Agricultural Decision Making under (Climate) Uncertainty

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Agricultural Decision Making under (Climate)
Uncertainty

• Elke Weber
• Columbia University
• AACREA, Buenos Aires, Nov. 29, 2005

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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

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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)

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• 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

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Argentina Research Team Members

• Collaboration between
• University and governmental institutions
  researchers
• AACREA leadership and technical advisors
• AACREA farmers
• In Argentina (only most relevant subset)
• 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)
• Guillermo Podesta
• Kenny Broad
• Sabine Marx
• Jim Hansen
• David Letson

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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

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My Research Interests

• Learning from personal experience and learning
  from others
• Role of cognition and emotion in decisions and
  behavior
• (C) Different decision making goals and decision
  making styles

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(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

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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

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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

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(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?

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Affective/Experience-based Processing of
Information

• Generally
• greater motivator to take action
• But, also has some downsides
• Recency effect leads to volatile responses to
  small-probability events
• Either get too little attention/weight or lead to
  overreaction
• Finite Pool of Worry Effect
• Single Action Bias

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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

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Finite Pool of Worry(0 to 10 ratings of concern)

• Risk Category Scenario1 Scenario2 p-value
• Climate Risk 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

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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)

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(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

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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

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Promotion/Prevention Scale

• assesses peoples subjective histories of
  effective promotion and prevention
  self-regulation
• distinguishes between promotion pridea
  subjective history of success with
  promotion-related eagernessand prevention
  pridea subjective history of success with
  prevention-related vigilance
• measures two types of success-related
  pridenamely, promotion pride and prevention
  priderather than success-related pride and
  failure-related shame
• both promotion pride and prevention pride are
  positively, reliably, and independently
  correlated with achievement motivation

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Locomotion/Assessment Scale

• assesses peoples 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

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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)

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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 19992001 (Díaz
  2002)
• ENSO has a marked influence on the regions
• 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

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Study Details

• Farmer Characteristics (n 31)
• 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
• 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

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Preliminary Results

• Perceptions of Climate Change
• Decision Goals and Climate Forecast Related
  Actions in Decision Exercise
• Influence of Personality Traits

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Climate Change Perceptions and Beliefs
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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)

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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

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Decision Goals (0 to 10 scale)
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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,
  plt.001)
• r(assessment, minimizing political risks) -.73,
  plt.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,
  plt.05)
• r(prevention, maximizing yields) .72, plt.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, plt.05)
• r(experimenting, regret) -.61, plt.05)

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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

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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

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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

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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)

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(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.

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(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.

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Prospect Theory
Valor
Punto de Referencia
Ganancias

• Relative Evaluation Value is judged relative
  to a reference point.

Ingreso
Perdidas
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Loss Aversion

• Pain gt Pleasure

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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 years profits)
• Different reference points result in different
  preferences

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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)

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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

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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?

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Thank You!