Day 13: Problems: Numbers and Causes - …...Day 13: Problems: Numbers and Causes Daniel J....
Transcript of Day 13: Problems: Numbers and Causes - …...Day 13: Problems: Numbers and Causes Daniel J....
Day 13: Problems: Numbers and Causes
Daniel J. Mallinson
School of Public AffairsPenn State [email protected]
PUBPL 304
Mallinson Day 13 September 20, 2017 1 / 18
Road map
Discuss how numbers and causal stories frameproblems
Mallinson Day 13 September 20, 2017 2 / 18
Problem Definition
Symbols
Numbers
Causes
Interests
Decisions
Mallinson Day 13 September 20, 2017 3 / 18
Who to count?
Inclusion/Exclusion Criteria
Who should do the counting?
Bureau of Labor and StatisticsDo not have a job
Actively looked in last 4 weeks
Currently available for work
Mallinson Day 13 September 20, 2017 4 / 18
Challenges to Criteria
Wrongful Exclusion
Wrongful Inclusion
Mallinson Day 13 September 20, 2017 5 / 18
Numbers as Metaphor
Example: Three-fifths Compromise
Mallinson Day 13 September 20, 2017 6 / 18
Numbers and Storytelling
Hidden Stories
Aura of expertise and authority
Precision, accuracy, objectivity
Enumeration implies importance (competence)
Assertion of clear boundaries
Creates a community
Implies a solution
Mallinson Day 13 September 20, 2017 7 / 18
The Rise of the Infographic
Mallinson Day 13 September 20, 2017 8 / 18
Causation
What is causing climate change?
Mallinson Day 13 September 20, 2017 9 / 18
Causation
Science
Quest to explain the world; find “the” cause
Politics
Assignment of responsibility
Mallinson Day 13 September 20, 2017 10 / 18
Types of Causal Theories
Consequences
Actions Intended Unintended
Unguided Mechanical Cause Accidental Cause
Guided Intentional Cause Inadvertent Cause
Mallinson Day 13 September 20, 2017 11 / 18
Accidental CauseUnintended consequences from unguided actionsExamples:
Natural disastersFateBad luck
Strong defenseAvoid blame
Mallinson Day 13 September 20, 2017 12 / 18
Intentional Cause
Intended consequencesfrom guided actionExamples:
OppressionConspiricyKnown, but ignored, sideeffects“Bad apples”Blame the victim (hard)
Strong offense
Demand accountability
Mallinson Day 13 September 20, 2017 13 / 18
Mechanical Cause
Intended consequences from unguided action
Examples:
Machines gone badEichmann Defense
Weak offense/defense
Mallinson Day 13 September 20, 2017 14 / 18
Inadvertent Cause
Unintended consequences from guided action
Examples:
Unanticipated side effectsAvoidable ignoranceCarelessnessBlaming the victim (soft)
Weak offense/defense
Mallinson Day 13 September 20, 2017 15 / 18
A Political Contest
Not just about finding “the” cause
Claimant
Show that previously-assumed natural cause is anintentional/inadvertent harm
Respondent
Claim strongest position possible, then blame shift, then take weakerposition, then claim inadvertent harm
Law and Science
Legitimize claims of causation
Mallinson Day 13 September 20, 2017 16 / 18
A Political Contest
“In the polis, causal stories need to be fought for,defended, and sustained.” (Stone, 223)
Mallinson Day 13 September 20, 2017 17 / 18
Using Causal Claims
Challenge or reinforcethe status quo
Assign responsibility andcost
Give authority to “fixers”
Create new alliances
Mallinson Day 13 September 20, 2017 18 / 18