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Multi-Criteria Resource Assessment - MIRA

Multi-Criteria Resource Assessment - MIRA. An Structured Approach for Applying “Clumsy” Solutions to “Wicked” Problems Alan J. Cimorelli November 28, 20011 ISCMEM Annual Meeting , NRC, DC. Presentation Overview. Environmental Decision Making Typical Approach Need for a different construct

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Multi-Criteria Resource Assessment - MIRA

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  1. Multi-Criteria Resource Assessment - MIRA An Structured Approach for Applying “Clumsy” Solutions to “Wicked” Problems Alan J. Cimorelli November 28, 20011 ISCMEM Annual Meeting , NRC, DC

  2. Presentation Overview • Environmental Decision Making • Typical Approach • Need for a different construct • Wicked Problems • Clumsy Solutions • Overview of MIRA • Mountain Top Mining Application • Demonstration of MIRA

  3. Environmental Decision Making • Characteristics: • Multi-disciplinary • Multi-stakeholder • Socio-economic, cultural & political dimensions • General Considerations: • Context is paramount: • What is the question and what are the constraints? • Gives relevance and meaning to data • Science cannot make the decision – only inform and constrain • All decisions are tradeoffs among relevant criteria • Value Judgments • There is No “Right” Answer.

  4. Typical Approaches • Information via briefings and discussions - Decisions based on: • Persuasion – Who has the best argument • Preconceived leanings • Gut • Use of decision analysis methodology: • Basic Purpose: Method for RANKING ……. • Types: • Optimization • Constrained or limited: e.g., cost-benefit; utility functions; fixed weighting among criteria • Deconstruction to simplify: e.g., AHP • Common Problem: • One-Pass approaches • Decision makers often don’t like the solution • Can work for “Tame” problems but not “Wicked” • Need new Construct: Clumsy Solutions for Wicked Problems

  5. Wicked Problems* • Those that cannot be defined so all agree on the problem to solve • Require complex judgment about the level of abstraction at which to define the problem • Have no clear stopping rules • Have no right/wrong answer; just better/worse conditions • Have no objective measure of success • Require iteration – every trial counts • The solution may need to be discovered • Often have strong moral, political or professional dimensions *Rittel and Webber, 1973

  6. Tame vs. Wicked Problems Environmental Decision Making Ecosystem Services Fate and Transport Modeling Risk Management Climate Change Integrated Modeling Sustainability Risk Assessment Tame Wicked Toxicology: Human & Ecosystems Uncertainty Analysis

  7. Clumsy Solutions • Stakeholder-inclusive • Transparent • Learning-based; cross- and trans-disciplinary • Iterative and Adaptive • Typical approaches are not adequate

  8. MIRA: A Clumsy Solutions Approach for Wicked Problems • Identify the stakeholders • Establish decision context • Select decision criteria and define data metrics • Construct a decision hierarchy • Indexing • Preferencing • Calculate a single valued metric for each PSE and rank • Analyze results and make changes • Iterate to concensus

  9. Where Does MIRA Fit In IEM • Enables the real-time use of IEM results in Policy and Decision Making • MIRA has been incorporated into FRAMES: • Integrated modeling results are use as data input for MIRA • Examine decision sensitivity to: • Input data • Indexing: expert judgments regarding data significance • Preferencing: relative importance among indicators (stakeholder value judgments) • Examine decision uncertainty relative to: • Data uncertainty • Arguments among experts • Differences of opinion among stakeholders

  10. CI Analysis for MTM: A Wicked Problem • Decision context: Define environmental condition of HUC12 Watersheds w.r.t. cumulative impacts surrounding MTM – Aquatic only • Stakeholders: EPA, COE, OSM • Purpose: Predict possible alternative futures • Why this problem is “Wicked” • Difficult to agree on definition of Env. Condition • Each stakeholder brought skill sets to the problem • Stakeholders roles and responsibilities were different • Multi-iterations were needed to learn and ultimately arrive at a definition of the problem

  11. MIRA Demo

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