In this paper we present a work process with associated operational research modeling and analysis tools for the policy formulation stage of the Lindblom policy cycle process model. The approach exploits the use of causal maps for problem structuring and scenario generation of policy options together with decision analysis for evaluating generated scenarios taking preferences of decision makers and stakeholders into account. The benefits of interest when exploiting this integrated modeling approach is to enable for; (i) problem structuring and facilitating understanding and communication of a complex policy problem, (ii) simulation of policy consequences and identification of a smaller set of policy options from a possible very large set of possible options, and (iii) structured decision evaluation of the generated alternative policy options.

The ultimate objective of studying, modeling and analyzing policy problems is to incorporate the newest management technologies in the public policy decision-making in a meaningful and practically feasible way that adds significant value to the process. Simulation techniques can support the policy decision process by allowing empirical evaluation of the system dynamics present in the policy situation at hand. This paper presents a decision support simulation model for the European Union (EU) Climate and Energy targets 2030 as a case study of public policy decision making on the EU level. The simulation model is based on the problem structuring or framing by derivation of a system dynamics model from verbal descriptions of the problem, the graphical representation and analysis of change scenarios using the ‘Causal Mapping and Situation Formulation’ method. This approach supports the analysis of qualitative and quantitative information in order to facilitate both the conceptualization and formulation stages of the system modeling process. The resulting model, which is simply a topology of quantified causal dependencies among the problem key variables, can be used to simulate the transfer of change. The aim of simulation herein is to apply cognitive strategic thinking and scenario-based planning in a public policy problem situation in order to design alternative options and provide foresight or ex-ante impact assessment in terms of economic, social, environmental and other impacts.

Policy making situations are real-world problems that exhibit complexity in that they are composed of many interrelated problems and issues. To be effective, policies must holistically address the complexity of the situation rather than propose solutions to single problems. Formulating and understanding the situation and its complex dynamics, therefore, is a key to finding holistic solutions. Analysis of text based information on the policy problem, using Natural Language Processing (NLP) and Text analysis techniques, can support modelling of public policy problem situations in a more objective way based on domain experts’ knowledge and scientific evidence. The objective behind this study is to support modelling of public policy problem situations, using text analysis of verbal descriptions of the problem. We propose a formal methodology for analysis of qualitative data from multiple information sources on a policy problem to construct a causal diagram of the problem. The analysis process aims at identifying key variables, linking them by cause-effect relationships and mapping that structure into a graphical representation that is adequate for designing action alternatives, i.e., policy options. This study describes the outline of an algorithm used to automate the initial step of a larger methodological approach, which is so far done manually. In this initial step, inferences about key variables and their interrelationships are extracted from textual data to support a better problem structuring. A small prototype for this step is also presented.