Updated 29 Nov2012

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LARC is SMU – Carnegie Mellon Partnership

“This extraordinary LARC opportunity takes our existing relationship with CMU to a new level of research intensity. The SMU-CMU collaboration gives us global edge in interdisciplinary research that integrates computation, management, and social sciences.”

- Professor Arnoud De Meyer, SMU President

“The Living Analytics Research Centre builds on CMU’s successful collaborations with SMU over the years. We are pleased to be partnering with SMU on such an exciting initiative - one that has great potential for groundbreaking work in the emerging field of computational social science.”

- Dr Jared L. Cohen, CMU President

LARC Project Settings & Partners

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What is Living Analytics?Consumer & Social Insights From

Experiment-Driven, Closed-Loop Analytics +Societal-Scale Human Networks

Framework for Living Analytics

• Observe complex behaviors in natural consumer and social settings via digital traces

• Progressively real-time

• Progressively societal-scale

• Network-centric

• Closed-loop, and iterative

• Experiment-driven

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Closed loop, network experimentation via LARC

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1. Collect data (historical, existing, new)

2. Construct network• Relevant observed links• Infer links as appropriate

3. Identify questions and predict behavior • Related to individual behaviour• Related to group or collective behaviour• Which method of personalization works best?

4. Design Experiments• Sample individuals and groups• Incentive and interaction design

5. Observe via digital traces and interactions

6. Analyze results of network-centric experiments and test predictions

7. Learn and adapt

8. Iterate around the loop

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The Living Analytics Adaptive Learning Loop

The loop begins with the Observe stage that involves observing user interaction and relationships within a network in real-time and gathering their digital traces.

The Analyze and Predict stage takes these digital traces, conducts analysis on them, discovers patterns in them, and uses these patterns for future user behavior and network trend prediction.

The Experiment stage involves testing how individual users and networked groups respond to changes in content, service offerings, interaction experience, pricing and incentives. The Experiment stage also tests how users respond to different types of guidance and feedback.

Finally, the Human Action stage is where users respond within the experiment, and to various types of feedback, and this generates the data that is picked up on the next cycle of observation.

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LARC•Experiment-driven

•Closed-loop, and iterative

•Network-centric

•Observing complex behaviors via digital traces

•Progressively real-time

•Progressively societal-scale

•Combining field realism & complexity with lab control

Plus LiveLabs• Context aware

• Using real-time context triggers for automating behavioral interactions

• Combining usage-adaptive 4G network management with end-user behavior

LiveAnalytics (LARC + LiveLabs) : New Concepts, Methods and Tools for Consumer & Social Insights that are

LiveAnalytics Vision(LARC + LiveLabs)

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Analytics that combinerealism, complexity and dynamics of social and consumer behavior observable in the field

withexperimental control and causal inference capability of the lab

in anetwork-centric world LiveLabs

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Living Analytics is an Interdisciplinary Fusion of Computer Science + Social Science

(Computational Social Science)

• Machine Learning & Data Mining for Data Analytics

• Real-Time Optimization & Adaptive Decision Support for Decision Analytics

• Social & Management Science for understanding, predicting and analysing the behaviour of individuals and networks via empirical analysis and experimentation

• Enabling computation and software applications• Enabling privacy and information security• Enabling protocols and administrative processes

for end-to-end Living Analytics insight experiments

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LARCFaculty Directors and Deputy Directors from SMU and CMU

LARCFaculty Directors and Deputy Directors from SMU and CMU

Funding for LARC

Research grant funds from the Singapore’s Interactive & Digital Media (R&D) Programme Office.

These funds come from Singapore’s National Research Foundation.

S$26M

Contributions from SMU, which includes in-kind contributions as well as financial contributions.

S$26MContributions from Carnegie Mellon, which are comprised of in-kind contributions.

