Risk Management BUSS 5292 代写
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Risk Management
BUSS 5292
Topic 1
History of risk management thinking
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Course Facilitator: Kesten Green
Study Period 1, 2016
Today’s session
• Introductions
• About the Risk Management
course
• Administrative matters
• History of risk management
thinking
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Risk Management - BUSS 5292
Course Objective
• Introduce risk management
concepts and tools, that you can
use to help you to…
• Structure and solve managerial
decision problems.
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Your objectives for the
course
• ?
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How to do well in Risk Management
• Make use of the Course Site
• Course Outline provides key administrative
information
• Use the News Forum for general communications
• Video clips and readings
• Study Guide is not used
• Add your photo and profile
• Read topic materials before lecture
• For assignments: Use good sources, and your
own words
• Think about applications
• Ask clarification questions
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Administration:
Course Outline & LearnOnline Site
• Refer to the Course Outline and course
internet site (LearnOnline) for information on
the administration of the course, including…
– Contacts
– Overview
– Dates
– Resources
– Assessment
– News
– Other stuff…
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Maths
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Why lectures?
• To help you understand the course
material
• To help you relate the material to
practical problems
• To test your understanding
• To get tips on how to get good marks
• To make useful contacts
• ?
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What do we mean by risk?
q “If the uncertainty associated with an event can be
quantified on the basis of empirical observations or
causal knowledge (physical design), the
uncertainty is called risk.
q Relative frequencies and probabilities are ways to
express risks.
q Contrary to everyday use of the term, a risk need
not be associated with harm; it can refer to a
positive, neutral, or negative event.
q The classical distinction between known risks
(‘risk’) and unknown risks (‘uncertainty’) is
attributed to the economist Frank Knight.”
Gerd Gigerenzer, Glossary of “Risk savvy”
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The risk management problem:
Old(ish) version
“We plan, God laughs”
Yiddish proverb
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“Nothing is certain but death
and taxes.”
From Benjamin Franklin’s 1789 letter to
Jean-Baptiste Leroy
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The risk management problem:
New(er) version (Franklin’s Law)
§ Ignorance
§ Mistakes
§ Deceit
§ Conflict
§ Forecasting failure
§ Forecast uncertainty
§ ...
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Why so little certainty?
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Summary history of
risk management thinking
Date Key ideas
3200 B.C. Certainty, given signs from gods (Asipu)
400 B.C. Behaviour now risks soul in afterlife (Plato)
400 A.D. Dominance principle, 2 x 2 matrix (Arnobius)
1657 A.D. Probability theory introduced (Pascal)
… development of mathematical probability theory,
and concepts of causality, systematic observation,
and the experimental method (Science)...
1792 A.D. Modern quantitative risk analysis (Laplace)
Recorded history indicates risk management was an
important service at least as far back as the 4 th Millenium B . C .
At the begining of history and in the absence of the scientific
method...
What would happen was assumed to be determined by the
whim of gods.
People nevertheless wanted advice to help make decisions
about risky or uncertain situations...
Demand was met with supply in the form of risk
management consultants (a . k . a . priests or seers), who
developed rituals they claimed divined the plans of the gods.
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Risk management at
the dawn of history
Demand for seers
persists to this day
• In 1978, Scott Armstrong summarized
evidence showing that in complex and
uncertain situations, expert forecasts*
were no more accurate than forecasts
by people with little expertise.
• People resisted this evidence, hence the
“Seer-sucker Theory.”
*Unaided by evidence-based principles on how
to forecast
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6Risk Management BUSS 5292 代写
Forecasts by experts
• Unaided experts’ forecasts are of no
value when the situation is complex and
uncertain.
• Does not help when judgments are
expressed in complex mathematics.
• Does not help when the experts get more
data
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Unaided expert forecasts:
recent evidence
• Tetlock’s Expert Political Judgment
(2005) also found experts’ forecasts
lacked value:
– evaluated forecasts from 284 experts in
politics and economics
– who made about 82,361 forecasts
– over two decades
Possible states
God exists God doesn’t exist
Accept BIG win Small loss
Christianity
Alternative
decisions
Remain
pagan BIG loss Small win
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Dominance principle (c400 A . D .)
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“Pascal’s Wager”: expected value,
& utility maximisation
Possible states
God exists God doesn’t exist
(p=.5? .00001?)
Accept BIG win Small loss
Christianity ∞?
Alternative
decisions
Remain
pagan BIG loss Small win
E (“wager for God”) = p x ∞ + (1-p) x “small loss” = ∞
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More on Pascal’s contribution:
The division problem or problem of points
Problem of dividing a prize when a series of games is
interrupted before agreed winning total is reached (e.g.
first to win 7 games) assuming equal chances of winning.
Equivalent to determining the ratio of the probabilities
that each player will win the remaining games he needs
to win before the other does…
applied to the value of the prize.
In other words, each player gets his calculated expected
value of the prize.
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Key developments in math-prob.
theory and scientific causality
Date Key ideas
1657 A.D. Probability theory introduced (Pascal)
1662 A.D. Life expectancy tables (Graunt)
1692 A.D. Causal probabilities calculable (Arbuthnot)
1693 A.D. Life expectancy table and annuities (Halley)
1792 A.D. Modern quantitative risk analysis (Laplace)
[Analysed the probability of death having
had a smallpox vaccination and without having had
one.]
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Graunt’s life expectancy table:
Empirical probabilities
Births and deaths data collected c1554 at behest of London
Merchants by Chancellor Thomas Cromwell
Life expectancy table published in 1661 from an analysis of
the data by “obscure haberdasher” John Graunt…
and so pioneered modern statistics, including PDFs.
(Table and chart from Thompson’s (Rice U) “John Graunt’s Life Table”)
Modern use of experimentation in the 16 th & 17 th Centuries
e.g. Galileo (1612) Bodies that stay atop water, or move in it
Key changes:
1. Observation to correct theory
(vs to support argument or established theory)
2. Experimentation, or active observation whereby the
situation of interest is manipulated to see what happens
3. Control extraneous influences that might bias
observations and lead to erroneous conclusions.
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Developments in causal knowledge:
Experimentation
A causal relationship exists if the effect…
1. Follows the cause
2. Is connected to the cause in some way
3. Cannot be plausibly explained in any way other than
by the cause*
From J. S. Mill’s formulation
Modern scientific experimentation is the best way to
determine whether the three necessary conditions of a
causal relationship exist.
*Importance of testing multiple reasonable hypotheses.
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Developments in causal knowledge:
Establishing cause and effect
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Why experimental findings?
Meta-analyses of experimental evidence from
tests of multiple reasonable hypotheses is the
basis for scientific advances (Chamberlin, 1890)
Infeasible to identify causality from analyses of
nonexperimental data in uncertain complex
situations. Illusions in regression analysis
Directions of effects from nonexperimental
studies often differ from those from experimental
studies. Armstrong & Patnaik (2009).
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Multiple hypotheses and
knowledge
Knowledge advances when multiple hypotheses
are tested, especially if hypotheses challenge
accepted wisdom, e.g.:
• Anti-inflammatory drugs harm head injury patients
• Duodenal ulcers are caused by bacteria, not spicy food
• Market-share objectives harm profits.
• Minimum-wage laws harm low-skilled workers
• Regulation harms consumers
• Pre-announced satisfaction surveys harm satisfaction
Risk Management BUSS 5292 代写
