Can Social Mobility Solve Saudi Arabia Unemployment Issue?
by Raid Zaini (WPI)


About the Speaker:
Raid Mahmoud Zaini is a graduate student in WPI System Synamics program. He worked as operation lead in the petrochemicals, rubber and chemicals industries in Saudi Arabia, and is interested in unemployment and industrial transformation research.

Abstract:
It is puzzling how a country like Saudi Arabia with its natural resources could have a relatively high unemployment rate among its citizens. The current unemployment rate is around 12% and it has been around this number for the at least the last 10 years. It is even more puzzling when the country has between 8 to 11 millions temporary foreign labor which account for around 65% of the total labor force. For over thirty-five years, Saudi Arabia has been “struggling” to increase the participation of Saudi labor force in the private sector which will help in reducing local unemployment rate. The government launched several initiatives and campaigns to increase the “Saudization” throughout the years, however, its strongest and most determined campaign is the one that started from 2007 till now. The campaign used a variety of incentives ranging from The initiatives ranging from incentives, rewards, subsidizing the new Saudi employees salary for two years and sometimes fines and restriction to employers from obtaining access to temporary foreign labor. Despite the government efforts, still there are no significant improvements in the labor market (at least for now). In this (ongoing) research, I will explore the main causes for unemployment among Saudis and whether adapting policies toward Schumpeterian “Entrepreneurial” could help the Saudis to resolve the unemployment issue. The model used in this research is based on Professor Khalid Saeed’s work. Specifically, the Schumpeter model and the Economic Growth and Income Distribution model.

 

 

The limits of on-demand urban mobility systems
by Dimitris Papanikolaou (Harvard)


About the Speaker:
Dimitris is a doctoral candidate at the Harvard GSD and a graduate from the MIT Media Lab. His research uses System Dynamics to explore the limits of efficiency of P2P vehicle sharing systems and how these limits are defined by urban form, land use, and economic constraints. At the Media Lab he co-developed Mobility on Demand (MoD), an intelligent vehicle sharing system of electric foldable cars that allows users to make point-to-point trips while minimizing parking space, named by TIME magazine as the best automotive invention of 2007. Dimitris’ research has received awards including the $100K Buckminster Fuller Challenge, the $15K Harvard’s Deans’ Design Challenge: Urban Life 2030, and the MIT Transportation program’s showcase award for the best research in Economics, Finance, Policy and Land Use categories. He holds an MSc in MAS from the MIT Media Lab, a SMArchS from MIT's SAP, and a Dipl. Arch. Eng. from NTUA in Greece.

Abstract:
With more than 1.2M bikes in 950 cities and 300 systems under construction, the global bike sharing industry is mobilizing 6M trips per day. About 30 percent of this trip volume is rebalanced each day through an army of trucks, gas, and employees who continuously move bikes from full to empty stations. Rebalancing costs more than $3B and 250K tons of CO2 emissions per year, yet half users complain about bike or dock availability. In motorized vehicle sharing, this is dramatically worse. Can better planning or operational decisions improve efficiency of on-demand mobility? I present a general theory on the costs (and thereby limits) of mobility-on-demand (MoD) systems like bike, car, or scooter sharing, and how these limits are defined by urban form, land use patterns, fleet sizing and rebalancing decisions, and basic economic constraints. Using Boston’s bike sharing system as a case, and working with interviews and a dataset covering a period of over a year of operations, I present a System Dynamics model that simulates the palindromic morning and evening flows (empty and loaded) of vehicles between residential and commercial districts and outputs the dynamics of stock accumulations over time. The model allows researchers to interactively explore how urban density and land use patterns define commuting patterns and how fleet sizing and rebalancing decisions jointly affect costs and environmental impact for mobilizing these trips as a result of a dynamic equilibrium. The model focuses on the nonlinear interaction between loaded and empty flows during dynamic equilibrium, and can be used to exploratory address questions like: What combination of fleet size, docks, trucks, and operational time windows (work shifts) minimizes system-wide costs for mobilizing a city? How does system utilization depend on urban form and land use? Why do MoD systems in different cities have different utilization rates? Are there cities in which MoD systems are economically unfeasible? Why is it that trucks rebalance always more bikes than those that are imbalanced by users? As part of my presentation I will demonstrate SD.js, a novel JavaScript System Dynamics library that I am developing as part of my dissertation, with which researchers can build, control, and visualize (using D3.js) interactive SD models directly in their web-browsers.


Modeling Obesity: From Individuals to National Trends
by Hazhir Rahmandad (MIT)


About the Speaker:

Hazhir Rahmandad is an Albert and Jeanne Clear Career Development Professor of Management and Assistant Professor of System Dynamics at the MIT Sloan School of Management. Hazhir's research applies dynamic modeling to complex organizational problems, from learning in presence of delays to capability development tradeoffs and adaptation traps. Hazhir’s methodological work contributes to aggregation of prior statistical findings and his research in health domain has focused on understanding the dynamics of obesity and depression. Hazhir has published in diverse journals including Management Science, Organization Science, Strategic Management Journal, and American Journal of Public Health among others and has served as a reviewer for dozens of NIH and NSF panels and several different journals. His research has been funded by the National Science Foundation, National Institutes of Health, Department of Housing and Urban Development, and private sector firms. Before joining Sloan in 2015, Hazhir was an Associate Professor of Industrial and Systems Engineering at Virginia Tech.

