Introduction to assessment performance Mikko Pohjola, THL

Contents Concepts General framework Common perspectives (& examples) Quality assurance/quality control Uncertainty Model performance Properties of good assessment Summary

Concepts & rationale Some basic concepts: Performance = goodness! Assessment, Management Model Process (making/using), Product Output, Outcome Assessor, Decision/Policy maker, Stakeholder Participant, User

Concepts & rationale Why evaluation of assessment performance is important? Efficient use of resources? Value of work done? Importance/meaning of information? Implications of information? Actual impacts of information? … …because funder, customer, user, boss, peer, stakeholder etc. wants/needs to know!

Roles and interests ExpertsData quality, analysis procedure, coherence, comprehensiveness, … FundersRelevance, efficiency, timeliness, importance, … Users (DM)Understandability, reliability (of source), acceptance, practicality, … Interested (SH)(same as DM, but different perspective)

General RA/RM framework Process, product, use

Common perspectives & examples Quality assurance/quality control Focus on assessment process An “engineering” perspective Uncertainty Focus on assessment output A scientists perspective??? Model performance Focus on modelling and model Combines QA/QC and uncertainty perspectives A modellers perspective

Quality assurance/quality control Principle: Good process guarantees good outputs/outcomes! Question: How should an assessment process be conducted? Examples: Ten steps by Jakeman et al.(2006) IDEA framework (Briggs, 2008) (Over)appreciation of randomized controlled trials (RCT’s)

Ten iterative steps in development and evaluation of environmental models Jakeman et al.: Ten iterative steps in development and evaluation of environmental models. Environmental Modelling & Software Issue 5, May 2006, Pages

IDEA framework (INTARESE) Briggs: A framework for integrated environmental health impact assessment of systemic risks. Environmental Health 2008, 7:61.

Uncertainty Principle: Performance is an intrinsic property of an information product! Question: How good is the answer provided by the assessment?

Uncertainty Examples: Statistical uncertainty analysis Mean, variance, confidence limits, distributions, … Cf. D. Lindley: Philosophy of Statistics, 2000 Sources of uncertainty E.g. model, parameter & scenario uncertainty (as applied e.g. by the U.S.EPA) Extensive approaches E.g. inclusion of qualitative aspects, sources of uncertainty as in NUSAP (

NUSAP N: numeral U: unit S: spread A: assessment (qualitative judgment) P: pedigree (historical path leading to result)

NUSAP - pedigree Jeroen van der Sluijs: NUSAP- some examples. Presentation. Available:

Model performance Principle: The model is the essence of the assessment! Question: How good is the model? Examples: Verification, validation, (reliability, usability, …) Outcome-oriented approach by Matthews et al. 2011

Outcome-oriented modelling approach Matthews et al.: Raising the bar? – The challenges of evaluating the outcomes of environmental modelling and software. Environmental Modelling & Software, March 2011, Pages

Summary of common perspectives Assessment process and product addressed in many ways Use of results mostly not considered The link between outputs and outcomes (cf. Matthews et al. 2011) Evaluation often a separate process Expert processes of making assessments and using their results Expert processes of evaluating performance Alternative perspectives?

Properties of good assessment

Ex post (after assessment) evaluation Ex ante (before/during assessment) evaluation Guidance of design and execution Links process and output with use Thereby also linking them to outcomes

Example: what makes a good hammer?

How is the hammer made? By whom? What properties does the hammer have? What do you want to do with the hammer? How does the hammer help you do it?

Summary Consideration of (intended) use is essential Consideration of process and product in light of use Consider the instrumental value of information Cf. absolute value (a common science view) Cf. Ad hoc solutions (a common practice view) Contextuality, situatedness, practicality, … In policy-support information is a tool (a means to an end) A model is a tool for producing information How does this relate to the previous lectures about DA and the DA study plan exercise?