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Discontinuity, Nonlinearity, and Complexity 7(4) (2018) 383--396 | DOI:10.5890/DNC.2018.12.003

Christopher R. Stephens$^{1}$,$^{2}$, Raúl Sierra Alcocer$^{3}$, Constantino González Salazar$^{1}$,$^{4}$

$^{1}$ Centro de Ciencias de la Complejidad and

$^{2}$ Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México, Circuito Exterior, A. Postal 70-543, México D.F. 04510

$^{3}$ CONABIO, Liga Perif´erico - Insurgentes Sur 4903, Parques del Pedregal, Ciudad de M´exico. C.P. 14010

$^{4}$ Departamento de Ciencias Ambientales, CBS Universidad Autónoma Metropolitana, Unidad Lerma, Estado de México 52006, M´exico

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Abstract

All systems - physical, biological, ecological and social - are composed of hierarchies of building blocks - atoms, molecules, cells, tissues, individuals, species etc. - with corresponding interactions, wherein the presence of interactions - attractive and repulsive - affects the relative spatio-temporal distribution of the building blocks. In physical systems, in particular, the structure of the building blocks and the nature of their interactions has been deduced via systematic observations of their positions in space and time. Unfortunately, Complex Adaptive Systems are highly multi-factorial, so that, unlike many physical systems, it is impossible to systematically observe and characterize each and every interaction that exists in such systems. In this paper we discuss a general framework - Complex Inference Networks - wherein interactions, particularly in Complex Adaptive Systems, may be studied and characterized using position data about their building blocks. We compare and contrast physical versus Complex Adaptive Systems and give as an explicit example the identification of disease hosts in the ecology of emerging and neglected diseases, where it has been possible to discover previously unknown ecological interactions from species co-occurrence data.

Acknowledgments

This work was supported by DGAPA-PAPIIT grants IN113414 and IG200217 and by the CONABIO.

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