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

Social Web Communities - 2008

One year later ….

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1" 5" 9" 13" 17" 21" 25" 29" 33" 37" 41" 45"

H.Index"F2F"Degree"F2F"Strength"

Physical-Cyber-Social behaviour

Table 1: Correlation Coe!cients of dimensions

Dispersion Engagement Contribution Initiation Quality PopularityDispersion 1.000 0.277 0.168 0.389 0.086 0.356Engagement 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 0.513Quality 0.086 0.151 0.086 -0.059 1.000 0.065Popularity 0.356 0.926** 0.909** 0.513 0.065 1.000

Figure 7: Cumulative density functions of each dimension showingthe skew in the distributions for initiated and in-degree ratio

same forum and do not deviate away, at the other ex-treme very few users are found to post in a large rangeof forums. For initiated (initiation) and in-degree ratio(popularity) the density functions are skewed towardslow values where only a few users initiate discussionsand are replied to by large portions of the community.Average points per post (quality) is also skewed to-wards lower values indicating that the majority of usersdo not provide the best answers consistently.These plots indicate that feature levels derived from

these distributions will be skewed towards lower values,for instance for initiated the definition of high for thisfeature is anything exceeding 1.55x10!5.The distribution of each dimension is shown in Fig-

ure 8 for each of the 11 induced clusters. We assessthe distribution of each feature for each cluster againstthe levels derived from the equal-frequency binning ofeach feature, thereby generating a feature-to-level map-

Figure 8: Boxplots of the feature distributions in each of the 11 clus-ters. Feature distributions are matched against the feature levels de-rived from equal-frequency binning

ping. This mapping is shown in Table 2 where certainclusters are combined together as they have the samefeature-level mapping patterns (i.e. 5,7 and 8,9). Wethen interpreted the role labels from these clusters, andtheir subsequent patterns, as follows:

• 0 - Focussed Expert Participant: this user typeprovides high quality answers but only within se-lect forums that they do not deviate from. Theyalso have a mix of asking questions and answeringthem.

• 1 - Focussed Novice: this user is focussed within afew select forums but does not provide good qual-ity content.

• 2 - Mixed Novice: is a novice across a mediumrange of topics

6

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 Coe!cients of dimensions

Dispersion Engagement Contribution Initiation Quality PopularityDispersion 1.000 0.277 0.168 0.389 0.086 0.356Engagement 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 0.513Quality 0.086 0.151 0.086 -0.059 1.000 0.065Popularity 0.356 0.926** 0.909** 0.513 0.065 1.000

Figure 7: Cumulative density functions of each dimension showingthe skew in the distributions for initiated and in-degree ratio

same forum and do not deviate away, at the other ex-treme very few users are found to post in a large rangeof forums. For initiated (initiation) and in-degree ratio(popularity) the density functions are skewed towardslow values where only a few users initiate discussionsand are replied to by large portions of the community.Average points per post (quality) is also skewed to-wards lower values indicating that the majority of usersdo not provide the best answers consistently.These plots indicate that feature levels derived from

these distributions will be skewed towards lower values,for instance for initiated the definition of high for thisfeature is anything exceeding 1.55x10!5.The distribution of each dimension is shown in Fig-

ure 8 for each of the 11 induced clusters. We assessthe distribution of each feature for each cluster againstthe levels derived from the equal-frequency binning ofeach feature, thereby generating a feature-to-level map-

Figure 8: Boxplots of the feature distributions in each of the 11 clus-ters. Feature distributions are matched against the feature levels de-rived from equal-frequency binning

ping. This mapping is shown in Table 2 where certainclusters are combined together as they have the samefeature-level mapping patterns (i.e. 5,7 and 8,9). Wethen interpreted the role labels from these clusters, andtheir subsequent patterns, as follows:

• 0 - Focussed Expert Participant: this user typeprovides high quality answers but only within se-lect forums that they do not deviate from. Theyalso have a mix of asking questions and answeringthem.

• 1 - Focussed Novice: this user is focussed within afew select forums but does not provide good qual-ity content.

• 2 - Mixed Novice: is a novice across a mediumrange of topics

6

Table 1: Correlation Coe!cients of dimensions

Dispersion Engagement Contribution Initiation Quality PopularityDispersion 1.000 0.277 0.168 0.389 0.086 0.356Engagement 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 0.513Quality 0.086 0.151 0.086 -0.059 1.000 0.065Popularity 0.356 0.926** 0.909** 0.513 0.065 1.000

Figure 7: Cumulative density functions of each dimension showingthe skew in the distributions for initiated and in-degree ratio

same forum and do not deviate away, at the other ex-treme very few users are found to post in a large rangeof forums. For initiated (initiation) and in-degree ratio(popularity) the density functions are skewed towardslow values where only a few users initiate discussionsand are replied to by large portions of the community.Average points per post (quality) is also skewed to-wards lower values indicating that the majority of usersdo not provide the best answers consistently.These plots indicate that feature levels derived from

these distributions will be skewed towards lower values,for instance for initiated the definition of high for thisfeature is anything exceeding 1.55x10!5.The distribution of each dimension is shown in Fig-

ure 8 for each of the 11 induced clusters. We assessthe distribution of each feature for each cluster againstthe levels derived from the equal-frequency binning ofeach feature, thereby generating a feature-to-level map-

Figure 8: Boxplots of the feature distributions in each of the 11 clus-ters. Feature distributions are matched against the feature levels de-rived from equal-frequency binning

ping. This mapping is shown in Table 2 where certainclusters are combined together as they have the samefeature-level mapping patterns (i.e. 5,7 and 8,9). Wethen interpreted the role labels from these clusters, andtheir subsequent patterns, as follows:

• 0 - Focussed Expert Participant: this user typeprovides high quality answers but only within se-lect forums that they do not deviate from. Theyalso have a mix of asking questions and answeringthem.

• 1 - Focussed Novice: this user is focussed within afew select forums but does not provide good qual-ity content.

• 2 - Mixed Novice: is a novice across a mediumrange of topics

6

Correlations

§  Between different behaviour roles

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Churn Rate

FPR

TPR

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User Count

FPR

TPR

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Seeds / Non−seeds Prop

FPR

TPR

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Clustering Coefficient

FPR

TPR

§  Between behaviour and activity

§  Between behaviours and community health

!

Composition and evolution of behaviour

macro level

micro level

And in the mean time …

GLOBAL WARMING

Solar panels

Smart Meters

www.efergy.com

greenenergyoptions.co.uk

fastcompany.com

powerp.co.uk

www.energycircle.com

indiegogo.com greentechadvocates.com

But the jury is still out

With Manfred’s permission

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

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

•  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

Making the invisible visible

Feedback

§  What’s the optimal level of detail ?

§  What feedback is suitable for what type of consumer?

§  What feedback tools? What visualisations?

Behaviour change models

http://www.enablingchange.com.au/7_doors_page.html

information

personalised drivers

tools

feedback

convenience social/competitions

behaviour change

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

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0.6

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Churn Rate

FPR

TPR

0.0 0.2 0.4 0.6 0.8 1.0

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0.6

0.8

1.0

User Count

FPR

TPR

0.0 0.2 0.4 0.6 0.8 1.0

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0.4

0.6

0.8

1.0

Seeds / Non−seeds Prop

FPR

TPR

0.0 0.2 0.4 0.6 0.8 1.0

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0.2

0.4

0.6

0.8

1.0

Clustering Coefficient

FPR

TPR

www.decarbonet.eu/

Stay tuned