npj Complexity最新文献

Assembly theory (AT) quantifies selection using the assembly equation, identifying complex objects through the assembly index, the minimal steps required to build an object from basic parts, and copy number, the observed instances of the object. These measure a quantity called Assembly, capturing causation necessary to produce abundant objects, distinguishing selection-driven complexity from random generation. Unlike computational complexity theory, which often emphasizes minimal description length via compressibility, AT explicitly focuses on the causation captured by selection as the mechanism behind complexity. We illustrate formal distinctions through mathematical examples demonstrating that the assembly index is fundamentally distinct from complexity metrics like Shannon entropy, Huffman encoding, and Lempel-Ziv-Welch compression. We provide proofs showing that the assembly index belongs to a different computational complexity class compared to these measures and compression algorithms. Additionally, we highlight AT's unique ontological grounding as a physically measurable framework, setting it apart from abstract theoretical approaches to formalizing life that lack empirical measurement foundations.

The adoption of individual behavioural patterns is largely determined by stimuli arriving from peers via social interactions or from external sources. Based on these influences, individuals are commonly assumed to follow simple or complex adoption rules, inducing social contagion processes. In reality, multiple adoption rules may coexist even within the same social contagion process, introducing additional complexity to the spreading phenomena. Our goal is to understand whether coexisting adoption mechanisms can be distinguished from a microscopic view at the egocentric network level without requiring global information about the underlying network, or the unfolding spreading process. We formulate this question as a classification problem, and study it through a likelihood approach and with random forest classifiers in various synthetic and data-driven experiments. This study offers a novel perspective on the observations of propagation processes at the egocentric level and a better understanding of landmark contagion mechanisms from a local view.

Social media has enabled the spread of information at unprecedented speeds and scales, and with it the proliferation of high-engagement, low-quality content. Friction-behavioral design measures that make the sharing of content more cumbersome-might be a way to raise the quality of what is spread online. In this perspective, we propose a scalable field experiment to study the effects of friction with a learning component to educate users on the platform's community standards. Preliminary simulations from an agent-based model suggest that while friction alone may decrease the number of posts without improving their quality, it could significantly increase the average quality of posts when combined with learning. The model also suggests that too much friction could be counterproductive. Experimental interventions inspired by these findings would be minimally invasive.

Autocatalysis is thought to have played an important role in the earliest stages of the origin of life. An autocatalytic cycle (AC) is a set of reactions that results in stoichiometric increase in its constituent chemicals. When the reactions of multiple interacting ACs are active in a region of space, they can have interactions analogous to those between species in biological ecosystems. Prior studies of autocatalytic chemical ecosystems (ACEs) have suggested avenues for accumulating complexity, such as ecological succession, as well as obstacles such as competitive exclusion. We extend this ecological framework to investigate the effects of surface adsorption, desorption, and diffusion on ACE ecology. Simulating ACEs as particle-based stochastic reaction-diffusion systems in spatial environments-including open, two-dimensional reaction-diffusion systems and adsorptive mineral surfaces-we demonstrate that spatial structure can enhance ACE diversity by (i) permitting otherwise mutually exclusive ACs to coexist and (ii) subjecting new AC traits to selection.

Understanding how misinformation affects the spread of disease is crucial for public health, especially given recent research indicating that misinformation can increase vaccine hesitancy and discourage vaccine uptake. However, it is difficult to investigate the interaction between misinformation and epidemic outcomes due to the dearth of data-informed holistic epidemic models. Here, we employ an epidemic model that incorporates a large, mobility-informed physical contact network as well as the distribution of misinformed individuals across counties derived from social media data. The model allows us to simulate various scenarios to understand how epidemic spreading can be affected by misinformation spreading through one particular social media platform. Using this model, we compare a worst-case scenario, in which individuals become misinformed after a single exposure to low-credibility content, to a best-case scenario where the population is highly resilient to misinformation. We estimate the additional portion of the U.S. population that would become infected over the course of the COVID-19 epidemic in the worst-case scenario. This work can provide policymakers with insights about the potential harms of exposure to online vaccine misinformation.