Search Results (56)

The Kerper–Bowron (KB) Method (patent pending) projects expected claims at the individual contract level. This paper extends that cash-flow engine to manufacturer-warranty accruals and to three proposed uses: collateralized risk transfer, lending against service-contract equity, and risk-adjusted customer lifetime value (CLV). Manufacturer warranties and separately priced service contracts are treated as related but distinct products under ASC 460, ASC 450, and IAS 37. Expected cost per unit of exposure is formed with a generalized linear model; a Tweedie mean–variance function is used as a working choice, not as a tested warranty distribution. Accident-month estimates are allocated to payment months, incurred-but-not-reported cost on pre-valuation months is isolated, and remaining paid cash flow is split into pre-valuation runoff and post-valuation occurrence. One present-value risk margin is taken from the predictive distribution as the present value of the gap between a stated percentile and the mean; a constant loading on the discounted mean is an illustrative substitute when simulation is not run. The contribution is contract-level granularity and a single paid path that can be refreshed as experience and assumptions change. The same paid path can be used as a financial-monitoring tool: expected claims, equity, and risk-adjusted values can be refreshed as time passes, actual results emerge, and model or economic assumptions change. Accuracy, balance sheet derecognition, investor diversification, and lendable capacity would be the subject of further research. Full article

This study asks whether chief executive compensation at private nonprofit four-year institutions reflects financial performance or financial scale. The sample is 569 institutions in the 1000 to 4999 enrollment band, matched to IPEDS finance data for 2018–19 through 2023–24 and IRS Form 990 compensation data. Financial scale dominates: when entered jointly, the revenue coefficient is 0.295, enrollment is insignificant, and a Wald test rejects the equality of coefficients, although compensation remains positively associated with performance (0.088, p = 0.001). As a test of agency theory, institutions whose revenue and net tuition both declined are compensated about 14 percent below prediction; in an exploratory severity analysis, the discount reaches about 16 percent when each fell more than 20 percent in constant dollars. These are cross-sectional associations, not causal effects of board policy. Panel estimates are consistent with the discount developing over the window (the growth differential is significant at the 10 percent level). Adjustment is incomplete: about half of institutions in real decline are paid above prediction; the breakaway core label for this group is descriptive, not a finding of excess, and its larger enrollment is not distinguishable from the sector-wide pattern. Because the sector’s recovery is nominal rather than real, above-benchmark compensation occurs amid real contraction. Full article

Retailers of non-instantaneously deteriorating items must jointly set inventory, preservation technology, and payment decisions. Preservation technology reduces deterioration, but excessive investment increases operational costs, making the determination of an optimal preservation level essential for maximizing profit. Although preservation technology, hybrid payment schemes, and prepayment discounts have been studied individually, their joint treatment alongside partially backlogged shortages remains largely unexplored. To address this gap, this study develops an inventory model that simultaneously incorporates preservation technology investment, a hybrid payment structure, advance payment combined with trade credit, optionally supplemented by a prepayment discount, and partially backlogged shortages for non-instantaneously deteriorating items. A classical optimization approach is employed, yielding quasi-closed-form solutions for the shortage and replenishment timing across four trade credit scenarios, while the profit-maximizing preservation investment level is identified through sensitivity analysis. Numerical examples and sensitivity analysis show that increasing the number of prepayment installments lowers the discount rate offered by the supplier; because this forgone discount outweighs the benefit of retaining capital longer, the retailer’s profit falls. Profit responds most strongly to purchasing cost, the advance payment period, and lead time. These results give retailers a practical basis for balancing preservation investment, payment structure, and shortage policy to maximize profitability. In the sensitivity analysis, profit varies by more than 45% over the tested range of the purchasing cost and by up to 21% depending on the number of prepayment installments negotiated with the supplier. That gives retailers a concrete ranked basis for prioritizing which contract terms to negotiate first. Full article

Railway idle land offers a large and underused space for solar power, yet its economic value has not been evaluated. Revenue depends on the volatile System Marginal Price (SMP) and Renewable Energy Certificate (REC) markets, whose uncertainty cannot be fully captured by static discounted cash flow (DCF) analysis. This study asks whether solar development on Korea’s railway idle land is worthwhile over the long term, and at what scale it should proceed. It applies an integrated DCF and real options analysis (ROA) framework to a proposed 438 MW project on the Honam Line in southern Korea. Price volatility is estimated from monthly SMP and REC data with a geometric Brownian motion model, and the options to expand and to contract are valued on a binomial lattice. The DCF yields a marginal net present value of USD 3.2 million. The expansion option adds USD 172.5 million and is exercised in 67% of states, raising the total project value to USD 175.7 million. Rising panel efficiency and falling capital costs move the project firmly into feasibility. Therefore, scale flexibility turns a marginal project into a strongly positive one, supporting the large-scale deployment of solar on railway idle land. Full article

