Using Decentralized Conflict-Abduction-Negation in Policy-Making

In many real-world events that would require additional regulation, the causal chain leading to the event can be hard to determine. This is partly due to the distribution of knowledge across multiple agents, the a-priori unknown number an competence of such agents and their heterogeneous expertise. In this case, coordination is key to the understanding of the phenomenon. In this paper, we informally describe a novel approach to analyze complex sequences. This method originates from the study of smart homes, where collaboration between heterogeneous components is required, too. Our proposal is named D-CAS, which stands for Decentralized Conflict-Abduction-Negation. It is a high-level process that coordinates components’ expertise to generate an explanatory reasoning in smart homes. We transfer our smart home solution to socio-technical systems in general. We illustrate the general concept via two fictional example cases: i) an autonomous car crash and ii) a crime perpetrated after social media fake news. In both cases, we examine how D-CAN could manage the communications and be used as a general framework to formalize interactions between experts and organize the discussion, helping to unravel a multi-domain causal chain. Using D-CAN helps identifying causes and responsible and can thus be helpful in a broader perspective of policy-making, e.g. to audit the potential flaws of current legislation.