Research Paper
nareshr
usingsimulationsforpolicymaking1.docxRunning Head: USING SIMULATIONS FOR POLICY MAKING 1
USING SIMULATIONS FOR POLICY MAKING 6
Using Simulations for Policy Making
Name
Institution Affiliation
Atkinson, J., Page, A., Wells, R., Milat, A., & Wilson, A. (2015). A modelling tool for policy analysis to support the design of efficient and effective policy responses for complex public health problems. Implementation Science, 10(1). doi: 10.1186/s13012-015-0221-5
This article explores the application of simulation models in the health sector and affirms that simulation models utilize the effect of contextual influences on policymaking. As such, successful simulation and positive results from the same hasten policy adoption as well as the implementation of changes in the health sector and health sector. In an unexpected twist, the article then explains that analytic tools are not sufficient to support evidence-informed decisions in instances where complex problems are involved. Further, it offers an alternative tool that analyzes policy, integrates that evidence gathered, and offers a platform to test alternatives in a bid to design efficient solutions. Importantly, the authors point out that simulation modeling tools could be improved to guarantee better-informed policy responses. As such, further research in this direction could be used to examine ways in which simulation models could be improved to be better positioned in policymaking.
Carlson, J., Alderson, D., Stromberg, S., Bassett, D., Craparo, E., Guiterrez-Villarreal, F., & Otani, T. (2014). Measuring and Modeling Behavioral Decision Dynamics in Collective Evacuation. Plos ONE, 9(2), e87380. doi: 10.1371/journal.pone.0087380
This paper looks at how social networking technologies influence human behavior and how the said networking technologies have influenced the development of associated applications. As such, the paper attempts to link simulation studies and empirical observations in widespread networks by conducting an experiment that involves information, dissemination, and sharing of the information on social networks, and implications on individual decision-making dynamics. The data gathered in the experiment is then simulated and it complements that of experimental observations. The authors, however, point out that similar decision models could produce different outputs. Additionally, they reveal that simulation studies sometimes extrapolate decision-making strategies to larger ones and still maintain their accuracy. Further, the authors explain that the distribution of risk for the study was dependent on demographics. It is therefore imperative for researchers to consider looking at ways in which they could control human behavior in one way or another to guarantee effective simulation and consequently give desired results for informed decision-making.
Freebairn, L., Atkinson, J., Kelly, P., McDonnell, G., & Rychetnik, L. (2018). Decision makers’ experience of participatory dynamic simulation modelling: methods for public health policy. BMC Medical Informatics And Decision Making, 18(1). doi: 10.1186/s12911-018-0707-6
The authors argue that technological advances, accessibility to the technology, and interest in the same have contributed greatly to the resolution of complex health policy matters. This is because they ensure the articulation of informed decisions which are crucial in health policies. An important part of the simulation is the participatory process by individuals which is contentious. Further, the authors recommend that the assessment of the value and application of the methods and tools for simulation should be done to assess if their adoption supports evidence and consequently informed policy and planning. The authors stress that the experience of end-users in health-related simulation models have been set back by the lack of scrutiny on their experience with participatory modeling. Additionally, there has been the omission of policy decision-makers in non-health simulation models which is detrimental. The authors also noted that stakeholders embrace simulation models that they are aware of; those that they have been involved in informing and grounding. While the participatory process is essential in dynamic simulation models, technology is equally as crucial since it fuels the process.
Orta, E., & Ruiz, M. (2014). A Simulation Approach to Decision Making in IT Service Strategy. The Scientific World Journal. doi: A Simulation Approach to Decision Making in IT Service Strategy
This article explains how technologies and tendencies such as the internet have given rise to web developers who employ different business models in their operations. The article then looks into simulation models and delves into the uses of simulation models as well as the different simulation approaches. Further, the authors explore different strategies for the IT services process and explain the benefits of the same. The conclusion is that the appropriateness of a simulation model is dependent on the nature of the problem being solved. The authors agree that simulation models are used in decision-making and this is supported in the article by the different experiments carried out and explained in the article. This article offers detailed information regarding simulation models, their numerous applications in IT, and how they are used to define and achieve strategic goals. The authors also recommend further research to be done regarding the development of simulation models and extending the functionality of the same. Though the scope of the study is limited to ITIL strategy of IT services, the research is but a canvas to what could be explored in the field of simulation, IT, other strategies for IT policy making and how they achieve their goal.
Wiklund, S. (2019). A modelling framework for improved design and decision-making in drug development. PLOS ONE, 14(8), e0220812. doi: 10.1371/journal.pone.0220812
This article explains that modeling and simulation enhance the decision-making process. The paper discusses two pillars that are critical in improving decision making which are embracing uncertainties and adapting the holistic perspective of developing a program and product lifecycle. The article further explains the algorithms used in the simulation as well as model attributes. Different metrics and factors are twitched and discussed in the article to give the desired result. The authors conclude that the choice of decision is informed by a combination of several factors. Notably, the decision-making is flexible in such a way that it could cater for different situations. The beauty of simulation in decision-making is that it allows for generalization in some cases and one model could be used to make decisions for numerous situations. This assumption should, however, be supported or countered by further research.
Reference
Carlson, J., Alderson, D., Stromberg, S., Bassett, D., Craparo, E., Guiterrez-Villarreal, F., & Otani, T. (2014). Measuring and Modeling Behavioral Decision Dynamics in Collective Evacuation. Plos ONE, 9(2), e87380. doi: 10.1371/journal.pone.0087380
Freebairn, L., Atkinson, J., Kelly, P., McDonnell, G., & Rychetnik, L. (2018). Decision makers’ experience of participatory dynamic simulation modelling: methods for public health policy. BMC Medical Informatics And Decision Making, 18(1). doi: 10.1186/s12911-018-0707-6
Orta, E., & Ruiz, M. (2014). A Simulation Approach to Decision Making in IT Service Strategy. The Scientific World Journal. doi: A Simulation Approach to Decision Making in IT Service Strategy
Wiklund, S. (2019). A modelling framework for improved design and decision-making in drug development. PLOS ONE, 14(8), e0220812. doi: 10.1371/journal.pone.0220812
