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

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.

Open Access Download

Keywords
Abstract
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.

Open Access Download

Keywords
Abstract 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.

T. Jeong, M. Kozlova, L. Leifsson, J.S. Yeomans (2025). "Simulation Decomposition Analysis of the Iowa Food-Water-Energy System", Environmental Modelling and Software, 188, 106415.

Open Access Download

Keywords
Abstract
This study applies global sensitivity analysis (GSA) to the Iowa Food-Water-Energy system, focusing on nitrogen export into the Mississippi River. A binning method combined with simulation decomposition (SimDec) quantifies and visualizes the influence of crucial aggregate input variables — manure nitrogen (MN), commercial nitrogen (CN), grain nitrogen (GN), and fixation nitrogen (FN) — on nitrogen surplus (NS) at the county level. Unlike traditional Sobol’ indices, the binning method captures dependent variables. In addition, the SimDec procedure provides a detailed visual representation of how these dependencies and interactions drive the nitrogen variability. MN is identified as the most influential factor, followed by CN, with FN and GN having less impact. The study also performs GSA on the low-level input variables, enhancing the overall interpretability of the sensitivity analysis. This approach offers actionable insights for improving nitrogen management practices and contributes to GSA literature by showcasing the analysis of aggregate variables.

Julian Scott Yeomans, M. Kozlova, R. Moss, J. Caers (2024). "Uncovering Heterogeneous Effects in Computational Models for Sustainable Decision-Making", Environmental Modelling and Software, 171, 105898.

Open Access Download

Keywords
Abstract Computational modeling is frequently incorporated into environmental decision-making in order to capture inherently complex relationships and system dynamics. The complexity of such models often lies in various heterogeneous effects that arise due to the interaction of different input factors or due to designed structural variation in the model. In the past, various sensitivity analysis approaches have been implemented in attempts to identify essential decision factors. However, existing sensitivity analysis methods fail to capture critical information in the presence of heterogeneous effects. In this paper, the recently introduced simulation decomposition (SimDec) visualization method is extended to include quantitative sensitivity analysis. The framework is tested on several decision-making problems and is shown to capture heterogeneous behavior. A formal definition and classification of heterogeneous effects for computational models is introduced. The framework is open-sourced in a variety of scientific programming languages.

with 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.

Open Access Download

Keywords
Abstract “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.