A Systems Approach to Modeling Corruption: Bridging the Disciplinary Divide Between the Social and Technical Sciences

Corruption is considered one of, if not the, largest issue hindering progress towards sustainable development and ultimately the global eradication of poverty. Corruption decreases the amount of aid that reaches its intending... [ view full abstract ]

Corruption is considered one of, if not the, largest issue hindering progress towards sustainable development and ultimately the global eradication of poverty. Corruption decreases the amount of aid that reaches its intending destination for those in need. It also reduces the efficiency of international aid thereby undermining its validity. Corruption depresses the incomes and overall GDP of many countries in Africa, especially those reliant on aid with rich deposits of natural resources. This paper focuses on the effects of corruption on natural resource governance. Specifically, the paper addresses the so called “natural resource curse” as a guiding framework, which is an especially problematic paradox in African countries. The premise is that an abundance of natural resources leads to more corruption, thereby to less economic growth, less democratic government, and lower levels of development, as measured by the Human Development Index, than countries with less natural resources endowments. A novel systems modeling methodology combining civil systems engineering, system dynamics, and political science is presented to analyze the interactions of corruption with the environment in the context of the natural resource curse in African countries. Until now, few systems approaches have been proposed to analyze different acts of corruption (rentier effect, repression effect, and modernization effect) in any fashion, and the ones that do rely entirely on perception-based data, namely the Transparency International’s Corruption Perception Index. The systems-based methodology presented herein is compatible with the social sciences and directly utilizes peer-reviewed literature as a tool to build models. In doing so, we advocate more collaboration and transdisciplinary research among technical scientists and engineers, system dynamicists, and social scientists (especially political scientists to address corruption issues and complex issues more broadly). New system-level understanding of corruption can be extracted from methods such as the one proposed and presented herein. To demonstrate this capability, a qualitative system dynamic stock-and-flow diagram is created where each variable and interaction is sourced to at least one peer-reviewed, social science article that can greatly increase model validation and confidence in accuracy; it depicts the world as it is, not as the modelers thinks it is. The proposed methodology can effectively aggregate the relevant data and literature into a high-level model, yielding significant insight and value to decision makers and those working with complex social and political issues.