approach enables efficient pre-implementation decision-making and facilitates the construction of diversified AI project portfolios. Notably, the framework requires no complex modeling—only qualitative executive judgments on key dimensions—thereby circumventing the common “build-to-evaluate” dilemma. Empirical application at Compass demonstrated its practical utility: high-value projects such as the Likely-to-Sell recommendation system, which generated nine-figure annual revenue, were successfully prioritized, while low-potential initiatives were terminated early, validating the framework’s discriminative power and operational effectiveness.

Traditional revenue forecasting approaches struggle to uncover the underlying customer behavioral drivers—such as customer acquisition, repeat purchase rates, and average transaction value—that influence revenue dynamics. To address this limitation, this work proposes the Customer-Based Multi-Task Transformer (CBMT), which uniquely integrates multi-task learning with a Transformer architecture to jointly model customer behavioral metrics and total revenue through shared representations. Furthermore, CBMT incorporates a downstream alignment mechanism to enhance both interpretability and predictive accuracy. Empirical evaluation on real-world customer transaction panel data demonstrates that CBMT outperforms existing methods across 23 out of 24 evaluation metrics, achieving a 30% reduction in total sales prediction error compared to the strongest baseline and significantly surpassing single-task models employed by 74.3% of firms.

This work addresses the challenges of low quality and poor transparency in build-or-buy decisions within enterprise software development, which often stem from reliance on unstructured experiential knowledge. To overcome these limitations—particularly in cold-start scenarios lacking historical data—the authors propose a structured approach that integrates a decision-factor ontology, rule-based reasoning, and reference-class matching. This method enables transparent, auditable evaluation of alternatives and represents the first application of combined ontology modeling and rule reasoning to build-or-buy decision-making. By revealing critical decision thresholds and supporting traceability, the approach enhances the rationality, transparency, and auditability of choices. Its practical efficacy is demonstrated through a lightweight tool validated in a financial industry case study, showing significant improvements in decision quality.

This study addresses the unclear relationship between initial capabilities and the computational returns of interaction scaling in long-horizon agents. By analyzing long-horizon benchmarks, we reveal how initial performance correlates with subsequent gains, finding that later-stage benefits are highly concentrated among a few models. Accordingly, we propose a category-specific logistic power-law scaling model that integrates trajectory extrapolation with cost calibration to construct a progress-prediction-based continuation strategy for optimizing compute allocation. Experiments demonstrate that this strategy reduces runtime by approximately one-third while incurring only marginal performance degradation of 2.4% on AutoLab and 3.3% on EdgeBench, achieving an effective balance between efficiency and accuracy.

本文提出了一种名为Return-to-Present (RTP)曲线的方法，解决了传统投资回报展示方式中固定起点或离散时间点的局限性，通过保持评估日期不变而让历史购买日期连续变化，从而提供了一个更全面的投资表现视图。