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Revisiting Decision Rationale

Revisiting Decision Rationale. Ren é Ba ñ ares-Alc á ntara. Reader, Department of Engineering Science Senior Engineering Fellow, New College University of Oxford rene.banares@eng.ox.ac.uk. Contents. Some history … Revision and extension of ECOSSE ideas Compendium

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Revisiting Decision Rationale

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  1. Revisiting Decision Rationale René Bañares-Alcántara Reader, Department of Engineering Science Senior Engineering Fellow, New College University of Oxford rene.banares@eng.ox.ac.uk

  2. Contents • Some history … • Revision and extension of ECOSSE ideas • Compendium • Extensions to Compendium • options vs. criteria matrix • global parameters • sensitivity analysis • Decision support (not only rationale) • ELECTRE-III method and preferences • access to the web • Future work: ontologies for semantic consistency

  3. Artefact = network of decisions objectives, goals, specifications, constraints DECISION n DECISION 2 INTENT DECISION 1 justifications alternatives / options (structure & models) RATIONALE ARTEFACT

  4. KBDS

  5. DRAMA (QuantiSci Ltd)

  6. KBDS and DRAMA applications (academic and industrial) • HDA process, continuous with single product • HF, steady state rigorous model in ASPEN Plus • acetates, methyl acetate and its re-use for the design of ethyl, isopropyl and propyl acetates • penicillin, discontinuous biotechnological process • urban WWTP, continuous biotechnological • fuel reprocessing plants (BNFL) • electricity generation plants (British Electricity) • storage tank farm (Shell Oil) • decision-making and corporate memory (Norsk Hydro)

  7. Current efforts at Oxford (OXF & OBU) Ontologies (Reasoners, rules and NL) DECISION n MCDM (Electre) DECISION 2 INTENT DECISION 1 Modified IBIS (extended Compendium) RATIONALE ARTEFACT

  8. Compendium Compendium is an • an open source, • Java-based, • dialogue mapping software developed by the Compendium Institute (Open University), see http://compendium.open.ac.uk/institute/ Compendium is a tool to organise ideas, images, texts, websites, emails, … “ … it is like MindManager on steroids …”

  9. Original functionality in Compendium

  10. Compendium+: Options vs. Criteria

  11. Compendium+: criteria created by users

  12. Compendium+: global parameters

  13. Compendium+: sensitivity analysis Does a change in the value of a Global parameter affect previous decisions?

  14. Decision support: MCDMs Multi Criteria Decision Methods (MCDMs) support decision makers choose among a set of (discrete and exclusive) alternative options based on their compliance with respect to a set of criteria.

  15. General Procedure of MCDMs • Determine alternatives and criteria. • Assign weights (relative importance) to the criteria and evaluate the impacts of the criteria on the alternatives. • Process the resulting matrix to determine a ranking for each alternative.

  16. MCDM methods normalised by the maximum value • Weighted Sum Model (WSM) • Weighted Product Model (WPM) • Analytic Hierarchy Process (AHP) revised version [Belton&Gear 83] • ELECTRE (I, II, III, IV, IS, TRI) (ELimination Et Choix Traduisant la Realité; ELimination and Choice Expressing Reality) [Roy 68]

  17. ELECTRE methods • Normalise the Decision Matrix. • Weight the Normalised Decision Matrix. • Calculate the Concordance and Discordance sets. • Construct the Concordance and Discordance Matrices (elements are concordance indexes). • Determine the Concordance and Discordance Dominance Matrices. • Determine the Aggregate Dominance Matrix. • Generate a partial preference ordering of the alternatives. Outranking relations (comparing each pair of alternatives) Veto thresholds

  18. Where to locate a business? [Belton&Stewart]

  19. helping the user selecting the threshold values

  20. ELECTRE III: partial order

  21. Access to the web

  22. Access to the web (2)

  23. Access to the web (3)

  24. Ontologies for semantic consistency One of the “selling points” of our approach is the flexibility to declare issues, alternatives, criteria and variables with any name and meaning. However, • alternatives should be related to the associated issue, • criteria should be related to the declared goals, • variables should be linked to the measured criteria, … We plan to use ontologies to represent the semantics (meaning) or each of these concepts {Protégé}, check their consistency {1st order logic: PELLET}, and perhaps even suggest “what to do next” {rules: JESS}.

  25. Application areas • Policy making • Renewable Energy {PhD: Julian} • Biofuels and food security {MSc: Minerva} • Cquestrate project {Araz} http://www.cquestrate.com/ (partly funded by Shell’s GameChanger project) • Ontologies {MSc: Krishna}

  26. Conclusions and Acknowledgements • KBDS and DRAMA live on! • we are adapting their ideas and extending Compendium to • support decision making (on top of maintaining decision rationale) • provide additional functionalities • apply it in other decision-making scenarios, e.g. policy, energy The work is being done in conjunction with the group of Dr. Arantza Aldea at the Department of Computing, Oxford Brookes University • Simon Skryzpczak • John Hedges • Krishna Sapotka

  27. Conclusions and Acknowledgements … but just as important, it could not have been done without the ideas, effort and support of ECOSSE people: • Geoff • Josh • Rama • Neal • Bill + the QuantiSci team and • Jack Thanks!

  28. The policy process Setting the agenda Problem definition Policy design Policy implementation Policy enforcement Policy evaluation Formulation (transport and energy) Record (biofuels / food security) Regulation (pharmaceutical processes)

  29. Show that an ontology knowledge base can be applied to the pharmaceutical process and regulation domains in order to facilitate the management of regulatory compliance Objectives of the research Objective 1: Process generate ensure Compliance Task Proof Document match Regulation parse Objective 2: Do it in a semantically explicit, tool independent and flexible fashion • Supporting the parsing of regulations into formal tasks • Representing the tasks in a machine readable and tool independent format • Developing an ontological informatics infrastructure that allows computerized reasoning and supports decision making • Tracking the execution of tasks with respect to regulations to evaluate the degree of compliance

  30. TBox Ontology Environment: Protégé – OWL 3.4 Knowledge Layer Developer 1)Classify class hierarchy 2)Report noncompliant indiv. Fire rules and modify the individuals respectively DL Reasoner: Pellet 1.5.2 Rule Engine: Jess 7.1 Inference Layer Software Language: Java 5.0 (Eclipse) ABox Integration Layer OntoReg architecture

  31. TBox ABox OntoReg example TBox is populated by the user to represent an individual process.

  32. Artefact = network of decisions

  33. Where to locate a business? Descending order Ascending order [Amsterdam] [Paris] [London] [Milan] [Brussels, Berlin] [Warsaw] Partial order (intersection) [Amsterdam] [Paris] [London] [Brussels, Milan] [Berlin] [Warsaw] [Amsterdam] [Paris] [London, Milan] [Berlin] [Warsaw, Brussels]

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