23rd MIT-UAlbany System Dynamics Ph.D. Colloquium
Friday, November 11, 2011

University at Albany
State University of New York

Milne 215
Address: 135 Western Avenue, Albany, NY 12222

Organizers: Junesoo Lee (UAlbany) and Ozge Karanfil (MIT)

Time Presentation Speaker
10:25 AM Introductory remarks

George Richardson and
David Andersen,University at Albany

10:30 AM Wash, Rinse, and Repeat: The Infinite Rework Cycle in the ERP Environment

Meg Fryling, Siena College

11:15 AM Incorporating Endogenous Electricity Demand into Capacity Expansion Models: the Case of the Tanzanian Power System

Rhonda Jordan, MIT

12:00 PM Lunch .
12:45 PM Travel Dynamics and Policy Options

Sreekumar Nampoothiri, University at Albany

1:30 PM Understanding Spreading Patterns of Hybrid-Electric Vehicle Adoption

David Keith, MIT

2:15 PM Experiential Learning and Variation in Medical Practice: Qualitative, Simulation, and Quantitative Data-Based Models

Navid Ghaffarzadegan, University at Albany

3:00 PM Closing Remarks

George Richardson and
David Andersen, University at Albany

Abstracts

Wash, Rinse, and Repeat: The Infinite Rework Cycle in the ERP Environment

Meg Fryling, Siena College

The rework cycle is widely known as a central concept in system dynamics project models.  This research incorporates case study data to explore the rework cycle in a prepackaged software environment, specifically an ERP system, with a focus on the post-implementation maintenance phase.  While there have been ERP implementation research using system dynamics tools, the work is limited and is focused on ERP planning and initial implementation not post-implementation. In fact, ERP studies related to post-implementation maintenance and upgrades are lacking even in areas outside of system dynamics.  The focus of this presentation is to provide insight into ERP post-implementation rework dynamics. ERP projects are different from homegrown software implementations and as such the rework cycle has some unique characteristics. A proposed extension to the traditional rework cycle for ERP systems is also presented.

Incorporating Endogenous Electricity Demand into Capacity Expansion Models: the Case of the Tanzanian Power System
Rhonda Jordan, MIT

Investment decisions made within electric power systems have typically been informed by the use of quantitative models, and researchers have used modeling to explore policy questions for decades. However, the use of (i) optimization models that represent the electric power grid but do not represent the complex dynamics of consumer demand or (ii) simulation models that represent endogenous consumer dynamics but not the operation of the power grid may provide misleading results when used to inform planning and policy in the context of developing countries. This research describes the development of an integrated simulation model that captures the interplay between social and technical factors in the electric power system. The model demonstrates an interdisciplinary approach to simulating the technical details of power grid operation and endogenous electricity demand dynamics that result from social processes of technology diffusion.  The model is used to simulate electric power system development and performance in the United Republic of Tanzania.  Finally, this work utilizes this holistic representation of the power system to demonstrate the importance of incorporating electricity demand dynamics into capacity expansion and electrification planning models. Preliminary results will be presented.

Travel Dynamics and Policy Options
Sreekumar Nampoothiri, University at Albany

This paper presents a modeling framework of auto and transit travel based on a system dynamics approach. This model tries to capture the interaction between auto travel and transit travel based on Sterman's map called ‘mass transit death spiral’ in Business Dynamics. It offers policy makers an assessment platform to understand the complex behavior of the system as well as various policy outcomes that have been in discussion or use.

Understanding Spreading Patterns of Hybrid-Electric Vehicle Adoption

David Keith, MIT

Hybrid-electric vehicles such as the iconic Toyota Prius have now been available in the United States for over a decade, growing to an installed base of approximately two million vehicles.  Prior work suggests that both growing consumer familiarity learned from marketing and word-of-mouth, and constraints in the supply of hybrid vehicles available, have governed the observed diffusion of hybrid vehicles to date.  The observation that motivates this research is that the majority of these hybrid vehicle sales have been clustered in regions such as the West Coast, around Washington DC and through New England.  Intuitively, much of this variation may be attributed to variation in both technological attributes (gasoline prices, government incentives) and consumer demographics (income, education, political preferences).  However, the aim of this paper is to test the extent to which, when controlling for these technological and demographic attributes, the spatial transmission of word-of-mouth through consumers’ social networks explains the observed spatial diffusion of the Toyota Prius over the past decade. 
Building on the diffusion modeling literature, I have developed a model of spatial technology diffusion that captures the accumulation of consumer familiarity, consumer choice between hybrid and conventional gasoline vehicle alternatives, and the supply of hybrid vehicles available.  If the spatial transmission of word-of-mouth is shown to be a significant determinant of diffusion, the most influential regions will be those that have both a high propensity to adopt and strong social influence over other regions.  The findings of this research promise to inform the development of focused marketing strategies and policies that are more effective at accelerating adoption of hybrid- and electric- vehicles.  More broadly, this research will help to understand how the adoption of clean energy technologies occurs spatially and over time.

Experiential Learning and Variation in Medical Practice: Qualitative, Simulation, and Quantitative Data-Based Models
Navid Ghaffarzadegan, University at Albany

Why physicians make different decisions for medically similar patients? I report on three studies that I have been involved in the last few years in collaboration with several of my colleagues including Erika Martin and Andrew Epstein. Specifically, we will discuss about (1) an analytical framework of practice variation based on a qualitative study of the literature, (2) a theoretical simulation model of the problem, and (3) a calibrated simulation model of practice variation to a data set of 100 obstetricians in the state of Florida. Then we discuss policy implications of the studies


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Last edited by JH 12/05/2011