npj Complexity最新文献

Social media platforms frequently prioritize efficiency to maximize ad revenue and user engagement, often sacrificing deliberation, trust, and reflective, purposeful cognitive engagement in the process. This manuscript examines the potential of friction-design choices that intentionally slow user interactions-as an alternate approach. We present a case against efficiency as the dominant paradigm on social media and advocate for a complex systems approach to understanding and analyzing friction. Drawing from interdisciplinary literature, real-world examples, and industry experiments, we highlight the potential for friction to mitigate issues like polarization, disinformation, and toxic content without resorting to censorship. We propose a state space representation of friction to establish a multidimensional framework and language for analyzing the diverse forms and functions through which friction can be implemented. Additionally, we propose several experimental designs to examine the impact of friction on system dynamics, user behavior, and information ecosystems, each designed with complex systems solutions and perspectives in mind. Our case against efficiency underscores the critical role of friction in shaping digital spaces, challenging the relentless pursuit of efficiency and exploring the potential of thoughtful slowing.

Understanding how microorganisms adapt to novel physical and chemical environments requires integrating evolutionary, regulatory, and phenotypic perspectives. Here, we examined Streptococcus mutans populations previously evolved for 100 days under simulated microgravity (sMG) or combined microgravity and silver nitrate (sMGAg), generating new transcriptomic and phenotypic datasets and integrating them with prior whole-genome sequencing. These environments model key pressures encountered in enclosed spaceflight habitats, including altered fluid shear, oxidative challenges, and exposure to disinfectants. Populations maintained under normal gravity (NG) largely preserved ancestral metabolic and redox characteristics. In contrast, sMG populations exhibited divergent physiological and transcriptional outcomes that were not predictable from genomic variants alone, including multiple ROS response patterns, broad reductions in carbohydrate metabolism, and consistent retention of trehalose utilization. Populations evolved under sMGAg showed more convergent patterns, characterized by broad activation of oxidoreductase and metal-handling pathways, elevated basal ROS relative to the ancestral strain with reduced inducibility, and a consistent gain in nitrate-reduction capability. These outcomes reflect condition-associated physiological states resolved only through combined genomic, transcriptomic, and phenotype-level data, as no single data type was sufficient to capture the full structure of adaptive responses. Together, these findings illustrate how distinct physical and chemical stress regimes reshape the landscape of accessible evolutionary responses, with microgravity alone permitting a wider range of adaptive trajectories and microgravity combined with silver favoring more uniform physiological states. More broadly, this work demonstrates that integrated multi-level datasets are essential for accurately characterizing adaptive outcomes in extreme or non-terrestrial environments.

The emergence of phototrophy is one of the most significant innovations in the history of life, vastly increasing available metabolic energy. Phototrophy is, however, known to have arisen only twice. This raises a curious question: if phototrophy was accessible enough to evolve twice, why has it never arisen again despite billions of years of subsequent evolution? Through physiological modeling, we demonstrate that chlorophototrophy and retinalophototrophy together saturate the bioenergetic landscape available to light-harvesting systems. They represent opposite solutions to key biophysical trade-offs: maximizing efficiency per photon versus maximizing metabolic flux, specialization versus versatility, and sophistication versus simplicity. Together they create an evolutionary priority effect, blocking any newly-arising phototrophic system from succeeding. By revealing the basis of this competitive exclusion, our work sheds light on a general principle - that early innovations can saturate ecological space such that they constrain future evolutionary possibilities, making apparently 'easy' innovations appear as rare events.

Collectives are often able to process information in a distributed fashion, surpassing each individual member's processing capacity. In fission-fusion dynamics, where group members come together and split from others often, sharing complementary information about uniquely known foraging areas could allow a group to track a heterogenous foraging environment better than any group member on its own. We analyse the partial overlaps between individual spider monkey core ranges, which we assume represent the knowledge of an individual during a given season. Sets of individuals with complementary overlaps are identified, showing a balance between redundantly and uniquely known portions, and we use simplicial complexes to represent these higher-order interactions. The structures of the simplicial complexes show holes in various dimensions, revealing complementarity in the foraging information that is being shared. We propose that the complex spatial networks arising from fission-fusion dynamics allow for adaptive, collective processing of foraging information in dynamic environments.

Primates utilize distributed neural circuits to learn habits in uncertain environments, but the underlying mechanisms remain poorly understood. We propose a formal theory of network energetics explaining how brain states influence sequential behavior. We test our theory on multi-unit recordings from the caudate nucleus and cortical regions of macaques performing a motor habit task. The theory predicts the energy required to transition between brain states represented by trial-specific firing rates across channels, assuming activity spreads through effective connections. We hypothesized that habit formation would correlate with lower control energy. Consistent with this, we observed smaller energy requirements for transitions between similar saccade patterns and those of intermediate complexity, and sessions exploiting fewer patterns. Simulations ruled out confounds from neurons' directional tuning. Finally, virtual lesioning demonstrated the robustness of observed relationships between control energy and behavior. This work paves the way for examining how behavior arises from changing activity in distributed circuitry.

