Presentation by Alain Starke on SPUDM, the conference on Subjective Probability, Utility, and Decision-Making. The topic is on energy recommender systems and social norms.

1. Crafting normative messages to promote energy
conservation in a recommender system
Dr.ir. Alain Starke
Eindhoven University of Technology, Netherlands
& University of Bergen, Norway (starting next month)
In collaboration with:
Dr.ir. Martijn Willemsen & Prof.dr. Chris Snijders
Eindhoven University of Technology
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2. We seek to promote household
conservation by supporting energy-
saving decisions

3. Household energy conservation
Government mass information campaigns typically prescribe
efficiency investments…
…but consumers prefer to make simple behavioral changes
(Attari et al, 2010; Gardner & Stern, 2008)

4. Problem of information filtering

5. Our sift is a Recommender system
Recommenders personalize content by filtering
information based on past user behavior

6. What is our ‘energy’
algorithm?

7. Performed rarely
(1% of users)
Performed often
(99% of users)
This is a ‘Rasch scale’
(a psychometric model)
This order of measures is
rather consistent across
different populations

8. Performed rarely
(1% of users)
Performed often
(99% of users)
Performs many
measures (99%)
Performs few
measures (1%)
Persons are
ordered on
the same
scale:
Behavioral
Costs
Energy-
saving
attitude

9. 5%
The position on the scale
serves as a starting point
for energy-saving
recommendations
Probability of already
performing a measure:
90%
50%
75%
25%
12%
95%
2%

10. Our Besparinghulp.nl recommender system
• ‘Recommended’ lists of nine measures, ordered on kWh savings
• Users could choose any measure they like, which were sent to
them by e-mail. Afterwards, they received a questionnaire.

11. Findings in 2 recommender
studies (N = 222; N = 288)
1. The ‘green’ strategy was
evaluated as more
satisfactory and led to
more choices
2. Users preferred
measures that fell just
below their position on
the scale
Government
strategy
The ‘easy
stuff’
Starke, A.D., Willemsen, M.C., Snijders, C. (2017). Effective user interface designs to increase energy-
efficiency behavior in a Rasch-based energy recommender system. Proceedings RecSys ’17, 65-73.

12. We seek to promote measures ‘higher
up the scale’
Perhaps that nudging can help?

13. Nudge based on hotel towel reuse studies
• Traditionally: “Help save the environment by… ”
• Social norms: “Join our fellow guests in… ”
(REF’s: Goldstein, Cialdini, Griskevicius (2008) – A room with a Viewpoint;
Bohner & Schlüter (2014) – A room with a Viewpoint Revisited)
Image: Pxhere.com13

14. Different social messages
• Goldstein et al. (2008): Social messages led to higher
compliance rates compared to an environmental message
– Through emphasis on majority (75% of guests)
– Through specific peer groups (“hotel guests” or “guests in room
408” more effective than “people” or “citizens”)
(REF’s: Goldstein, Cialdini, Griskevicius (2008) – A room with a Viewpoint;
Bohner & Schlüter (2014) – A room with a Viewpoint Revisited)14

15. Towel reuse is just an example of an easy,
low-effort behavior
• A lot of people already do this (social norms useful (75%))
– But do not apply for e.g. Install Solar PV (~20%)
• Towel reuse is once a day, has no costs, bears little effort
– Different from large-scale investments (perceived as effortful)
Image: Pxhere.com15

16. How can we motivate users to not only select relatively
easy options among personalized measures?
5%
Probability for
Person A:
90%
50%
75%
25%
12%
95%
2%
50%
99%
95%
98%
90%
75%
99.9%
25%
Probability for
Person B:

17. We seek to promote measures ‘higher
up the scale’ through social norms
of specific peer groups with higher
adoption rates
Starke, A.D., Wilemsen, M.C., Snijders, C. (under review). Crafting normative messages to
promote energy conservation in a recommender system

18. Social Norms: We add the compliance rate of
each behavior
Image: Besparingshulp.nl18

19. Study 2: We compared an environmental baseline
score vs 3 social norms (N=207) in a
recommender study
1. KWh savings (0-100)
2. Descriptive adoption probability (15%-75%)
3. Adoption prob. of similar peer (20%-80%)
4. Adoption prob. of expert peers (40%-90%)
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20. Results
No differences between in the total
number of chosen measures, but
differences within lists
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01234
No.ofChosenMeasures
Savings Global Similar Experienced
Weak attitude Strong attitude
0.1.2.3
Proportionchosen
0-20 20-40 40-60 60-80 80-100
Savings Score categories
Chosen measures per Score bin
0-20 20-40 40-60 60-80 80-100
Norm % categories
Chosen measures per Norm bin

21. Odds Ratio
(S.E.)
Odds Ratio
(S.E.)
Interface score 0.78 (.32) 1.19 (.56)
Score X Global 3.80 (2.22)* 2.43 (1.58)
Score X Similar 2.26 (1.27) 1.19 (.75)
Score X Exper. 2.99 (1.66)* 1.46 (.90)
Score X Effort 0.15 (.14)*
Score X Effort X Global 6.29 (7.59)
Score X Effort X Similar 6.46 (7.68)
Score X Effort X Exper 12.52 (14.93)*
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Logistic Regression on Within-list Choice

22. Take-aways
• Communicating norms of specific peers can be used to
boost the adoption rate, and still influence preferences
• Users followed expert advice more for effortful measures
• The social norms had no mean treatment effect in our
personalized context
– Are nudges still effective under personalization?
– This is not a study on ‘which one is better’, but shows
that they are both relevant
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23. Thank you!
A.D.Starke@tue.nl
Twitter: @alainstarke
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