Current Directions in Behavioral Energy Economics
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Transcript of Current Directions in Behavioral Energy Economics
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Current Directions in Behavioral Energy Economics
Laurens Rook
July 17, 2015
Alpen-Adria University Klagenfurt
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Who am I?
Assistant Professor at Delft University of Technology (TPM)
Lecturer Research Methods and Statistics / Group Dynamics / Organizational Psychology
PhD at Erasmus University Rotterdam -> individual and small group research (2008)
MA at University of Amsterdam -> mass psychology (2001)
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Research Interests
(1) Creative cognition research
(2) Behavioral economics: biases and heuristics in the making of choices -> applied to future energy business
My research methods: laboratory / online experiments and surveys
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Key Collaborators in Behavioral Energy Economics research
Sudip Bhattacharjee (University of Connecticut)
Wolfgang Ketter (Rotterdam School of Management)
Markus Zanker (Alpen-Adria University, Klagenfurt)
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Outline for today
Introduction into the problem of (renewable) energy
Behavioral economics and future energy preferences
Personality psychology and future energy preferences
Directions for future research
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Today’s energy landscape
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Future Energy Business
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Future Energy Tariffs and Their Consequences
Fixed tariffs =energy consumption relatively insensitive to fluctuations in energy prices (energy markets in most countries currently employ fixed tariffs)
Flexible tariffs = energy consumption is subject to fluctuations in energy prices (i.e., renewable but imbalanced energy)
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Future Energy Tariffs and Their Consequences
Hedging Cost Premiums (Faruqui & Wood, 2008)
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Energy Tariffs and Their Behavioral Consequences
Fixed tariffs =energy consumption relatively insensitive to fluctuations in energy prices (a safe and certain situation)
Flexible tariffs = energy consumption is subject to fluctuations in energy prices (i.e., renewable but imbalanced energy; a risky and uncertain situation)
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Our Core Experimental Paradigm
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Behavioral economics and today’s energy landscape
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Behavioral Economics:Valenced framing: when people’s choices are influenced by the manner in which options are presented
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The Asian disease problemImagine that the United States is preparing for an outbreak of an unusual Asian disease that is expected to kill 600 people. A number of alternative programs to combat the disease have been proposed. Scientific estimates of the consequences of the programs are:
Program A: If Program A is adopted, 200 people will be saved.Program B: If Program B is adopted, there is a one-third probability that 600 people will be saved and a two-thirds probability that no people will be saved.
Program C: If Program C is adopted, 400 people will die.Program D: If Program D is adopted, there is a one-third probability that 600 people will be saved and a two-thirds probability that no people will be saved.
Tversky & Kahneman, 1981
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Major framing effects
Risky choice framing = when people evaluate an object / event based on its (positive-negative; risky-safe) characteristics
Attribute framing = when people evaluate an object / event based on its (positive-negative) characteristics
Goal framing = when the goal (end-state) of an action or behavior is (positively-negatively) framed
Levin et al., 1998
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Framing effects (general predictions)
Risky choice framing = people are more willing to take risks [to avoid a loss] under negative (vs. positive) risky choice frames
Attribute framing = positive attribute frames are more effective than negative attribute frames
Goal framing = negative goal frames are more effective than positive goal frames
NB – intrinsic self-relevance – Krishnamurthy et al., 2001
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Our hypotheses
H1 - Risky choice framing = people will prefer riskier energy tariffs under a negative than under a positive frame
H2 - Attribute framing = people will evaluate a RTP tariff better under a positive than under a negative attribute frame
H3 - Goal framing = people will prefer a RTP tariff under a negative than under a positive goal frame
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Energy Preferences: Individual Differences?
