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Updated 29 Nov 2012 PowerPoint Presentation
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Updated 29 Nov 2012 - PowerPoint PPT Presentation

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Updated 29 Nov 2012

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  1. Updated 29 Nov 2012

  2. 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

  3. LARC Project Settings & Partners 3

  4. What is Living Analytics? Consumer & Social InsightsFrom Experiment-Driven, Closed-Loop Analytics+Societal-Scale Human Networks Societal-scale data via digital traces Experiment-driven, closed-loop analytics Network experimentation Data confidentiality, privacy and security

  5. Framework for Living Analytics • Observe complex behaviors in natural consumer and social settings via digital traces • Progressively real-time • Progressively societal-scale • Experiment-driven • Closed-loop, and iterative • Network-centric

  6. Closed loop, network experimentation via LARC • Collect data (historical, existing, new) • Construct network • Relevant observed links • Infer links as appropriate • Identify questions and predict behavior • Related to individual behaviour • Related to group or collective behaviour • Which method of personalization works best? • Design Experiments • Sample individuals and groups • Incentive and interaction design • Observe via digital traces and interactions • Analyze results of network-centric experiments and test predictions • Learn and adapt • Iterate around the loop

  7. 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.

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

  9. LiveAnalytics Vision(LARC + LiveLabs) • Analytics that combine • realism, complexity and dynamics of social and consumer behavior observable in the field • with • experimental control and causal inference capability of the lab • in a • network-centric world LiveLabs

  10. 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

  11. LARC Faculty Directors and Deputy Directors from SMU and CMU LARC Faculty Directors and Deputy Directors from SMU and CMU

  12. Funding for LARC

  13. Utilization of LARC Funding Over 5 Years

  14. “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 and tools to reliably disentangle, quantify and understand critical effects-- e.g. interactions and influences across: Individual people Networks of people • 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 Attributes of services, content, experience Consumer preferences Preference formation & evolution Preference Influencing • How to design, execute and interpret an ongoing program of overlapping short-running & long-running experiments

  15. Human Action: Individual Responses Group & Network Responses For example: • How will people respond over time to new content, new services, new interactions, or new bundling or services? • How are these responses influenced by • network interactions? • context ? • learning and experience accumulation? • How will people respond to specific types of incentives in a given context? • How will people respond to specific types of price alterations ? How does this change with context? With time? • What content will people create ? How does this evolve over time? • What will people share? How does this evolve over time? • How will people respond to customized recommendations? To explanations of consequences ? • Who do people trust? Who has influence over others in the network? How does trust and influence evolve over time?

  16. Key R&D Challenges for LARC

  17. Hard Challenges to Realising Living Analytics, con’t • Administrative Challenges • Gaining access to the data of participating companies • Legal agreements to work with companies (confidentiality, publishing, IP issues, exclusivity, no warranties, indemnities, etc) • Gaining permissions to work with participating communities • Institutional Review Board (IRB) clearances • Privacy and security issues for working with confidential data and human subjects • Training industry counterparts (project side, legal side, business side) on how to work with the university, on accelerating capability development vs procuring “product” • Training LARC faculty, staff and students to understand the criticality of these • legal agreements and giving them tools and processes to abide by requirements • Practical and future oriented solutions to all of these challenges are part of LARC’s work. • SMU and CMU are giving these issues the highest levels of senior management support. • 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

  19. 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

  20. Engaging with LARC

  21. Engaging with LARC

  22. Living Analytics and LARC

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

  24. Acknowledgments • LARC Data Set Partners • BuzzCity • Citibank Singapore • Starhub • ResortWorlds Sentosa • Sentosa Leisure Group • LiveLabs Urban Lifestyle Innovation Platform • LARC Research Affiliates

  25. Contacting LARC For any enquires or more information about LARC, please contact us at 6808 5227 or email to Visit us at Like us at Follow us on