Public-sector policy design is inherently complex: there are multiple stakeholders, multiple objectives, and ambiguous or unknown evidence regarding effective policy interventions. One response to this problem is the design of decision support systems (DSS) to help ordinary citizens as well as policymakers make choices that enrich their daily lives and participate in policy and planning processes. DSS typically combine information technology tools for data storage, analysis, and visualization with decision models that help users formulate and solve problems that might otherwise appear impossible to formulate clearly or are computationally intractable. This paper proposes a framework for developing DSS for public sector policy making that reflects a number of key principles. First, DSS should be values-based-reflective of ethical and moral considerations that motivate users to address problems of public interest. Second, it should be evidence-based-containing data and functional relationships that represent best knowledge and practices. Third, it should be model-based-containing representations of real-world systems that generate actionable recommendations based on multiple choice alternatives. Last, it should facilitate creativity and negotiation-enabling multiple stakeholders to collaborate and explore "what-if" questions easily and identify "best-compromise" solutions quickly. Evidence to support this theory of public DSS comes from current projects on increasing citizen participation in initiatives to reduce energy use, policy design for senior services provision, and choosing new neighborhoods for use of tenant-based housing subsidies.
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