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.
Kozlova, M. and Yeomans, J.S. (2022). "Extending Simulation Decomposition Analysis into Systemic Risk Planning for Domino-Like Cascading Effects in Environmental Systems", Journal of Environmental Informatics Letters , 7(2), 64-68.
AbstractIn interconnected environmental systems, the innocuous failure of one component can sometimes trigger a subsequent domino-like effect resulting in a cascading collapse of the entire system. Risk analysis in “real world” contexts frequently requires the need to simultaneously contrast numerous uncertain factors and difficult-to-capture dimensions. Monte Carlo simulation modelling has often been employed to integrate uncertain inputs and to construct probability distributions of the resulting outputs. Visual analytics and data visualization can be used to support the processing, analyzing, and communicating of the influence of multi-variable uncertainties on the decision-making process. In this paper, the novel Simulation Decomposition (SimDec) analytical technique is extended into complex assessments of cascading risk analysis and used to quantitatively examine situations involving potentially catastrophic, domino-like collapses of an entire system. SimDec analysis proves to be beneficial due to its ability to reveal interdependencies in complex models, its ease of decision-maker perception, its visualizable analytic capabilities, and its significantly lower computational burdens. The case example visually demonstrates that when a system collapse is a low-probability/high-impact event, more expensive, reactive policies minimize the overall value loss under conditions of system survival, while more proactive policies enable better loss prevention under system survival. However, proactive approaches significantly decrease the likelihoods and magnitudes of losses for scenarios resulting from the collapse of the system. Such findings would not have been revealed without the visualization provided by SimDec.
Kozlova, M. and Yeomans, J.S. (2022). "Sustainability Analysis and Environmental Decision-Making Using Simulation, Optimization, and Computational Analytics", Sustainability, 14(3), 1655.
AbstractThis issue contains applied computational analytics papers that either create new methods or provide innovative applications of existing methods to assist with sustainability analysis and environmental decision-making applications. In practice, environmental analytics is an integra-tion of science, methods, and techniques that involves a combination of computers, computational intelligence, information technology, mathematical modelling, and system science to assess re-al-world, sustainability, and environmental problems. The contributions to this issue all inves-tigate novel approaches of computational analytics – modelling, computational solution proce-dures, optimization, simulation, and technologies—as applied to sustainability analysis. The papers emphasize both the practical relevance and the methodological contributions of the work to environmental decision-making. Areas of application encompass a wide spectrum of environ-mental decision-making and sustainability, from waste, water, energy, climate change, industrial ecology, resource recovery, to recycling.
Kozlova, M. and Yeomans, J.S. (2020). "Visual Analytics in Environmental Decision-Making: A Comparison of Overlay Charts Versus Simulation Decomposition", Journal of Environmental Informatics Letters, 4, 2, 93-100.