simulation/modelling in social/policy sciences ppol225...

Simulation/Modelling in Social/Policy Sciences PPOL225/SOC125

Mills College Spring 2009 Dan Ryan

PPOL225-Simulation-Modeling-2009-SYLLABUS.docx 1 5 January 2011

BOOKS, POLICIES, GRADING 2 SCHEDULE PRELIMINARIES 5

1. (Th.1.22) The What, the why and the how of this course 5 2. [Tu.1.27] Flow Charts 5

[We.1.28] LAB (1) Excel, chart drawing, functions, etc. 6 3. [Th.1.29] Other charts as models 6 4. [Tu.2.3] Models I: Concepts, Motivations, and Examples 7

SYSTEMS 8 Deterministic 8

5. [Th.2.5] Difference Equations I :Concepts and Techniques 8 6. [Tu.2.10] Difference Equations II : Applications and Extensions 8

[We.2.11] LAB (2) DE and Excel 8 7. [Th.2.12] Thermostats, Lemons, and Feedback 9 8. [Tu.2.17] Tipping and Networks 9 9. [Th.2.19] Stock and Flow: Systems Dynamics 10 10. [Tu.2.24] Extra 10

[We.2.25] LAB (3) System Dynamics Models 10 Non-Deterministic 10

11. [Th.2.26] Probability: EYAWTKAPIAOH and Queuing Models 10 12. [Tu.3.3] Queuing Models (continued) 11 13. [Th.3.5] Markov Models 11 14. [Tu.3.10] Communities of Organizations 12

[We.3.11] LAB (4) Markov and Queues and Monte Carlo in Excel 12 DECISIONS 12 Deterministic 12

15. [Th.3.12] Choice and the Ordering of Preferences 12 16. [Tu.3.17] Benefit Cost Analysis I 13 17. [Th.3.19] Benefit Cost Analysis II 13 18. [Tu.3.31] Discounting 14

[We.4.1] LAB (5) Benefit Cost Analysis in Excel 14 19. [Th.4.2] Extra 14 20. [Tu.4.7] Linear Programming I 14 21. [Th.4.9] LP II: What Would It Be Worth If We… 15

Non-Deterministic 15 22. [Tu.4.14] Decision Analysis: "to do" vs. "to happen" 15

[We.4.15] LAB (6) Linear Programming Lab 16 23. [Th.4.16] Decision Analysis: Adding Uncertainty and Risk 16 24. [Tu.4.21] Decision Analysis: The Value of Information 16

REFLECTIONS 17 25. [Th.4.23] Social Welfare: What's the Point? 17 26. [Tu.4.28] Market Failure and Remedies 17

[We.4.29] LAB (7) DA & Excel 17 27. [Th.4.30] Cautions and Caveats 17 28. [Tu.5.5] Marching Orders 18

Ryan: Simulation/Modeling in Social/Policy Sciences Spring 2009


Books

I've selected three books that cover more or less the same turf but from slightly different perspectives. My strategy here is to counter the tendency to get too committed to one or another "spin" on things ("that's not the way we do it in X…") and to expand our thinking beyond simplistic "applications" of a bag of tricks remembered from some course we once took.

The backbone of the course is provided by Stokey & Zeckhauser's A Primer of Policy Analysis. Some supplemental material will be drawn from Lave & March's An Introduction to Models in the Social Sciences. Students who are not public policy majors will probably want to make more extensive use of this book. Finally, we'll draw some material from Schelling's Micromotives and Macrobehavior. This latter is a classic in every way and should be on the book shelf (as well as the "I've read it list") of every student of the social and policy sciences.

Course Policies

Students are expected to attend all class meetings, arriving early and having completed all readings and other assignments due on that date. Students are responsible for keeping track of the syllabus and where we are on it.

Written assignments should be submitted at the start of class on the day on which they are due. Unannounced late work will not be accepted. If one expects to be unable to complete something on time, one must give prior notice (the night before is not considered "prior") with an indication of the date on which a finished product can be expected AND submit on-time a presentable version of that which could be completed as a down payment. A letter grade penalty should be expected per week of tardiness.

