Abstract
A common problem facing an organisation of autonomous agents is to track the dynamic value of a signal, by aggregating their individual (and possibly inaccurate or biased) observations (sensor readings) into a commonly agreed result. A meta-problem is to explain the observation of the value: to say what rules produced the signal value that has been observed. In this paper, we use the Regulatory Theory of Social Influence and self-organising multi-agent systems to simulate a Distributed Information Processing unit (DIP) trying to solve such a meta-problem. Specifically, we examine what configuration of initial conditions on the DIP produce what type of epistemic condition for the collective, and determine the explanatory adequacy of this condition, i.e. to what extent does the DIP’s explanation of the rules match the actual rules. The results offer some further insight into the need for epistemic diversity for self-improvement in dynamic self-organising systems.
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We are particularly grateful to the three anonymous reviewers whose many insightful comments helped to revise and improve the presentation of this work.
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Mertzani, A., Pitt, J., Nowak, A., Michalak, T. (2022). Epistemic Diversity and Explanatory Adequacy in Distributed Information Processing. In: Ajmeri, N., Morris Martin, A., Savarimuthu, B.T.R. (eds) Coordination, Organizations, Institutions, Norms, and Ethics for Governance of Multi-Agent Systems XV. COINE 2022. Lecture Notes in Computer Science(), vol 13549. Springer, Cham. https://doi.org/10.1007/978-3-031-20845-4_2
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