Predictive modelling, complexity analysis and assessment barometer for gender-based violence evaluation : a system dynamics approach

Abstract

Gender-Based Violence (GBV) is a social problem that has grown in magnitude, severity and complexity in recent times. Social, human systems like GBV presents unique challenges that span across various disciplinary boundaries, hence making it difficult to proffer a sustainable solution. Currently, there are a very limited number of engineering-based solutions available in this problem space while most solutions being proffered by the social science disciplines are mostly human centred with little or no holistic considerations or systems thinking and dynamics. This makes it difficult to replicate such solutions in the diverse human societies. It is on this ground that this research has proposed a holistic and integrated engineering-based solution, premised on systems thinking principles, to address GBV as a complex social problem. Thus far, a need currently exists for a more reliable and comprehensive solution mechanism in order to significantly minimise the occurrence of GBV and its conglomerative effects on individuals and the society at large. The specific objectives addressed in this research include: the development of a holistic and integrated network of GBV driving factors with the aid of systems thinking; prioritisation and quantification of the identified GBV driving factors for further objective analysis; modelling of the complexity of the GBV problem for proper management guidance towards the provision of effective solutions and lastly, adaptation of the GBV solution mechanism to an interactive dashboard to serve as a measurement barometer capable of assessing and estimating the likelihood of GBV occurrence between any two interactors of the opposite genders. This research has utilised a case study research methodology in combination with different engineering-based problem-solving mechanisms premised on the Hybrid Structural Interactive Matrix (HSIM) for prioritisation of the system drivers while Systems Thinking and Systems Dynamics principles were utilised towards gaining holistic understanding of the depth of interaction amongst the diverse driving factors and how the system behaviour changes over time. In a mostly data-less system with qualitative centred driving factors, the quantification and analysis of the system was made feasible through the generation of weights emanating from the prioritisation process of the driving factors. The weights as applied in this research are symbolic and were used to understand the criticality status of the driving factors and their corresponding analysis. Based on the symbolic quantification per driving factor as generated in the prioritised weights, complexity analysis was carried out on the GBV system of factors by deploying the spider diagram approach effected on three separate iterations. Different levels of complexity were arrived at viz 95.2748%, 95.2341% and 95.2662% respectively for iterations 1, 2 and 3. The implication herein is that the operational dimension of these factors from a holistic point of view poses a high level of complexity hence requiring a significant management effort. Furthermore, by varying the weights of selected factors, sensitivity analysis was conducted using the system dynamics methodology. However, this was preceded via the development of a comprehensive causal loop diagram premised on the system drivers as a measure towards understanding the intricacy of interactions per driving factor. This was followed by the stock and flow diagram development for dynamic simulation over a horizon period of 100 months. Different intervention measures were proposed in respect of managing the top driving factors on the hierarchy diagram which are considered to be critical to the GBV system. These factors in a descending order include: gender equality, education equality, income generation opportunities, pandemic situations, population density related problem and lastly health and health care driving factor. The six most prioritised factors were considered as the KPIs for the GBV system. These were utilised to develop a simple GBV barometer for assessment of the likelihood of GBV occurrence for both victims and perpetrators. Three case scenarios were hypothetically explored and validated to ascertain the functionability of the GBV system. The output from the system was categorised into such responses as low, moderate, medium and high likelihood of GBV perpetration. In a nutshell, this research has provided a comprehensive objective architecture for GBV analysis, evaluation and mitigation. It is hoped that with the availability of datasets for specific drivers, more simulation-based studies can be conducted for an enhanced GBV management process.