Decision Analysis & Decision Support Systems: DADSS Lecture 1: Introduction to Modeling John Gasper

Course Details What will we talk about in this course:  Management Science  Operations Research  Decision Analysis  Economics / Finance  Monte Carlo Simulation  Optimization  Business and Public Policy decision making It’s a quantitative course that stresses applied use of Excel for Managerial Decision Making

Course Details Attendance and participation Required. I expect you on time Blackboard + Piazza discussion site Cell phones and laptops Turn off your phones. Airplane mode is good. Laptops GOOD for for taking notes and working through data. I would suggest you bring your laptop to most classes. This is a very applied Excel modeling course. You learn by doing – not by watching But laptops NOT OK to check news, facebook, twitter, youtube…

Course Details I want to teach you the information as best as I can. That requires that I get to know you.  Please stop by my office this week for at least 5-10 minutes.  Office Hours: Mon/Wed 1-2pm and 4:30-5:30pm.  And by appointment Who am I? TA: Aniish Sridhar

Course Details: Grades Grades will be earned as follows: 20% Homework or Quizzes Likely electronic submission, but still business quality 30% Midterm exams: 15% each x2 20% Final exam 20% Final (group) project Should be thought of as a real business / policy presentation 10% Participation %

Exams Midterm exams in this class are different… Multiple choice BUT unlike any exam you've probably every had. We will cover the details later, but the grading scheme allows you to receive negative scores (you can score < 0) Being confident and wrong is often worse than admitting that you don’t know an answer. Risk Analysis. Where can mistakes happen? Questions about the course?

Introduction to Modeling What is a model? A model is a selective abstraction of reality A model is a symbolic representation of reality that “assumes away” less important details in order to highlight the critical ones Not Enough DetailToo Much Detail

Selective Abstraction Models that are too simple aren’t useful Insufficient information Multiple interpretations Average data Models that are too detailed aren’t useful Too complex Unable to extract major patterns/trends Everything should be made as simple as possible, but not simpler Albert Einstein

Two Levels of Modeling Detail Same data Which mathematical model is better?

Why Build Models? The purpose of modeling is insight Understanding complex phenomena Improving decision making Two types of models Descriptive: Models that “explain” a situation What is the average capacity on US Air’s flights? Prescriptive: Models that suggest a course of action How can US Air change its flight routing to increase average capacities?

Types of Models Possible divides: Deterministic vs Stochastic  Deterministic: What is the optimal portfolio of assets when risk and return are known with certainty?  Stochastic: What is the optimal portfolio of assets in the real world (risk and return are unknown)? Analytic vs Simulation  Analytic: What is the average wait time for a single-server line where arrivals are Poisson?  Simulation: What is the average wait time for a multi-server line with balking and preferential access? Qualitative vs Quantitative  Qualitative: Influence diagrams, flow charts  Quantitative: Spreadsheets, systems of equations

Building a Model What goes into a model? Basic Model Ingredients Objective What is the decision or problem? Decision Variables What can you change? What do you have control over? Dependent Variables What is affected by your actions or choices? Quantitative models require making the problem explicit If you cannot state a problem in a symbolic (mathematical) representation, you probably don’t understand it well enough!

…in practice (unfortunately true some times)

Seven Modeling Steps 1. Define the problem Define the problem 2. Observe the system and collect data Observe the system and collect data 3. Formulate a model Formulate a model 4. Verify and use it for prediction Verify and use it for prediction 5. Selection of an alternative Selection of an alternative 6. Presentation of the results Presentation of the results 7. Implementation plan Implementation plan

Seven Modeling Steps: Step One Define the Problem What is the objective? Minimize, maximize, increase, decrease… Is there more than one problem? Who identified the problem? Is that classification person-specific? Is the objective coherent? Often not clear Note: Common homework dialogue: Student: “I have a question about the homework.” TA: “What’s your question?” Student: “I don’t get it.” What decisions/actions/choices are possible? Why is that the set of possible actions? How is it limited?

Seven Modeling Steps: Step Two Observe the System and Collect Data What do you know about the problem/system? What is the baseline? Has anyone even measured performance over time? Has the problem/system changed over time? Do you actually know how good or bad it is now? What data are important? How reliable is the data?

Seven Modeling Steps: Step Three Formulate a Model What type of model should you create? How will the model be used? Who will use the model? Does the it need to be integrated with other models? What kind of output do you want the model to produce?

Seven Modeling Steps: Step Four Verify the Model and Use it for Prediction Does the model’s output make sense? How would you know if the model’s output is correct? Is there historical data to use for testing? Can the model “back-cast”? Cross validation?

Seven Modeling Steps: Step Five Selection of an Alternative Exact or Approximate solution Some problems are sufficiently complicated to solve that only “good enough” solutions can be found Heuristic methods are often used to find “good” solutions quickly to very complex problems Dealing with multiple solutions Is have multiple solutions a good or a bad thing? Does the solution achieve the objective? How are you measuring success? If not, what would have to change about the problem in order to achieve it?

Seven Modeling Steps: Step Six Presentation of the Results “The big problem with management science models is that managers practically never use them” -- John Little, 1970 Resistance to quantitative models: ``I go with my gut” I don’t understand it I don’t trust it It doesn’t sufficiently address the problem (it’s too abstract) I don’t know how it works (the “black box”) “Your objective is not my objective – you solved the wrong problem” Anticipating questions: what if…

Seven Modeling Steps: Step Seven Implementation If a model is not implemented, it is useless. Is it easy to use? Does it require significant readjustment by the client? Does your solution suggest an implementation plan? Modeler: “After months of study, we have determined that you should do X” Client: “OK. How do I get from where we are now to X?” Modeler: “Uh…that wasn’t part of the study.” Client: “Go away.”