Contributions from external organizations, which can be in the form of financial or tangible in-kind investments. $10M

Total (2011 thru 2015) S$62M

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Utilization of LARC Funding Over 5 Years

Research grant funds from the Singapore’s Interactive & Digital Media (R&D) Programme Office

S$26M

• 15 research staffs per year supported at SMU• 3 full time admin staffs at SMU• 8 LARC/SIS PhD students spend 10 months at

CMU each year• Over 5 years, a total of 40 PhD students, each with 10

month training at CMU• Additional 4 year SIS PhD scholarships (8)• 110 months of CMU faculty residency time at SMU• CMU PhD student residency time at SMU• Conference travel and Int’l workshops on LA

Contributions from SMU

S$26M• LARC facility at SMU• In-Kind faculty time from SMU• In-Kind faculty time from CMUContributions from

Carnegie Mellon

Contributions from external organizations

$10M• Infrastructure• Field pilots• Support staff

Total S$62M

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“Behavioral Insight” Experiments:

Methodological Research Work

How to Design and Execute “Behavioral Insight” Experiments

•In NETWORKED DATA Context•Subject to PRIVACY CONSTRAINTS•At Scale, At Speed, With Smart Use of Network Resources

Experimental methods, tools and practices for •Sampling•Design of interventions•Execution of interventions, both near-real time (e.g. on mobile device) and non-real time (e.g. Web applications) •Inference, Interpretation, and conclusions

How to design, execute and interpret an ongoing program of overlapping short-running & long-running experiments

Individual peopleNetworks of

people

Attributes of services, content,

experience

Consumer preferences

Preference formation &

evolution

Preference Influencing

Experimental methods and tools to reliably disentangle, quantify and understand critical effects-- e.g. interactions and influences across:

• How will people respond to customized recommendations? To explanations of consequences ?

• How will people respond to specific types of price alterations ? How does this change with context? With time?

• How will people respond to specific types of incentives in a given context?

• Who do people trust? Who has influence over others in the network? How does trust and influence evolve over time?

• What will people share? How does this evolve over time?

• How will people respond over time to new content, new services, new interactions, or new bundling or services?

Human Action:Individual Responses

Group & Network ResponsesFor example:

• What content will people create ? How does this evolve over time?

How are these responses influenced by• network interactions?• context ?• learning and experience accumulation?

Key R&D Challenges for LARC1. Analytics, Systems, and Computing Challenges

• Methods and Computation for Observation, Analytics, Prediction, Guidance and Learning

• In near real-time • At scale • Across multiple sources

• Across multiple contexts and extended time periods • Analytics at individual level of granularity while preserving privacy • Economically so that firms can eventually afford to make use of these powerful

capabilities

2. Social & Management Science Challenges • Understanding behavioural choices, preferences and intent of individuals, in the context of networks of individuals, depending on context• Designing & delivering guidance and incentives • Evaluating how participants respond to guidance & incentives• The theory and practice of experimental design in evolving, network-centric

settings as observations cannot be assumed to be independent

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Hard Challenges to Realising Living Analytics, con’t

o Practical and future oriented solutions to all of these challenges are part of LARC’s work.o SMU and CMU are giving these issues the highest levels of senior management support.o These types of administrative capabilities are important strategic competencies

that enable LARC to work with private and public sector organisations.18

Assuring the Security, Privacy and Confidentiality of our Partner Information

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Post-graduate PhD students who enter our programme within the next two years wills be elgible to participate in the 10 month training residency at CMU through LARC

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Engaging with LARC

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Engaging with LARC

Data Set Partner • Commitment to joint experimentation with LARC• Data access for LARC (SMU & CMU)• Major investment required for specific project

initiative• Need additional manpower at SMU and

CMU since all manpower is committed to ongoing projects

Research Affiliate Member

• Ability to sponsor and co-supervise LARC-related Student Projects with SMU and CMU students

• Annual private briefing • Periodic affiliates workshop • Preferential access to students and LARC team• Invitations to LARC seminars and events

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Living Analytics and LARC

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LARC: experimentation and learning in a digitally connected, network centric world

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Acknowledgments

LARC Data Set Partners• BuzzCity• Citibank Singapore• Starhub• ResortWorlds Sentosa• Sentosa Leisure Group•LiveLabs Urban Lifestyle Innovation Platform

LARC Research Affiliates

Contacting LARC

For any enquires or more information about LARC, please contact us at 6808 5227

or email to larc@smu.edu.sg

Visit us at http://www.larc.smu.edu.sg/

Like us at http://www.facebook.com/larc.cmu.smu

Follow us on https://twitter.com/larc_cmu_smu

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