Policy Simulation Of Seaweed Aquaculture Development In Kupang Regency, East Nusa Tenggara Province By Household Economics Approach

This research to analyze alternative policies could be done to improve the income of seaweed farmers. The method is survey. Analysis of the data used simulation analysis policy. From the results, alternative policies could be done: a). rising labor costs 15%, the addition of one year of experience and the addition of long talus 15%, b). the addition of one year of experience, the addition of one year of education, and increasing the length of the talus 15%, c). rising labor costs 15%, the addition of one year of experience and the addition of one year of education, d). rising labor costs 15% and improving sanitation 5%, and e). the addition of one year of experience, the addition of one year of education and the addition of 5% level of cleanliness. The suggestions: a). to take the policy in a way to rising labor costs 15%, the addition of one year of experience and a 15% increase in length talus together, b). seaweed farmers need to be tested first, in order to determine the level of success of the policy alternatives, c). in the future the policy makers can simulate their own policy by using existing software

Policy Advice Derived from Simulation Models

When advising policy we face the fundamental problem that economic processes are uncertain. Consequently, policy can err. In this paper we show how the use of simulation models can reduce policy errors by inferring empirically reliable and meaningful statements about economic processes. We suggest that policy is best based on so-called abductive simulation models, which help to better understand how policy measures can influence economic processes. We show that abductive simulation models use a combination of theoretical and empirical analysis based on different data sets. By way of example we show what policy can learn with the help of abductive simulation models, namely how policy measures can influence the emergence of a regional cluster.Policy Advice, Simulation Models, Uncertainty, Methodology

Policy Advice Derived From Simulation Models

When advising policy we face the fundamental problem that economic processes are connected with uncertainty and thus policy can err. In this paper we show how the use of simulation models can reduce policy errors. We suggest that policy is best based on so-called abductive simulation models, which help to better understand how policy measures can influence economic processes. We show that abductive simulation models use a combination of theoretical and empirical analysis based on different data sets. This helps inferring empirically reliable and meaningful statements about how policy measures influence economic processes. By way of example we show how research subsidies by the government influence the likelihood that a regional cluster emerges.Policy Advice, Simulation Models, Uncertainty, Methodology

Decision makers\u27 experience of participatory dynamic simulation modelling: Methods for public health policy

Background: Systems science methods such as dynamic simulation modelling are well suited to address questions about public health policy as they consider the complexity, context and dynamic nature of system-wide behaviours. Advances in technology have led to increased accessibility and interest in systems methods to address complex health policy issues. However, the involvement of policy decision makers in health-related simulation model development has been lacking. Where end-users have been included, there has been limited examination of their experience of the participatory modelling process and their views about the utility of the findings. This paper reports the experience of end-user decision makers, including senior public health policy makers and health service providers, who participated in three participatory simulation modelling for health policy case studies (alcohol related harm, childhood obesity prevention, diabetes in pregnancy), and their perceptions of the value and efficacy of this method in an applied health sector context. Methods: Semi-structured interviews were conducted with end-user participants from three participatory simulation modelling case studies in Australian real-world policy settings. Interviewees were employees of government agencies with jurisdiction over policy and program decisions and were purposively selected to include perspectives at different stages of model development. Results: The ‘co-production’ aspect of the participatory approach was highly valued. It was reported as an essential component of building understanding of the modelling process, and thus trust in the model and its outputs as a decision-support tool. The unique benefits of simulation modelling included its capacity to explore interactions of risk factors and combined interventions, and the impact of scaling up interventions. Participants also valued simulating new interventions prior to implementation in the real world, and the comprehensive mapping of evidence and its gaps to prioritise future research. The participatory aspect of simulation modelling was time and resource intensive and therefore most suited to high priority complex topics with contested options for intervening. Conclusion: These findings highlight the value of a participatory approach to dynamic simulation modelling to support its utility in applied health policy settings

Hierarchical Knowledge-Gradient for Sequential Sampling

We consider the problem of selecting the best of a finite but very large set of alternatives. Each alternative may be characterized by a multi-dimensional vector and has independent normal rewards. This problem arises in various settings such as (i) ranking and selection, (ii) simulation optimization where the unknown mean of each alternative is estimated with stochastic simulation output, and (iii) approximate dynamic programming where we need to estimate values based on Monte-Carlo simulation. We use a Bayesian probability model for the unknown reward of each alternative and follow a fully sequential sampling policy called the knowledge-gradient policy. This policy myopically optimizes the expected increment in the value of sampling information in each time period. Because the number of alternatives is large, we propose a hierarchical aggregation technique that uses the common features shared by alternatives to learn about many alternatives from even a single measurement, thus greatly reducing the measurement effort required. We demonstrate how this hierarchical knowledge-gradient policy can be applied to efficiently maximize a continuous function and prove that this policy finds a globally optimal alternative in the limit

What are the consequences of the AWG-projections for the adequacy of social security pensions? ENEPRI Research Report No. 65, 16 January 2009

This paper starts by describing the model MIDAS in detail. It next presents and discusses some simulation results for Belgium, Germany and Italy. Finally, the simulation results of two alternative policy scenarios are presented and discussed