The Modeling Process Esmaeil Khedmati Morasae Center for Community-Based Participatory Research in Health Tehran University of Medical Sciences Winter.

Slides:



Advertisements
Similar presentations
Chapter 2 Comparing Political Systems. How we Study Politics Describe Describe Predict Predict Explain Explain A. Description Conceptual Framework – set.
Advertisements

Performance Assessment
Study Objectives and Questions for Observational Comparative Effectiveness Research Prepared for: Agency for Healthcare Research and Quality (AHRQ)
Roesfiansjah Rasjidin Teknik Industri – FT - UEU.
INTRODUCTION TO MODELING
Virtual University - Human Computer Interaction 1 © Imran Hussain | UMT Imran Hussain University of Management and Technology (UMT) Lecture 16 HCI PROCESS.
Chapter 1 Introduction to Modeling DECISION MODELING WITH MICROSOFT EXCEL Copyright 2001 Prentice Hall.
Using the Crosscutting Concepts As conceptual tools when meeting an unfamiliar problem or phenomenon.
Chapter 1 Introduction to Modeling DECISION MODELING WITH MICROSOFT EXCEL Copyright 2001 Prentice Hall Publishers and Ardith E. Baker.
System Dynamics Modeling with STELLA software. Learning objective  After this class the students should be able to: Understand basic concepts of system.
Causal Loop Diagrams Esmaeil Khedmati Morasae Center for Community-Based Participatory Research in Health Tehran University of Medical Sciences January.
Marzano Art and Science Teaching Framework Learning Map
Gautam Sanka. Analyze and Elucidate the behavior of complex systems Complex Systems Collection of interconnected elements (system) Behavior and Characteristics.
Effective Math Questioning
System Dynamics 1. What is System Dynamics  Computer simulation modeling for studying and managing complex feedback systems, such as business and other.
Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-Hall Chapter Chapter 4: Modeling Decision Processes Decision Support Systems in the.
COMM : Applied Organizational Communication Instructor: Dan Lair Day Six: Systems Thinking Approaches to Organizational Communication September.
Systems Thinking Progression Bob Landel October 27, 2011 Systems Design and Business Dynamics Class #4.
Creating Architectural Descriptions. Outline Standardizing architectural descriptions: The IEEE has published, “Recommended Practice for Architectural.
Descriptive Modelling: Simulation “Simulation is the process of designing a model of a real system and conducting experiments with this model for the purpose.
Copyright 2007 by Linda J. Vandergriff All rights reserved. Published 2007 System Engineering in the 21st Century - Implications from Complexity.
Science and Engineering Practices
The Research Process. Purposes of Research  Exploration gaining some familiarity with a topic, discovering some of its main dimensions, and possibly.
Systems Dynamics and Equilibrium
By Saparila Worokinasih
Monitoring Evaluation Impact Assessment Objectives Be able to n explain basic monitoring and evaluation theory in relation to accountability n Identify.
Public Policy Modeling Causal Loop Diagrams Friday, April 21, 2017
1 Validation & Verification Chapter VALIDATION & VERIFICATION Very Difficult Very Important Conceptually distinct, but performed simultaneously.
Fish Infectious Disease Model Case Study BSC417/517.
Business Analysis and Essential Competencies
CJT 765: Structural Equation Modeling Class 7: fitting a model, fit indices, comparingmodels, statistical power.
Putting Research to Work in K-8 Science Classrooms Ready, Set, SCIENCE.
ENM 503 Lesson 1 – Methods and Models The why’s, how’s, and what’s of mathematical modeling A model is a representation in mathematical terms of some real.
Lecture 2 BSC 417/517. Today’s class Course website Schedule & topics for rest of semester Software tools and introductions Basic system components and.
