Abstract
Recently emerging data-driven citizen sciences need to harness an increasing amount of massive data with varying quality. This paper develops essential theoretical frameworks, example models, and a general definition of complexity measure, and examines its computational complexity for an interactive data-driven citizen science within the context of guided self-organization. We first define a conceptual model that incorporates the 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 an exhaustive optimization. Conjectures of criticality are obtained on the 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 management within open complex systems.
Highlights
Recent innovation of information and communication technologies (ICT) embedded in real environments is drastically changing the way society interacts with computation
We consider the generalization of complexity measures with respect to essential information processing in citizen science, based on the inter-subjective objectivity model with buoy–anchor–raft constructs
We demonstrate the application of the model developed in this article to actual citizen science observation data, taking a biodiversity observation activity supported by interactive database as a typical example [17]
Summary
Recent innovation of information and communication technologies (ICT) embedded in real environments is drastically changing the way society interacts with computation. The sampling time series from a power-law distribution encounters intermittent shifts of the sample average due to the infinite variance of distribution—even with the upper-bounded power law in the real world (e.g., in the magnitude distribution of earthquakes) This situation addresses a statistical limit of prediction solely by the modelling and simulation of the phenomena, and presents a positive reason to engage human elements as a practical solution in actual management—especially those involving semantic and cognitive judgements [12,13]. In solving global agendas such as sustainability goals, a comprehensive approach is required that should make use of the full potential of self-organisation in coupled social–ecological systems [5,18,19] These efforts practically take on the engagement of citizens and multi-disciplinary stakeholders as important actors in the data acquisition and the implementation of an interactive management through guided self-organization, as a novel type of collective intelligence in the era of the fourth industrial revolution [3,20,21]. Section 3: How can we generalize the concept of complexity measures in application to the human–computer hybrid systems in citizen science?
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