Transcript Document

Agricultural Decision Making
under (Climate) Uncertainty
Elke Weber
Columbia University
AACREA, Buenos Aires, Nov. 29, 2005
Outline
• Background and scope of current research
collaboration with AACREA
• My background
• Introduction to cognitive-style assessment
• Preliminary results from Argentina
• Brief tutorial on Prospect Theory
• Future questions for investigation
Sources of Research Funding
• Pilot project funding
– National Science Foundation (NSF) Incubation Grant
– International Research Institute for Climate Prediction
(IRI)
• National Oceanographic and Atmospheric
Administration (NOAA)
– Funded two three-year follow-up projects
• National Science Foundation (NSF)
– Funded large three-year Biocomplexity initiative (led
by Guillermo Podesta)
– Funded five-year Center for Research on
Environmental Decisions (CRED)
Societal
Environment
Natural
Environment
CLIMATE
Soils
Topography
Land use history
Pests & Diseases
Other
Commodity prices
Exchange rates
Tax policies
Political stability
Institutions
Other
Decision-Making
Cognitive limitations
Personality traits
Risk attitudes
Objective functions
Institutions
Informational
Environment
Salience
Credibility
Legitimacy
Access
Compatibility
Place
• Mission
– Investigate decision processes underlying adaptation to uncertainty and change,
in particular uncertainty and change related to climate change and climate
variability
• Coordinates and integrates 16 projects conducted by an
interdisciplinary set of 24 researchers
– Anthropology, cognitive and social psychology, economics, history, geography,
environmental engineering, agronomy
– Headquarters at Columbia University in New York City
• Field research on a wide range of decision makers
– e.g., farmers, water resource managers, policy makers
• Research conducted across a wide range of cultures around the globe
– USA, Brazil, Argentina, Europe, Uganda, Greater Horn of Africa, South Africa,
Middle East
Argentina Research Team Members
• Collaboration between
– University and governmental institutions’ researchers
– AACREA leadership and technical advisors
– AACREA farmers
• In Argentina (only most relevant subset)
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Emilio Satorre
Fernando Ruiz Toranzo
Carlos Laciana (and Xavier Gonzalez)
Federico Bert
CENTRO (Hilda Herzer and her team)
David and Laura Hughes and other AACREA farmers
• In the United States (only most relevant subset)
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Guillermo Podesta
Kenny Broad
Sabine Marx
Jim Hansen
David Letson
My Background
• Trained in psychology and economics at Harvard
in 1980s
• First academic job in the American Midwest (U.
of Illinois) in 1985
– Worked with agricultural economists on perceptions of
and reactions to climate change
• Moved to a joint position in Psychology and the
Business School at Columbia U. in 1999
– Worked with colleagues at IRI who subsequently
moved to U. Miami and introduced me to Guillermo
Podesta
My Research Interests
(A) Learning from personal experience and
learning from others
(B) Role of cognition and emotion in
decisions and behavior
(C) Different decision making goals and
decision making styles
(A) Learning from Personal Experience
• Personal experience is a powerful teacher
– Touching a hot stove once is very memorable
• However, even learning from experience often
not so simple
– Beliefs and expectations influence perception and
interpretation
• Historical example: Colonial English settlers in North
America
– Beliefs and expectations influence perception and
memory
• Weather memories of Illinois farmers in 1980s
Historical Example: Colonial
English settlers in North America
• Believed that climate was a function of latitude
– Newfoundland expected to have the climate of London
– Virginia expected to have the climate of Spain
– Experience of consistently colder weather ignored for
many years
• Samuel de Champlain interpreted single mild winter
in 1610 to mean that milder climate expectations
were justified after all; previous six years were seen
as aberrations or “statistical outliers”
Contemporary Example: Weather
Memories of Illinois Farmers in late 1980s
• Illinois cash-crop farmers interviewed in late
1980s about climate change beliefs and
expectations
– About half believed that there was a warming trend
(climate change) and half did not
• Farmers also asked to remember key weather
variables over past 7 years (e.g., average July
temperature)
– Weather memories of both groups were distorted in
direction of their expectation
(B) Role of Emotion and Cognition
in Decision Making
• Two human processing systems
– analytic, rule-based system
• effortful, slow, unique to humans, requires conscious
awareness, and explicit learning
– e.g., probability theory, formal logic
– association- and similarity-based system
• evolutionarily older, hard-wired, fast, automatic
– greater emphasis on outcomes than probabilities
– emotions as a powerful class of associations
» risk represented as a “feeling” that serves as an “early
warning system”
– Two systems operate in parallel
• “Is a whale a fish?”
