1) Assessing the Quality of Simulations
Simulation is the imitation of the operation that exists in the processes and systems of the real world over a particular period. Simulation requires the development of models which represent the main characteristics and behaviors of the preferred abstract system or processes. The developed models will represent the system itself while the simulation stands for the operation of the system over time (Loonen et al. 2015). Simulation is applicable in different cases like scientific modelling pf natural systems and the simulation of technology for training, education, and video gaming.
The quality of simulations can be assessed and ascertained using different verified methods. These methods vary according to the field and the models developed for simulation. In the case of a learning environment, the quality of simulation can be assessed by comprehending what simulation is intended to achieve. This begins with the initial evaluation of whether simulation precludes the perfect delivery of information cardinal to student performance; this can necessitate the provision of relevant learning environments. The quality of a legislative simulation is assessed by determining the extent of its coverage. A good one might cover the entire legislative process with the learner taking their positions.
The use of qualitative methods which involves an investigation of simulation through observations, interviews, reviewing artefacts, and studying different cases in their natural settings to consider variables the way they appear in the complexity of their context. Such context includes collaborations and local engagements (Susanto 2016). These qualitative methods are deemed inferior in causal inference, but they are made strong by providing models describing the way simulation operate. Combined methods are also efficient since it provides debates on the merits of radically diverse points of view. The quality of simulation is thus assessed by determining the nature and implementation of the simulation.
Loonen, R. C. G. M., Loomans, M. G. L. C., & Hensen, J. L. (2015). Towards predicting the satisfaction with indoor environmental quality in building performance simulation. Proceedings of Healthy Buildings Europe 2015: 18-20 May 2015, Eindhoven, The Netherlands, 1-8.
Susanto, R. (2016, May). Quality and Consistency Check, and PVT Data Tuning Simulation Approach-Case Study of Gas Field. In 78th EAGE Conference and Exhibition 2016.
2) Quality in Social Simulation
The basis to ensure the quality simulation is to have the right target agents (Janssen et al., 2015). Agents are able to provide the right feedback on a product or service they have been using for a period of time. Thus the simulation model should identify specific agents that relate directly to the policy model that is being formulated. For instance, to have the desired accuracy when developing a policy that can evaluate the use of a property in a country of 750,000, it is advisable to directly sample the population to enable the policymakers to have firsthand data.
To make the process viable, the task force team can make use of tools like the INFO-SKIN application, which was designed to help the policymakers evaluate the relationship between the research data and the policy is formulated (Janssen et al., 2015). To ensure that the policy in question represents and can solve the problem of the population, a sufficient number of agents should be selected and the right empirical questions should be asked. At every stage of the simulation process, there should be an analysis of varying data, to gauge the differences and similarities and how they can affect the final model policy. Finally, INFO-SKIN should be used to evaluate all the results if the final data is in sync with the interactions exhibited among various agents.
Janssen, M., Wimmer, M. A., & Deljoo, A. (Eds.). (2015). Policy practice and digital science: Integrating complex systems, social simulation and public administration in policy research (Vol. 10). Springer.
3) Quality of simulations
Quality of simulation defines the standard problems which evaluate the social simulations that are considered towards the user community systems. It explains the policy model that is discussed over the mechanism of the policy systems, and quality simulation techniques describe the information data, and quality modeling techniques are determined over transforming the data. Three different views explain more in detail about the simulation quality that is described as a standard view, constructivist view, and user community view. These views explain the quality simulation will manage the methods to perform policymaking systems (Janssen & Helbig, 2018). I lead a taskforce where I used to develop the strategic planning will be developed, and it is recommended to consider that 750,000 residents of the country. A decision was made towards the development of the strategy, which discussed zoning areas of the county. Dynamic factors include age, race, and income status are also considered, and simulation view helps for the taskforce.
Comparing to the three views for this development of taskforce user community view is the best-suited simulation view to handle it. This view probably identifies the simulation technique that could manage the areas where the taskforce implements the quality assessment. It consists of defining the standard policy that could help better for developing the social simulation will construct the dynamic factors. These factors help the residents to manage the color, race, age, and income status are considered as the key terms to process the context of the zoning board (Janssen, Wimmer & Deljoo, 2015). It ensures the highest simulation view could make changes in the task force that could probably manage the accuracy of the residents. It completely reliable over the data that consider the presentation of the clustering techniques is discussed and the simulation model describes the income that is significant towards the residents. Hence I would implement the user-community view that could probably determine the accuracy of the task force.
Janssen, M & Helbig, N. (2018). Innovating and changing the policy-cycle: Policy-makers be prepared. Government Information Quarterly, 3(5), 99-105. DOI: 10.1016/j.giq.2015.11.009.
Janssen, M., Wimmer, M. A., & Deljoo, A. (Eds.). (2015). Policy practice and digital science: Integrating complex systems, social simulation, and public administration in policy research (Vol. 10). Springer.