Decentralised Detection of Emergence in Complex Adaptive Systems

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Title: Decentralised Detection of Emergence in Complex Adaptive Systems
Authors: O'Toole, Eamonn
Nallur, Vivek
Clarke, Siobhán
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Date: 1-May-2017
Abstract: This article describes Decentralised Emergence Detection (DETect), a novel distributed algorithm that enables agents to collaboratively detect emergent events in Complex Adaptive Systems (CAS). Non-deterministic interactions between agents in CAS can give rise to emergent behaviour or properties at the system level. The nature, timing, and consequence of emergence is unpredictable and may be harmful to the system or individual agents. DETect relies on the feedback that occurs from the system level (macro) to the agent level (micro) when emergence occurs. This feedback constrains agents at the micro level and results in changes occurring in the relationship between an agent and its environment. DETect uses statistical methods to automatically select the properties of the agent and environment to monitor and tracks the relationship between these properties over time. When a significant change is detected, the algorithm uses distributed consensus to determine if a sufficient number of agents have simultaneously experienced a similar change. On agreement of emergence, DETect raises an event, which its agent or other interested observers can use to act appropriately. The approach is evaluated using a multi-agent case study.
Type of material: Journal Article
Publisher: ACM
Copyright (published version): 2017 ACM
Keywords: EmergenceComplex adaptive systems
DOI: 10.1145/3019597
Language: en
Status of Item: Peer reviewed
Appears in Collections:Computer Science Research Collection

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