1. 1 Course Intro Scott Matthews 12-706 / 19-702 Lecture 1

2. Lecture 1:2 Objectives  Prepare you to construct, assess, and explain models to aid in public decision making  Build a framework on which you can add additional courses and knowledge  Understand issues of estimation, economics, uncertainty, coping with multiple parties and objectives in decision making.

3. Lecture 1:3 History: Merged Course – Economists and Engineers  Seemed to work well during the past 8 years.  Courses overlapped in content - need for practical decision making aids.  Engineers need economic perspective; economists need an engineering (practical problem solving) perspective.

4. Lecture 1:4 Course History  I’ve taught this course for 10+ years  1995: Benefit-cost analysis (73-359)  1997: Merged with CEE 12-706  2005: Merged with EPP 19-702

5. Lecture 1:5 About Me U2 Fan Married, 2 sons. Get no sleep I have 2 great other helpers x3

6. Lecture 1:6 TAs: Paulina and Amanda  Contact info on syllabus  When should office hours be?  Decide today, Wednesday  Goal - before HWs (due Wed in general)

7. Lecture 1:7 Scott Matthews  Associate Prof., CEE/EPP  Research Director and Faculty  Green Design Institute  B.S. ECE/Engineering & Public Policy, M.S. Economics, PhD. Economics (all CMU)  Research  Sustainable infrastructure and green product/system design  Help stakeholders understand all private and social costs of decisions.

8. Lecture 1:8 Course Web Page  Course web page:  http://www.ce.cmu.edu/~hsm/bca2007/  Lecture notes, problem sets and schedule

9. Lecture 1:9 Course Grade Components  ~6 Problem Sets  Case Study Writeups  Take-Home Final Examination  Several Group Projects  Participation: Borderline cases  (I will learn all names)

10. Lecture 1:10 Text and Handouts  Clemen and Reilly “Making Hard Decisions” (aka Clemen)  Optional: Schaum’s Guide to Engineering Economics  Lecture notes- available on web page.  Application cases.  Miscellaneous: articles, problems, etc.

11. Lecture 1:11 Graduate Course “Rules”  Whose first grad course?  Students do readings in advance  I supplement reading with discussion and examples  I do not re-lecture what you’ve read  Class time will be mostly spent on applications and demos  Should reconsider if not comfortable

12. Lecture 1:12 Cheating / etc. Guidelines  I will not tolerate it  The whole point of this class is to teach you how to build your own models and use them  Stealing what others have done, aside from being against policy, undermines the purpose of the course, and your education  If you use external sources to help you build models, make sure you cite them

13. Lecture 1:13 Application Areas  Methods and techniques are general.  Emphasize environmental / civil systems applications as examples.  Roadways, transit systems.  Air and water pollution  Water and wastewater systems.  Public, private and mixed investment/finance decisions (e.g. Stadium construction).

14. Lecture 1:14 Planning Process versus Analysis  Benefit-Cost Analysis and Design support planning processes, often performed by consultants or staff.  Planning processes tend to involve many different parties (current terminology - “stakeholders”), all with their own agendas.

15. Lecture 1:15 Our Course Scope: The Real World  Scary thought.

16. Lecture 1:16 The Policy World  Normative vs. Positive theories  N - based on ‘norms’ - ‘should be done’  P - based on ‘reality’ - ‘actually done’  This reinforces the idea of perspective  Guardian vs. Spender mentality  Guardians bottom-line oriented, see only tolls  Tend to underestimate costs  Spenders see everything (inc. costs) as benefits  Tend to overestimate benefits

17. Lecture 1:17 A Teaser of What We Will Learn  Risk Analysis Models  Cost-Effectiveness  Environmental Valuation  Simulation  Effective Visuals and Documentation

18. Lecture 1:18 Open Ended Questions - Examples  Its 1990. Should we spend $5-10 billion improving the levee / storm protection system around New Orleans in case of a Category 4 hurricane?

19. Lecture 1:19 How Will Our Answer Vary?  How Would Cost of Electricity Change if we replaced light bulbs with photo sensors

20. Lecture 1:20 Should we travel by plane or train?

21. Lecture 1:21 Preview: Estimation  The first concept we will go over (Wednesday) is on structured estimation problems.  How do we construct an estimate for a number when we do not know the answer?

22. Lecture 1:22 Estimation in the Course  We will encounter estimation problems in sections on demand, cost and risks.  We will encounter estimation problems in several case studies.  Projects will likely have estimation problems.  Need to make quick, “back-of-the-envelope” estimates in many cases.  Don’t be afraid to do so!

23. Lecture 1:23 Problem of Unknown Numbers  If we need a piece of data, we can:  Look it up in a reference source  Collect number through survey/investigation  Guess it ourselves  Get experts to help you guess it  Often only ‘ballpark’, ‘back of the envelope’ or ‘order of magnitude needed  Situations when actual number is unavailable or where rough estimates are good enough  E.g. 100s, 1000s, … (10 2, 10 3, etc.)  Source: Mosteller handout

24. Lecture 1:24 Notes on Estimation  Move from abstract to concrete, identifying assumptions  Draw from experience and basic data sources  Use statistical techniques/surveys if needed  Be creative, BUT  Be logical and able to justify  Find answer, then learn from it.  Apply a reasonableness test ** very important

25. Lecture 1:25 How Many TV Sets in the US?  Work in groups of 2-3

26. Lecture 1:26 How many TV sets in the US?  Can this be calculated?  Estimation approach #1: Survey/similarity  How many TV sets owned by class?  Scale up by number of people in the US  Should we consider the class a representative sample? Why not?

27. Lecture 1:27 TV Sets in US – another way  Estimation approach # 2 (segmenting):  Work from # households and # TV’s per household - may survey for one input  Assume x households in US  Assume z segments of ownership (i.e. what % owns 0, owns 1, etc)  Then estimated number of television sets in US = x*(4z 5 +3z 4 +2z 3 +1z 2 +0z 1 )

28. Lecture 1:28 TV Sets in US – sample  Estimation approach # 2 (segmenting):  work from # households and # tvs per household - may survey for one input  Assume 50,000,000 households in US  Assume 19% have 4, 30% have 3, 35% 2, 15% 1, 1% 0 television sets  Then 50,000,000*(4*.19+3*.3+2*.35+.15) = 125.5 M television sets

29. Lecture 1:29 TV Sets in US – still another way  Estimation approach #3 – published data  Source: Statistical Abstract of US  Gives many basic statistics such as population, areas, etc.  Done by accountants/economists - hard to find ‘mass of construction materials’ or ‘tons of lead production’.  How close are we?

30. Lecture 1:30

31. Lecture 1:31 Lessons Learned?  What were primary sources of our “error” in estimating this number?  What can we learn from sources of error?  Reading for Wednesday.