1 Course Intro Scott Matthews 12-706 / 19-702 Lecture 1.
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Transcript of 1 Course Intro Scott Matthews 12-706 / 19-702 Lecture 1.
1
Course Intro
Scott Matthews12-706 / 19-702 Lecture 1
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.
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.
Lecture 1: 4
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
Lecture 1: 5
About Me
U2 Fan
Married, 2 sons.
Get no sleep
I have 2 great other helpers
x3
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)
Lecture 1: 7
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.
Lecture 1: 8
Course Web Page
Course web page: http://www.ce.cmu.edu/~hsm/bca2007/
Lecture notes, problem sets and schedule
Lecture 1: 9
Course Grade Components
~6 Problem Sets Case Study WriteupsTake-Home Final ExaminationSeveral Group Projects Participation: Borderline cases
(I will learn all names)
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.
Lecture 1: 11
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
Lecture 1: 12
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
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).
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.
Lecture 1: 15
Our Course Scope: The Real World
Scary thought.
Lecture 1: 16
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
Lecture 1: 17
A Teaser of What We Will Learn
Risk Analysis ModelsCost-EffectivenessEnvironmental
ValuationSimulationEffective Visuals and
Documentation
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?
Lecture 1: 19
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
Lecture 1: 20
Should we travel by plane or train?
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?
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!
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, … (102, 103, etc.)
Source: Mosteller handout
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
Lecture 1: 25
How Many TV Sets in the US?
Work in groups of 2-3
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?
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*(4z5+3z4+2z3+1z2+0z1)
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
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?
Lecture 1: 30
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.