Every effort will be made to make this class accessible for students regardless of disability. Students with needs for accomodation should contact for Students with Disabilities (Cowell Building, x2130) and inform the instructor in order for access to be arranged adequately and promptly.

Customary academic standards academic integrity (including proper bibliographic citation) apply. It is your responsibility to know what these are and to follow them. Collaborative learning is encouraged, but work that is submitted under your name as a demonstration of your skills and competence must represent YOUR work. Plagiarism, as defined under the Mills College Honor Code, will be cause for, at a minimum, a failing grade in this course. Please consult with instructor if you have any questions, or even the slightest doubt, about how to follow these requirements.

Work for Evaluation and Grading

The evaluated work for this course will consist of 7 labs and associated problem sets (49%), 3 "Project Applications" (30%) and a final exam (21%).

Labs/Problem Sets

Each lab session will have an associate problem set covering material from a section of the course and employing techniques introduced in the lab. Overall, each problem set will be graded on a scale of ten points with 12 awarded for excellence, 10 for demonstrated competence, 8 for satisfactory, 6 for insufficient. At the instructor's discretion, insufficient results can be resubmitted within one week for a satisfactory grade.



Project Applications

During the semester you will have the opportunity to apply course material to your master's paper project. The first will be mandatory and the other two selected in consultation with the instructor. Each will require a short memo/paper that will go through two drafts. Project applications will be graded in two parts. A first draft should show evidence of serious effort and be on-time. Extensive feedback will be offered both to suggest where to go on second draft and to justify categorization as "good" or "not-so-good" (the latter should be understood strictly as an exhortation to push toward moving your future first drafts farther along). Based on feedback on first draft, second draft should be in the form of a professional memo that could be submitted to either your client or your project supervisor. It will be graded Excellent, Satisfactory, Satisfactory but weak, or Unsatisfactory. Overall grade for the assignment is then graded according to the following matrix:

First Draft

on-time and

good on-time and not-

so-good not on time

Second Draft

Excellent* A A- B

Satisfactory* B+ B B-

Weak Satisfactory* B- B- C

Unsatisfactory F F F

*Definitions

Excellent. The work(1) clearly demonstrates competence in skills under consideration and the (2) results essentially correct; the final product (3) communicates clearly what was done, how, and why, and is presented in a (4) professional manner.

Satisfactory. Fundamentally sound but could be better on one or more of above criteria.

Weak Satisfactory. Middling performance with significant gaps

Unsatisfactory. Not acceptable as is. Should be resubmitted in one week for possible C credit.

Keep in mind that the purpose of these exercises is two-fold. First, you are practicing applying this material to your own project. Second, it is an opportunity to demonstrate your competence. With the latter in mind you need to shift from thinking of it in terms of "what is required?" and "what does the teacher want?" to "what have I learned how to do and how can I demonstrate it?"



PREL I MINARIES

1. (Th122) The What, the why and the how of this course

We are all, already, modelers and simulators. All science, all human activity, is based on models. We observe the world around us, abstract and infer a model of it, and act in a manner that is consistent with our model's predictions ("teachers like it when you ask questions, so ask whether modeling always has to be conscious or not"). We look at the status quo and conjure up processes that lay behind it ("he looks grumpy this morning; probably didn't get much sleep last night…") to either explain and de-scribe or to predict and prescribe.

This course is an attempt to push this natural, everyday process in three directions: make it more systematic (vs. ad hoc, haphazard, etc.); use proven techniques (vs. im-provisation and intuition); focus on problems in social and policy sciences. We start by defining "model," describing the range of models and purposes for which they can be used, and then talking about the mechanics of the course.

Reading 1 Start reading chapters 1 and 2 of Stokey & Zeckhauser, chapter 1 of Lave & March, and

chapter 1 of Schelling. All three should be read by class #3.

