How emergence arises

Haan, J. de

doi:10.1016/j.ecocom.2007.02.003

Cited by 129 publications

(79 citation statements)

References 12 publications

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“…Rather, patterns emerge from interactions between a full range of niche and neutral processes, through feedback with the environment (figure 1a). This may lead to nonlinear behaviours [16] and to patterns with values that lie beyond those expected between pure niche and pure neutral processes. The phase space therefore allows the differences between the values of different community patterns to be visualized, with respect to their positions along the niche and neutral axes.…”

Section: The Community Assembly Phase Spacementioning

confidence: 99%

“…Understanding how particular patterns vary with changes in the neutral and niche processes is nonetheless important because the information value of multiple patterns may be redundant or complementary. Different patterns may contain similar information on processes, and therefore be redundant [22], or may be differently sensitive to changes in processes [23], and behave nonlinearly [16]. We therefore examined how the four following community patterns varied with changes in the parameters of the three models (neutral, niche and composite).…”

Section: (B) From Process To Patternmentioning

confidence: 99%

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Beyond the continuum: a multi-dimensional phase space for neutral–niche community assembly

2015

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Neutral and niche processes are generally considered to interact in natural communities along a continuum, exhibiting community patterns bounded by pure neutral and pure niche processes. The continuum concept uses niche separation, an attribute of the community, to test the hypothesis that communities are bounded by pure niche or pure neutral conditions. It does not accommodate interactions via feedback between processes and the environment. By contrast, we introduce the Community Assembly Phase Space (CAPS), a multidimensional space that uses community processes (such as dispersal and niche selection) to define the limiting neutral and niche conditions and to test the continuum hypothesis. We compare the outputs of modelled communities in a heterogeneous landscape, assembled by pure neutral, pure niche and composite processes. Differences in patterns under different combinations of processes in CAPS reveal hidden complexity in neutral-niche community dynamics. The neutral-niche continuum only holds for strong dispersal limitation and niche separation. For weaker dispersal limitation and niche separation, neutral and niche processes amplify each other via feedback with the environment. This generates patterns that lie well beyond those predicted by a continuum. Inferences drawn from patterns about community assembly processes can therefore be misguided when based on the continuum perspective. CAPS also demonstrates the complementary information value of different patterns for inferring community processes and captures the complexity of community assembly. It provides a general tool for studying the processes structuring communities and can be applied to address a range of questions in community and metacommunity ecology.

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Section: The Community Assembly Phase Spacementioning

confidence: 99%

Section: (B) From Process To Patternmentioning

confidence: 99%

Beyond the continuum: a multi-dimensional phase space for neutral–niche community assembly

2015

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“…On this problem there is a vast literature that has explored the so called "micromacro link" in complex social systems, while dealing with the crucial and disputed idea of "social emergence" (e.g., Gilbert 1996;Sawyer 2005;De Haan 2006;Squazzoni 2008). According to this literature, it is possible to identify certain types of emergent properties that characterize the societal systems that are called "first order" and "second-order" emergent properties.…”

Section: Discussionmentioning

confidence: 99%

A (computational) social science perspective on societal transitions

Squazzoni

2008

Comput Math Organiz Theor

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This paper aims to illustrate how social sciences, sociology in particular, have theorized on societal transitions. The first section introduces some preliminary definitions. The assumption is that a societal transition is more than a social, economic or technological change. It is a large-scale and long-term macro process through which a given social system radically changes its structural basis, in terms of new socio-technical practices, governance rules, social and economic institutions, cultural frames, and patterns of social life. The second section provides an excursus on social science accounts on transitions. In particular, the attention has been given to Norbert Elias' famous study on the rise and fall of the court society in France, between 17th and 18th century and to Manuel Castells' recent analysis of the emergence of the network society. The third section discusses problems and challenges of standard approaches and suggests some building blocks of societal transition models, taken from complexity and social simulation literature. The concluding section suggests some way-forward measures towards a computational social science approach to societal transitions.

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“…These are: Type-I (where the micro causes the macro level phenomenon) Type-II (where the macro level feeds back onto the micro level) and Type-III (where the micro-level agents learn and adapt to this feedback). Type-III is the most significant type of emergence as it results in the systems as a whole becoming reflexive [de Haan 2006]. This occurs because the macro-level now has a causal effect on the agents on the micro-level, causing the agents to learn and adapt their behaviour in response to the detected emergent behaviour.…”

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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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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How emergence arises

Cited by 129 publications

References 12 publications

Beyond the continuum: a multi-dimensional phase space for neutral–niche community assembly

Beyond the continuum: a multi-dimensional phase space for neutral–niche community assembly

A (computational) social science perspective on societal transitions

Decentralised Detection of Emergence in Complex Adaptive Systems

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