Emergence as Mesoscopic Coherence

Minati, Gianfranco; Licata, Ignazio

doi:10.3390/systems1040050

Cited by 27 publications

(20 citation statements)

References 32 publications

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“…Radical changes due to processes of emergence; 2 An example occurs when considering that a medical problem may simultaneously have biochemical and psychological components. Furthermore, economic, political, and sociological aspects of any social system are simultaneously present.…”

Section: Foreseeing and Understanding In Structural Dynamicsmentioning

confidence: 99%

“…Collective behaviours are intended as a source of large amounts of data; however, they must be coherent. In this case, the phenomena of emergence are intended to have mesoscopic coherence [2] among the interacting agents when, "...mesoscopic variables are intended to transversally intercept and represent values adopted by aggregates of microscopic variables. Values of mesoscopic variables are considered to represent the effective application of interaction rules" ([2], p. 55).…”

Section: Approaches To the Model Structural Dynamics Of Collective Bementioning

confidence: 99%

“…Values of mesoscopic variables are considered to represent the effective application of interaction rules" ([2], p. 55). In the meta-structure project, we consider the properties of the clustered variables considered to be suitable to model the collective behaviours [2,57].…”

Section: Approaches To the Model Structural Dynamics Of Collective Bementioning

confidence: 99%

“…This article's purpose is to contribute to the introduction of fresh understandings of and approaches to Big Data to allow multiple comprehensions that are suitable for the realisation of clues and tendencies that are appropriate for conjecture, identify processes, properties, and forecasts. As we introduce later, Big Data are not only considered to be representative of well-defined phenomena [1], but also as having autonomously acquired properties that are detectable at the mesoscopic (or topological) level, such as collective behaviours [2].…”

Section: Introductionmentioning

confidence: 99%

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Big Data: From Forecasting to Mesoscopic Understanding. Meta-Profiling as Complex Systems

Minati¹

2019

Systems

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We consider Big Data as a phenomenon with acquired properties, similar to collective behaviours, that establishes virtual collective beings. We consider the occurrence of ongoing non-equivalent multiple properties in the conceptual framework of structural dynamics given by sequences of structures and not only by different values assumed by the same structure. We consider the difference between modelling and profiling in a constructivist way, as De Finetti intended probability to exist, depending on the configuration taken into consideration. The past has little or no influence, while events and their configurations are not memorised. Any configuration of events is new, and the probabilistic values to be considered are reset. As for collective behaviours, we introduce methodological and conceptual proposals using mesoscopic variables and their property profiles and meta-profile Big Data and non-computable profiles which were inspired by the use of natural computing to deal with cyber-ecosystems. The focus is on ongoing profiles, in which the arising properties trace trajectories, rather than assuming that we can foresee them based on the past.We assume that dealing with Big Data as a phenomenon contains not only traces of past events, but also the assumption of having properties that are independent from the original event, when studied as a virtual collective being [6]. Thus, we consider the occurrence of ongoing, non-equivalent, multiple properties with any initial temporal beginning, duration, and variable combination in conceptual correspondence with the nature of quasi-systems, which occur when a system is not always the same system and or even a system at all [7,8].We then mention the criticality of the level of description, as well as the scalarity assumed to detect the properties as well as the conceptual ineffectiveness of increasing the quantity of data available. Before proposing new approaches allowed by the conceptual 'definitions' mentioned above, we consider the differences between models and profiles. Models (especially ideal) are expected to support understanding, while profiling is intended to represent non-ideal, data-driven models, and their emergent, ongoing properties, for example, correlations and coherences [9]. Another differentiation considered is between forecasting and understanding. Forecasting is based on the importance of the past, is based on analogies and repetitiveness, and takes some contextual conditions into account. Understanding is considered to be the ability to conjecture, hypothesise, speculate, and, finally, realise the nature of the phenomenon under study, for instance, chaotic, hosting bifurcations, fluctuations, and the presence of multiple dynamic coherences.We specify that the differences introduced above should be considered in a constructivist conceptual framework in which, instead of trying to find how a phenomenon 'really' is, one looks for the most effective way to think of it (multiple modelling) [10]. For instance, we consider the conceptual framework of the t...

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Section: Foreseeing and Understanding In Structural Dynamicsmentioning

confidence: 99%

Section: Approaches To the Model Structural Dynamics Of Collective Bementioning

confidence: 99%

Section: Approaches To the Model Structural Dynamics Of Collective Bementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Big Data: From Forecasting to Mesoscopic Understanding. Meta-Profiling as Complex Systems

Minati¹

2019

Systems

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“…In the second case (second Systemics), interactions occur in non-structured, non-organised ways such as for self-organisation and emergence, as introduced in Section 2.2. This case is matter of multiple, but coherent properties [7]. For this case, examples of acquired systemic properties include behaviours of markets, patterns and behaviours of traffic, flocks, and swarms.…”

Section: First and Second Systemicsmentioning

confidence: 99%

The Non-Systemic Usages of Systems as Reductionism: Quasi-Systems and Quasi-Systemics

Minati¹

2018

Systems

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Usual reductionism considers systemic, acquired properties as non-systemic, possessed properties. We consider here the non-systemic usages of systems, misunderstood as non-interacting virtual objects or devices, and the misunderstanding between non-complex (first Systemics) and complex systems (second Systemics) as another form of reductionism. This reductionism leads to inappropriate and ineffective approaches, particularly dealing with complex systems such as socioeconomic systems, whose complexity is often misunderstood and neglected. However, this reductionism should be distinguished from mixed usages of systemic approaches suitable to deal with multiple, dynamic, temporary, and partial systemic natures of phenomena related to complex systems. We consider that we should move from the well-defined, often simplistic, world of Systemics to Quasi-Systemics, which is intended as constructionist Systemics, always in progress, non-ideological, multiple, contradiction-tolerant, incomplete, and in its turn emergent. Rather than recommending a pragmatic attitude, we mention two approaches, one methodological approach called Logical Openness and another, the Meta-Structure approach, which is suitable to more formally deal with such multiple aspects and-based on mesoscopic representations-suitable to represent quasiness.

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Prospective New Conceptual Categories

Minati¹,

Pessa

2018

From Collective Beings to Quasi-Systems

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Emergence as Mesoscopic Coherence

Cited by 27 publications

References 32 publications

Big Data: From Forecasting to Mesoscopic Understanding. Meta-Profiling as Complex Systems

Big Data: From Forecasting to Mesoscopic Understanding. Meta-Profiling as Complex Systems

The Non-Systemic Usages of Systems as Reductionism: Quasi-Systems and Quasi-Systemics

Prospective New Conceptual Categories

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