2. [Tu127] Flow Charts

If a picture is worth a thousand words, a good diagram must be worth at least a mil-lion. Visual diagrams fulfill two important func-tions: (1) reduce or tame complexity to permit fo-cus on features of a problem relevant to the task at hand (it stabilizes my model so I know what I am thinking); (2) externalize our un-derstanding of a problem so that the same model can be subject to multiple cogni-tions (it permits multiple thinkers to think about the same object).

Flow charts and their variants are among the most useful diagrams you will ever en-counter. They can be used both prescriptively (telling us what procedure to follow under different conditions) and descriptively (as when the organizational ethnog-rapher discerns the factors that lead to decisions in practice). It's important to achieve a level of competence beyond simply recognizing what a flow chart is. A few of the techniques involved in making them are incredibly useful, general purpose heuristics.

Here we want to learn how to read and make flowcharts, understand the concepts of stepwise refinement, top-down design, and "black-boxing," and how to translate verbal (and ethnographic) descriptions into flow charts.

Project Application: Charts



Readings 1. Read Wikipedia articles on Flow Charts 2. Flow Charts for Simple Tasks: Tutorial with exercises at Univ Plymouth, UK

http://www.cimt.plymouth.ac.uk/projects/mepres/book8/bk8i1/bk8_1i2.htm 3. Flow Charts for Classification: Tutorial with exercises at Univ Plymouth, UK

http://www.cimt.plymouth.ac.uk/projects/mepres/book8/bk8i1/bk8_1i3.htm 4. An overview by HCI consulting in Australia

http://www.hci.com.au/hcisite2/toolkit/flowchar.htm 5. Human Subject Regulations Decision Charts at U.S. Department of Health and Human Services

(HHS) http://www.hhs.gov/ohrp/humansubjects/guidance/decisioncharts.htm

[We128] LAB (1) Excel, chart drawing, functions, etc.

The purpose of this lab is to get us all on the same page as far as playing with MS Excel goes and to push us all to a "next level" in terms of using functions, autofill, drawing diagrams, making charts, adding controls to worksheets, etc. In addition to acquiring (or reviewing) specific skills, a goal of the lab is to give students a definitive sense of what more there is and how to find it.

Readings 1. Skim through all the entries on MS Excel Help page for "Entering and editing data" 2. Read MS Excel Help: Create or change a cell reference 3. Read MS Excel Help: Overview of formulas 4. Read MS Excel Help: Understanding Array Formulas 5. Read MS Excel Help: FREQUENCY function 6. Read MS Excel Help: Under "absolute cell reference" view the video: Understanding relative

and absolute in Excel (Brainstorm Inc.)

3. [Th129] Other charts as models

As already suggested, simply being able to draw a picture and allowing the mind's eye to process it is, perhaps, the most familiar way to "model" and "simulate." The right picture can be worth a lot if (1) it IS the right picture and (2) your mind's eye knows how to read it. Today we will introduce a wide range of diagram types and focus in on the how and why of a few. Among those considered: PERT, Gantt, trees, organ-izational charts, network diagrams, Sankey diagrams, stock and flow, state transition diagrams.

Our goals include acquiring a sense of the variety that's out there, an appreciation of the value of "the right diagram for the job," and a beginner's ability to handle a few of the more common types.

Readings 1. Read Wikipedia articles on

a. PERT chart b. Gantt chart

http://www.cimt.plymouth.ac.uk/projects/mepres/book8/bk8i1/bk8_1i2.htm

http://www.cimt.plymouth.ac.uk/projects/mepres/book8/bk8i1/bk8_1i3.htm

http://www.hci.com.au/hcisite2/toolkit/flowchar.htm

http://office.microsoft.com/search/redir.aspx?assetid=XT102879531033&QueryID=xZnEh518n&respos=2&rt=2&ns=EXCEL&lcid=1033

http://office.microsoft.com/search/redir.aspx?assetid=XT102879531033&QueryID=xZnEh518n&respos=2&rt=2&ns=EXCEL&lcid=1033



c. Decision Trees d. Sankey Diagrams e. Stock and Flow

REMINDER: Lab/Problem Set #1 (charts) Due Next Class

REMINDER: First draft Project Application #1 (charts) Due Next Class

4. [Tu23] Models I: Concepts, Motivations, and Examples

The title of this course suggests a double doubling. Models and simulation. Policy and social sciences. In this section of the course we want to formalize what we mean by model and on how the style of thinking about models differs when we are wear-ing our social science hat or our policy science hat.

