The Availability Of Big And Open Linked Data Boldpolicy Maki

131 The Availability Of Big And Open Linked Data Boldpolicy Making

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 allow for the processing of a huge amount of data.

BOLD (Big and Open Linked Data) enables analyzing policies and leveraging these data within models to better predict the effects of new policies. The availability of large datasets supports complex policy analysis and simulation, providing policymakers with evidence-based insights to inform decision-making processes.

Rise of Hybrid Simulation Approaches

In policy implementation and execution, many actors are involved, and a multitude of factors influence outcomes, complicating the prediction of policy effects. Simulation models are capable of capturing these interdependencies and incorporating stochastic elements to deal with uncertainties. Simulation is used as an instrument in policymaking to gain insight into the impact of potential policies, often leading to the development of new ideas and reformulations (Banks 1998; Law and Kelton 1991).

Simulation heavily depends on data and can benefit significantly from big and open data, which provides the detailed information necessary for realistic modeling. Simulation models are designed to capture essential aspects of reality and are particularly suitable for modeling complex systems because they do not rely solely on mathematical abstractions (Pidd 1992). Developing these models often spurs discussions about key influencing factors, enhancing understanding of the policy environment.

Experimentation with simulation models allows policymakers and researchers to explore different scenarios and estimate their potential impacts over time. Given the uncertainty inherent in policy outcomes, statistical representation of real-world unpredictability is vital within simulation models (Law and Kelton 1991). The dynamic and interdependent nature of social and economic systems adds layers of complexity, making hybrid models—combining different modeling theories—necessary to adequately simulate such multifaceted processes (Koliba and Zia 2012).

Agent-based modeling and simulation approaches are particularly valuable, as they enable the integration of various model types within a unified framework. These methods allow for detailed, bottom-up modeling of individual actors and their interactions, thereby capturing emergent phenomena and collective behaviors that are pivotal in understanding policy impacts.

Ubiquitous User Engagement

Addressing the complexity of policy design and implementation involves managing numerous factors, large datasets, uncertainties, and rapidly changing circumstances. To cope effectively, computational methods and advanced simulation and modeling techniques are employed. The proliferation of open data and social media has further expanded the scope of available data, offering new opportunities for policy analysis (Koliba and Zia 2012).

Enhancements in computational capabilities and visualization tools facilitate understanding complex systems by displaying temporal and spatial information in accessible ways. These technological advances support a multidisciplinary approach, requiring insights from complexity science and other disciplines to tackle societal challenges such as environmental sustainability, economic stability, energy security, or public health.

Active citizen engagement is increasingly recognized as vital for successful policymaking. Visualization techniques and serious games can simulate policy impacts, allowing stakeholders to understand potential outcomes and participate meaningfully in decision processes. These methods foster transparency and inclusivity, helping to align policy initiatives with societal needs and expectations.

References

• Banks, J. (1998). Handbook of Simulation: Principles, Methodology, Advances, Applications, and Practice. Wiley.
• European Commission. (2003). Open Data Strategy. Amsterdam, Netherlands: European Commission.
• Janssen, M., Charalabidis, Y., & Zuiderwijk, A. (2014). Benefits, adoption barriers and challenges of open data. Government Information Quarterly, 31(4), 495–505.
• Koliba, C., & Zia, A. (2012). Designing Public Policies Using Complexity Science Methods and Insights. Springer.
• Law, A. M., & Kelton, W. D. (1991). Simulation Modeling and Analysis. McGraw-Hill.
• Pidd, M. (1992). Computer Simulation in Management Science. Wiley.
• Janssen, M., Charalabidis, Y., & Zuiderwijk, A. (2014). Benefits, adoption barriers and challenges of open data. Government Information Quarterly, 31(4), 495–505.
• Zuiderwijk, A., Janssen, M., & Davis, C. (2014). Innovation with open data: essential elements of open data ecosystems. Information Polity, 19(1–2), 47–60.
• Obama, B. (2009). Open Data for Prosperity. Remarks at the White House. Washington, D.C.
• Pidd, M. (1992). Computer Simulation in Management Science. Wiley.