This paper investigates an infinite-horizon discounted stochastic maintenance optimization problem within the framework of dynamic programming. The system degradation is modeled by a discrete-time stochastic process affected by maintenance actions and random disturbances, while the objective is to minimize the expected discounted maintenance and degradation costs. The analysis is carried out on the Banach space of continuous functions equipped with the supremum norm. It is proved that the associated Bellman operator is well defined, maps the function space into itself, and is a contraction with contraction modulus equal to the discount factor. Consequently, the existence and uniqueness of the optimal value function follow from the Banach Fixed Point Theorem, and the convergence of value iteration is established. In addition, rigorous a priori and a posteriori error estimates are derived, providing theoretical stopping criteria for numerical computation. The theoretical results are complemented by numerical experiments illustrating the optimal stationary maintenance policy, the stability of the computed solution under grid refinement, and the influence of the discount factor on both the optimal policy and the convergence rate of value iteration. Full article

This study develops a scenario-based analytical framework for assessing smart microgrids in industrial zones as infrastructure investments and for analyzing multi-actor risk allocation and governance. No empirically validated return is reported for a specific Bulgarian industrial zone. Public institutional, market, financial, technology and environmental sources are instead used to benchmark the scenario assumptions. Three scenarios are evaluated for a reference zone with annual consumption of 12,000 MWh. The model incorporates photovoltaic (PV) degradation, battery round-trip efficiency of 85–90%, annual usable-capacity degradation, one modeled battery replacement within a 12–15-year service interval, component-based capital and operating expenditures, and an author-defined semi-quantitative likelihood–impact risk matrix. Net present value (NPV) is EUR −1,114,726 in the conservative scenario, EUR 697,836 in the baseline scenario and EUR 3,424,835 in the favorable scenario. Discounted payback is not achieved within 20 years in the conservative scenario and is approximately 14.1 and 7.0 years in the baseline and favorable scenarios, respectively. Deterministic one-at-a-time sensitivity analysis identifies electricity price, capital expenditures, and the direct PV self-consumption ratio as the dominant financial drivers.The indicative reduction in location-based emissions associated with grid electricity purchases is 802.9–1385.5 tonnes of carbon dioxide equivalent (tCO₂e) per year under the selected grid-average electricity emission factor. The contribution lies in integrating public-data availability assessment, external parameter benchmarking, battery service life, degradation and replacement economics, investment appraisal, threshold analysis, semi-quantitative risk prioritization and contractual risk allocation within one reproducible framework. The outputs remain illustrative and require project-level validation using measured hourly loads, binding prices, financing terms and enforceable contracts. Full article

Public responses during health crises are shaped by interacting risk perceptions, affect, trust, information needs, overload, misinformation, and protective behavior. Existing applications of the Risk Information Seeking and Processing (RISP) model are largely static and therefore cannot represent stochastic multichannel exposure, delayed correction, or policy feedback. We develop the Stochastic Probabilistic Dynamic RISP (SP-D-RISP) model, which recasts RISP as a bounded stochastic state-space system. Its symmetry structure is explicit: the channel-allocation mechanism is equivariant under simultaneous relabeling of channels and their parameter blocks, while the multi-agent dynamics are invariant to agent relabeling under exchangeable sampling and a label-independent policy. Channel-specific effects, heterogeneous traits, rumor shocks, and interventions generate symmetry breaking. The model combines softmax–multinomial channel competition, discounted Bayesian trust updating, and policy-coupled state transitions. Projection guarantees feasible states by construction, whereas stronger stochastic stability is conditional on a coefficient-level small-gain criterion. For the stationary bounded-memory specification, this criterion is sufficient for Wasserstein contraction, uniqueness of the invariant distribution, and geometric forgetting of initial conditions. The criterion is formulated at the coefficient level and is kept distinct from finite-horizon simulation diagnostics. For the fully disclosed semi-synthetic coefficient vector, the scenario-specific gain matrices have spectral radii between $0.852765$ and $0.857123$; the worst-case column-sum norm is $0.983948$. Thus, the fixed-policy kernels satisfy the stated contraction certificate. For deterministic time-varying paths, the calculation is used only as a common-path one-step certificate, and for the threshold-adaptive rule, it is used only mode by mode rather than as a stationary invariant-law claim. While concentration bounds and Monte Carlo inference quantify population and replication uncertainty, a semi-synthetic experiment with 2500 heterogeneous agents over 90 days examines trust and literacy heterogeneity, clarification delays, communication volume, and intervention portfolios. Within the calibrated SP-D-RISP scenarios, the simulations suggest that higher communication volume may reduce modeled protective behavior when overload effects dominate knowledge gains, delayed clarification may increase transient misinformation, and an integrated portfolio can yield a more favorable simulated outcome profile than the evaluated single-lever strategies. Full article