The first of the two lessons in this section will be about how to think like a social scientist. We start their because (1) it best introduces the discipline of problem ori-ented thinking and the mental shift required to do it, and (2) because it is the style we will be more or less leaving behind (but not forgetting) as we move on in the course.

The second lesson is to make a transition from backward looking explanatory mod-els to more forward looking and predictive models.

Our overarching learning goal here is an understanding of "modeling" as a specific process and the cultivation of a capacity to generate and consider alternative explana-tions. Secondarily, we will try to lay out a roadmap of five skills we want to acquire in this course:

1 Capacity to abstract from reality to model 2 Ability to use model to derive meaningful implications 3 Competence at assessing how good a model something is 4 Competence at implementing model with computer where appropriate 5 Acquire a familiarity with common models and techniques

Finally, we will try to sketch the beginnings of a taxonomy of approaches to model-ing – deterministic and not, micro and macro, systems and decisions.

Readings 1. Lave & March, ch. 1, "What We Are Up To," pp.1-7. 2. Lave & March, ch. 2, "An Introduction to Speculation," pp.9-42. 1. Stokey & Zeckhauser, ch. 1, "Thinking About Policy Choices," pp. 3-7. 2. Stokey & Zeckhauser, ch. 2, "Models" A General Discussion," pp. 8-21. 3. Schelling, Ch. 1, "Micromotives and Macrobehavior," pp. 9-44.



SYSTEMS

D E T E R M I N I S T I C

5. [Th25] Difference Equations I :Concepts and Techniques

Almost every field has a trove of problems whose basic structure is the exploration of how things change over time. Some of the underlying processes are continuous (your kids growing taller) and some are arguably discrete (demograph-ic change due to births, deaths and migration). It turns out that in many cases we can treat the process as proceeding stepwise, with fi-nite changes happening each time period and accumulating over time.

Here we introduce the relatively simple idea of difference equations: if we can write an equation that tells us how the system looks one day based on how it looked the day before, then we can make predictions about what it will look like in the long term.

To begin, we want to get comfortable with the mathematical notation and under-stand the range of phenomena to which the technique can be applied.

Readings 1. Stokey & Zeckhauser, ch. 4, "Difference Equations," pp.47-61. 2. Start reading Schelling , ch. 3, "Thermostats, Lemons, and Other Families of Models," pp.81-133.

6. [Tu210] Difference Equations II : Applications and Extensions

Further explorations.

Readings 1. Stokey & Zeckhauser, ch. 4, "Difference Equations," pp.62-73. 2. Continue reading Schelling , ch. 3, "Thermostats, Lemons, and Other Families of Models," pp.81-

133.

REMINDER: First draft Project Application #1 returned today

[We211] LAB (2) DE and Excel

It turns out you can model a whole bunch of different things with some relatively basic spreadsheet techniques and relatively elementary application of difference equa-tions. We will explore a few of these in this lab. Specifically, we will learn how to build simple iterative models in Excel and to visualize their output.

Project Application Difference Equations



7. [Th212] Thermostats, Lemons, and Feedback

The basic tools of difference equations can be extended conceptually to a whole family of models feedback. Actors look at the world around them and decide how to act; their (and everyone else's) actions have an effect on the world, they then react to the new state of the world.

Readings 1. Schelling , ch. 3, "Thermostats, Lemons, and Other Families of Models," pp.81-133.

