Governments are currently confronting major challenges such as financial, economic and spending crises, impending resource shortages, migratory pressures and climate change. At the same time, policymakers are required to be more inclusive and to co-create policy with stakeholders. Policies must address the complexity of a situation in a holistic way, rather than propose solutions to single problems. Therefore, formulating and understanding the situation and its complex dynamics is key in finding holistic solutions. This indicates a systems approach to policy analysis that is integrative and inter-subjective. There is a need to develop robust, intuitive decision support tools that provide an environment for model-based collaborative governance. These tools should enable multiple policy actors to: (i) build and share a mental model of the problem under analysis; (ii) design policy options, each structured as a particular combination of various types of policy instruments, and assess the likely impacts of a policy option in terms of the economic, financial, social, environmental and other impacts; and (iii) conduct an in-depth evaluation of competing policy options, taking into account the preferences of policymakers and stakeholder groups. A new policy-oriented quantitative problem structuring method that is facilitated by decision support system (DSS) technologies is a novel research opportunity that was explored in the SENSE4US research project, which designed and demonstrated systems modelling, simulation and decision analysis tools for prescriptive policy analysis. This paper reports on the results of that research project and provides a reflection on the contributions of this research to theory and practice and what areas further research can take place in the future.

Resilience of interdependent infrastructures increasingly depends on collaborative responses from actors with diverse backgrounds that may not be familiar with cascade effects into areas beyond their own sector. A simulation-game can enable societal actors to obtain a deeper understanding of the interdependencies between their infrastructures and their respective crisis responses. Following a design science approach, a simulation-game has been developed that combines role-playing simulation and computer simulation. The simulation-game challenges participants to address the interaction between payment disruptions, food and fuel supply, security problems (riots, robberies) and communication challenges (preventing hoarding). A number of crucial design choices were handled while developing the simulation-game. The main design challenges were: How to validate an unthinkable escalation scenario?; How to give the simulation a sufficient level of detail on all aspects and keep the complexity graspable so it can be played instantly?; and How much time should each playing round take?

The ultimate aim of support for public policy decision making is to develop ways of facilitating policymaking that can create policies that are consistent with the preferences of policymakers and stakeholders (such as an increase in economic growth, the reduction of social inequalities, and improvements to the environment), and that are at the same time based on the available knowledge and evidential information.

Using the design science research methodology, an iterative design process was followed to build and evaluate a research artefact in the form of an analytical method that is also operationalised as a decision support system (DSS) – in order to facilitate the problem analysis, the impact assessment and the decision evaluation activities carried out at the policy formulation stage of the policymaking process. The DSS provides a web-based, user-friendly interface for two main software modules: (i) a tool for modelling and simulation of policy scenarios; and (ii) a tool for multi-criteria evaluation of policy decisions. The target end-users of the DSS tools are policymakers, the support staff of politicians, policy analysts and researchers within governmental departments and parliaments at the various institutional levels of the European Union.

The proposed model-based decision support approach integrates systems thinking, problem structuring methods and multi-criteria decision analysis, in what can be described as a ‘sense-making’ approach. A new policy-oriented quantitative problem structuring method is introduced in this research, the ‘labelled causal mapping’ method, which aims to reduce the cognitive overload involved in representing complex mental models using system dynamics simulation modelling in order to facilitate knowledge representation and system analysis. One contribution of this work is an object-oriented implementation of a prototype tool for systems modelling and simulation of policy decision situations based on the labelled causal mapping method. The method provides a basis for further computational decision analysis. We proposed criteria models and data formats for common (generic) policy appraisal, and a preference elicitation method for in-depth decision evaluation based on the results of scenario simulation and the preferences of decision makers and stakeholder groups.

The artefact evaluation clarifies how well the proposed approach and the DSS tool prototype support a solution to the problem and the extent to which the outcomes in two policy analysis use cases are useful in terms of output analysis and knowledge synthesis.

The contributions of this research to theory and practice were articulated based on the design knowledge obtained through an iterative design process, notably the emergence of the concepts of transparency and intelligibility in policymodelling, (i.e., the need for explicit and interpretable models that can provide justification of a specific decision).

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.

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.

We provide an agent based simulation model of the Swedish payment system. The simulation model is to be used to analyze the consequences of loss of functionality, or disruptions of the payment system for the food and fuel supply chains as well as the bank sector. We propose a gaming simulation approach, using a computer based role playing game, to explore the collaborative responses from the key actors, in order to evoke and facilitate collective resilience.

The elicitation and processing of relevant information is the core of any policy decision-making process. Modelling is about making sense of the available information. Models are able to incorporate the contextual influences on policy making (e.g. political and economic environments, community sentiment…etc). 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.

In an effort to enable adoption of the systems thinking approach to address the central problem of empirical political study, this thesis presents a framework for prescriptive policy analysis that provides decision support to: the problem definition, ex-ante impact assessment and evaluation activities carried out at the policy formulation stage of the policymaking process.

We contribute a new tool for systemic modelling and simulation of public policy decision situations. It aims to facilitate the cognitive activity of representing complex mental models using system dynamics simulation modelling. Using the ’labelled causal mapping’ method, a policy-oriented problem structuring method introduced in this research, the tool bridges the gap between the user’s mental model and the explicit graphical representation in order to enable knowledge representation and system analysis. The method provides a basis for further computational decision analysis using a common policy appraisal format, a multi-criteria model with main evaluation criteria (effectiveness, efficiency, relevance, coherence and added value), linked to a set of measurable, context dependent attributes (targeted impact variables from the policy model).

A web-based tool prototype has been implemented in a Node.js environment and is accessible both from a web-based graphical user interface as well as a hosted API.  Multiple demonstration and test cases, from various policy areas and different EU policymaking levels, were used in several iterations of the build-evaluate cycle. This approach lead to the different studies that make up this research.

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.

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.

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.