SYSTEM DYNAMICS MODELING AND SIMULATION OF FOOD SUPPLY FOR MPANDA DISTRICT IN TANZANIA

The Mpanda District in the Rukwa region is one of many areas in Tanzania which for a long time has faced food supply related problems. This paper has analyzed the problem of food supply for Mpanda district in by using system dynamics modeling and simulation analysis methodology. The basic methods and definitions of system dynamics were developed by J. W. Forrester. The main objective, original point and contribution of this study is to introduce the application and use of system dynamics modeling and simulation approach in Tanzania and Africa. To implement the application and use, this study has developed a food supply-analytical tool for Mpanda district, Tanzania. This is an essential component in the programmatic efforts for solving food supply and agricultural production problems to develop an integrated decision support program to assist policy analyses, policy makers, decision makers, regional resource allocation for food supply and agricultural management in the Mpanda district and Tanzania in general. This research facilitates better understanding of the food supply and agricultural production in Mpanda district and other areas in Tanzania. These areas are likely to be impacted in the short and long term by family farm decisions about agricultural production methods. The system dynamics approach is a new methodology for food supply in Tanzania. Most research studies have used other analytical methods, such as econometric and optimization models. Unfortunately the research problems analyzed by econometric and optimization models remain unsolved and offer no promising sustainable solutions. Due to limitations and the merits of system dynamics as compared with other analytical methods, using system dynamics to analyze the food supply problem is a wise choice. For this analysis, the Mpanda system dynamics food supply model (MSDFSM) was developed and simulated. Factors, such as population, agricultural production, and land resources, were modeled and simulated using Vensim, system dynamics computer software, to demonstrate implications through interactions and causative effect of the variables in this study. The conclusions and summary are the results of six cases, divided into three cases of analysis of the Mpanda system dynamics food supply model and the results of three cases of marginal returns of labor and land resources. This study provides a comprehensive analysis of system dynamics modeling and simulation for the food supply in Mpanda district and concludes with policy recommendations beneficial to both policy and decision makers

Using system dynamics modelling to assess the economic efficiency of innovations in the public sector - a systematic review

© 2022 Jadeja et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.Background: Decision-makers for public policy are increasingly utilising systems approaches such as system dynamics (SD) modelling, which test alternative interventions or policies for their potential impact while accounting for complexity. These approaches, however, have not consistently included an economic efficiency analysis dimension. This systematic review aims to examine how, and in what ways, system dynamics modelling approaches incorporate economic efficiency analyses to inform decision-making on innovations (improvements in products, services, or processes) in the public sector, with a particular interest in health. Methods and findings: Relevant studies (n = 29) were identified through a systematic search and screening of four electronic databases and backward citation search, and analysed for key characteristics and themes related to the analytical methods applied. Economic efficiency analysis approaches within SD broadly fell into two categories: as embedded sub-models or as cost calculations based on the outputs of the SD model. Embdedded sub-models within a dynamic SD framework can reveal a clear allocation of costs and benefits to periods of time, whereas cost calculations based on the SD model outputs can be useful for high-level resource allocation decisions. Conclusions: This systematic review reveals that SD modelling is not currently used to its full potential to evaluate the technical or allocative efficiency of public sector innovations, particularly in health. The limited reporting on the experience or methodological challenges of applying allocated efficiency analyses with SD, particularly with dynamic embedded models, hampers common learning lessons to draw from and build on. Further application and comprehensive reporting of this approach would be welcome to develop the methodology further.Peer reviewedFinal Published versio

Measuring Sustainable Development: The Use of Computable General Equilibrium Models

This paper advocates computable general equilibrium models as a methodological tool that is particularly suitable for measuring the impacts of policy interference on the three dimensions of sustainable development, i.e. environmental quality, economic performance (gross efficiency) and equity. These dimensions are inherently intertwined and subject to trade-offs. Computable general equilibrium models can incorporate various important sustainable development indicators in a single consistent framework and allow for a systematic quantitative trade-off analysis. --computable general equilibrium modeling (CGE),sustainability impact assessment (SIA),sustainable development (SD)

New directions in the analysis of inequality and poverty

Over the last four decades, academic and wider public interest in inequality and poverty has grown substantially. In this paper we address the question: what have been the major new directions in the analysis of inequality and poverty over the last thirty to forty years? We draw attention to developments under seven headings: changes in the extent of inequality and poverty, changes in the policy environment, increased scrutiny of the concepts of ‘poverty’ and inequality’ and the rise of multidimensional approaches, the use of longitudinal perspectives, an increase in availability of and access to data, developments in analytical methods of measurement, and developments in modelling

Controlling complex policy problems: a multimethodological approach using system dynamics and network controllability

Notwithstanding the usefulness of system dynamics in analyzing complex policy problems, policy design is far from straightforward and in many instances trial-and-error driven. To address this challenge, we propose to combine system dynamics with network controllability, an emerging field in network science, to facilitate the detection of effective leverage points in system dynamics models and thus to support the design of influential policies. We illustrate our approach by analyzing a classic system dynamics model: the World Dynamics model. We show that it is enough to control only 53% of the variables to steer the entire system to an arbitrary final state. We further rank all variables according to their importance in controlling the system and we validate our approach by showing that high ranked variables have a significantly larger impact on the system behavior compared to low ranked variables

Which is more appropriate: a multi-perspective comparison between systems dynamics and discrete event simulation

System Dynamics (SD) and Discrete Event Simulation (DES) are two established simulation tech-niques for simulating the dynamics of a system. Both have been widely used in modelling business de-cisions. This paper presents meta-comparison between the two approaches based on literature survey. Upon reviewing the existing literature it has been identified that existing comparisons could be classi-fied under three main perspectives: Systems perspective, Problems perspective and Methodology per-spective. The nature of system and nature of problem have been argued as primary factors for decid-ing modelling methodology. Therefore SD and DES comparisons have been classified on the basis of systems, problems and inherent aspects and capabilities of both modelling methods. It has been ar-gued that development of sound models need fit between system, problem and methodology. The suc-cess of model depends on it’s technical soundness as well as it’s successful implementation. In order to develop successful models this vision has been further extended to incorporate stakeholders, re-sources and time

Using system dynamics modelling to assess the economic efficiency of innovations in the public sector - a systematic review.

BackgroundDecision-makers for public policy are increasingly utilising systems approaches such as system dynamics (SD) modelling, which test alternative interventions or policies for their potential impact while accounting for complexity. These approaches, however, have not consistently included an economic efficiency analysis dimension. This systematic review aims to examine how, and in what ways, system dynamics modelling approaches incorporate economic efficiency analyses to inform decision-making on innovations (improvements in products, services, or processes) in the public sector, with a particular interest in health.Methods and findingsRelevant studies (n = 29) were identified through a systematic search and screening of four electronic databases and backward citation search, and analysed for key characteristics and themes related to the analytical methods applied. Economic efficiency analysis approaches within SD broadly fell into two categories: as embedded sub-models or as cost calculations based on the outputs of the SD model. Embdedded sub-models within a dynamic SD framework can reveal a clear allocation of costs and benefits to periods of time, whereas cost calculations based on the SD model outputs can be useful for high-level resource allocation decisions.ConclusionsThis systematic review reveals that SD modelling is not currently used to its full potential to evaluate the technical or allocative efficiency of public sector innovations, particularly in health. The limited reporting on the experience or methodological challenges of applying allocated efficiency analyses with SD, particularly with dynamic embedded models, hampers common learning lessons to draw from and build on. Further application and comprehensive reporting of this approach would be welcome to develop the methodology further