Reminder: Lab/Problem Set #2 (Difference Equations) Due Next Class

8. [Tu217] Tipping and Networks

In Schelling's chapter we encountered the idea of a tipping point. This term has been popularized in a book by journalist Malcom Gladwell. In Schelling's picture, everyone has access to global information in an amorphous world; in Gladwell's, some actors are more influential than others. Underlying this is the idea of a social network: the world can be more or less connected, individuals can be more or less central. We can only scratch the surface of the surface of the study of social net-works, but a basic appreciation for the idea that the world is "clumpy" is an im-portant supplement to the basic economic model that underlies most of this course.

Here we want first to provide a quick overview of what a network is and how varia-ble network properties can affect something like tipping. Beyond this, we'll manage an introduction to WHY and HOW you might include network analysis as a part of policy analysis, but this will be more at the level of pointers to further study.

Readings 1. Read Wikipedia entry on "social networks" 2. Interview and excerpts from The Tipping Point by Malcom Gladwell

[http://www.gladwell.com/tippingpoint/index.html] 3. "Is the Tipping Point Toast?" By: Clive Thompson Thu Sep 11, 2008 at 7:39 PM

[http://www.fastcompany.com/node/641124/print] 4. Or listen to this podcast [http://www.veotag.com/player/?u=vvqxwrgqnr or

http://media.podcastingmanager.com/72206-80605/Media/Duncan%20Watts.mp3] 5. Look over Valdis Krebs' material on OrgNet [http://www.orgnet.com/sna.html] 6. Read pages 1-3 of Charles Kadushin's introductory text on social networks

[http://home.earthlink.net/~ckadushin/Texts/Basic%20Network%20Concepts.pdf]

REMINDER: Final draft Project Application #1 (charts) Due Next Class

9. [Th219] Stock and Flow: Systems Dynamics

Queuing models are appropriate when we have servers and a flow of clients and our job is to "handle" the flow optimally (meaning a balance between excess re-Project Application: Stocks and Flows

http://www.gladwell.com/tippingpoint/index.html

http://www.fastcompany.com/node/641124/print

http://www.veotag.com/player/?u=vvqxwrgqnr

http://media.podcastingmanager.com/72206-80605/Media/Duncan%20Watts.mp3

http://www.orgnet.com/sna.html

http://home.earthlink.net/~ckadushin/Texts/Basic%20Network%20Concepts.pdf



sources and wait time). Here the issue is the raw material waiting for the system. A related set of problems goes by the name "inventory" and is the obverse: the pro-cesses of the system need raw material to work on so as to produce outputs. One distinction that will start to become clear is that between "wholist" or "aggregate" models and individualist models. We'll see here a new way to think about feedback and to focus our attention again on full system performance over time. We'll cover concepts like stock, flow, valve, feedback, reinforcing loops, causal loops, balancing loops.

Readings 1. Kirkwood, Craig W. System Dynamics Methods: A Quick Introduction

a. Ch 1. " System Behavior and Causal Loop Diagrams" (PDF) b. Ch 2. " A Modeling Approach" (PDF)

10. [Tu224] Extra

[We225] LAB (3) System Dynamics Models

This lab will involve a brief hands-on experience with the program VenSim which can be used to create and run systems dynamics simulations.

N O N - D E T E R M I N I S T I C

11. [Th226] Probability: EYAWTKAPIAOH and Queuing Models

One reason to make a pit stop called "Everything You Want To Know About Prob-ability In About One Hour" is that most of us have an intuitive sense of probability, but it's an intuition that is very frequent-ly wrong; it be- hooves us to train ourselves in some systematic thinking about probability. Another reason is that the mathematics of probability figures pretty prominently in most of what we do in this course. A gut level appreciation of the basic idea and competence in the notation and basic operations is pretty im-portant.

We'll do this in the context of introducing queuing models so that we can do the proverbial one stone, two birds thing. On the probability front, we will learn how to calculate simple probabilities, joint probabilities, and expected value. We'll ground ourselves in the concept of distributions and remind ourselves about a few common ones and their relevance to our work.

