Agricultural Decision Making under (Climate) Uncertainty

Elke WeberColumbia 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)

Informational Environment

SalienceCredibilityLegitimacyAccessCompatibilityPlace

SocietalEnvironment

Commodity pricesExchange ratesTax policiesPolitical stabilityInstitutionsOther

NaturalEnvironment

CLIMATESoilsTopographyLand use historyPests & DiseasesOther

Decision-MakingCognitive limitationsPersonality traitsRisk attitudesObjective functionsInstitutions

• 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)– 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

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 small-

probability 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 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

“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 promotion-related eagerness—and “prevention pride”—a subjective history of success with prevention-related vigilance

• measures two types of success-related pride—namely, promotion pride and prevention pride—rather than success-related pride and failure-related 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)– 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

Preliminary Results

• Perceptions of Climate Change

• Decision Goals and Climate Forecast Related Actions in Decision Exercise

• Influence of Personality Traits

Belief /Statement of Fact Proportion agreement

Climate in region changed over last several years

.38

Change has affected farm management decisions

.36

Affected by drought anytime over last 12 years

.33

Source of belief in climate change:

Personal memory .29

Other farmers .18

Press/TV .15

Other .11

Climate Change Perceptions and Beliefs

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

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

Decision Goals (0 to 10 scale)

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

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

Punto deReferencia

Ingreso

Ganancias

Perdidas

Valor

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!