BACKGROUND: Reasoning biases have been suggested as risk factors for delusional ideation in both patients and non-clinical individuals. Still, it is unclear how these biases are longitudinally related to delusions in the general population. We hence aimed to investigate longitudinal associations between reasoning biases and delusional ideation in the general population. METHODS: We conducted an online cohort study with 1184 adults from the German and Swiss general population. Participants completed measures on reasoning biases (jumping-to-conclusion bias JTC, liberal acceptance bias LA, (...) bias against disconfirmatory evidence BADE, possibility of being mistaken PM) and delusional ideation at baseline, and delusional ideation 7 to 8 months later. RESULTS: A greater JTC bias was associated with a greater increase in delusional ideation over the following months. This association was better described by a positive quadratic relationship. Neither BADE, LA nor PM were associated with subsequent changes in delusional ideation. CONCLUSIONS: This study suggests that jumping-to-conclusions predicts delusional ideation in the general population but that this association may follow a quadratic trajectory. While no other associations turned significant, future studies with shorter temporal distances may shed further light on the role of reasoning biases as risk factors for delusional ideation in non-clinical samples. (shrink)

The social aspects of causality in medicine and healthcare have been emphasized in recent debates in the philosophy of science as crucial factors that need to be considered to enable, among others, appropriate interventions in public health. Therefore, it seems central to recognize the bearing of social causes (broadly understood, e.g., social inequalities and socio-economic status) in bringing about certain concrete pathologies. Being aware of the relevance of social causes in medicine and healthcare is particularly important in the face of (...) the role that artificial intelligence-based systems (such as machine learning algorithms) are increasingly playing in these high-stakes fields. In fact, these systems bear the dangerous potential of concealing relevant social causes. This is highly problematic not only because it reinforces issues of distributive injustice but also because it can pave the way for issues of epistemic injustice. The central aim of this chapter is to make a first effort to point out possible connections between the importance of recognizing social causes in medicine and healthcare and forms of epistemic injustice. (shrink)

Mathematical modeling is a convenient way for characterization of complex ecosystems. This approach was applied to study the dynamics of zooplankton in Lake Sevan (Armenia) at different stages of anthropogenic eutrophication with the use of a novel method called discrete modeling of dynamical systems with feedback (DMDS). Simulation demonstrated that the application of this method helps in characterization of inter- and intra-component relationships in a natural ecosystem. This method describes all possible pairwise inter-component relationships like “plus–plus,” “minus–minus,” “plus–minus,” “plus–zero,” “minus–zero,” (...) and “zero–zero” that occur in most ecosystems. Based on the results, a working hypothesis was formulated. It was found that the sensitivity to weak external influence in zooplanktons was the greatest during the mid period of eutrophication in Lake Sevan, whereas in the final stages of eutrophication, an outbreak in the biomass production of cyanobacteria was evident. To support this approach, a weak external disturbance in the form of magnetic storm was used to see its effect on species Daphnia longispina sevanica. A statistically significant correlation between the frequency of magnetic storms and the number of this species was revealed and an increase in the number of toxic cyanobacteria species as a consequence of eutrophication. This paper, for the first time, suggests a DMDS method, to diagnose impact of anthropogenic eutrophication on environment. (shrink)

In the article "The Epistemological Nature of Information Modeling" [Gnoseologicheskaia priroda informatsionnogo modelirovaniia], Voprosy filosofii, 1963, No. 10, Academician V. M. Glushkov examines two important methodological aspects of the modeling of natural objects and phenomena in the contemporary natural sciences. The first consists of the informational nature of the models created, and the second of their dynamism. The dynamic nature of today's models in the natural sciences means that it is not so much the material structure of the object that (...) is modeled as its behavior and functions in some sphere of natural phenomena. In this sense, the models under examination may also be termed functional, as distinct from the structural models pertaining to a static description of objects. (shrink)

The state-of-art research in the field of life’s organization confronts the need to investigate a number of interacting components, their properties and conditions of sustainable behaviour within a natural system. In biology, ecology and life sciences, the performance of such stable system is usually related to homeostasis, a property of the system to actively regulate its state within a certain allowable limits. In our previous work, we proposed a deterministic model for systems’ homeostasis. The model was based on dynamical system’s (...) theory and pairwise relationships of competition, amensalism and antagonism taken from theoretical biology and ecology. However, the present paper proposes a different dimension to our previous results based on the same model. In this paper, we introduce the influence of inter-component relationships in a system, wherein the impact is characterized by direction (neutral, positive, or negative) as well as its (absolute) value, or strength. This makes the model stochastic which, in our opinion, is more consistent with real-world elements affected by various random factors. The case study includes two examples from areas of hydrobiology and medicine. The models acquired for these cases enabled us to propose a convincing explanation for corresponding phenomena identified by different types of natural systems. (shrink)

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