Quantitative Emergence

Mnif, Moez; Müller-Schloer, Christian

doi:10.1007/978-3-0348-0130-0_2

Cited by 20 publications

(20 citation statements)

References 5 publications

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“…While [13] and [14] have already given definitions for the emergence and autonomy, the novel contribution of this paper are the quantitative definitions for some other properties of self-organizing systems:…”

Section: Resultsmentioning

confidence: 99%

“…For modeling continuous self-organizing systems and a comparison between discrete and continuous modeling see [12]. Quantitative definitions of autonomy, emergence and globalstate awareness, which use the information theoretical concept of entropy, can be found in [13], [14] and [15]. The methods of [13] for modeling a system with stochastic automatons are also used in Section 3 of this paper.…”

Section: Related Workmentioning

confidence: 99%

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Quantitative Modeling of Self-organizing Properties

Holzer

Meer

2009

Lecture Notes in Computer Science

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Abstract. For analyzing properties of complex systems, a mathematical model for these systems is useful. In this paper we give quantitative definitions of adaptivity, target orientation, homogeneity and resilience with respect to faulty nodes or attacks by intruders. The modeling of the system is done by using a multigraph to describe the connections between objects and stochastic automatons for the behavior of the objects. The quantitative definitions of the properties can help for the analysis of existing systems and for the design of new systems. To show the practical usability of the concepts, the definitions are applied to a slot synchronization algorithm in wireless sensor networks.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Quantitative Modeling of Self-organizing Properties

Holzer

Meer

2009

Lecture Notes in Computer Science

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“…Quantitative measures of autonomy, emergence, adaptivity, target orientation, homogeneity, resilience and global-state awareness, can be found in [1], [2], [13], [16] and [3]. Other approaches to quantitative emergence are given in [16] and [17]. In [16] emergence is defined as an entropy difference by considering the change of order within the system.…”

Section: Related Workmentioning

confidence: 98%

“…For modeling continuous self-organizing systems and a comparison between discrete and continuous modeling see [15]. Quantitative measures of autonomy, emergence, adaptivity, target orientation, homogeneity, resilience and global-state awareness, can be found in [1], [2], [13], [16] and [3]. Other approaches to quantitative emergence are given in [16] and [17].…”

Section: Related Workmentioning

confidence: 99%

Methods for Approximations of Quantitative Measures in Self-Organizing Systems

Holzer

Meer

2011

Lecture Notes in Computer Science

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Abstract. For analyzing properties of complex systems, a mathematical model for these systems is useful. In micro-level modeling a multigraph can be used to describe the connections between objects. The behavior of the objects in the system can be described by (stochastic) automatons. In such a model, quantitative measures can be defined for the analysis of the systems or for the design of new systems. Due to the high complexity, it is usually impossible to calculate the exact values of the measures, so approximation methods are needed. In this paper we investigate some approximation methods to be able to calculate quantitative measures in a micro-level model of a complex system. To analyze the practical usability of the concepts, the methods are applied to a slot synchronization algorithm in wireless sensor networks.

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“…Entropy, a measure of disorder in a system, is used by several variable-based approaches for detecting emergence [Fisch et al 2010;Procházka and Olševičová 2015]. Emergence is the difference in system entropy from the process start to end, measured by the number of events [Mnif and Muller-Schloer 2006]. A mathematical approach for representing strong emergent properties of a system has been proposed by measuring entropy at different levels of abstraction [Bar-Yam 2004].…”

Section: Related Workmentioning

confidence: 99%

Decentralised Detection of Emergence in Complex Adaptive Systems

O'Toole

Nallur

Clarke

2017

ACM Trans. Auton. Adapt. Syst.

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This paper describes Decentralised Emergence Detection (DETect), a novel distributed algorithm that enables agents to collaboratively detect emergent events in Complex Adaptive Systems (CAS). Nondeterministic 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.

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Quantitative Emergence

Cited by 20 publications

References 5 publications

Quantitative Modeling of Self-organizing Properties

Quantitative Modeling of Self-organizing Properties

Methods for Approximations of Quantitative Measures in Self-Organizing Systems

Decentralised Detection of Emergence in Complex Adaptive Systems

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