Publications Database
Welcome to the new Schulich Peer-Reviewed Publication Database!
The database is currently in beta-testing and will be updated with more features as time goes on. In the meantime, stakeholders are free to explore our faculty’s numerous works. The left-hand panel affords the ability to search by the following:
- Faculty Member’s Name;
- Area of Expertise;
- Whether the Publication is Open-Access (free for public download);
- Journal Name; and
- Date Range.
At present, the database covers publications from 2012 to 2020, but will extend further back in the future. In addition to listing publications, the database includes two types of impact metrics: Altmetrics and Plum. The database will be updated annually with most recent publications from our faculty.
If you have any questions or input, please don’t hesitate to get in touch.
Search Results
Melville, G., and Yeomans, J.S. (2026). "Decomposing the Theta Cliff: A SIMDEC Filtering of Asymptotic Time-Decay in Long-Call Options with a Real-Money Intraday Illustration", AI, 7(7)-257.
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Previous research has shown sector-conditional asymmetry in implied volatility levels and in option returns. However, no prior work has parameterised that asymmetry at the effective-theta layer in a form that fires a non-discretionary rule trigger. This study supplies the parameterisation, its formulation, the first observation, and the data evidence. An effective theta is defined as 𝛩𝑒=𝛼(𝑠,𝑟)⋅𝛩𝐵𝑆, where 𝛩𝐵𝑆 is the standard Black–Scholes (BS) theta and 𝛼(𝑠,𝑟) is a sector- and regime-conditional scaling factor. A SIMDEC decomposition is used to filter the input space and to determine the corner where 𝛼 matters most. The framework is a bounded retrieval-and-deterministic compute system. The instruments are retrieved from cached market data and the learned layer’s outputs are constrained to that admissible set. Therefore, by construction, it cannot confabulate a fictitious or out-of-bounds instrument and the generative-class hallucination failure mode cannot occur. This concerns the groundedness and bounds of every output and is distinct from the accuracy of the regime and quality labels. SIMDEC supplies the joint-state filtering partition and, together with the Sobol variance decomposition, an explainability and attribution layer in which every position-level evaluation maps to an interpretable joint-state bin and a variance-share attribution. A “first observation” arising from a three-position long-call cohort traversing terminal decay is deployed using eight intraday states tracked on the trajectory at primary-source resolution and illustrates the relationship of the 𝛼 parameterisation to existing market conditions. To examine the effectiveness of the approach, a SIMDEC dataset from the same deployment supplies population-level support across 12 sectors and a three-tier quality stratification. The dataset is the output of the THETA AI/ML pipeline—a multi-architecture deep-learning inference system that treats SIMDEC joint-state partitioning and Sobol variance decomposition as complementary interpretability inputs, with the regime classifier carrying the labels and the composite quality scorer carrying the stratification. The PC-based, token-free analytical procedure for regulated decision-making settings, together with an illustrative example of the asymmetry in the effective-theta provide a “next level” contribution to traditional option methodology.Melville, G., Ghiassi, D., Inthathirath, S., and Yeomans, J.S. (2026). "Filter Before You Solve: A Deterministic-First/Learned-Second Architecture for AI-Driven Portfolio Management with Real-Money Training-Investment Calibration", Journal of Software Engineering and Applications, 19(8)-301-348.
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This study investigates a deterministic-first/learned-second AI/ML framework that is deployed in regulated retail brokerage accounts. A two-stage calibration system is employed that combines historical back-testing with live recalibration via continuous position snapshots. The framework includes a unique explainability layer that employs the global sensitivity analysis method, SimDec, to identify the most influential components. SimDec renders every admissible decision fully attributable to a joint state of the input space. Because the attribution is an input to the trigger rather than a commentary on it, no generative step exists in the decision path and hallucination is structurally unreachable and, therefore, absent by construction. An intraday options trading financial framework is illustrated through a live training-investment cycle on long-call positions using “real money”. The major contribution of this research is the overall architectural and methodological framework. The filter-before-you-solve approach enables contributions to be evaluated independently of specific implementations. Beyond financial applications, it is described how the complete architectural pattern can actually generalize to many AI/ML deployment contexts that require auditable deterministic gating prior to learned inference.Melville, G., Ghiassi, D., Inthathirath, S., and Yeomans, J.S. (2026). "Architectural Transferability in Bounded AI: Five Conditions for Transfer from Options Trading to Regulated Decision Domains", AI, 7(9)-366.
