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Mathematics for the Digital Economy Building Stones

Mathematics for the Digital Economy Building Stones. Roland Potthast, Reading, UK. The Digital Economy. “Novel design or use of information and communication technologies to help transform the lives of individuals, society or business.” (EPSRC). New Products + Services. Behaviour Lifestyle.

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Mathematics for the Digital Economy Building Stones

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  1. Mathematics for the Digital EconomyBuilding Stones Roland Potthast, Reading, UK

  2. The Digital Economy “Novel design or use of information and communication technologies to help transform the lives of individuals, society or business.” (EPSRC) New Products + Services Behaviour Lifestyle New Technology = + Highly multidisciplinary: high impact Technology + People/Society = Resonance = New Opportunities

  3. Math and Digital Economy Services Products Algorithms Modelling Team Structures Area/Data People

  4. People • Example CV (Potth.) Bridging the Gaps: • Education Physics and Mathematics • Project Manager in IT Industry • Trainer for Siemens ICN • Partner for Spin-Off Companies in IT/Maths • Mathematics Reader/Professor • Team Builder

  5. Math and Digital Economy Services Products Algorithms Modelling Team Structures Area/Data People

  6. Becoming data rich… Data from many sources • Behaviour of people and groups • Transactions B2C • Communication/networking P2P • Outreach and information • Participation • Monitoring/surveillance • Measurement • New Imaging Technologies • In many areas we are data rich but model poor! • Especially when the “atoms” are people

  7. Math and Digital Economy Services Products Algorithms Modelling Team Structures Area/Data People

  8. … the information revolution The information age just started! • More information and data on various levels than we could ever imagine:economy, society, science • Sincere demand of models, order, understanding, monitoring, control • Scaling, Micro vs. Macro Analysis, • Hierarchy of Models, • New Mathematics, continuous or discrete! New Insight, Applications, Products, Services

  9. Math and Digital Economy Companies Services Products Algorithms Modelling Team Structures Area/Data People

  10. Some Digital PlayersNow part of the fabric of our lives Online commerce Pure plays Existing Finance Social interaction Networking Commerce Services Communications Utilities…

  11. Math and Digital Economy Services Products Algorithms Modelling Team Structures Area/Data People

  12. Customer Relationship Management EXAMPLE • Explosive growth though IT “reach” … 105-106 customers • Using behaviour to discover/define addressable groups • Highly responsive : near real time • Finding markets that are predictive • Predicting behaviour and churn SORTING OUT THE CROWD Maths Inside Unsupervised discrimination over data bases: EM algorithm and its variants Hidden Markov models for rates of transition between behavioural states Supervised discrimination: Bayes factors and probability theory Discrete searches model optimisation: genetic algorithms Simulation : Agent Based Modelling e.g. possible spin out from Maths@UoR

  13. Cognitive Neuroscience / Healthcare EXAMPLE • Exploding number of new imaging technologies • Aging society with new needs of diagnostics • Societies growing strongly in developing countries • Time-resolved multi-source data, need for model hierarchy and evaluation Maths Inside Discrete Theories and Field Theories, Integro-Differential Equations Medical Imaging, Monitoring, Data Analysis, Remote Analysis Automated algorithms, Remote Health Care Inverse Problems, Data Assimilation, Stochastic Estimation Theory New Multi-Level Structures, Models, Analysis and Numerics

  14. Monitoring and security EXAMPLE • Searching for aberrant or low probability events • Classifying behaviour • Prioritising for different types of intervention • Supply Chain Management and Monitoring (SiroTechnologies, EADS, VW, RLS, KMW etc) • e.g. Health-check data in the home and online (www.brainpanrel.co.uk bid to LLHW prog), • e.g. fraudulent behaviour detection for online poker companies (Valeo Associates Ltd) SiroTechnologies Maths Inside Bayesian multiple hypotheses testing Forecasting trends/uncertainties : application of MCMC in adaptive forecasts Supervised discrimination: Log Bayes factors / probability theory

  15. Graphs and Networks EXAMPLE • The growth and evolution of dynamical networks • Small world and range dependent graphs • e.g. • Inverse problems: calibrating graph parameters from data • Dealing with very large networks – sensitivity to data • Comparison of alternative concepts/models Maths Inside New classes of random graphs Numerical linear algebra & spectral theory: clustering within networks Generalised clustering methods, e.g. SVD-based for stochastic graphs Maximum likelihood representations of data within classes of graphs Stability of results with respect to data, Inverse Problems for Graphs

  16. Behaviour based profiling segmentation EXAMPLE • Segmenting populations with behavioural metrics for product and service development • e.g. Smart 24/7 energy metering data in the home – “current insight” mining pilot project • e.g. Analysing m-banking data in Africa (current 2M customer pilot UoOx start-up ARK MF Ltd) Maths Inside Unsupervised discrimination over data bases: EM algorithm and its variants Markov models for rates of transition between behavioural states Supervised discrimination: Bayes factors and probability theory Discrete search algorithms: genetic algorithms Simulation : Agent Based Modelling

  17. Fuel Cell Quality Monitoring EXAMPLE • Monitoring of current distributions in fuel cells via magnetic tomography • Development of fuel cells and fuel cell stacks, energy patterns, applications • Production and quality control • Maintenance, Diagnostics, Control Maths Inside Integral equations and Potential Theory, PDE, Numerical Analysis Inverse Problems, Imaging, Data Assimilation, Optimization Algorithms Data analysis, Large ODE systems, FEM/FIT/BEM Unsupervised discrimination over data bases: EM algorithm and its variants

  18. Math and Digital Economy KT Services Products Algorithms Modelling Team Structures Area/Data People

  19. KT Opportunities • Many companies have these topics • Need for new concepts and new practical applications • Data is very often confidential which is a barrier • The maths community would benefit from anonymous problem banks • Algorithms and methods are difficult to protect • Secrecy rather than publication

  20. The Horizon Hub • University of Nottingham and “spokes” at Reading, Cambridge, Exeter • Highly multidisciplinary: • ICT, • Maths, • Business, • Social science • Art, Performance • Starts is a few months • Large and growing number of industrial partners • Grindrod, UoR, will manage an interface with UK advertising companies on behalf of the national DE community – The IPA

  21. Services Products Algorithms Modelling Team Structures Area/Data People Math and Digital Economy:Future Trends • Integration of diverse and multilevel technologies • Simplification and complexity • Individual empowerment: customers’ perceptions and activities and ideas becoming paramount • Commerce P2P exchange • Control, Security, Sustainability • Ethics and individual/subjective issues Thank you!

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