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TED and IDs Overview and research issues David R os Insua, URJC Varenna, September 03

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TED and IDs Overview and research issues David R os Insua, URJC Varenna, September 03

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    1. TED and IDs Overview and research issues David Ros Insua, URJC Varenna, September 03

    2. The role of IDs in TED Overview of DA DIs from outside Inside IDs Evaluating IDs Research issues Reader

    4. Facilitating DA methods through the web

    5. Need tools to: - Model the problem structure as described on the Problem Structuring session. What can we do and in what order? What do I know when I make the decisions? What am I uncertain about? What do I obtain by making the decision. - Do this in an intuitive and compact fashion - Communicate to others our perception of problem .. One possibility is through influence diagrams

    6. The Prestige problem

    9. Decision Analysis Cycle

    10. Example: How to recover a land?

    12. Basic elements in an ID A (directed, acyclic) graph with - Nodes of three types Circles. Chance nodes Squares. Decision nodes Diamonds Value node - Arcs of two types Pointing a decision node Pointing a chance or value node

    13. Some DI chunks

    14. Think of complete DIs with one chance node, One decision node, one value node

    15. Some standard examples Decision making under risk A nuke accident. The major should decide whether to evacuate or not a city. He doesnt know in which direction the wind will blow. Decision making with imperfect information but we have access to a weather forecast Deterministic nodes Multiple objectives

    18. Sequential decisions

    19. Probabilistic influence diagrams A dispnea may be due to tuberculosis, cancer or SARS, several of them or None of them. A recent visit to Asia increases the probability of SARS, Whereas smoking is a risk factor for cancer and tuberculosis. The results Of an X-ray may not discriminate between cancer and tuberculosi, neither The presence or absence of the dispnea

    20. Building IDs For probabilistic IDs, automatic methods. But they require large Databases For decision IDs, an art essentially

    24. Preferences

    26. Three basic operations Chance node removal Decision node removal Arc inversion For a properly formulated diagram, we may apply sequentially those rules Until all nodes are removed and the optimal policy is determined

    27. Reducing chance node

    28. Reducing decision node

    29. Inverting arc

    30. Proceeding step by step

    31. The same qualitative approach may be taken in continuous problems. But computations are intractable MCMC methods (eg augmented simulation) For discrete probabilistic IDs lots of methods Many software products

    35. A reader Shachter (1986) OR paper Clemen (1997) Making hard decisions Bielza, Muller, Rios Insua (1999) M. Science paper Cowell, Spiegelhalter, Lauritzen, Dawid (2000) Springer TED Visit program!!!!

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