Robert rosen: Fundamentals of Measurement and Representation of Natural Systems. New York: Elsevier North-Holland, 1978, 221 pp.

Bosserman, Robert W.

doi:10.1002/bs.3830250409

Cited by 4 publications

(6 citation statements)

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“…For a formal construct to be a formal representation of a real-world system, binding the formal terms to observations and measurement in addition to mathematical soundness is required, and using a term with established meaning in an expression of the theory language needs appropriate justification [33]. Models in epidemiology are motivated by the desire to understand real world events, requiring the model language as referring to the real world, but in the absence of a binding anywhere as reliable as in physics or chemistry, caution is required.…”

Section: Interpretation Of Modelmentioning

confidence: 99%

Conjectures concerning the explanation of recurrent patterns in COVID-19 spread dynamics based on time-variant networks.

Krall

2023

Preprint

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The paper discusses conjectures concerning patterns in virus spread observed in the Covid-19 event, including fluctuations of activity under constant environmental conditions, plateaus of near-constant activity, and rapid wild-type replacement at rather low levels of population immunity. Based on models of time-variant networks, it is shown that the patterns can be explained if spread dynamics result from superposition of three components: Continuous activity in reservoirs with limited fluctuation where fluctuation of group members allows transmissions from active cases to new arrivals before the active cases leave or reach the end of the contagious period. Hospitals are the obvious candidates for these reservoirs. The second component is propagation within stable groups, such as families, office staff, or religious groups. In addition, there are outbursts in temporary groups. The second and third components multiply case numbers but, due to strong segmentation of the population, do not support self-sustained spread which would allow the previous wild-type to coexist with a mutant strain until population immunity gets too high.

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Section: Interpretation Of Modelmentioning

confidence: 99%

Conjectures concerning the explanation of recurrent patterns in COVID-19 spread dynamics based on time-variant networks.

Krall

2023

Preprint

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“…Therefore, in this paper, we propose a conceptual and technical solution, borrowed from the SEM approach and graph theory relying on DAG representations, to take causal relationships into account in SDMs exercises. From a pure conceptual-level perspective, we introduce Robert Rosen's modelling relation framework (Rosen, 1978(Rosen, , 1986(Rosen, , 1993 as a causal scheme to guide the design of SDMs. Robert Rosen (1934Rosen ( -1998, a theoretical biologist, introduced the conceptual framework called 'modelling relation' as a fundamental principle in understanding and representing complex systems like living organisms, arguing that traditional mathematical models often fall short in capturing their complexity (Rosen, 1978(Rosen, , 1986.…”

Section: Introductionmentioning

confidence: 99%

“…From a pure conceptual-level perspective, we introduce Robert Rosen's modelling relation framework (Rosen, 1978(Rosen, , 1986(Rosen, , 1993 as a causal scheme to guide the design of SDMs. Robert Rosen (1934Rosen ( -1998, a theoretical biologist, introduced the conceptual framework called 'modelling relation' as a fundamental principle in understanding and representing complex systems like living organisms, arguing that traditional mathematical models often fall short in capturing their complexity (Rosen, 1978(Rosen, , 1986. The modelling relation highlights the idea that a model should capture the essential organizational relationships and constraints of a system, capturing the underlying organizational principles that guide the system's behaviour rather than merely describing its components and interactions (Rosen, 1993).…”

