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Course Intro

Scott Matthews12-706 / 19-702 Lecture 1

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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.

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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.

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Course History

I’ve taught this course for 10+ years1995: Benefit-cost analysis (73-359)1997: Merged with CEE 12-7062005: Merged with EPP 19-702

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About Me

U2 Fan

Married, 2 sons.

Get no sleep

I have 2 great other helpers

x3

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TAs: Paulina and Amanda

Contact info on syllabus

When should office hours be? Decide today, Wednesday Goal - before HWs (due Wed in general)

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Scott MatthewsAssociate Prof., CEE/EPPResearch Director and Faculty

Green Design InstituteB.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.

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Course Web Page

Course web page: http://www.ce.cmu.edu/~hsm/bca2007/

Lecture notes, problem sets and schedule

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Course Grade Components

~6 Problem Sets Case Study WriteupsTake-Home Final ExaminationSeveral Group Projects Participation: Borderline cases

(I will learn all names)

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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.

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Graduate Course “Rules”

Whose first grad course? Students do readings in advance I supplement reading with discussion

and examplesI do not re-lecture what you’ve readClass time will be mostly spent on

applications and demosShould reconsider if not comfortable

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Cheating / etc. Guidelines

I will not tolerate itThe 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

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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).

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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.

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Our Course Scope: The Real World

Scary thought.

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The Policy WorldNormative vs. Positive theoriesN - 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

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A Teaser of What We Will Learn

Risk Analysis ModelsCost-EffectivenessEnvironmental

ValuationSimulationEffective Visuals and

Documentation

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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?

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How Will Our Answer Vary?

How Would Cost of Electricity Change if we replaced light bulbs with photo sensors

SensIt - Sensitivity Analysis - Tornado

5

20

3

0.025

30

50

10

0.075

$1,400 $1,600 $1,800 $2,000 $2,200 $2,400 $2,600 $2,800 $3,000 $3,200 $3,400

Bulb Change Time (mins)

Labor cost/hour

Bulb Cost

Mean Elec Cost ($/kWh)

Total Cost

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Should we travel by plane or train?

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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?

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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!

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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, … (102, 103, etc.)

Source: Mosteller handout

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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

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How Many TV Sets in the US?

Work in groups of 2-3

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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?

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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*(4z5+3z4+2z3+1z2+0z1)

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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

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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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Lessons Learned?

What were primary sources of our “error” in estimating this number?

What can we learn from sources of error?

Reading for Wednesday.