Emergence in stigmergic and complex adaptive systems: A formal discrete event systems perspective

Mittal, Saurabh

doi:10.1016/j.cogsys.2012.06.003

Cited by 64 publications

(54 citation statements)

References 21 publications

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“…It is a fundamental concept in complex systems and a necessary part of what qualifies complex systems as complex [Flake 1998;Mittal 2013]. An extensive body of literature exists that attempts to characterise emergence but, despite this, there is no widely accepted definition of emergence, though a number of characteristics are commonly discussed Dessalles and Phan 2001;Fromm 2005a].…”

Section: Background and Challengesmentioning

confidence: 99%

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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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Section: Background and Challengesmentioning

confidence: 99%

“…The observer of the emergent behaviour or property is also a key consideration [Mittal 2013]. Muller uses the observer's identity to distinguish between weak and strong emergence [Muller 2004].…”

Section: Emergence In Casmentioning

confidence: 99%

Decentralised Detection of Emergence in Complex Adaptive Systems

O'Toole

Nallur

Clarke

2017

ACM Trans. Auton. Adapt. Syst.

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“…In addition, CSs can have multiple spatial and temporal scales through a global hierarchy and intra-entity hierarchy, e.g. a society is composed of humans, themselves composed of cells (Elsner et al, 2015;Lichtenstein, 2014;Mittal, 2013;Nicolet, 2010). § Distributed decision-making: In a CS, the highest level of the hierarchy doesn't manage and guide the system.…”

Section: Introductionmentioning

confidence: 99%

“…These latter are therefore autonomous and spontaneous (Nicolet, 2010;Wolf and Holvoet, 2005). § Dynamism and complicated interactions: A CS can be in incremental growth since new entities can dynamically be created (Mittal, 2013). These entities are constantly evolving, and the interactions between them and the environment are complex with mechanisms of flow diffusion and propagation.…”

Section: Introductionmentioning

confidence: 99%

Simulating Complex Systems - Complex System Theories, Their Behavioural Characteristics and Their Simulation

Aziza

Borgi

Zgaya

et al. 2016

Proceedings of the 8th International Conference on Agents and Artificial Intelligence

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Abstract:Complexity science offers many theories such as chaos theory and coevolutionary theory. These theories illustrate a large set of real life systems and help decipher their nonlinear and unpredictable behaviours.Categorizing an observed Complex System among these theories depends on the aspect that we intend to study, and it can help better understand the phenomena that occur within the system. This article aims to give an overview on Complex Systems and their modelling. Therefore, we compare these theories based on their main behavioural characteristics, e.g. emergence, adaptability, and dynamism. Then we compare the methods used in the literature to model and simulate Complex Systems, and we propose a simple guide to select the appropriate method for modelling a Complex System.

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“…Mittal [6] pointed out that strong emergent behavior results in generation of new knowledge about the system representing previously unperceived complex interactions. This can occur in the form of one or more of new abstraction levels and linguistic descriptions, new hierarchical structures and couplings, new component behaviors, and new feedback loops.…”

Section: Introductionmentioning

confidence: 99%

Emergence at the Fundamental Systems Level: Existence Conditions for Iterative Specifications

Zeigler¹,

Muzy

2016

Preprint

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Abstract:Conditions under which compositions of component systems form a well-defined systemof-systems here are formulated at a fundamental level. Statement of what defines a well -defined composition and sufficient conditions guaranteeing such a result offers insight into exemplars that can be found in special cases such as differential equation and discrete event systems. For any given global state of a composition, two requirements can be stated informally as: 1) the system can leave this state, i.e., there is at least one trajectory defined that starts from the state, and 2) the trajectory evolves over time without getting stuck at a point in time. Considered for every global state, these conditions determine whether the resultant is a well-defined system and if so, whether it is nondeterministic or deterministic. We formulate these questions within the framework of iterative specifications for mathematical system models that are shown to be beha viorally equivalent to the Discrete Event System Specification (DEVS) formalism. This formalization supports definitions and proofs of the afore-mentioned conditions. Implications are drawn at the fundamental level of existence where the emergence of a system from an assemblage of components can be characterized. We focus on systems with feedback coupling where existence and uniqueness of solutions is problematic.

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Emergence in stigmergic and complex adaptive systems: A formal discrete event systems perspective

Cited by 64 publications

References 21 publications

Decentralised Detection of Emergence in Complex Adaptive Systems

Decentralised Detection of Emergence in Complex Adaptive Systems

Simulating Complex Systems - Complex System Theories, Their Behavioural Characteristics and Their Simulation

Emergence at the Fundamental Systems Level: Existence Conditions for Iterative Specifications

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