As for queuing, nobody likes to wait, but we all spend a lot of time waiting. It is, we might say, just one of the prices we pay. But to whom do we pay it? A system with more waiting than necessary has people paying a cost that no one receives. It's like burning money. We call that a deadweight loss. In this section of the course we in-

Project Application: Queuing Models

www/docs/kirkwood-ch-1.pdf

www/docs/kirkwood-ch-2.pdf



troduce the idea of a queuing model so that we can think constructively about wait-ing as a policy problem.

Readings 1. Stokey & Zeckhauser, ch. , "Queues," pp.74-83. 2. Queuing Model in Wikipedia [http://en.wikipedia.org/wiki/Queueing_model]

Supplemental

1. Schwartz, Barry. Queuing and Waiting : Studies in the Social Organization of Access and Delay. Chicago, Ill.: The University of Chicago Press, 1975.

2. “Queuing Theory in Operations Management” @ St. Norbert College [http://www.snc.edu/socsci/chair/333/quethry.htm]

3. MS Excel Help: "Introduction to Monte Carlo simulation"

REMINDER: Lab/Problem Set #3 (Systems Dynamics) Due Next Class

12. [Tu33] Queuing Models (continued)

Readings 1. Stokey & Zeckhauser, ch. 5 “Queuing,” pp. 83-88. 2. Wikipedia on Poisson distribution and Poisson Process

REMINDER: First draft Project Application #2 Due Next Class

13. [Th35] Markov Models

Many systems consist of a population of entities that transition be-tween states over time. At any given time, for example, some people are in school, some have jobs, and some are unemployed. Over time, people get hired, fired, graduate, look for work, go back to school etc. The rates at which these different processes occur determine the number of people in each state. Whenever the next state depends on-ly on where you are now, we can model the situation as a "Markov" process.

Here we will learn to recognize things that can be modeled as Markov processes, how to set up and "solve" a Markov model and what the limits of this approach are.

Readings 1. Stokey & Zeckhauser, ch. 7, "Markov Models," pp. 98-107. 2. Stokey & Zeckhauser, ch. 7, "Markov Models," pp.104-114.

14. [Tu310] Communities of Organizations

Almost everything a policy maker thinks about involves organizations and takes place in communities, but both of these are commonly ignored in conversations about policy modeling and analysis.

Project Application

Markov Models

http://en.wikipedia.org/wiki/Queueing_model

http://www.snc.edu/socsci/chair/333/quethry.htm



Today we move off that traditional path to introduce the idea of "communities of organizations." Two basic ideas comprise the intended take-away here: organizations are not like people, they are like organizations (and so the analyst needs some tools for thinking about how organizations behave); communities consist not, primarily, of people, but of organizations.

Readings 1 Ryan, Dan. “'Everything Here is So Political': Separating the Organizationally Normal from the

Political in Communities of Organizations.” 2006. Journal of Drug Issues, 36,2. 2 Kadushin, Lindholm, & Ryan. “Why is it so difficult to form community coalitions?” City and

Community 4, 3 (September 2005).

REMINDER: First draft Project Application #2 Returned Today

[We311] LAB (4) Markov and Queues and Monte Carlo in Excel

DECI S I ONS

D E T E R M I N I S T I C

15. [Th312] Choice and the Ordering of Preferences

The background for much of the modeling and simulation that goes on in the social and policy sciences is the rational actor model we learn about in introductory micro-economics. To be sure, a lot of our models are "corrections" to this naïve approach, but this does not amount to rejecting it. Indeed, it provides a heuristic scaffolding on which we erect most of the techniques of analysis that we'll learn in this course. And thus, we begin with a review.

The next three chapters all relate to the problem of figuring out what we should do when we want to accomplish many things and where things happen over time.

We want to learn to describe our simple model of choice, set up two-dimensional production and indifference curves, understand marginal analysis, think about how to combine values and preferences to compare outcomes.

Readings 1. Stokey & Zeckhauser, ch. 3, "The Model of Choice," pp. 22-44. 2. Lave & March, ch. 5, "Exchange," pp. 157-174. 3. Stokey & Zeckhauser, ch. 8, "Defining Preferences," pp.115-133.