Affective/Experience-based
Processing of Information
• Generally
– greater motivator to take action
• But, also has some downsides
– Recency effect leads to volatile responses to smallprobability events
• Either get too little attention/weight or lead to overreaction
– “Finite Pool of Worry” Effect
– “Single Action” Bias
“Finite Pool of Worry” Effect
• As concern about one type of risk increases,
worry about other risks decreases
[Linville and Fischer, 1991]
– Argentine Farmers
• ratings of political, economic, and climate risk of
farm decision without or with a La Niña forecast
(Hansen, Marx, Weber, 2004)
– as concern with climate risk increased, concern with
political risk decreased
Finite Pool of Worry
(0 to 10 ratings of concern)
Risk Category
Climate Risk
Scenario1 Scenario2 p-value
7.5
8.4
.05
Political Risk
8.6
8.1
.10
Input Price Risk
4.7
6.5
.05
Crop Price Risk
8.1
8.3
“Single Action” Bias
• Propensity to take only one action to respond to a
problem where a whole set of remedies would be
more fitting (Weber 1997)
– First action taken reduces feeling of worry
– Removal of affective marker (“flag”) reduces
motivation for further action
• Radiologists: detect single abnormality on X-ray, miss others
• Illinois farmers: engaged in single adaptation to climate change
(either production practice, pricing practice, or endorsement of
government intervention, but not two or all three)
(C) Different Decision Making
Goals and Decision Making Styles
• Different “strokes” for “different folks”
– Identification of different types of people/farmers may
help to target (climate forecast) communication and
education
• Heterogeneity in decision makers usually defined
as differences in
– Demographic variables (e.g., age, education)
– Economic variables (e.g., income, farm size)
• Heterogeneity in decision makers in psychology
also defined as differences in
– Personality traits
Farmer Personality Traits Measured
• Herrmann Brain Dominance Instrument
– Preferred Thinking Style
• Rational/Planning
• Feeling/Experimenting
• Regulatory Focus (Higgins 1999)
– Promotion Focus
• Make good things happen
– Prevention Focus
• Prevent bad things from happening
• Regulatory State (Kruglanski et al. 2000)
– Locomotion Orientation
• Action orientation; make quick decision and move on
– Assessment Orientation
• Consideration orientation; make best possible decision
Promotion/Prevention Scale
• assesses people’s subjective histories of effective
promotion and prevention self-regulation
• distinguishes between “promotion pride”—a
subjective history of success with promotionrelated eagerness—and “prevention pride”—a
subjective history of success with preventionrelated vigilance
• measures two types of success-related pride—
namely, promotion pride and prevention pride—
rather than success-related pride and failurerelated shame
• both promotion pride and prevention pride are
positively, reliably, and independently correlated
with achievement motivation
Locomotion/Assessment Scale
• assesses people’s chronic assessment and
locomotion tendencies
• Assessment measures tendency to critically
evaluate alternative goals or means to decide
which are best to pursue
• Locomotion measures tendency to want to move
from decision to decision and state to state,
including commitment of psychological resources
to initiate and maintain such movement
Personality scales scores
• Promotion/prevention
– Range: 1 to 6, midpoint 3.5
– AACREA farmer sample medians and ranges:
• Promotion Score: 3.5 (2.8 to 4)
• Prevention Score: 3.4 (2.2 to 4.6)
• Locomotion/assessment
– Range: 1 to 5, midpoint 3
– AACREA farmer sample medians and ranges:
• Assessment Score: 3.1 (2.8 to 3.6)
• Prevention Score: 2.5 (1.7 to 3)
Study of farmers perceptions and actions
regarding climate change and climate
variability in the Argentine Pampas
• Pampas one of the most productive agricultural areas
in the world (Hall et al. 1992)
• Major importance to the Argentine economy
– 51% of exports; 12% of GDP over 1999–2001 (Díaz 2002)
• ENSO has a marked influence on the region’s
– climate (Vargas et al. 1999; Grimm et al. 2000)
– crop yields (Podestá et al. 1999)
• Similarity in production scale, crops grown and
technology to other major production areas, including
the US Midwest
Study Details
• Farmer Characteristics (n = 31)
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93% male; aged 25-57 years, with mean of 41.5
84% full-time farmers
avg. level of education “some university, no degree”
Avg. income level $100-150 k
members of AACREA for avg. of 9 years
• Farm Characteristics
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670 ha to 6,500 ha, with mean of 2,402 ha
1-10 employees, with mean of 5.4
46% had noncontiguous land
main crops: soy, corn, wheat
Preliminary Results
• Perceptions of Climate Change
• Decision Goals and Climate Forecast
Related Actions in Decision Exercise
• Influence of Personality Traits
Climate Change Perceptions and Beliefs
Belief /Statement of Fact
Climate in region changed over last several
years
Change has affected farm management
decisions
Affected by drought anytime over last 12
years
Source of belief in climate change:
Personal memory
Other farmers
Press/TV
Other
Proportion
agreement
.38
.36
.33
.29
.18
.15
.11
Personality Traits and Beliefs
about Climate Change
• Promotion-focused farmers more likely to
believe in
– changed climate (r = .51)
– hold belief based on personal experience (r = .50)
• Prevention-focused farmers more likely to
– hold belief about climate change based on
information from other farmers (r = .59)
