Week 1 Discussion

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1.3.1 The Availability of Big and Open Linked Data (BOLD)

Policy-making heavily depends on data about existing policies and situations to make decisions. Both public and private organizations are opening their data for use by others. Although information could be requested for in the past, governments have changed their strategy toward actively publishing open data in formats that are readily and easily accessible (for example, European Commission 2003; Obama 2009). Multiple perspectives are needed to make use of and stimulate new practices based on open data (Zuiderwijk et al. 2014). New applications and innovations can be based solely on open data, but often-open data are enriched with data from other sources. As data can be generated and provided in huge amounts, specific needs for processing, curation, linking, visualization, and maintenance appear. The latter is often denoted with big data in which the value is generated by combining different datasets (Janssen et al. 2014). Current advances in processing power and memory allows for the processing of a huge amount of data. BOLD allows for analyzing policies and the use of these data in models to better predict the effect of newpolicies.

1.3.2 Rise of Hybrid Simulation Approaches

In policy implementation and execution, many actors are involved and there are a huge number of factors influencing the outcomes; this complicates the prediction of the policy outcomes. Simulation models are capable of capturing the interdependencies between the many factors and can include stochastic elements to deal with

the variations and uncertainties. Simulation is often used in policy-making as an instrument to gain insight in the impact of possible policies which often result in new ideas for policies. Simulation allows decision-makers to understand the essence of a policy, to identify opportunities for change, and to evaluate the effect of proposed changes in key performance indicators (Banks 1998; Law and Kelton 1991). Simulation heavily depends on data and as such can benefit from big and open data. Simulation models should capture the essential aspects of reality. Simulation models do not rely heavily on mathematical abstraction and are therefore suitable for modeling complex systems (Pidd 1992). Already the development of a model can raise discussions about what to include and what factors are of influence, in this way contributing to a better understanding of the situation at hand. Furthermore,

experimentation using models allows one to investigate different settings and the influence of different scenarios in time on the policy outcomes.

The effects of policies are hard to predict and dealing with uncertainty is a key aspect in policy modeling. Statistical representation of real-world uncertainties is an integral part of simulation models (Law and Kelton 1991). The dynamics associated with many factors affecting policy-making, the complexity associated with the interdependencies between individual parts, and the stochastic elements associated with the randomness and unpredictable behavior of transactions complicates the simulations. Computer simulations for examining, explaining, and predicting social processes and relationships as well as measuring the possible impact of policies has become an important part of policy-making. Traditional models are not able to address all aspects of complex policy interactions, which indicates the need for the development of hybrid simulation models consisting of a combinatory set of models built on different modeling theories (Koliba and Zia 2012). In policy-making it can be that multiple models are developed, but it is also possible to combine various types of simulation in a single model. For this purpose agent-based modeling and simulation approaches can be used as these allow for combining different type of models in a single simulation.

1.3.3 Ubiquitous User Engagement

Efforts to design public policies are confronted with considerable complexity, in which (1) a large number of potentially relevant factors needs to be considered, (2) a vast amount of data needs to be processed, (3) a large degree of uncertainty may exist, and (4) rapidly changing circumstances need to be dealt with. Utilizing computational methods and various types of simulation and modeling methods is often key to solving these kinds of problems (Koliba and Zia 2012). The open data and social media movements are making large quantities of newdata available. At the same time enhancements in computational power have expanded the repertoire of instruments

and tools available for studying dynamic systems and their interdependencies. In addition, sophisticated techniques for data gathering, visualization, and analysis have expanded our ability to understand, display, and disseminate complex, temporal, and spatial information to diverse audiences. These problems can only be addressed from

a complexity science perspective and with a multitude of views and contributions from different disciplines. Insights and methods of complexity science should be applied to assist policy-makers as they tackle societal problems in policy areas such as environmental protection, economics, energy, security, or public safety and health.

This demands user involvement which is supported by visualization techniques and which can be actively involved by employing (serious) games. These methods can show what hypothetically will happen when certain policies are implemented.