Current Directions in Behavioral Energy Economics

Laurens Rook

July 17, 2015

Alpen-Adria University Klagenfurt

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)

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

Key Collaborators in Behavioral Energy Economics research

Sudip Bhattacharjee (University of Connecticut)

Wolfgang Ketter (Rotterdam School of Management)

Markus Zanker (Alpen-Adria University, Klagenfurt)

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

Today’s energy landscape

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Future Energy Business

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)

Future Energy Tariffs and Their Consequences

Hedging Cost Premiums (Faruqui & Wood, 2008)

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)

Our Core Experimental Paradigm

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

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

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

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

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

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

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

The Experimental Paradigm

NOTE – participants could for each three tariff types choose between a grey and a green version, yielding six possibilities

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

Manipulation risky choice frame

As in Kahneman and Tversky’s Asian disease problem, but adapted to energy tariffs

Results

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)

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

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

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

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…

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

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

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

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 )

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

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

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

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]

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

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

Self-report measures for cognitive neuroscience

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Source: sachaepskamp.com

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)

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

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

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

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

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