Entropy-based soft-min operators and Gibbs policies are widely used in reinforcement learning to smooth greedy decisions and regulate stochastic action selection. Their behavior depends critically on an inverse temperature parameter, which is often chosen through fixed values, annealing schedules, entropy targets, or worst-case bounds. These rules do not directly control the local error induced by soft action selection and do not account for the geometry of the action-value vector. This paper develops a geometry-aware conformal calibration framework for selecting inverse temperatures in discounted finite-action reinforcement learning. The analysis distinguishes the operator-level soft-min approximation error from decision-level Gibbs excess and shows how both quantities depend on local value gaps and a near-optimal action structure. A conformal order statistic rule is then used to obtain finite-sample marginal control of the selected local score under exchangeability. A geometry-conditional extension assigns different temperatures to different action-value geometries. The Bellman analysis clarifies that fixed temperature maps preserve contraction, while data-dependent conformal selectors are best interpreted as post-training action selection rules. A finite-MDP experiment empirically confirms the conformal coverage behavior and shows that calibration must be applied to a decision-relevant cost representation when value estimates contain optimistic value estimation traps. An Atari SpaceInvaders experiment with a frozen deep Q-network shows that the learned action-value geometry is heterogeneous, that decision-level calibration is less conservative than operator-level calibration, and that gated geometry-conditional selection preserves most of the greedy return while providing explicit marginal score control of randomized decision excess with low online overhead. Full article

Large online marketplaces increasingly rely on multi-stage ranking pipelines where a learned relevance model is complemented by business-aware constraints such as contractual pacing, exposure caps and commercial alignment objectives. This paper develops a second-stage, contract-aware re-ranking layer for real-estate recommendation that explicitly balances user–item relevance with plan fulfillment, lead value and operational guardrails. The proposed multi-objective re-ranker (PMOR) combines a calibrated base relevance score with multiplicative business adjustments and subtractive penalties for approaching contractual caps and for within-slate similarity. The method supports heterogeneous settlement models, including pay-per-action and fixed-fee contracts, via contract-specific weights. Because the scoring function is deterministic and structured, it admits exact component-wise contribution analysis and counterfactual ablations without relying on surrogate explainability methods. Offline evaluation on production logs from an anonymized marketplace covers 4219 recommendation requests and 184,147 candidate items, joined with daily business snapshots using an as-of strategy to prevent look-ahead bias. Under a profit proxy based on effective lead value, position discounting and billability, PMOR achieves an indexed expected-revenue proxy of 487.4 (baseline = 100), corresponding to a lift of 387.4% over a model-only baseline and 48.4% over a legacy production re-ranker (LPR). The gain is primarily associated with improved billable exposure, increasing the share of billable positions in TOP-3 to 78.22% compared with 37.85% for LPR. We discuss parameter sensitivity, operational considerations and limitations of offline proxy objectives for deployment. Full article

