Corruption in social organizations has been a ubiquitous phenomenon hindering the development at institutional, local, state, and national levels. Bracketing out important moral, philosophical, and cultural discussions on corruption, and with no illusion of providing a complete account, the main focus on quantify the dynamics of illegal human behavior. Systematic procedures to help experts recognize developing patterns of legal and illegal forms of corruption, quantify them, and create adequate representations (analytical frameworks) are generally lacking (e.g., complex systems and game theory models that help us abstract and understand the different types illicit behavior like tax evasion, embezzlement, fraud, bribery, extortion, speed money, collusion and kickbacks on public contracts). Choosing an appropriate formalism to model corruption, based on microeconomics ideas of incentives and information and with considerations of social organizational structures and human dynamics, often poses a significant challenge. Many modeling efforts have been frustrated by their highly abstract nature. Advances in data technology, modeling, and validation techniques, however, promise to find the right level of abstraction, providing fine-grained representation of the fundamental properties of corruption hidden in reams of subjective perceptions and objective data and closing the gap between systemic thinkers and policy-makers.
Many engineering and social networks exhibit highly complex coupling rules that lead to the emergence of generic behavioral and structural features. For example, one of the main structural features of so-called “scale-free networks” is their heavy-tailed connectivity distribution, which can be found in computer networks such as the Internet, genetic regulatory networks, networks created by the formation of sexual partnerships, and many more. The main interest is to develop mathematical frameworks that capture the broad connectivity dynamics of growing networks and their emerging features.
Examples of cooperative systems include the distributed decision-making systems for a network of agents tasked with a search and rescue operation, a surveillance and attack mission, or a flexible manufacturing system. The spatially distributed nature of cooperative control problems implies that allocation algorithms must be distributed across multiple moving agents, and even though these agents may only sense local information about their immediate surroundings, they must still cooperate in order to accomplish a global common objective. The focus is on mathematical modeling of global spatial distributions (e.g., developing asynchronous discrete event system models), stability analysis of group behavior (e.g., using extensions of Lyapunov theory), and the impact of information flow constraints.