Short presentation at Dagstuhl seminar on Physical-Cyber-Social Computing, September 29 to October 4, 2013. http://www.dagstuhl.de/en/program/calendar/semhp/?semnr=13402

1. From smart meters
to smart behaviour
Harith Alani
http://people.kmi.open.ac.uk/harith/
@halani
harith-alani
@halani
Dagstuhl seminar on Physical-Cyber-Social Computing, 2013

2. Social Web Communities - 2008

3. One year later ….

4. 0"
0.2"
0.4"
0.6"
0.8"
1"
1" 5" 9" 13" 17" 21" 25" 29" 33" 37" 41" 45"
H.Index"
F2F"Degree"
F2F"Strength"
Physical-Cyber-Social behaviour

5. Table 1: Correlation Coefficients of dimensions
Dispersion Engagement Contribution Initiation Quality Popularity
Dispersion 1.000 0.277 0.168 0.389 0.086 0.356
Engagement 0.277 1.000 0.939** 0.284 0.151 0.926**
Contribution 0.168 0.939** 1.000 0.274 0.086 0.909**
Initiation 0.389 0.284 0.274 1.000 -0.059 0.513
Quality 0.086 0.151 0.086 -0.059 1.000 0.065
Popularity 0.356 0.926** 0.909** 0.513 0.065 1.000
Behaviour analysis of online communities
§ Bottom Up analysis
§ Every community member is
classified into a “role”
§ Unknown roles might be
identified
§ Copes with role changes
over time
ini#ators
lurkers
followers
leaders
Structural, social network,
reciprocity, persistence,
participation
Feature levels change with the
dynamics of the community
Associations of roles with a collection of
feature-to-level mappings
e.g. in-degree -> high, out-degree -> high
Run rules over each user’s features
and derive the community role
composition
Table 1: Correlation Coefficients of dimensions
Dispersion Engagement Contribution Initiation Quality Popularity
Dispersion 1.000 0.277 0.168 0.389 0.086 0.356
Engagement 0.277 1.000 0.939** 0.284 0.151 0.926**
Contribution 0.168 0.939** 1.000 0.274 0.086 0.909**
Initiation 0.389 0.284 0.274 1.000 -0.059 0.513
Quality 0.086 0.151 0.086 -0.059 1.000 0.065
Popularity 0.356 0.926** 0.909** 0.513 0.065 1.000
Table 1: Correlation Coefficients of dimensions
Dispersion Engagement Contribution Initiation Quality Popularity
Dispersion 1.000 0.277 0.168 0.389 0.086 0.356
Engagement 0.277 1.000 0.939** 0.284 0.151 0.926**
Contribution 0.168 0.939** 1.000 0.274 0.086 0.909**
Initiation 0.389 0.284 0.274 1.000 -0.059 0.513
Quality 0.086 0.151 0.086 -0.059 1.000 0.065
Popularity 0.356 0.926** 0.909** 0.513 0.065 1.000
Figure 7: Cumulative density functions of each dimension showing Figure 8: Boxplots of the feature distributions

6. Correlations
§ Between different behaviour
roles
0.0 0.2 0.4 0.6 0.8 1.0
0.00.20.40.60.81.0
Churn Rate
FPR
TPR
0.0 0.2 0.4 0.6 0.8 1.0
0.00.20.40.60.81.0
User Count
FPR
TPR
0.00.20.40.60.81.0
TPR
§ Between behaviour and activity
§ Between behaviours and community
health

7. !
Composition and evolution of behaviour
macro level
micro level

8. And in the mean time …

9. GLOBAL
WARMING

10. Solar panels

11. Smart Meters
www.efergy.com
greenenergyoptions.co.uk
fastcompany.com
powerp.co.uk
www.energycircle.com
indiegogo.comgreentechadvocates.com

12. But the jury is still out

13. With Manfred’s perm

14. Fine, but what does this
have to do with behaviour?!

15. Need to change consumption behaviour
Nov 2012
• Behaviour can be changed
• Individual/community approaches
• Multiple motivating factors
• Behaviour change is sustainable
key findings
• Quantitative impact of specific changes
• Socio-demographic factors
• Gas vs electricity vs water
• Cost-effectiveness of interventions
• Longevity of change
gaps
August 2012

16. • Personal energy-saving targets
• Community/social initiative lead to long-term change
• Dynamic pricing schemes don’t always work
• The “rebound effect” can emerge from short-term measures
• Role of technology, age, economic situation, culture, marketing, etc.
• Consumer ability to handle new technology, capital cost, trade-offs, and
expected convenience

17. Making the invisible visible

18. Feedback
§ What’s the optimal level of
detail ?
§ What feedback is suitable
for what type of
consumer?
§ What feedback tools?
What visualisations?

20. Behaviour change models
http://www.enablingchange.com.au/7_doors_page.html
information
personalised drivers
tools
feedback
conveniencesocial/
competitions
behaviour
change

21. Ø Effectiveness of different strategies
Ø Quantitative impact of change
Ø Cause-effect indicators
Ø Socio-demographic factors
Ø Longevity of change
0.0 0.2 0.4 0.6 0.8 1.0
0.00.20.40.60.81.0
Churn Rate
FPR
TPR

22. www.decarbonet.eu/
Stay tuned