Skip to main content

Introduction to MIRA, an Open Solution Approach

  • Chapter
  • First Online:
Environmental Public Policy Making Exposed

Part of the book series: Risk, Systems and Decisions ((RSD))

  • 276 Accesses

Abstract

The Multi-criteria Integrated Resource Assessment (MIRA) open solution approach is a framework and a process. MIRA is a transparent policy framework that embraces diversity, adversity, and discovery, with a process that allows for the policy-specific evaluation of Decision Uncertainty and the emergence of stakeholder agreement. The three major unique aspects of an open solution approach are the inclusion of Decision Uncertainty; stakeholder-directed, but structured, iterations of the Requisite Steps; and trans-disciplinary learning. Trans-disciplinary learning becomes key to ensuring that each iteration is informed by the previous. These components distinguish the open solution approach from conceptual social science approaches that lack methodologies to compare specific policy alternatives and the reductionist decision-analytic approach that is currently the dominant public policy making paradigm. In this chapter, the Requisite Steps as applied in a MIRA approach are described, including terminology and guiding principles specific to MIRA.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 119.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Often, the differing levels of detail reflect the availability of data due to the limitations in knowledge, time, or expertise at the time of the analysis.

  2. 2.

    Data metrics are those data that are either represented by a single datum or are constructed from more than one datum and are used to represent the stakeholders’ CN.

  3. 3.

    While assessment of the compliance or attainment status of a county for ozone appears to be purely factual (i.e., whether an air quality monitor is measuring complying ozone readings), this assessment is complicated by many factors, including the lack of monitors in every county, the regional transport of ozone precursor emissions, and the atmospheric chemistry of ozone formation.

  4. 4.

    Simplifying the EJS and VS for the Baseline Run allows stakeholders to focus on refining their PN, CNs, and CMs. Customization of their analysis with a non-uniform EJS and an unequal VS follows with iteration.

References

  1. Groot A, van Dijk N, Jiggins J, Maarleveld M (2002) Three challenges in the facilitation of system-wide change. In: Leeuwais C, Pyburn R (eds) Wheelbarrows full of frogs. Koninklijke Van Gorcum, Assen, pp 199–213

    Google Scholar 

  2. Benessia A, Funtowicz SO, Giampietro M, Guimaraes Pereira A, Ravetz JR, Saltelli A, Strand R, van der Sluijs JP (2016) The rightful place of science: science on the verge. Consortium for Science, Policy and Outcomes, Arizona State University, Charleston

    Google Scholar 

  3. Lawrence RJ (2010) Beyond disciplinary confinement to imaginative transdisciplinarity. In: Brown VA, Harris JA, Russell JY (eds) Tackling wicked problems: through the transdisciplinary imagination. Earthscan, London, pp 16–30

    Google Scholar 

  4. Röling N (2002) Beyond the aggregation of individual preferences. In: Leeuwais C, Pyburn R (eds) Wheelbarrows full of frogs: social learning in rural resource management. Koninklijke Van Gorcum, Assen, pp 25–47

    Google Scholar 

  5. Leeuwais C, Pyburn R (eds) (2002) Wheelbarrows full of frogs: social learning in rural resource management. Koninklijke Van Gorcum, Assen

    Google Scholar 

  6. Guijt I, Proost J (2002) Monitoring for social learning: insights from Brazilian NGOs and Dutch farmer study groups. In: Leeuwais C, Pyburn R (eds) Wheelbarrows full of frogs. Koninklijke Van Gorcum, Assen, pp 215–232

    Google Scholar 

  7. Curtin CG (2015) The science of open spaces: theory and practice for conserving large complex systems. Island Press, Washington, D.C.

    Book  Google Scholar 

  8. Bond A, Morrison-Saunders A, Gunn JAE, Pope J, Retief F (2015) Managing uncertainty, ambiguity and ignorance in impact assessment by embedding evolutionary resilience, participatory modelling and adaptive management. J Environ Manag 151:97–104. https://doi.org/10.1016/j.jenvman.2014.12.030

    Article  Google Scholar 

  9. Endter-Wada J, Blahna D, Krannich R, Brunson M (1998) A framework for understanding social science contributions to ecosystem management. Ecol Appl 8(3):891–904

    Article  Google Scholar 

  10. Isaacs W (1999) Dialogue and the art of thinking together: a pioneering approach to communicating in business and in life. Doubleday, New York

    Google Scholar 

  11. Bell S, Morse S, Shah RA (2012) Understanding stakeholder participation in research as part of sustainable development. J Environ Manage 101:13–22. https://doi.org/10.1016/j.jenvman.2012.02.004

    Article  Google Scholar 

  12. Andrews CJ (2002) Humble analysis: the practice of joint fact-finding. Praeger, Westport

    Google Scholar 

  13. Webler T, Renn O (1995) A brief primer on participation: philosophy and practice. In: Renn O, Webler T, Wiedemann P (eds) Fairness and competence in citizen participation: evaluating models for environmental discourse, Technology, risk and society, vol 10. Kluwer, Dordrecht, pp 17–34

    Chapter  Google Scholar 

  14. U.S. Environmental Protection Agency (1998) Guideline for data handling conventions for the 8-hour ozone NAAQS. U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park

    Google Scholar 

  15. U.S. Environmental Protection Agency (2015) National ambient air quality standards for ozone. Fed Regist 80(208):177

    Google Scholar 

  16. Saaty TL (1990) Multicriteria decision making: the analytic hierarchy process. RWS Publications, Pittsburgh

    Google Scholar 

  17. Clean Air Act (1990) 42 U.S.C. 7407 et seq

    Google Scholar 

  18. Stahl CH, Fernandez C, Cimorelli AJ (2004) Technical support document for the Region III 8-hour ozone designations 11-factor analysis. U.S. Environmental Protection Agency, Region III, Philadelphia

    Google Scholar 

  19. U.S. Environmental Protection Agency (2004) Air quality designations and classifications for the 8-hour ozone National Ambient Air Quality Standard; early action compact areas with deferred dates. Fed Regist 84(69):23858–23951

    Google Scholar 

  20. Corporate Finance Institute (2018) Dow Jones Industrial Average (DJIA). https://corporatefinanceinstitute.com/resources/knowledge/trading-investing/dow-jones-industrial-average-djia/. Accessed 12/13/18

  21. Susskind LE, Levy PF, Thomas-Larmer J (2000) Negotiating environmental agreements: how to avoid escalating confrontation, needless costs, and unnecessary litigation. Island Press, Washington D.C.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Stahl, C.H., Cimorelli, A.J. (2020). Introduction to MIRA, an Open Solution Approach. In: Environmental Public Policy Making Exposed. Risk, Systems and Decisions. Springer, Cham. https://doi.org/10.1007/978-3-030-32130-7_3

Download citation

Publish with us

Policies and ethics