Abstract:
In this seminar I will provide an overview of three related studies on modeling obesity. At its core obesity is controlled by the (im)balance between energy intake (i.e. eating) and energy expenditure (due to metabolism and physical activity). Energy gap, the difference between energy intake and expenditure, regulates not only body weight but also human growth and body composition. I discuss an individual-level model of human growth and body weight dynamics that accurately replicates empirical regularities in this domain and offers a building block for various health applications. For example such models could be used to infer energy intake and energy gap trajectories for various population groups based on their weight data over time. Estimating energy gap is important because measuring it is very hard, but that is the main variable people have control over and is subject to social and environmental influences which constitute the policy levers in addressing obesity. Yet computational costs prohibit accurate estimation of energy gap profiles for large populations of individuals. I discuss a method for more accurately simulating the population level changes in the distributions of an outcome and apply this method to estimating energy gap for US population over the past three decades, finding significant disparities among different ethnicity groups. This talk is based on joint work with Saeideh Fallah-fini and a few other colleagues.


“We need to train more”: a dynamic model on the organization wide impacts of a training policy implementation
by Babak Bahaddin & Felippe Cronemberger (UAlbany)


About the Speaker:
Babak is a second year PhD student in Informatics at UAlbany. His primary specialization is Data Analytics with concentration on System Dynamics. He also works at System Dynamics Society as a Graduate Student.

Abstract:
The pursuit of efficient methods to understand ever-changing organizational environments are a constant in management literature. Increasingly, the strategic role of human resources initiatives as a catalyzer for change and performance has receiving attention. While the vast majority methods are still mostly dedicated to assess isolated achievements in specific HR domains – such as recruitment, training and talent management - and at a micro-level, not enough attention is given to analytical approaches that evaluate the interaction across those domains. This paper proposes that proper and more complete understanding of organization-wide impacts of HR initiatives is possible through systemic thinking and dynamic modelling methods. More specifically, this study will shed light to the effects of introducing a training policy in a hypothetical scenario – a case study based on facts experienced by the authors. A System Dynamics model was developed to depict the effects the introduction of a training policy. The goal is to demonstrate the impact an increase in the average amount of hours of training per employee will have on organizational performance. We expect to elucidate leveraging points such as productivity and motivation and reveal other points of sensitivity where unintended consequences may arise. Finally, we will point out how the model can be utilized in similar scenarios to address future questions and other relevant collateral questions.


Research proposal: sustainable mobility
by Samuel Allen (WPI)


About the Speaker:
Samuel Allen is a PhD student in the Foisie School of Business at WPI studying Operations Mgt & SD. His interests include organizational approaches to complex environmental problems, policy analysis, and "shoe-string" systems methods.

Abstract:
In this short talk I will solicit feedback on my research plans. First, I will describe two ideas relating to the alternative fuel vehicle market transition (AVMT) family of models: improving the models' ability to explain a "transition backlash" with the GM diesel in the early 1980s; and the "P1" model. Second, I will describe a project around community bicycle projects and how they emerge, exploring sensemaking and applying "shoestring" methods.


Serial Approval Vote Elections
by Thomas Cavin (WPI)


About the Speaker:
Tom has worked in the software industry for about 35 years, and as a sys-admin at MIT. He is currently completing an SD masters degree. His focus at the moment is on why classic SD systems are mishandled and a lot of good SD research ignored, which has led to a strong interest in nominally democratic voting systems and their unintended consequences.

Abstract:
This paper develops and analyzes a new system of voting, Serial Approval Vote Elections, compares this system to both Plurality systems and Plurality with Runoff elections, and lays out a general method for examining behaviors of voting systems in simulation. It then illustrates the basic scenario behind Arrow’s Impossibility Theorem and argues for a class of iterative voting systems that allow the candidate slate to adjust to the desires of the electorate and discover or create a Condorcet winner. The specific SD connection is that most if not all of the big problems we face with complex dynamic systems involve large scale activities and will only be addressed when we have large scale coordination among groups with different local interests. The only way to do that without a global dictatorship is to fix our democracies.


 

You Eat What You Kill: Pure commission compensation optimizes against long-term performance of beginning sales agents
by James Houghton (MIT)


About the Speaker:
James Houghton is a PhD student in System Dynamics at the MIT Sloan School. He is interested in the dynamics of social movements, and in analytical methods for complex systems models.

Abstract:
The life insurance industry has notoriously high rates of turnover in its beginning sales force. Agents who make it through this initial startup period, however, generally go on to experience full careers. In this paper, we simulate the startup dynamics of a commission-based sales agent, and ask how variances in the agent’s resource base (skill, natural market, and lifestyle buffer) influence their three-year survival rates and overall career productivity. We show that a pure-commission compensation policy selects a sales force optimized for surviving startup dynamics, not for long-term profitability. We examine a mix of policy interventions designed to align the sales force with a company’s long-term interest.