RHS 303. TRANSITION OF THEORY AND TREATMENT nature of existence and gives meaning to and guides the action Philosophical Base: Philosophy of occupational.
URBDP 591 I Lecture 3: Research Process Objectives What are the major steps in the research process? What is an operational definition of variables? What.
Systemic Perspective Necessary and Sufficient Activities.
CROSS-CUTTING CONCEPTS IN SCIENCE Concepts that unify the study of science through their common application across the scientific fields They enhance core.
Exploratory Research and Proper Problem Definition Lecture 3.
Comparing Political Systems. Why Compare To develop perspective on the mix of constants and variability which characterize the world’s governments and.
Reconstructing unruly ecological complexities. ecological complexity poses many challenges to conventional scientific ways of knowing.
Lecture 9 BSC 417. Outline Last homework, question #2 Homework: some background first – Chapter 3, Q6-8 (on the board) Sensitivity analysis: infectious.
Systems Thinking © Jane Qiong Zhang and Linda Vanasupa 1 Storyboard 3 properties that determine system behavior Open vs. closed thermodynamic systems.
Introduction to Planning
1 Rockefeller College of Public Affairs and Policy University at Albany Tools for Systems Thinking and Modeling Dynamics: Graphs over time Structure:Causal-loop.
Global Environmental Change and Food Systems Scenarios Research up to date Monika Zurek FAO April 2005.
Developing the theoretical and conceptual framework From R.E.Khan ( J199 lecture)
Basic building blocks of SD Levels (Stocks), Rates (Flows), Auxiliary variables and Arrows Essential building blocks Represent the way dynamic systems.
SD modeling process One drawback of using a computer to simulate systems is that the computer will always do exactly what you tell it to do. (Garbage in.
Developing a Framework In Support of a Community of Practice in ABI Jason Newberry, Research Director Tanya Darisi, Senior Researcher
Ch 10 Methodology.
Analysis Yaodong Bi. Introduction to Analysis Purposes of Analysis – Resolve issues related to interference, concurrency, and conflicts among use cases.
Constructivism: The Social Construction of International Politics POL 3080 Approaches to IR.
Using Simulations and Inquiry to Teach Nature of Science Jennifer Maeng, Lindsay Wheeler, & Amanda Gonczi VAST 2015 Chantilly, VA.
Chapter 4 Motor Control Theories Concept: Theories about how we control coordinated movement differ in terms of the roles of central and environmental.
Systems Thinking Progression Bob Landel October 28, 2008 Systems Design and Business Dynamics Class #3.
Using Qualitative Methods to Identify System Dynamics and Inform System Evaluation Design Margaret Hargreaves Mathematica Policy Research American Evaluation.
Systems Thinking Storyboard 3 properties that determine system behavior Open vs. closed thermodynamic systems Map events Link events in causal loops Events.
Building Valid, Credible & Appropriately Detailed Simulation Models
Value network analysis for complex service systems: Author : Juite Wang Jung-Yu Lai Li-Chun Hsiao Professor : Soe-Tsyr Daphne Yuan Presenter : Po-Wei Chiang.
Bringing Diversity into Impact Evaluation: Towards a Broadened View of Design and Methods for Impact Evaluation Sanjeev Sridharan.
ACCOUNTING THEORY AND STANDARDS
More Vensim and “Stuff” Fall /14/20162 TODAY Recitation Lecture Hands-on.
Structure and Behavior of Dynamic Systems
1 Design and evaluation methods: Objectives n Design life cycle: HF input and neglect n Levels of system design: Going beyond the interface n Sources of.
Computational Reasoning in High School Science and Math
Formulate the Research Problem
Cross-cutting concepts in science
Conceptual Frameworks, Models, and Theories
CAP4800/5805 Systems Simulation
Presentation transcript:

