Alexei A. Gaivoronski Norwegian University of Science and Technology

1. Stochastic optimization and modeling of network risk and uncertainty: the case of telecommunication services Alexei A. Gaivoronski Norwegian University of Science and Technology Joint work with Josip Zoric, Denis Becker, Adrian Werner, Paolo Pisciella IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007

2. Risk adapted performance networks • Electric power generation and distribution • Gas production, transportation, dirstribution • Telecommunications and internet • Transportation • Hierarchical networks with nodes of different levels of complexity: from equipment to enterprises • Nodes designed to meet local risk adjusted performance targets locally • Network should satisfy risk/performance tradeoff globally • Inherent uncertainty IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007

3. Quantitative evaluation of business models for collaborative service provisionWork directions • Getting qualitative understanding of business models, input from qualitative part, SPICE scenarios, surveys • Development of quantitative models • Implementation in a prototype of decision support system • Testing on SPICE scenarios, cases • Deliverable on quantitative evaluation IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007

4. Status Task 1.4, quantitative analysis of business models • The Edition 1 of the set of models for investment business analysis of collaborative service provision has been developed: top static view • Architecture of the prototype of decision support system for analysis of business models is selected • Parts of this prototype is under implementation IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007

5. Work in progress • Edition 2 of the model set: service lifetime, different constellations of actors • Build up of the prototype of decision support system for business analysis • Analysis of SPICE scenarios using the model set • Analysis of possible business models using qualitative input from other participants • Further dissemination effort IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007

6. Quantitative business models • What is it? • Well understood in theory of corporate finance and in business practice • BUT focus is on one single enerprise who selects industrial project or project portfolio • Identify and measure and commeasure all cash flows related to a given business activity • Give integrated assessment of cash flow/profit performance based on different business principles • Return on investment • NPV • Risk/performance tradeoff • Decision about business activity • Recent emphasis on risk control IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007

7. Challenges to this view • Networked industrial environment • Different independent agents are contributing to the common goal being in complex relations of competition and collaboration • How all this functions in such networked environment? • Corporate finance theory needs further development for this case • Risk control issues • Good example: evaluation of business models in context of SPICE IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007

8. Objective • Starting from theory of corporate finance and optimal decisions under uncertainty and risk develop methods and tools for quantitative evaluation of business models in networked environment • Utilize this methodology in SPICE context for evaluation of collaborative service provision on SPICE platform IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007

9. Risk/performance networks • State of the art: different attempts but no universally accepted answers • Growing importance in different fields • Telecommunications • Supply chain management • Energy IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007

10. Example of structural description of service provision IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007

11. Different constellations of roles IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007

12. Service architecture IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007

13. Services, roles and actors services Components, enablers, roles users actors SPICE IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007

14. Economic requirements • Platform should be attractive for all actors • Actors should feel incentive to join service provision, that is they should want to join cooperative effort because they will benefit from it • Services should provide to actors a competitive source of profit • Risk/return considerations: risk that users will not accept the service as expected, cannibalizing, etc IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007

15. Approach of modern financial theory • Actors participate in service(s) provision assuming roles and providing components for services • Quantify cash flow, profits and risks • Each actor will select tradeoff between profit and risk exposure according to its preferences • This will result in service portfolio for each actor • Coordination tools should assure that the actors will select on their own accord participation in service provision in required proportion IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007

16. Risk/return tradeoffNobel prise winning concept IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007

17. Quantitative modelDescription of service • Services consist of components which my be provided by different actors N components indexed by i and M services indexed by j lij - share of component i in service j. Description of service through components: • Service generate revenue vj • Revenue sharing coefficients • Actor who contribures with component i recieves revenue IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007

18. Description of actors • Actors assume roles by providing service components • This incurs costs and brings revenue K actors indexed by k cik–unit provision costs for actor k providing component i Wik– provision capability of component i by actor k xijk–the portion of provision capability for component i of actor k dedicated to participation in provision of service j. Profit model for actor k xijkWik - the volume of provision of component i dedicated by actor k to service j IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007

19. Profit model for actor k • xijkWik/λij- volume of service j in which the actor k participates • vjxijkWki/λij - the total revenue from this service • vjxijkWkiγij/λij - the part of the revenue which goes to actor k • Profit of actor k: IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007

20. Profit model for actor k • Basic case: an actor provides only one component • Profit • Return • Portfolio viewpoint: an actor chooses portfolio of services to which contribute IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007

21. Portfolio viewpoint • Return coefficients associated with participation in each service • expected return coefficients • expected return • Risk that actual return will be different from expected return or even become loss IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007

22. Efficient service portfolios • Problem to solve for computing eficient frontier IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007

23. Next level: quantitative coordination • What is necessary is that the whole service provision platform functions properly • And this means that different actors should independently make decisions to participate in different services which nevetherless will provide coordinated result. • Revenue sharing coefficients should be chosen in order to achieve this IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007

24. Coordinator (service provider) problem Paper is available on Edition 1 of the model set IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007

25. Architecture of the DSS prototype IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007

26. Screenshot 1 of demo of DSS prototype IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007

27. Screenshot 2 of demo of DSS prototype IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007

28. Example: business person on the move IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007

29. Risk/performance preferences IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007

30. Market shares IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007

31. Price competition IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007

32. Summary • Modern theory of corporate finance and risk management together with optimization under uncertainty provides a foundation for quantitative analysis of risk/performance networks in the context of collaborative service provision IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007

33. Conclusions • Traditional risk management paradigm should be augmented and developed further: noncommeasurable risks • Modern theory of corporate finance and risk management provides a foundation for quantitative analysis of risk/performance networks but much more work is needed • Many possibilities for stochastic programming approaches • Three components: Modern computing technology, off-shelf optimization software, custom algorithm design • It is possible to solve highly nonlinear and nonconvex problems in industrial quantities IIASA, Workshop on Coping with Uncertainty, 10-12.12.2007