1,721,319 research outputs found
Replication data for: Assortative mixing of opinions about COVID-19 vaccination in personal networks
No full textThis data set corresponds to the study "Assortative mixing of opinions about COVID-19 vaccination in personal networks" (authored by Marian-Gabriel Hâncean, Jürgen Lerner, Matjaž Perc, José Luis Molina, and Marius Geantă).
This data set includes raw data and the corresponding code for data analysis replication. The code (R Markdown script) includes explanatory comments spatially arranged for human readability. In the code, we made the statistical models exactly reproducible by pseudo-random number generators which are explicitly seeded.
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The dynamics of human gait
No full textAbstract We analyse the dynamics of human gait with simple nonlinear time series analysis methods that are appropriate for undergraduate courses. We show that short continuous recordings of the human locomotory apparatus possess properties typical of deterministic chaotic systems. To facilitate interest and enable the reproduction of presented results, as well as to promote applications of nonlinear time series analysis to other experimental systems, we provide user-friendly programs for each implemented method. Thus, we provide new insights into the dynamics of human locomotion, and make an effort to ease the inclusion of nonlinear time series analysis methods into the curriculum at an early stage of the educational process
Success-driven distribution of public goods promotes cooperation but preserves defection
No full textHigh-performance parallel computing in the classroom using the public goods game as an example
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Stability of subsystem solutions in agent-based models
No full textThe fact that relatively simple entities, such as particles or neurons, or even ants or bees or humans, give rise to fascinatingly complex behavior when interacting in large numbers is the hallmark of complex systems science. Agent-based models are frequently employed for modeling and obtaining a predictive understanding of complex systems. Since the sheer number of equations that describe the behavior of an entire agent-based model often makes it impossible to solve such models exactly, Monte Carlo simulation methods must be used for the analysis. However, unlike pairwise interactions among particles that typically govern solid-state physics systems, interactions among agents that describe systems in biology, sociology or the humanities often involve group interactions, and they also involve a larger number of possible states even for the most simplified description of reality. This begets the question: When can we be certain that an observed simulation outcome of an agent-based model is actually stable and valid in the large system-size limit? The latter is key for the correct determination of phase transitions between different stable solutions, and for the understanding of the underlying microscopic processes that led to these phase transitions. We show that a satisfactory answer can only be obtained by means of a complete stability analysis of subsystem solutions. A subsystem solution can be formed by any subset of all possible agent states. The winner between two subsystem solutions can be determined by the average moving direction of the invasion front that separates them, yet it is crucial that the competing subsystem solutions are characterized by a proper composition and spatiotemporal structure before the competition starts. We use the spatial public goods game with diverse tolerance as an example, but the approach has relevance for a wide variety of agent-based models.</jats:p
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