Strategic planning models and information provision for decision-making in complex strategic situations are frequent subjects for scientific research. This research deals with the problem of supporting strategic planning decision-making in public higher education (HE) institutions by designing a Decision Support System (DSS) to be used by HE decision makers in implementing their strategic planning process, considering that the DSS would be anchored in on all databases of the institution’s information systems. This paper adopts a model for the strategic planning process, advocates the incorporation of technologies of participation (ToP) and introduces a collaborative framework for the planning activities at the different institutional levels to develop the institution’s strategic plan using a bottom-up approach. Based on the strategic planning process model, a DSS framework is proposed and decision support methods are suggested for the different modules of the DSS. The DSS provides intelligent support (on the individual, group and organizational levels) to strategic planning decisions in all stages of the process. By utilizing this DSS, it is possible to create better conditions for implementing the objectives of the future-oriented activity of the institution.

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.

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.

Systems analysis allows quantitative empirical testing of models that exist in the study of public policy. Simulation and visualisation techniques can help policy makers to reduce uncertainties on the possible impacts of policies. This paper presents a new tool for systems modelling and simulation of policy problems to support the problem analysis, the design of policy options and impact assessment activities. The tool facilitates the cognitive activity of representing complex mental models using system dynamics simulation modelling by defining standards and a procedure for policy modelling. We propose a new policy-oriented problem structuring method (PSM), the ‘labelled causal mapping’. The method supports scenario-based dynamic simulation and provides graphical representations of the involved actors, key variables, control flows and causal dependencies in a policy decision situation. A web-based tool prototype has been implemented in a Node.js environment. Two real policy problems are presented for demonstration of the use of the prototype.

While the simulation-based impact assessment of public policy proposals allows policy makers to identify the feasible policy options and verify their economic, social and environmental impacts, it does not provide the explicit evaluation of policy options. Multi-criteria decision analysis (MCDA) techniques can support an in-depth performance evaluation of policy options taking into account the preferences of decision makers and stakeholders. These preferences reflect acceptable trade-offs of performance among objectives. This study reviews multi-attribute decision-making (MADM) technique and presents a common policy appraisal format using main evaluation criteria linked to a set of measurable, context dependent attributes. We argue for a rank-based approach for eliciting preferences, select a novel method for attribute weight elicitation, and show how it can be integrated within a public policy multi-criteria evaluation framework. A use case for policymaking, ‘Ultra-Low Emission Vehicles (ULEV) Uptake in UK’, is used for demonstration of the proposed approach for policy decision analysis. This approach seeks to couple systems modelling and simulation of policy scenarios with MCDA, stakeholder analysis and preference elicitation. The outputs can further provide analytical insights in controversy/acceptability of policy options, and consequently guide further policy formulation and the design of better options.

Elicitation and processing of the relevant information is the core of any policy decision-making process. This study presents a prescriptive policy analysis for the “fossil-free fuel deployment for public transport buses at the national level in Sweden”, using a policy-oriented modelling and simulation tool, Sense4us , that supports systems analysis for policy involving:: (i) Structuring of policy problems using the labelled causal mapping method [11], (ii) Ex ante Impact assessment using scenario-based dynamic simulation modelling and (iii) Ex-ante evaluation of the considered policy options based on the simulation results and using a set of standard criteria for evaluation of EU policy interventions. The results are most of all useful for designing a policy pathway for public bus transport that is both environmentally sustainable and economically feasible. Moreover, the process allows involvement of the key stakeholders to reflect various priorities and preferences.

A key challenge for policy planning and risk assessment in governmental institutions is to ensure that relevant information, expertise and knowledge are fully integrated and taken into account during the policy process. A systems thinking approach can help gathering and organizing the necessary policy intelligence and involving a diverse range of stakeholders. This study is done as part of a design-science research (DSR) project to build and evaluate a new systems tool for structuring of public policy problems and the design of policy options to be used by policy makers, analysts and researchers in governmental institutions and parliaments. The study aims at evaluation of the tool to establish its utility and efficacy for achieving its stated purpose and assess the end-user benefits. That is performed through application of the tool to analysis of a real public policy problem, Finnish government’s action in response to the rapidly growing numbers of asylum seekers entering Finland in recent years. The resulting simulation model allows designing a pathway for immigration policy that aims to mitigate the resulting problems.