Decision Exercise
• Hypothetical farm in two locations with
multiple plots in each location
– Choice of crop: Maize, Soy, Wheat, Wheat/Soy
– If Maize, then
• Choice of hybrid
• Date of planting and planting density
• Amount of fertilizer
• Same decisions made twice by 14 farmers and
3 AACREA technical advisors
– Scenario 1: No information about expected
climate during growing season
– Scenario 2: La Niña forecast introduced
Decision Goals (0 to 10 scale)
Goals
Farmers
Advisors
p-value
Maximize Farm Profitability
7.92
7.17
Maximize Crop Yields
7.75
5.67
.05
Maximize Crop Prices
6.54
3.17
.03
Minimize Cost of Production Inputs
6.25
2.66
.06
Minimize Impact of Political Uncertainty
6.43
3.00
.06
Make Best Possible Decisions Given
Circumstances
9.14
9.00
Make Reasonable Decisions Given
Circumstances
6.82
3.00
.03
Minimize Possible Regret about Decisions
After the Fact
6.89
3.83
.04
Personality Traits and Decision Goals
• Assessment-oriented farmers rated subgoals to the overall goal
of farm maximization as less important
– r(assessment, maximizing crop prices) = -.93, p<.001)
– r(assessment, minimizing political risks) = -.73, p<.05)
• Prevention-focused farmers rated goal of making best possible
decision as less important and individual subgoals as more
important
– r(prevention, best possible decision) = -.68, p<.05)
– r(prevention, maximizing yields) = .72, p<.05)
• Rational/planning farmers rated regret minimization as a
decision goal as more important and feeling/experimenting
farmers as less important
– r(planning, regret) = .60, p<.05)
– r(experimenting, regret) = -.61, p<.05)
Personality Traits and Actions Taken
• In both scenarios of decision experiment
– more promotion-focused farmers
• used higher-cycle maize hybrid
• grew it at higher density and using more fertilizer
– more prevention-focused and assessment-oriented
farmers
• made a smaller number of changes overall
• In allocation of actual farm expenditures to
different categories, more rational and more
assessment-oriented farmers allocated
– more money to farm administration and infrastructure
– less money to labor and debt repayment
Future Work Planned
• Larger samples of farmers, and in different regions of
Argentina
• Empirical investigation of goals and objectives of farmers’
decisions
– “objective functions”
• Relationship between personality traits and decision goals
and objectives
• Estimation of value of information (VOI) of climate
forecasts using different objective functions
Empirical investigation of goals and
objectives of farmers’ decisions
• Candidate “objective functions”
– Expected Utility (EU) maximization
• Assess degree of risk aversion
– Regret avoidance
• Comparison of obtained outcome(s) to outcomes that
other actions would have produced
– Often a social comparison (“what did my neighbor get?”)
– Requires information about outcomes of alternative actions
– Prospect theory
• Assess reference point, risk aversion, and loss aversion
Prospect Theory
• Psychological Extension of Expected Utility theory
– by Kahneman and Tversky (1979)
and Tversky and Kahneman (1992)
• Received Nobel Prize for Economics in 2001
• Risky Prospects/Choice Options are evaluated by
– Value function
– Decision Weights
• Value Function:
– Concave for gains (risk-averse), convex for losses (risk-seeking)
– Defined over gains and losses on deviations from some reference
point
– Steeper for losses than for gains (“losses loom larger”)
(Question 1)
If you were faced with the following choice, which
alternative would you choose?
(A) A sure gain of $240.
(B) A 25% chance to gain $1,000 and 75% chance of
getting $0.
(Question 2)
If you were faced with the following choice, which
alternative would you choose?
(A) A 100% chance of losing $50.
(B) A 25% chance of losing $200 and a 75% chance
of losing nothing.
Prospect Theory
Valor
Punto de
Referencia
• Relative Evaluation:
Value is judged
relative to a reference
point.
Ganancias
Ingreso
Perdidas
Loss Aversion
| Pain| > Pleasure
Reference Point
• Reference point assigned a value of 0
(neutral)
• Reference point determines if outcomes are
psychologically coded as gain or loss
• may be status quo (current asset position)
• could be an aspiration level or remembered level
(last year’s profits)
• Different reference points result in different
preferences
Maximizing vs. Satisficing
• Satisficing
– Sometimes “good enough” is good enough
• Flat utility function for returns beyond satisfactory
levels
– Elimination of decision alternatives because
they do not meet minimum requirements
• Implications for search behavior
– sequential pursuit of goals (e.g., first yields, then prices)
Estimation of value of information
(VOI) of climate forecasts
• Need to use different objective functions
– So far only EU maximization
• different degrees of constant relative risk aversion
• Objective function might affect
– VOI
• Difference between farm profitability with and without
climate forecast
– Best practice recommendations
• Combination of production and pricing decisions that
achieve maximal profitability
Questions for You
• Do you think some additional characterization of
farmers by personality traits (goals and
management style) is useful?
• How do farmers think about their farm
profitability?
– Do they value performance on subgoals?
– Use sequential strategies to rule out management
options?
– What reference points do farmers use to evaluate their
performance in a given year?
– Do they compare their performance to those of others?
If so, who do they choose for such comparisons?
Thank You!