Mapping the complexity of ecological models

Boschetti, Fabio

doi:10.1016/j.ecocom.2007.09.002

Cited by 26 publications

(16 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With increased model complexity we are less able to manage and understand model behaviour. As a result, the ability of a model to simulate complex dynamics is no more an absolute value in itself, rather a relative one: we need enough complexity to realistically model a process, but not so much that we ourselves can not handle [46]. For example, if we want to model biophysical processes on environmental interfaces we meet r c a lot of uncertainties in time series of calculated physical quantities.…”

Section: Numerical Simulations With Maps Of Exchange Processes On Thementioning

confidence: 99%

Coupled maps serving the exchange processes on the environmental interfaces regarded as complex systems

Mihailoviĉ

Budinčević²,

Balaz³

et al. 2012

View full text Add to dashboard Cite

We have defined the environmental interface through the exchange processes between media forming this interface. Considering the environmental interface as a complex system we elaborated the advanced mathematical tools for its modelling. We have suggested two coupled maps serving the exchange processes on the environmental interfaces spatially ranged from cellular to planetary level, i.e. 1) the map with diffusive coupling for energy exchange simulation and 2) the map with affinity, which is suitable for matter exchange processes at the cellular level. We have performed the dynamical analysis of the coupled maps using the Lyapunov exponent, cross sample as well as the permutation entropy in dependence on different map parameters. Finally, we discussed the map with affinity, which shows some features making it a promising toll in simulation of exchange processes on the environmental interface at the cellular level.

show abstract

Section: Numerical Simulations With Maps Of Exchange Processes On Thementioning

confidence: 99%

Coupled maps serving the exchange processes on the environmental interfaces regarded as complex systems

Mihailoviĉ

Budinčević²,

Balaz³

et al. 2012

View full text Add to dashboard Cite

show abstract

“…Some of the issues related to points 1 and 2 have been addressed in [14] and [7] and we refer the reader to that work and the references listed therein. Here we address point 3.…”

Section: Studying a Complex System Via Numerical Modelingmentioning

confidence: 99%

“…Unlike Kolmogorov's definition though, very little information is needed to statistically reproduce a random time series, since no amount of memory (effort) can help improving our predictive ability, i.e., an ''optimal'' prediction can be performed with zero memory (there is no point in storing the outcomes of roulette draws to bet on the next draw). Since these information-theoretic views of complexity are closely related to predictability and in particular to the amount of information required to achieve useful or optimal prediction, they can be applied to scientific modeling in a straightforward manner [7].…”

Section: Definitions and Measures Of Complexitymentioning

confidence: 99%

Complexity of a modelling exercise: A discussion of the role of computer simulation in complex system science

2008

Self Cite

View full text Add to dashboard Cite

Shifting the emphasis from a model to a modeling task, which involves both a computer model and a modeler, we ask what makes a problem complex. We propose that a modeling task can be seen as a set of questions-and-answers, nested at multiple levels. The role of the modeler then lies in posing the questions and choosing the best procedure to answer them, while the role of the model lies in answering the questions, via algorithmic, thus logically simple, procedures. Within this framework, complexity is broadly related to the number of question-answer levels involved in the process and the nature of the questions posed. Addressing this complexity depends crucially on the ingenuity and creativity of the modeler. This may lead to a view of complexity, which is no more observer-independent, but rather accounts for both historical and cultural development, that is the context of the problem at hand.

show abstract

“…A large number of definitions have been proposed in the literature and because a review is beyond the scope of this work, we adopt here as definition of complexity the amount of information needed to describe a process, a system, or an object. This definition is computable (at least in one of its forms), is observer-independent (once resolution is defined), applies to both data and models [20] and provides a framework within which selforganization and emergence can also be consistently defined.…”

Section: Complexity 31 Conceptmentioning

confidence: 99%

An information‐theoretic primer on complexity, self‐organization, and emergence

2008

Self Cite

View full text Add to dashboard Cite

An Information-Theoretic Primer on Complexity, Self-Organization, and Emergence Periodicals, Inc. Complexity 15: 11-28, 2009 Key Words: complexity; information theory; self-organization; emergence; predictive information; excess entropy; entropy rate; assortativeness; predictive efficiency; adaptation Complex Systems Science aims to understand concepts like complexity, selforganization, emergence and adaptation, among others. The inherent fuzziness in complex systems definitions is complicated by the unclear relation among these central processes: does self-organisation emerge or does it set the preconditions for emergence? Does complexity arise by adaptation or is complexity necessary for adaptation to arise? The inevitable consequence of the current impasse is miscommunication among scientists within and across disciplines. We propose a set of concepts, together with their possible information-theoretic interpretations INTRODUCTIONC omplex Systems Science studies general phenomena of systems comprised of many simple elements interacting in a nontrivial fashion. Currently, fuzzy quantifiers like "many" and "nontrivial" are inevitable. "Many" implies a number large enough so that no individual component/feature predominates the dynamics of the system, but not so large that features are completely irrelevant. Interactions need to be "nontrivial" so that the degrees of freedom are suitably reduced, but not constraining to the point that the arising structure possesses no further degree of freedom. Crudely put, systems with a huge number of components interacting trivially are explained by statistical mechanics, and systems with precisely defined and constrained interactions are the concern of fields like chemistry and engineering. In so far as the domain of complex systems science overlaps these fields, it contributes insights when the classical assumptions are violated. This article was submitted as an invited paper resulting from the "UnderstandingIt is unsurprising that a similar vagueness afflicts the discipline itself, which notably lacks a common formal framework for analysis. There are a number of reasons for this. Because complex systems science is broader than physics, biology, sociology, ecology, or economics, its foundations cannot be reduced to a single discipline. Furthermore, systems which lie in the gap between the "very large" and the "fairly small" cannot be easily modeled with traditional mathematical techniques.Initially setting aside the requirement for formal definitions, we can summarize our general understanding of complex systems dynamics as follows:(1) complex systems are "open," and receive a regular supply of energy, information, and/or matter from the environment;(2) a large, but not too large, ensemble of individual components interact in a nontrivial fashion; in others words, studying the system via statistical mechanics would miss important properties brought about by interactions; (3) the nontrivial interactions result in internal constraints, leading to symmetry breaking in th...

show abstract

Mapping the complexity of ecological models

Cited by 26 publications

References 34 publications

Coupled maps serving the exchange processes on the environmental interfaces regarded as complex systems

Coupled maps serving the exchange processes on the environmental interfaces regarded as complex systems

Complexity of a modelling exercise: A discussion of the role of computer simulation in complex system science

An information‐theoretic primer on complexity, self‐organization, and emergence

Contact Info

Product

Resources

About