Abstract
Abstract With the increased availability of data and the capacity to make sense of these data, computational approaches to analyze, model and simulate public policy evolved toward viable instruments to deliberate, plan, and evaluate them in different areas of application. Such examples include infrastructure, mobility, monetary, or austerity policies, policies on different aspects of societies (health, pandemic, skills, inclusion, etc.). Technological advances along with the evolution of theoretical models and frameworks open valuable opportunities, while at the same time, posing new challenges. The paper investigates the current state of research in the domain and aims at identifying the most pressing areas for future research. This is done through both literature research of policy modeling and the analysis of research and innovation projects that either focus on policy modeling or involve it as a significant component of the research design. In the paper, 16 recent projects involving the keyword policy modeling were analyzed. The majority of projects concern the application of policy modeling to a specific domain or area of interest, while several projects tackled the cross-cutting topics (risk and crisis management). The detailed analysis of the projects led to topics of future research in the domain of policy modeling. Most prominent future research topics in policy modeling include stakeholder involvement approaches, applicability of research results, handling complexity of models, integration of models from different modeling and simulation paradigms and approaches, visualization of simulation results, real-time data processing, and scalability. These aspects require further research to appropriately contribute to further advance the field.
Highlights
Policy modeling can be applied to make decision-making processes during the design, planning, and creation of a policy more intelligent, evidence-based, and agile (Lampathaki et al, 2011)
Methods and technologies for policy modeling Policy modeling is about studying a complex policy field and trying to understand the main influence factors and how these are interacting toward particular policy objectives
If dynamic behavior is applied in microsimulation, microsimulation models can transition to the paradigm of agent-based modeling (Heppenstall et al, 2012, p. 6)
Summary
Policy modeling can be applied to make decision-making processes during the design, planning, and creation of a policy more intelligent, evidence-based, and agile (Lampathaki et al, 2011). The suitability of each article for inclusion in the reference dataset was evaluated by the researchers after perusing the article’s abstract and keywords and a quick review of the article’s full text With this in mind, the paper is structured as follows: Section 2 reviews relevant literature on the different aspects of policy modeling to provide the necessary foundations for the project analysis. The effects of an invention of artificial intelligence (AI) may be difficult to predict based on data, because it is a unique situation that has never happened before This area has been transformed with the increase of computational capacity: the input from the experts can be used to build formal simulation models improving and validating the predictions (Kopec et al, 2010; Stach et al, 2010). Different approaches may be used: from supporting the simulation models with narrative scenarios and visualizations to the creation of platforms for facilitating the feedback to the specific aspects of the model (Scherer et al, 2015; Mureddu, 2019)
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