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.
Yeomans, Julian Scott and M. Kozlova (2023). "Extending System Dynamics Modelling Using Simulation Decomposition to Improve the Urban Planning Process", Frontiers in Sustainable Cities, 5, 1129316.
AbstractUrban planning often involves decision-making under highly uncertain circumstances. System dynamics and multi-agent modeling frameworks are commonly employed to model the social phenomena in this type of urban planning. However, because the outputs from these approaches are regularly characterized as a function of time, the majority of studies in this modeling domain lack appropriate sensitivity analysis. Consequently, important insights into model behavior are frequently overlooked. Monte Carlo simulation has been used to incorporate uncertain features in urban planning with the outputs displayed as probability distributions. Recently simulation decomposition (SimDec) has been used to enhance the visualization of the cause-effect relationships of multi-variable combinations of inputs on the corresponding simulated outputs. SimDec maps each output value of a Monte Carlo simulation on to the multivariable groups of inputs or scenarios from which it originated. By visually projecting the subdivided scenarios onto the overall output, SimDec can reveal previously unidentified influences between the various combinations of inputs on to the outputs. SimDec can be generalized to any Monte Carlo method with insignificant computational overhead and is, therefore, extendable to any simulated urban planning analysis. This study demonstrates the efficacy of adapting SimDec for the sensitivity analysis of urban dynamics modeling on a paradigmatic simplified version of Forrester's Urban Dynamics- URBAN1 model. SimDec reveals complexities in model behavior that are not, and can not be, captured by standard sensitivity analysis methods and highlights, in particular, the intricate joint effect of immigration and outmigration on system development.
Yeomans, Julian Scott and M. Kozlova (2022). "Discovering Optionality in Corporate Strategic Decisions with Simulation Decomposition", Real Options: Theory Meets Practice, 28(1).
AbstractCorporate strategic decisions are often supported by evaluation with real options framework. However, the majority of real options studies choose to analyze generic types of real options. In this extended abstract, we demonstrate how customized real options can be systematically discovered by employing a recently developed approach called Simulation Decomposition or SimDec. SimDec is an enhancement of Monte Carlo simulation, which allows tracing how different input factors and their interactions affect the outcome(s) while preserving the holistic picture of the overall uncertainty. We show the application of SimDec to the two cases of corporate strategic decisionmaking: 1. Investment strategy and 2. Compliance with emissions regulations. Both cases demonstrate that SimDec reveals previously hidden interactions of factors in a model and provides actionable outcome by discovering optionality in strategic decisions
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., T. Nykänen and Yeomans, J.S. (2022). "Technical Advances in Aviation Electrification: Enhancing Strategic R&D Investment Analysis Through Simulation Decomposition", Sustainability, 14(1), 414.
AbstractComputational decision-making in “real world” environmental and sustainability contexts frequently requires the need to contrast numerous uncertain factors and difficult-to-capture dimensions. Monte Carlo simulation modelling has frequently been employed to integrate the 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 used to quantitatively examine carbon emission impacts resulting from a transformation of the aviation industry toward a state of greater airline electrification. SimDec is used to decompose a Monte Carlo model of the flying range of all-electric aircraft based upon improvements to batteries and motor efficiencies. Since SimDec can be run concurrently with any Monte Carlo model with only negligible additional overhead, it can easily be extended into the analysis of any environmental application that employs simulation. This generalizability in conjunction with its straightforward visualizations of complex stochastic uncertainties makes the practical contributions of SimDec very powerful in environmental decision-making.
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.
AbstractMonte Carlo (MC) simulation is widely used in many different disciplines in order to analyze problems that involve uncertainty. Simulation decomposition has recently provided a simple, but powerful, advancement to the standard Monte Carlo approach. Its value for better informing decision making has been previously shown in the investment-analysis field. In this paper, we demonstrate that simulation decomposition can enhance problem analysis in a wide array of domains by applying it to three very different disciplines: geology, business, and environmental science. Further extensions to such disciplines as engineering, natural sciences, and social sciences are discussed. We propose that by incorporating simulation decomposition into pedagogical practices, we expect students to significantly advance their problem-understanding and problem-solving skills.
Deviatkin, I., Kozlova, M. and Yeomans, J.S. (2021). "Simulation Decomposition for Environmental Sustainability: Enhanced Decision-Making in Carbon Footprint Analysis", Socio-Economic Planning Sciences, 75, 1, 1-10 .
AbstractEnvironmental sustainability problems frequently require the need for decision-making in situations containing considerable uncertainty. Monte Carlo simulation methods have been used in a wide array of environmental planning settings to incorporate these uncertain features. Simulation-generated outputs are commonly displayed as probability distributions. Recently simulation decomposition (SD) has enhanced the visualization of the cause-effect relationships of multi-variable combinations of inputs on the corresponding simulated outputs. SD partitions sub-distributions of the Monte Carlo outputs by pre-classifying selected input variables into states, grouping combinations of these states into scenarios, and then collecting simulated outputs attributable to each multi-variable input scenario. Since it is a straightforward task to visually project the contribution of the subdivided scenarios onto the overall output, SD can illuminate previously unidentified connections between the multi-variable combinations of inputs on the outputs. SD is generalizable to any Monte Carlo method with negligible additional computational overhead and, therefore, can be readily extended into most environmental analyses that use simulation models. This study demonstrates the efficacy of SD for environmental sustainability decision-making on a carbon footprint analysis case for wooden pallets.
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.
AbstractVarious components within environmental decision-making problems often contain considerable uncertainty. Monte Carlo simulation approaches have frequently been used to incorporate a wide array of this uncertainty into environmental planning. Simulated outputs summarizing these uncertainties are commonly portrayed in the form of probability distributions. Visualization of the disparate uncertainties within these distributions is a key aspect for effective decision support in Monte Carlo analysis. This study contrasts the performance and benefits of two visual analytics tools – overlay charts and simulation decomposition. Overlay charts enable the display of multiple sources of uncertainty overlaid on top of each other in a single graphical representation and come as a standard feature in numerous commercial Monte Carlo software packages. Conversely, simulation decomposition combines user-defined sub-distributions of the simulation uncertainties and collectively displays them in a combined graphical output figure. This paper contrasts the efficacy of overlay charts versus simulation decomposition for the visual analysis uncertainty into the environmental decision-making process.
Kozlova, M. and Yeomans, J.S. (2019). "Multi-Variable Simulation Decomposition in Environmental Planning: An Application to Carbon Capture and Storage", Journal of Environmental Informatics Letters, 1(1), 20-26.