The Modeling Process Esmaeil Khedmati Morasae Center for Community-Based Participatory Research in Health Tehran University of Medical Sciences Winter 2012

Modeling does not take place in splendid isolation. It is embedded in a social context. The real world problem determines the nature of the model. And always there are clients that you are trying to change their mental model and finally behavior. Steps of The Modeling Process: The steps are inherently creative yet all successful modelers follow a disciplined process that involves the following activities.

But, we have to consider that modeling is not a one-shot activity that yields The Answer but an ongoing process of continual cycling between the virtual world of the model and the real world of action. Indeed, modeling is an iterative process:

Problem articulation: What is the real problem, not just the symptom of difficulty? What is the purpose of model? No model can represent an entire system (simplicity vs. complexity). Models try to address a specific problem and must simplify. The art of model building is knowing what to cut out, and the purpose of the model acts as a logical knife (purpose-driven models). In sum: always model a problem. Never model a system.

There are many methods to elicit the information needed to define the problem dynamically; Tow of most successful processes are establishing reference modes and explicitly setting the time horizon. Reference mode, literally a set of graphs and other descriptive data showing the development of the problem over time. (recognition of relevant variables and concepts) (break out of event-oriented worldview) Time Horizon, it extend far enough back in history to show how problem emerged describe its symptoms. It should far enough into the future to capture the delayed and indirect effects of potential policies. A good rule of thumb is to set the time horizon several times as long as the longest time delays in the system.

Formulating a Dynamic Hypothesis Once the problem has been identified and characterized over an appropriate time, modeler must develop a theory called “dynamic hypothesis” that accounts for the problematic behavior. It is dynamic because it provides an explanation of the dynamics characterizing the problem in terms of the underlying feedback and stock and flow structure of the system. A dynamic hypothesis is a working theory of how the problem arose. Theory development often happens in a team of clients. But, system dynamics seeks endogenous explanations for phenomena.

Endogenous= arise from within An endogenous theory generates the dynamic of a system through the interaction of the variables and agents represented in the model. By specifying how the system is structured and the rules of interaction (the decision rules in the system), you can explore the patterns of behavior created by those rules and that structure. Exogenous (arise from without ) explanation, explains the dynamics of variable in terms of other variables whose behavior you have assumed. Is system dynamics blind to exogenous variables?

Mapping System Structure System dynamics includes a variety of tools to communicate the boundary of your model and represent its casual structure. Model boundary chart: it summarizes the scope of model by listing which key variables are included endogenously, which are exogenous and which are excluded from the model. Usefulness: they are tools of inquiry but not weapons in a war of advocacy. Example: feedbacks between energy system and economy

Subsystem diagram It is the overall architecture of a model. Each major subsystem is shown along with the flows of material, money, goods, information, and so on coupling the subsystems to one another. Subsystem diagrams convey information on the boundary and level of aggregation in the model by showing the number and type of different organizations or agents represented.

Causal loop diagrams Model boundary charts and subsystem diagrams show the boundary and architecture of the model but do not show how the variables are related. Causal Loops Diagrams (CLDs) are flexible and useful tools for diagramming the feedback structure of systems in any domain. Causal diagrams are simply maps showing the causal links among variables with arrows from a cause to an effect.

Stock and flow maps Causal loop diagrams emphasize the feedback structure of a system Stock and flow diagrams emphasize their underlying physical structure. Stocks and flows track accumulations of material, money, and information as they move through a system. Stocks, population Flow, birth and death Stock state of the system information policies rate of the flow stocks (closing the feedback loops) Population policies?

Policy structure diagrams A causal diagram showing the information inputs to a particular decision rule. It focuses on information cues the modeler assumes decision makers use to govern the rates of flow in the system. The show the causal structure and time delays involved in particular decisions rather than the feedback structure of the overall system.

Formulating a Simulation Model Dynamic hypothesis, model boundary, and conceptual model, We must test them. Virtual world comes in. To do so, you must move from conceptual realm of diagrams to a fully specified formal model, complete with equations, parameters, and initial conditions. Conceptual model formalization, real test of your understanding of system.

Testing Testing begins as soon as you write the first equation. Part of testing, is comparing the simulated behavior to the actual behavior of the system. Every variable must correspond to a meaningful concept in the real world. Every equation must be checked for dimensional consistency. The sensitivity of model behavior must be assessed in light of the assumptions uncertainty, both parametric and structural. Model must be tested under extreme conditions It shows the flaws of your mode set the stage for improved understanding.

Policy Design and Evaluation Developing confidence in the structure and behavior of he model, it can be used to design and evaluate policies for improvement. Since the feedback structure of a system determines its dynamics, most of the time high leverage policies will involve changing the dominant feedback loops by redesigning the stock and flow structure, eliminating time delays, changing the flow and quality of information available at key decision points. Interactions of different policies must be considered. Commination policies interact in non-linear ways and they can balance or reinforce each other.