A Survey of Methods for Data Inclusion in System Dynamics Models

In 1980, Jay Forrester enumerated three types of data needed to develop the structure and decision rules in models: numerical, written and mental data, in increasing order of importance. While this prioritization is appropriate, it is numerical data that has experienced the most development in the 25 years since Forester made his enumeration. In this paper, we’ll focus on how numerical data can be incorporated into models when written and mental data are known, and survey the techniques for doing so

Analysis of a top-down bottom-up data analysis framework and software architecture design

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, System Design and Management Program, 2014.Cataloged from PDF version of thesis.Includes bibliographical references (pages 70-71).Data analytics is currently a topic that is popular in academia and in industry. This is one form of bottom-up analysis, where insights are gained by analyzing data. System dynamics is the opposite, a top-down methodology, by gaining insight by analyzing the big picture. The merging of the two methodologies can possibly provide greater insight. What greater insight that can be gained is research that will be required in the future. The focus of this paper will be on the software connections for such a framework and how it can be automated. An analysis of the individual parts of the combined framework will be conducted along with current software tools that may be used. Lastly, a proposed software architecture design will be described.by Anton Wirsch.S.M. in Engineering and Managemen

A survey of methods for data inclusion in system dynamics models: Methods, tools and applications

Numerical data is experiencing a renaissance because 1) traditional data such as census and economic surveys are more readily accessible 2) new sensors are measuring things that have never been measured before, and 3) previously 'unstructured' data - such as raw text, audio, images, and videos - is becoming more amenable to quantification. Because of this explosion and the popular buzz surrounding ‘Big Data’, clients expect to see strong incorporation of data methods into dynamic models, and it is imperative that System Dynamics Modelers are fully versed in the techniques for doing so. The SD literature contains surveys that explain methods for including data in system dynamics modeling, but techniques have continued to develop. This paper attempts to bring these surveys up to date, and serve as a menu of modern techniques.This material is based on work supported by the U.S. Office of Naval Research, Grant No. N00014-09-1-0597. Any opinions, findings, conclusions or recommendations therein are those of the author(s) and do not necessarily reflect the views of the Office of Naval Research

Making Diabetes Electronic Medical Record Data Actionable: Promoting Benchmarking and Population Health Improvement Using the T1D Exchange Quality Improvement Portal

   This article describes how the T1D Exchange Quality Improvement Collaborative leverages an innovative web platform, the QI Portal, to gather and store electronic medical record (EMR) data to promote benchmarking and population health improvement in a type 1 diabetes learning health system. The authors explain the value of the QI Portal, the process for mapping center-level data from EMRs using standardized data specifications, and the QI Portal’s unique features for advancing population health.</p