REMINDER: Lab/Problem Set #4 (Markov/Queues) Due Next Class



16. [Tu317] Benefit Cost Analysis I

Benefit cost- analysis has, perhaps more than any other analytical technique covered in this course, become an everyday concept. And, fortu-nately, the real thing is, more or less, the same as its popularization. But even if the concept is well known, how to carry out a cost-benefit analysis well requires a little training.

This approach is generally intended to be ex-ante – that is, we will attempt to evalu-ate projects before they are implemented. We lay out how to deal with multi-attributed outcomes, set up and carry out benefit/cost and cost effectiveness analysis and zero in on when they are appropriate and what limitations they are subject to.

Readings 1. Stokey & Zeckhauser, ch. 9, "Project Evaluation: Benefit-Cost Analysis," pp.134-157.

Supplemental 2. Weimer & Vining, ch. 7, "Benefit-Cost Analysis"

REMINDER: Final draft Project Application #2 Due Next Class

17. [Th319] Benefit Cost Analysis II

Continued

Readings 1. Stokey & Zeckhauser, ch. , "," pp..

Supplemental 2. Tversky, Amos, and Daniel Kahneman, "The Framing of Decisions and the Psychology

of Choice," Science 211: 453-458, 1981

18. [Tu331] Discounting

A bird in the hand is worth two in the bush. OK, but can we get more precise? How can we compare options when the costs or benefits may be spread out over time?

We want to understand what discounting is, how to value non-market outcomes, and critiques of the process.

Readings 1. Stokey & Zeckhauser Ch. 10, "The Valuation of Future Consequences: Discounting" 2. Read Excel help for functions FV, FVSCHEDULE, NPV, PV

Project Application: Costs, Benefits and Discounting



[We41] LAB (5) Benefit Cost Analysis in Excel

The goal of this lab will be to set up an Excel environment that you can use to ex-plore the examples in the text as well as the problems in the problem set that follows this section of the course.

19. [Th42] Extra

REMINDER: Lab/Problem Set #5 (Benefit-Cost Analysis) Due Next Class

20. [Tu47] Linear Programming I

Anyone who has tried to allocate a limited budget to fulfill a set of com-peting interests is familiar with the kind of problems we will look at in this section. "Linear programming" is a technique for determining the optimal allocation of resources subject to a given set of constraints.

We want to walk away with a sense of why "linear" and why "program-ming," knowledge of how it's done, and an awareness of the assumptions of the ap-proach.

Readings 1. Stokey & Zeckhauser, ch.11 , "Linear Programming," pp.177-200.

REMINDER: First draft Project Application #3 Due Next Class

21. [Th49] LP II: What Would It Be Worth If We…

Suppose we find an optimal solution given a set of constraints. We're done, right? Maybe. But another thing we often want to know about the world is how things we change if we could manage to relax one or another constraint. Suppose you come up with an austerity budget that lets your agency maximize services subject to a set of constraints and commitments. Some of these might, through the expenditure of some political capital, be relaxed. Which ones represent the most bang for the buck – in other words, where should you spend political capital to relax constraints so as to have the biggest impact on the level of services you can provide?

Here we cover shadow prices, the classical problems and the limitations and caveats that attend to this technique.

Readings 1. Stokey & Zeckhauser, ch.11 , "Linear Programming," pp.177-200.

Project Application:

Linear Programming



N O N - D E T E R M I N I S T I C

22. [Tu414] Decision Analysis: "to do" vs. "to happen"

Everything we have looked at so far seems to be about how to arrive at a decision as to the best course of action in given situation. We now turn to a slightly more com-plex (and more realistic) situation: sequential decisions in which some decisions de-pend on the outcome of previous decisions and some depend on the results of events we can't control.

Each path from the root of the tree on the left to the end of a branch on the right represents a sequence of decisions (things we can control) and events (things we can't control).

We'll divide our work into three parts. First, we'll learn how to build a basic decision tree and how to compare various branches in terms of the expected value of the se-

quence it represents. Next we'll learn how to incorporate into the model the amount of riskiness we are willing to accept. Final-

ly, we will look at the value of information (for example, of tests or inspections that can reduce the uncertainty about the outcomes of particular courses of action).