very slightly extremelyor not at all
Using renewable energy does not make any difference to me 1 2 3 4 5 6 7
Whether the energy used in my household is renewable is of no concern to me 1 2 3 4 5 6 7
Using renewable energy is not worth the price I would have to pay 1 2 3 4 5 6 7
The fact that my household uses renewable energy would make me feel better of myself 1 2 3 4 5 6 7
The possibility of renewable energy being used in my household means a lot to me 1 2 3 4 5 6 7
Concern about using renewable energy influences my decisions about the energy consumption 1 2 3 4 5 6 7
Bang et al., 2000
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Methodology
Three (30 min pencil-and-paper) experiments with similar procedure:
Measuring campus students’ attitude toward renewable energy
Experimental treatment (a valenced frame)
An energy tariff selection task
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The Experimental Paradigm
NOTE – participants could for each three tariff types choose between a grey and a green version, yielding six possibilities
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Experiment 1
One hundred and four students (71 men and 33 women, M age = 22.83, SD = 3.81)
Random assignment to a (positive, negative) risky choice frame
Individual attitude toward renewable energy, age, and gender added as covariates
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Manipulation risky choice frame
As in Kahneman and Tversky’s Asian disease problem, but adapted to energy tariffs
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Results
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Results (II)
Individual attitude toward renewable energy added as covariate
High: over-representation of green flat (under negative frame), and green time of use & real time tariffs (under positive frame)
Low: mild preference for green flat (under negative frame), and over-representation of all gray tariffs (under positive frame)
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Experiment 2
Ninety nine students (63 men and 36 women, M age = 22.82, SD = 4.40)
Random assignment to a (positive, negative) attribute frame
Individual attitude toward renewable energy , age, and gender added as covariates
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Manipulation attribute frame
As in Kahneman and Tversky’s paradigm, each energy tariff was presented either in positive or negative terms – depending on experimental conditions
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Results
Positive attribute frame: M = 2.239, SD = 1.239 Negative attribute frame: M = 4.163, SD = 1.632
mean difference -1.822, ts = -5.930, p < .0001
People prefer a positively attributed green real time pricing tariff over a negatively framed one
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Results (II)
Individual attitude toward renewable energy, age, and gender added as covariates
Same pattern: people prefer a positively attributed green real time pricing tariff over a negatively framed one regardless of attitudinal preferences…
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Experiment 3
One hundred and seven students (60 men and 47 women, M age = 23.59, SD = 5.28)
Random assignment to a (positive, negative) goal frame
Individual attitude toward renewable energy, age, and gender added as covariates
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Manipulation goal frame
As in Kahneman and Tversky’s paradigm, each energy tariff was presented either in positive or negative terms – depending on experimental conditions – and:
modified such that it tapped into (either) a risk-seeking or risk-avoidant end-state regarding energy consumption terms – depending on experimental conditions
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Results
Positive goal frame: M = 3.229, SD = 1.627 Negative goal frame: M = 3.568, SD = 1.797
mean difference -0.339, tp = -0.950, p < .345
Goal framing did not significantly influence people’s energy tariff selection
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No effects for goal framing. Why?
We did something wrong (i.e., a confounded design)
There was something special to our sample (analogous to the notion of intrinsic self-relevance )
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Results (II)
Individual attitude toward renewable energy:
High: Positive goal frame: M = 3.095, SD = 1.671Negative goal frame: M = 2.778, SD = 1.865
mean difference 0.317, tp = 0.560, p = 0.578
Low: Positive goal frame: M = 3.333, SD = 1.617Negative goal frame: M = 4.155, SD = 1.558
mean difference 0.782, tp = -1.790, p = 0.079
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Conclusion
Valenced-based framing does influence customer energy tariff selection
We can steer people’s choice toward choosing “green” (when we apply risky choice or attribute -but not goal – frames)
We confirmed that individual attitude toward renewable energy is important (but not necessary) to establish that
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Limitations
Our Experiment 1 – large number of tariff attributes without a proper control
Our Experiments 2 & 3 – a single attribute of one type of tariff (green RTP)
Solution = We are currently running a simplified risky choice framing study
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Limitations (II)
Our Experiment 3 (on goal framing) did not work, because of a confounded design
[A] take action and get gain[B] not take action and do not get gain[C] take action and avoid loss[D] not take action and incur loss
[Rothman & Salovey, 1997]
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Limitations (III)
Our Experiments 1-3 rely on a student sample instead of real households involved in tariff selection on an annual basis
We are currently running the same study on Amazon’s MechTurk among a more representational sample
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Personality psychology and today’s energy landscape
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Kurt Lewin’s law of interaction
B = f (P, E)
B = the behavior of the personP = personal characteristics of the individual E = environmental (task type) factors
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Self-report measures for cognitive neuroscience
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Source: sachaepskamp.com