Section: Introductionmentioning

confidence: 99%

Towards causal relationships for modelling species distribution

Da Re,

Tordoni,

Lenoir

et al. 2023

Journal of Biogeography

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AimUnderstanding the processes underlying the distribution of species through space and time is fundamental in several research fields spanning from ecology to spatial epidemiology. Correlative species distribution models rely on the niche concept to infer or explain the distribution of species, though often focusing only on the abiotic component of the niche (e.g. temperature, precipitation), without clear causal links to the biology of the species under investigation. This might result in an oversimplification of the complex niche hypervolume, resulting in a single model formula whose estimates and predictions lack ecological realism.LocationNot applicable.Time PeriodNot applicable.Major Taxa StudiedVirtual species.Materials and MethodsWe believe that a causal perspective associated with a finer definition of the modelling target is necessary to develop more ecologically realistic outputs. Here, we propose to infer the geographical distribution of a species by applying the modelling relation approach, a causal conceptual framework developed by the theoretical biologist Robert Rosen, which can be formalized through structural equation modelling.ResultsOur findings suggest that building a model relying on a strong conceptual basis improves the stability of the estimated model's coefficients, without necessarily increasing the predictive accuracy metrics of the model.Main ConclusionsIncluding causal processes underlying the spatial distribution of species into an inferential formal system highlights the methodological steps where uncertainty can arise and results in model outputs which are tightly linked to the ecology of the target species.

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“…Therefore, in this paper, we propose a conceptual and a technical solution, borrowed from the SEM approach and graph theory relying on DAG representations, to take causal relationships into account in SDMs exercises. From a pure conceptual-level perspective, we introduce the Robert Rosen's modelling relation framework (Rosen 1978;1986;1993) as a causal scheme to guide the design of species distribution models. Robert Rosen (1934Rosen ( -1998, a theoretical biologist, introduced the conceptual framework called "modelling relation" as a fundamental principle in understanding and representing complex systems like living organisms, arguing that traditional mathematical models often fall short in capturing their complexity (Rosen, 1978(Rosen, , 1986.…”

Section: Introductionmentioning

confidence: 99%

“…From a pure conceptual-level perspective, we introduce the Robert Rosen's modelling relation framework (Rosen 1978;1986;1993) as a causal scheme to guide the design of species distribution models. Robert Rosen (1934Rosen ( -1998, a theoretical biologist, introduced the conceptual framework called "modelling relation" as a fundamental principle in understanding and representing complex systems like living organisms, arguing that traditional mathematical models often fall short in capturing their complexity (Rosen, 1978(Rosen, , 1986. The modelling relation highlights the idea that a model should capture the essential organizational relationships and constraints of a system, capturing the underlying organizational principles that guide the system's behaviour rather than merely describing its components and interactions (Rosen 1993).…”

Section: Introductionmentioning

confidence: 99%

Towards causal relationships for modelling species distribution

Da Re,

Tordoni,

Lenoir

et al. 2023

Preprint

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1. Understanding the processes underlying the distribution of species through space and time is fundamental in several research fields spanning from ecology to spatial epidemiology. Correlative species distribution models (SDMs) involve popular statistical tools to infer species geographical distribution thanks to spatiotemporally explicit observations of species occurrences coupled with a set of environmental predictors. 2. So-called SDMs rely on the niche concept to infer or explain the distribution of species, though often focusing only on the abiotic component of the niche (e.g., temperature, precipitation), without clear causal links to the biology of species under investigation. This might result in an over-simplification of the complex niche hypervolume, resulting in a single model formula whose estimates and predictions lack ecological realism. 3. We believe that a causal perspective associated with a finer definition of the modelling target is necessary to develop ecologically more realistic outputs. Here, we propose to infer the geographical distribution of a species by applying the modelling relation approach, a causal conceptual framework developed by the theoretical biologist Robert Rosen, which can be formalized through structural equation modelling (SEM). 4. Implementing the modelling relation into SDMs would improve the inclusion of the causal processes underlying the spatial distribution of species into an inferential formal system, potentially highlighting the methodological steps where uncertainty arises and eventually resulting in model outputs which are tightly linked to the ecology of the target species.

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Robert rosen: Fundamentals of Measurement and Representation of Natural Systems. New York: Elsevier North-Holland, 1978, 221 pp.

Cited by 4 publications

References 0 publications

Conjectures concerning the explanation of recurrent patterns in COVID-19 spread dynamics based on time-variant networks.

Conjectures concerning the explanation of recurrent patterns in COVID-19 spread dynamics based on time-variant networks.

Towards causal relationships for modelling species distribution

Towards causal relationships for modelling species distribution

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