Readings 1. Stokey & Zeckhauser, ch. 12, "Decision Analysis," pp.201-208. 2. Lave & March, ch. 4, "Choice" 85-155

REMINDER: First draft Project Application #3 Returned Today

[We415] LAB (6) Linear Programming Lab

Readings 1. Review Stokey & Zeckhauser, ch. 12

23. [Th416] Decision Analysis: Adding Uncertainty and Risk

How should we analyze decisions when outcomes are subject to chance? How should we take account of the amount of downside risk we are willing to tolerate?

Here we learn how to add uncertainty and risk to our previous approaches by intro-ducing the methods of decision analysis.

Readings 1. Stokey & Zeckhauser, ch. 12, "Decision Analysis," pp.208-219.

2. Tversky and Kahneman, "Judgment under Uncertainty: Heuristics and Biases," Science

185: 1124-1131, 1974.

Project Application: Decision Analysis



REMINDER: Lab/Problem Set #6 (Linear Programming) Due Next Class

24. [Tu421] Decision Analysis: The Value of Information

More information may always be better, but if the information costs resources or time, how do you decide how much is enough or how much you are willing to pay for it? And when you are estimating parameters, how sensitive is your model to the values you choose?

Readings 1. Stokey & Zeckhauser, ch. 12, "Decision Analysis," pp.219-236.

REMINDER: Final draft Project Application #3 Due Next Class

Re f le ct ions

25. [Th423] Social Welfare: What's the Point?

How can we keep our eye on the prize? The importance of going back to basics and understanding the fundamental assumptions and arguments that provide the back-drop of what we are doing.

26. [Tu428] Market Failure and Remedies

Why not let markets do everything? The short answers are that few markets are per-fectly competitive, that efficiency is not the only thing we care about, and sometimes we, collectively, want things that are different from what we want as individuals.

Our goals today are to move toward a proper appreciation of market successes and failures and the arguments, ideological and substantive, pro and con about govern-ment action. Ultimately, the point is to temper one's own "gut" position (whatever it is) with the valuable points made by the "other" side.

Readings 1. Stokey & Zeckhauser, ch. 14, "Achieving Desirable Outcomes," pp.291-319.

REMINDER: Lab/Problem Set #7 (Decision Analysis) Due Next Class

[We429] LAB (7) DA & Excel



27. [Th430] Cautions and Caveats

The great danger at the end of a first course in anything is that one becomes a danger to the world because one knows a little bit about a lot of somethings. That's because we almost always learn the "good news" (all the amazing things this tool can do or this concept explains) before (or more than) we learn the "bad news" (the limited conditions under which it applies, the errors that can be made, the other factors that must be taken into consideration).

This last session is intended to add a little chastening to our salad of tools, to remind us to be as wise in knowing what we don't know as in what we do, and to, hopefully, succeed in making us realize that competence and expertise are often best exercised by seeking out the assistance of others with more of both than we ourselves have. Our expertise often lies in knowing whom to call, what to ask them to do, and how to use what they can give us.

Our specific learning goal is to move toward constructing a list of techniques, what they are appropriate for, and what one must be on the guard for.

28. [Tu55] Marching Orders

Now what? There's always more to learn, so where next for the working profession-al? We'll try to draw a map showing how much of different techniques we covered and where the next steps in each are to be found.

One goal in this course has be to develop the capacity to look at a problem and fig-ure out what kind of a problem it is. In other words, what family of models we would turn to start thinking about it. The successful student will now have sentences like these in her repertoire: "That's a collective action problem," "That’s the standard maximization problem," "That's a critical mass problem," and ask questions like, "What constraints are we operating under here?" or "Where's the feedback here?"

Readings 1. Stokey & Zeckhauser, ch. 15, "Putting Analysis to Work," pp.320-329.

simulation/modelling in social/policy sciences ppol225...

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