A sequence of technological inventions over several centuries has dramatically lowered the cost of producing and distributing information. Because societies ride on a substrate of information, these changes have profoundly impacted how we live, work, and interact. This paper explores the nature of information architectures (IAs)-the features that govern how information flows within human populations. IAs include physical and digital infrastructures, norms and institutions, and algorithmic technologies for filtering, producing, and disseminating information. IAs can reinforce societal biases and lead to prosocial outcomes as well as social ills. IAs have culturally evolved rapidly with human usage, creating new affordances and new problems for the dynamics of social interaction. We explore societal outcomes instigated by shifts in IAs and call for an enhanced understanding of the social implications of increasing IA complexity, the nature of competition among IAs, and the creation of mechanisms for the beneficial use of IAs.

From pathogens and computer viruses to genes and memes, contagion models have found widespread utility across the natural and social sciences. Despite their success and breadth of adoption, the approach and structure of these models remain surprisingly siloed by field. Given the siloed nature of their development and widespread use, one persistent assumption is that a given contagion can be studied in isolation, independently from what else might be spreading in the population. In reality, countless contagions of biological and social nature interact within hosts (interacting with existing beliefs, or the immune system) and across hosts (interacting in the environment, or affecting transmission mechanisms). Additionally, from a modeling perspective, we know that relaxing these assumptions has profound effects on the physics and translational implications of the models. Here, we review mechanisms for interactions in social and biological contagions, as well as the models and frameworks developed to include these interactions in the study of the contagions. We highlight existing problems related to the inference of interactions and to the scalability of mathematical models and identify promising avenues of future inquiries. In doing so, we highlight the need for interdisciplinary efforts under a unified science of contagions and for removing a common dichotomy between social and biological contagions.

Democratic governments comprise a subset of a population whose goal is to produce coherent decisions solving societal challenges while respecting the will of the people. New governance frameworks represent this as a social network rather than as a hierarchical pyramid with centralized authority. But how should this network be structured? We model the decisions a population must make as a satisfiability problem and the structure of information flow involved in decision-making as a social hypergraph. This framework allows to consider different governance structures, from dictatorships to direct democracy. Between these extremes, we find a regime of effective governance where small overlapping decision groups make specific decisions and share information. Effective governance allows even incoherent or polarized populations to make coherent decisions at low coordination costs. Beyond simulations, our conceptual framework can explore a wide range of governance strategies and their ability to tackle decision problems that challenge standard governments.

Contagions spreading across space-including epidemics, infodemics, and socio-economic turbulence - generate complex geo-spatial patterns shaped by contagion state and risk-driven population mobility. Distribution of resources for mitigating these contagions adds further complexity. We present a concise, generic framework to model various contagion types within a space characterized by bounded risk disposition parameters and generalized resource effectiveness. Specifically, we explore how (i) risk-averse behavior of "inoculated" individuals and (ii) resource effectiveness in reducing contagion "incidence" influence pattern formation and spread of infection, opinion polarization, social myths, and socio-economic disruptions. We show that "inoculated" individuals interacting with affected populations may help minimize contagion impact by curbing further transmission. We identify this as a generalized form of shield immunity and explain its emergence in terms of individual risk disposition. This shielding effect is strongest in socio-economic turbulence, moderate in epidemics, limited in social myth spreading, and not observed in polarization dynamics.

Collaboration enables groups to solve problems beyond the reach of their individual members in contexts ranging from research and development to high-energy physics. While communication networks play a pivotal role in group success, there is a longstanding debate on the optimal network topology for solving complex problems. Prior research reaches contradictory conclusions-some studies suggest networks that slow information transmission help maintain diversity, leading groups to explore more of the problem space and find better solutions in the long run, while others argue that networks that maximize communication efficiency allow groups to exploit known solutions, boosting overall performance. Many existing models assume that individuals use their network connections only to copy better-performing group members, but we show that such groups often perform worse than if individuals worked independently. Instead, our model introduces a crucial distinction: in addition to copying, individuals can actively collaborate, leveraging diverse perspectives to uncover solutions that would otherwise remain inaccessible. Our findings reveal that the optimal network structure depends on the balance between copying and collaboration. When copying dominates, inefficient, exploration-focused networks lead to better outcomes. However, when individuals primarily collaborate, highly connected, efficient networks win out. We also show how groups can reap the benefits of both strategies by employing a collaborate first-copy later heuristic in highly connected networks. The results offer new insights into how organizations should be structured to maximize problem-solving performance across different contexts.