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AI and machine learning deployments in regulated decision contexts face an intolerance for inadmissible outputs (“hallucinations” when the model is generative) that current explainability methods address only after the fact. Bounded AI denotes prevention by architectural design. This study establishes five conditions (C1–C5) under which a bounded artificial intelligence (AI) architecture transfers from one regulated decision domain to another. Conditions C1 through C4 adapt or combine previously established principles. The most significant contribution is the discrete joint-state topology condition, C5, for which no precedent was found in this role. The claim is that these five conditions are jointly necessary for the closure property to survive an architectural transfer—while sufficiency is not claimed. Two of the five conditions are structural prerequisites governing whether the architecture’s operators can be constructed in a destination at all. The remaining three provide warrant conditions governing whether it is the appropriate instrument or not. In existing runtime-assurance architectures, the constraint acts after inference, on the output of the learned component. In contrast, the pattern developed in this paper reverses the assurance steps via a deterministic-first/learned-second approach. Namely, the assurance architecture acts before inference on the input domain: a deterministic filter admits only rule-compliant objects, and the trigger fires non-discretionarily on joint-state cell occupancy rather than on the learned score. The architecture’s domain-neutral type signatures are formalized, and three structural transfers are developed in depth: predictive maintenance, energy-grid management, and credit underwriting, each concluding with a closure proof. All three transfers remain conceptual and report no deployment outcomes. The proofs are conditional on three stated premises that establish soundness with respect to a rule set rather than a safety case. The strongest evidence of transferability is a market-surveillance destination classified as admissible in advance and later realized on a live venue. C5 is what discriminates the transferability. It is shown that the autonomous-vehicle perception case satisfies C1 through C4, but fails C5 because the required distinctions are absent from the representation at every granularity—which is a failure that no additional compute can resolve. The architecture becomes domain-neutral through the act of transfer, not before it.S. Liukkonen, M. Kozlova, R. Stepanov, J.S. Yeomans (2025). "Comparative Analysis of the Datar-Mathews Real Options Method and Simulation Decomposition for Strategic Environmental Decision-Making", Journal of Environmental Informatics Letters, 14, 1, 30-42.
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Incorporating uncertainty into environmental evaluations improves decision-making and methods taken from various disciplines have been used to support this task. This paper contrasts the performance of two distinct approaches: the Datar-Mathews method pertains to real options valuation inherited directly from finance theory, while Simulation Decomposition (SimDec) has its origins in global sensitivity analysis. The two methods provoke different research questions, different modeling resolutions, and supply different analytics. Our analysis suggests that global sensitivity analysis should be used in the exploration and project planning stages, while real options are more useful for explicitly pricing tasks.
M. Kozlova, A. Ahola, P. Roy, J.S. Yeomans (2025). "Simple Binning Algorithm and SimDec Visualization for Comprehensive Sensitivity Analysis of Complex Computational Models", Journal of Environmental Informatics Letters, 13, 1, 38-56.
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Models of complex environmental systems inherently contain interactions and dependencies among their input variables that affect their joint influence on the output. Such models are often computationally expensive and few sensitivity analysis methods can effectively process such complexities. Moreover, the sensitivity analysis field as a whole pays limited attention to the nature of interaction effects, whose understanding can prove to be critical for the design of safe and reliable systems. In this paper, we introduce and extensively test a simple binning approach for computing sensitivity indices and demonstrate how complementing it with the smart visualization method, simulation decomposition (SimDec), can permit important insights into the behaviour of complex models. The straightforward binning computation generates first-, second-order effects, and a combined sensitivity index, and is considerably more computationally efficient than the “industry standard” measure for Sobol’ indices introduced by Saltelli et al. The cases vary from business and engineering to environmental applications. The totality of the sensitivity analysis framework provides an efficient and intuitive way to analyze the behaviour of complex environmental systems containing interactions and dependencies.A. Alam, M. Kozlova, L. Leifsson (2023). "The Importance of Intelligent Colouring for Simulation Decomposition in Environmental Analysis", Journal of Environmental Informatics Letters , 10, 2, 63-73.
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“Real world” risk analysis in environmental contexts frequently requires the need to contrast numerous uncertain factors simultaneously and to communicate difficult-to-capture interactions. Monte Carlo simulation modelling of complex environmental sytems is frequently employed to integrate uncertain inputs and to construct probability distributions of the resulting outputs. Visual analytics and data visualization can then be employed for the processing, analyzing, and communicating of the influence of any multi-variable uncertainties on the system. The simulation decomposition (SimDec) analytical technique has recently been employed in the complex assessments of environmental systems. SimDec has proved to be beneficial in revealing interdependencies in complex models, lowering computational burdens, facilitating decision-maker perceptions, and especially, making analytical components visualizable. It has been demonstrated that many analytical findings would not have been revealed without the coloured visualizations provided by SimDec. However, an ad hoc colouring scheme of the distribution output is neither sufficient nor capable of producing much of the key visualizable information requisite for an effective SimDec analysis. Instead, an approach that has recently been referred to as an intelligent colouring has been proposed. This paper outlines, highlights, and demonstrates the importance of and best-practices in an intelligent colouring scheme needed for an effective SimDec analysis of complex environmental systems.Kozlova, M. and Yeomans, J.S. (2022). "Monte Carlo Enhancement with Simulation Decomposition: A “Must-Have” for Many Disciplines", INFORMS Transactions on Education, 22(3), 147-159.
