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Designing statistical surveys and statistical systems – a complex decision process Bo Sundgren 2010

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1 Designing statistical surveys and statistical systems – a complex decision process Bo Sundgren 2010

2 A complex decision problem A large number of theoretically possible decision alternatives (solutions) –possibly expressible in a space spanned by a number of decision variables –in practice a selection of reasonable decision alternatives has to be made Each decision alternative will lead to an outcome –possibly expressible in terms of a number of outcome variables that are relevant and important for the stakeholders (costs, qualities,...) Some variables may be easy to measure and quantify, others not The relationships between decision alternatives and outcomes may be more or less predictable, more or less possible to investigate and describe in an objective way, acceptable for all stakeholders There are many stakeholders with different interests, different evalua- tions of the values of the outcome variables, and different preferences, rankings, and weights, possibly implicit, when it comes to evaluating and comparing the total outcomes of the selected decision alternatives The final decision will typically be made after some kind of reconciliation and negotiation process

3 The DSV-DECIDE model and toolbox Participative decision analysis and decision support Tested in a number of projects in some municipalities Typical applications: –localisation of unattractive activities (waste, nuclear power,...) –projects associated with complex environmental factors Enable citizens concerned by a decision to provide input in a constructive way in all steps of the process Enable all kinds of people involved to get a multi-faceted understanding of all factors affecting and being affected by the decision, both more or less objective, fact-oriented factors, and more subjective and value-oriented factors

4 The DSV-DECIDE model and toolbox Stakeholders and decision-makers can vary assumptions concerning different factors, and relationships between factors, thus increasing the transparency of the decision-making process, and getting a good feeling for the sensitivity or robustness of the outcome of the decision to variations of different assumptions in the decision model, such as –changes in preferences –priority weights or rankings of criteria taking into account that different stakeholders may very well differ in their values and views Among other things, the method may help to find solutions more satisfactory to some stakeholders than alternatives, without hurting the interests of others – Pareto optimality

5 The DSV-DECIDE model and toolbox The decision process model is structured into three layers: –The stakeholder layer, containing the political process and the interaction with the citizens –The investigation layer, consisting of the local governments internal administrative process, where decision makers (politicians) and civil servants cooperate to arrive at a reasonable set of alternatives, to be further investigated with assistance by experts/consultants –The analysis layer, or inner decision layer, into which the data from the other two layers are entered and modelled by using techniques and tools from the area of multi-criteria decision analysis the values and views of the decision makers (and stakeholders) are incorporated as weights or rankings of the criteria together with assessments of how the different decision alternatives score (or fulfil requirements) with respect of each criterion

6 Quantifiable and non-quantifiable variables Traditional approach: transform the multidimensional complex of quantifiable and non-quantifiable outcome dimensions into one common dimension, expressed in one common quantitative variable, money Alternative approach: agree upon a limited number of decision alternatives, outcome dimensions, and outcomes, and let each stakeholder –rank the outcomes for each outcome dimension –give weights to each outcome dimension

7 The reconciliation process Negotiate with the stakeholders to define a limited number of decision alternatives, outcome dimensions, and outcomes Let each stakeholder –rank the outcomes for each outcome dimension –give weights to each outcome dimension Analyse the outcomes together with the stakeholders in an open and transparent way Use sensitivity analyses Aim for satisfying and Pareto optimal solutions, solutions that most stakeholders can live with, rather than overall optimal solutions (which hardly exist) Use transparent and easy-to-grasp tools for supporting the discussions, analyses, and negotiations

8 Statistical design as a decision problem Designing a statistical survey is a complex process with –multiple, potentially conflicting goals and decision criteria –multiple stakeholders: multiple users and usages, known and unknown, present and future respondents and data providers funders managers professional staff: designers, methodological experts, operators,... other parties concerned: the public at large, potential victims Designing a statistical system of many surveys, registers, databases, etc, is of course even more complex

9 Customers - Users How to reconcile conflicting interests between many customers, some of whom are even unknown, in a general-purpose system? Figure 7. How to satisfy partially conflicting needs of different users of official statistics? From Sundgren (2004a).

10 Stakeholders Customers – Users Respondents Sponsors (the taxpayers and their representatives) Producing staff: designers, operators, managers, business partners and professional colleagues Researchers on official statistics Others concerned, victims – cf group privacy

11 Conflicting interests Contents Qualities –relevance –timeliness –accuracy –clarity –comparability –coherence Costs Response burden Confidentiality, privacy (for individuals and groups)

12 Conflicting interests Between stakeholders of different categories Between stakeholders within the same category For one and the same individual stakeholder

13 Great variety of decision alternatives and outcomes A large and complex space of possible designs, spanned by a large number of design variables (in a broad sense) A large and complex space of possible outcomes in terms of outcome variables Complex functions for evaluating and comparing the outcomes for different decision alternatives Different functions for different stakeholders Difficult to transform implicit, inconsistent evaluation fuctions into explicit, consistent evaluation functions for each stakeholder Difficult to reconcile decision alternatives, outcomes, and evaluations between stakeholders

14 Literature D. Bollinger and J. Pictet (2003): Potential use of e-democracy in MCDA processes. Analysis on the basis of a Swiss Case. Journal of Multi-Criteria Decision Analysis 12, pp. 65-76. D. Rios Insua, G. E. Kersten, J. Rios, and C. Grima (2007): Towards decision support for participatory democracy. Information Systems and E-Business Management 6(2), pp. 161-191. M. Danielson, L. Ekenberg, A. Larsson, and M. Riabacke (2009a): Transparent public decision making. Forthcoming. M. Danielson, L. Ekenberg, A. Larsson, and M. Riabacke (2009b): Structured eDemocracy beyond eDiscussion. Proceedings of eChallenges 2009. M. Danielson, L. Ekenberg, Å. Grönlund, and A. Larsson (2005): Public decision support – using a DSS to increase democratic transparency. International Journal of Public Information Systems 2005:1, pp 3-25 M. Danielson, L. Ekenberg, A. Ekengren, T. Hökby, and J. Lidén (2008): Decision process support for participatory democracy. Journal of Multi-Criteria Decision Analysis 15, pp. 15-30 R.P. Hämäläinen, E. Kettunen, M. Marttunen, H. Ehtamo (2001): Evaluating a framework for multistakeholder decision support in water resources management. Group Decision and Negotiation 10(4), pp. 331–353

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