1 Course Intro Scott Matthews / / Lecture 1 - 8/29/2005

Lecture 1: 8/29/052 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.

Lecture 1: 8/29/053 Scott Matthews  Asst. Prof., CEE/EPP  Research Director and Faculty  Green Design Institute  B.S. ECE/Engineering & Public Policy, M.S. Economics, PhD. Economics  Research  Sustainable infrastructure and green product/system design  Make sure corporations understand all private and social costs of decisions.

Lecture 1: 8/29/054 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.

Lecture 1: 8/29/055 Course History  I’ve taught this course for 10 years  1995: Benefit-cost analysis (73-359)  1997: Merged with CEE  2005: Merged with EPP

Lecture 1: 8/29/056 Changes Over Time I’ve gotten old. Some of you weren’t Born when the greatest Album of all time came Out! I got married and had 2 boys. Now I get no sleep I have 2 great other helpers

Lecture 1: 8/29/057 TAs: Joe and Paulina  Contact info on syllabus  When should office hours be?  Would an (infrequent) Friday review be helpful? When would we schedule it?

Lecture 1: 8/29/058 Course Web Page  Course web page:   Lecture notes, problem sets and schedule

Lecture 1: 8/29/059 Course Grade Components  5-6 Problem Sets  Midterm Quiz  Final Examination  Several Group Projects (grad students)  Participation: Borderline cases  (I will learn all names)

Lecture 1: 8/29/0510 Text and Handouts  Campbell and Brown “BCA with spreadsheets” (aka Campbell)  Clemen and Reilly “Making Hard Decisions” (aka Clemen)  Lecture notes- available on web page.  Application cases.  Miscellaneous: articles, problems, etc.

Lecture 1: 8/29/0511 Graduate Course “Rules”  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

Lecture 1: 8/29/0512 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  If you use external sources to help you build models, make sure you cite them

Lecture 1: 8/29/0513 Application Areas  Methods and techniques are general.  Emphasize environmental / civil systems investment and pricing applications as examples.  Ports, roadways, transit systems.  Air and water pollution  Water and wastewater systems.  Public, private and mixed investment/finance decisions (e.g. Stadium construction).

Lecture 1: 8/29/0514 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.

Lecture 1: 8/29/0515 Example Planning Process: New Plant  Initiated by Owner or Developer  Projected benefits/costs reviewed by financiers.  Local government and public often involved in planning for land acquisition.  Local government, utilities, regulatory agencies and public participate in permitting process.

Lecture 1: 8/29/0516 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  See Guardian vs. Spender mentality in chapter  Guardians bottom-line oriented, see only tolls  Tend to underestimate costs  Spenders see everything (inc. costs) as benefits  Tend to overestimate benefits

Lecture 1: 8/29/0517 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?

Lecture 1: 8/29/0518 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!

Lecture 1: 8/29/0519 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

Lecture 1: 8/29/0520 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

Lecture 1: 8/29/0521 How Many TV Sets in the US?

Lecture 1: 8/29/0522 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?

Lecture 1: 8/29/0523 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 )

Lecture 1: 8/29/0524 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* ) = M television sets

Lecture 1: 8/29/0525 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?

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