Augmented Control Systems: Enhancing the Management of Expert Professionals in Reconstituted Work Environment
The aim of this thesis is to find a control system that will improve the control system used in organisations and offer a solution to the problem of manipulation and sabotage by expert professionals. Previous theories have neglected the possibility and advantages of closer collaboration between algorithms and humans, a deficiency this study addresses. We ask to what extent an augmented algorithm will improve the efficiency of control systems in modern organisations. We do so to better enable academics and policymakers to understand the nuances of a less dichotomous deployment of algorithms and humans in a control process. We show that independently deployed algorithms are susceptible to errors and manipulations. We also show that humans possess unique attributes that can reduce various susceptibilities of algorithms in a control process. The importance of the thesis is that it enlightens our notional understanding of modern control by presenting a focus on augmentation previously absent, and informs our practical understanding of modern control systems.
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