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Biopsychological approaches to personality
Temperament and Character Inventory = a four-factor neurobiological model and measurement scale (Cloninger)
The BIS/BAS Scales = a multifactor neurobiological model that accounts for risk-seeking vs. risk-avoidant tendencies (Carver & Schreier, 1994)
The Big Five = a pragmatic five-factor model of personality (Costa & McCrae, 1993, 1997)
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Illustration: The TamagoCar project
Researchers: Ksenia Koroleva and Wolf Ketter (RSM), Laurens Rook
The TamagoCar app investigates (1) how different prices for battery charging influence efficient driving of an e-vehicle in competition, and (2) under which circumstances people may experience range anxiety
Part of the project was a behavioral pre-survey with self-reports on BIS/BAS, the Big Five, and energy-related attitudes
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Presented: Pre-analysis (correlations)
BIS/BAS
PANAS
IPIP / Five-Factor
Attitude toward
Renewable Energy
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The BIS/BAS Scales
Three fundamental emotional processes exist in the human brain (Gray, 1987, 1989):
1. Behavioral Inhibition System (BIS; avoidance behavior in response to threats and novel stimuli)
2. Behavioral Activation System (BAS; approach behavior in response to incentives)
3. Fight-Flight System (rapid responses to immediate threats)
BIS and BAS explain goal-directed behavior beyond emergency settings: how people may respond to rewards, stimuli (information), and threats
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The BIS/BAS scales
Carver and White (1994) developed a self-report measure for BIS and BAS, and is widely used in cognitive neuroscience to complement fMRI and other brain scanning studie.:
The BIS scale is 7 items, unidimensional
The BAS scale is 13 items, 3 sub-dimensions
4 items are fillers / distractors
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The BIS scale and prediction
Example item: “I worry about making mistakes”
Someone high (vs. low) on BIS is generally more nervous and may experience any sort of anxiety in novel or threatening situations
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The BAS Scale (I)
Example item (BAS Reward Responsiveness): “When I get something I want, I feel excited and energized”
Example item (BAS Drive): “When I want something, I usually go all-out to get it”
Example item (BAS Fun Seeking): “I’m always willing to try something new if I think it will be fun”
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The BAS Scale and prediction
Someone high on BAS is generally more sensitive to positive signals of rewards in novel or threatening situations, and may experience less anxiety
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The Positive Affect Negative Affect Scale (PANAS)
Watson, Clark and White (1988) developed the PANAS scales to measure self-reported PA and NA:
The PA scale is 10 items, unidimensional
The NA scale is 10 items, unidimensional
Consistent with the literature, we took the trait (“in general”) version of the PANAS
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The PANAS scales and predictions
Negative Affect will correlate highly with overall BIS sensitivity (cf., Gomez et al., 2002)
Positive Affect will correlate highly with overall BAS sensitivity (cf., Gomez et al., 2002)
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Theory
The Big Five or Five-Factor Model is the dominant model of personality structure in personality psychology (cf., Costa & McCrae, 1992) consisting of:
1. Extraversion; outgoing / energetic vs. solitary / reserved2. Agreeableness; 3. Conscientiousness; 4. Neuroticism; sensitive / nervous vs. secure / confident5. Openness;
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Visual: The Big Five
Source: sachaepskamp.com
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The Mini-IPIP scales
The Big Five (Costa & McCrae, 1985) is very large (240 items)
The Mini-IPIP was developed as a psychometrically acceptable, short, measure of the Big Five factors of personality (Donnellan, Oswald, Baird, & Lucas, 2006)
4 measures per Big Five trait with comparable convergent, discriminant and criterion-related validity
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The Mini-IPIP scales and predictions
The BIS is believed to underlie Neuroticism (cf., Watson et al., 1999) and thus can be assumed to correlate with Neuroticism
The BAS is believed to underlie Extraversion (cf., Watson et al., 1999) and thus can be assumed to correlate with Extraversion
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Three behavioral moderators
1. The BIS/BAS scales : people either approach or avoid action in presence of novel stimuli and threats, and with affective consequences (occurrence of general anxiety)
The BIS/BAS scales have two neighboring personality constructs:
2. The PANAS: PA correlates with BAS; NA correlates with BIS
3. The Mini-IPIP – Five-Factor Model: Neuroticism correlates with BIS; Extraversion with BAS
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In a conceptual model
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BIS/BAS
Self-reportedRange
AnxietyPANAS
IPIP / Five-Factor
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The sample
A total of 264 participated in the study
Data of 57 participants were excluded due to missing values
The sample used in the analyses consisted of 207 students (142 men and 65 women; Mage = 22.87; SD= 1.94)
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Reliability and correlations
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Summarizing
The TamagoCar project illustrates how:
You can use self-report measures from cognitive neuroscience to predict and test individual differences in human preferences
Also in research on energy-related topics
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Future Directions
Cognitive Neuroscience: Biopsychological self-report measures set the stage for fMRI-studies
Behavioral Energy Informatics: When experimental designs include (smart) devices (i.e., apps), psychological methods can be linked to other analytical tools
from highly controlled to bigger, messier data higher external validity
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