We establish a new fixed point result for measurable-selection-valued correspondences with nonconvex and possibly disconnected values arising from the composition of Caratheodory functions with an upper Caratheodory ($uC$) correspondence. Using Caratheodory approximation methods, we show that for any such upper Caratheodory composition correspondence, if in each state, the upper semicontinuous part of the underlying upper Caratheodory correspondence contains an upper semicontinuous sub-correspondence taking contractible values, then the underlying upper Caratheodory correspondence is Caratheodory approximable, further implying that the induced measurable-selection-valued correspondence has fixed points—all accomplished without the induced selection correspondence being convex-valued or upper semicontinuous in the appropriate topologies (in the case the weak star topologies). An excellent example of such a composition correspondence is provided by discounted stochastic games ($DSG$). In particular, the Nash payoff selection correspondence of the parameterized collection of state-contingent one-shot games underlying a discounted stochastic game is gotten by composing players’ parameterized collection of state-contingent Caratheodory payoff functions with the upper Caratheodory Nash equilibrium correspondence (i.e., the $uC$ Nash correspondence). We are able to conclude via our fixed point result that if the $uC$ Nash correspondence has an upper semicontinuous part containing a contractibly valued upper semicontinuous sub-correspondence, implying that the $uC$ Nash correspondence is Caratheodory approximable, then the Nash payoff selection correspondence induced by the $uC$ Nash correspondence has fixed points. It then follows from Blackwell’s Theorem (1965–extended to games) that the $DSG$ to which the selection correspondence belongs has stationary Markov perfect equilibria. Full article

Federated data spaces, established through initiatives such as IDSA and GAIA-X, enable organizations to share and monetize datasets under contractual terms. However, enforcing these contracts—particularly detecting unauthorized reuse or modification of datasets—remains an open challenge. We present the Off-Platform Contract Inspector, a component of the PISTIS framework, that implements a modular similarity-detection pipeline combining path-value Jaccard similarity, field-aware type-specific comparisons, and sentence-embedding-based semantic analysis across structured, semi-structured, and unstructured datasets. This contributes as follows: (i) an Inverse Document Frequency (IDF)-weighted structural similarity mechanism that discounts common domain vocabulary via Inverse Document Frequency weighting over the data space catalog, combined with a schema-evidence-gated fusion that reduces false positives from domain vocabulary overlap; (ii) an adaptive threshold optimization mechanism that learns modality-specific fusion weights and decision thresholds via cross-validated grid search; and (iii) a privacy-preserving similarity layer based on MinHash Locality-Sensitive Hashing signatures, Bloom filters with OR folding alignment, and Laplace noise for differential privacy, enabling cross-organizational dataset comparison without exposing raw data. Further, we contribute a threat taxonomy of seven dataset modification types ordered by detection difficulty, and evaluate the system on dataset pairs derived from real-world datasets across three smart-city application domains (Mobility, Energy, Automotive), with controlled augmentations applied to model adversarial behaviors. The IDF-weighted pipeline achieves high precision on intra-domain hard negatives—pairs of different tables from the same data space that share domain vocabulary—where text-similarity baselines produce false positives. The adaptive scheme learns per-modality fusion weights via cross-validated grid search. The privacy-preserving mode operates without accessing raw data and runs noticeably faster than the full pipeline, enabling screening while preserving data confidentiality. Full article

Formally stipulating rental terms in farmland transfer contracts is essential to safeguarding transacting parties’ rights, anchoring market price signals, and underpinning the rule-based governance of rural land markets. Drawing on survey data from 1496 rural households across three Chinese provinces, this study empirically examines how connection strength between transacting parties shapes the decision to formally stipulate rental terms in farmland transfer contracts. Baseline estimates show that greater connection strength is significantly and negatively associated with the probability of formal rental term stipulation, a pattern robust to alternative model specifications and variable operationalizations. Mechanism analysis reveals that stronger connections inhibit formal stipulation by concurrently heightening reputational constraints among parties suppressing demand for formal enforcement mechanisms and attenuating perceived transactional risk, which erodes the perceived value of the risk-bounding function that written clauses provide. Heterogeneity analysis further shows that this inhibitory effect is concentrated among ordinary farm household transfers and disappears among new-type agricultural business entities, where institutional rationality crowds out connection-based governance logic. Beyond its direct effect on contract formalization, greater connection strength indirectly undermines the price-anchoring function of written agreements, exposing realized rents to systematic connection-based discounting. These findings carry direct implications for the demand-side redesign of contract formalization policy and the development of county-level rental price guidance systems in rural China. Full article

