Preprint Article Version 1 NOT YET PEER-REVIEWED

Citizen Science and Topology of Mind: Complexity, Computation and Criticality in Data-Driven Exploration of Open Complex Systems

Version 1 : Received: 14 April 2017 / Approved: 14 April 2017 / Online: 14 April 2017 (10:51:07 CEST)

A peer-reviewed article of this Preprint also exists.

Funabashi, M. Citizen Science and Topology of Mind: Complexity, Computation and Criticality in Data-Driven Exploration of Open Complex Systems. Entropy 2017, 19, 181. Funabashi, M. Citizen Science and Topology of Mind: Complexity, Computation and Criticality in Data-Driven Exploration of Open Complex Systems. Entropy 2017, 19, 181.

Journal reference: Entropy 2017, 19, 181
DOI: 10.3390/e19040181

Abstract

Recently emerging data-driven citizen sciences need to harness increasing amount of massive data with varying quality. This paper develops essential theoretical frameworks and example models and examine its computational complexity for interactive data-driven citizen science within the context of guided self-organization. We first define a conceptual model that incorporates quality of observation in terms of accuracy and reproducibility, ranging between subjectivity, inter-subjectivity, and objectivity. Next, we examine the database's algebraic and topological structure in relation to informational complexity measures, and evaluate its computational complexities with respect to exhaustive optimization. Conjectures of criticality are obtained on self-organizing processes of observation and dynamical model development. Example analysis is demonstrated with the use of biodiversity assessment database, the process that inevitably involves human subjectivity for the management in open complex systems.

Subject Areas

inter-subjective objectivity; complexity measure; computational complexity; criticality; citizen science; open complex system;

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