Brazil possesses globally competitive offshore wind resources; however, financial viability is constrained by high capital expenditure (CAPEX) and industry risk. This study evaluates the investment feasibility of a 1 GW offshore wind project in northeast Brazil using a discounted cash flow (DCF) model. For the key parameter of CAPEX, a Baseline Case was established, assuming a 1.53% commodity price escalation from 2021 until the Financial Investment Decision (FID) date of 2027, and was sensitivity tested against an Optimistic Case, assuming 0% cost escalation and a Stress Case based on twice the commodity price escalation of 3.06% up to 2027. Each CAPEX Case was evaluated against 12 financing scenarios involving varying levels of public support through a blend of concessional debt and grants. Financial performance was measured using net present value (NPV) and Equity Internal Rate of Return (EIRR). Results indicate that project financial viability is achieved under the Baseline Case only with levels of grant funding and concessional debt that exceed realistic thresholds, unless PPA tariffs are raised by about 50% relative to current market benchmarks. The Optimistic Case is viable at current tariffs under more realistic financing structures but represents an unattainable degree of capital cost containment. The Stress Case is not viable at all without a doubling of current PPA tariffs. Sensitivity analysis further demonstrates that even the most promising financial scenarios are vulnerable to any shortening of the 20-year PPA contracting period, leading to greater merchant risk exposure. The paper concludes that catalysing Brazil’s nascent offshore wind market will therefore call for a combination of policy measures that: permit (and recoup) a transitional premium over current PPA prices; adopt structural measures to reduce associated CAPEX through local supply chain development; combine public and private sources of capital to soften financial terms; and incorporate price risk mitigation measures. Full article

The transition toward carbon-neutral cities and sustainable infrastructure requires massive capital mobilization, yet traditional static valuation models like discounted cash flow (DCF) systematically undervalue green projects due to high initial capital expenditures and long-term uncertainty. To address this critical gap in sustainable finance, this study proposes a novel Artificial Intelligence–Blockchain–Multiple Real Options (AI-MRO) integrated framework. This model aligns infrastructure profitability with Environmental, Social, and Governance (ESG) criteria and United Nations Sustainable Development Goals (SDGs), specifically SDG 11 (Sustainable Cities), SDG 13 (Climate Action), and SDG 9 (Industry, Innovation, and Infrastructure). The core approach integrates AI-based probabilistic forecasting for carbon footprint optimization and cash flow prediction, MRO-based operational flexibility assessment, and blockchain-based smart contracts (Security Token Offerings, STOs) to ensure transparent green finance governance and social inclusion. Through empirical validation at Singapore’s Punggol Digital District (PDD)—a flagship smart city project featuring a district-level smart grid reducing 1700 tonnes of CO₂ and generating 3000 MWh of solar energy annually—this model successfully captured investment resilience (Extended Net Present Value, ENPV > 0) even in crisis scenarios where conventional DCF models failed. The results demonstrate that integrating digital twins and AI-driven ESG metrics structurally reduces the risk premium and amplifies the strategic value of sustainable investments. This study represents a substantial methodological contribution toward data-driven, automated, and transparent governance, offering a scalable financial framework for global net-zero infrastructure development. Full article

The Build–Operate–Transfer (BOT) model is one of the most widely used Public–Private Partnership (PPP) methods for financing large-scale infrastructure projects. In this model, the concession period, which is the most critical parameter of the contract between the government and the private-sector investor, is a decision variable that directly affects the interests of both parties and varies depending on many uncertainty factors. The vast majority of models in the existing literature have been tested on hypothetical projects, and it is observed that parameters such as country-specific legal regulations, traffic volume guarantees, and financing conditions affecting discounting over time are not sufficiently incorporated into existing models. This study develops an integrated stochastic financial model, building on the established NPV–Monte Carlo–bargaining framework in the literature, that determines the optimum concession period for highway projects to be tendered via the BOT model in Türkiye. In the proposed model, uncertain parameters (construction cost, inflation, loan interest rate, traffic volume, toll increase rate, operation and maintenance costs) are defined with probability distributions; the Net Present Value (NPV) based financial model is solved via Monte Carlo simulation; and the obtained concession range is narrowed using a Rubinstein-type alternating-offers bargaining-game framework. The model simultaneously integrates parameters that prior studies addressed only in isolation: the equity–debt structure, loan repayment conditions, the government’s traffic volume guarantee, expropriation costs, and legal limits specific to Türkiye. The proposed model was validated by applying it to the Ankara–Niğde Highway Project, which was tendered in 2017. The results indicate that the concession range calculated by the model (11 years, 9 months, 2 days–24 years, 4 months) is consistent with the actual bids in the tender process. Following the application of bargaining-game theory, the range was narrowed to between 13 years, 4 months, and 16 days and 13 years and 5 months; this interval represents the concession range that best balances the profitability of both parties. This study provides a multidimensional evaluation framework for decision-makers by presenting comprehensive profitability analyses under different scenarios (including/excluding guaranteed traffic volumes and the project being fully constructed by the state). Full article