arXiv subjects

Yaneer Bar-Yam

Publications and source records attributed to Yaneer Bar-Yam.

At least 19 recordsLinked to original sources

arXiv2025

A Formal Definition of Scale-dependent Complexity and the Multi-scale Law of Requisite Variety

Ashby's law of requisite variety allows a comparison of systems with their environments, providing a necessary (but not sufficient) condition for system efficacy: a system must possess at least as much complexity as any set of environmental behaviors that require distinct responses from the system. However, to account for the dependence of a system's complexity on the level of detail -- or scale -- of its description, a multi-scale generalization of Ashby's law is needed. We define a class of complexity profiles (complexity as a function of scale) that is the first, to our knowledge, to exhibit a multi-scale law of requisite variety. This formalism provides a characterization of multi-scale complexity and generalizes the law of requisite variety's single constraint on system behaviors to a class of multi-scale constraints. We show that these complexity profiles satisfy a sum rule, which reflects a tradeoff between smaller- and larger-scale degrees of freedom, and we extend our results to subdivided systems and systems with a continuum of components.

physics.soc-ph↗

arXiv2025

Predicting System Dynamics of Universal Growth Patterns in Complex Systems

Predicting dynamic behaviors is one of the goals of science in general as well as essential to many specific applications of human knowledge to real world systems. Here we introduce an analytic approach using the sigmoid growth curve to model the dynamics of individual entities within complex systems. Despite the challenges posed by nonlinearity and unpredictability in system behaviors, we demonstrate the applicability of the sigmoid curve to capture the acceleration and deceleration of growth, predicting an entitys ultimate state well in advance of reaching it. We show that our analysis can be applied to diverse systems where entities exhibit nonlinear growth using case studies of (1) customer purchasing and (2) U.S. legislation adoption. This showcases the ability to forecast months to years ahead of time, providing valuable insights for business leaders and policymakers. Moreover, our characterization of individual component dynamics offers a framework to reveal the aggregate behavior of the entire system. We introduce a classification of entities based upon similar lifepaths. This study contributes to the understanding of complex system behaviors, offering a practical tool for prediction and system behavior insight that can inform strategic decision making in multiple domains.

stat.AP↗

arXiv2024

Self-similarity in pandemic spread and fractal containment policies

Although pandemics are often studied as if populations are well-mixed, disease transmission networks exhibit a multi-scale structure stretching from the individual all the way up to the entire globe. The COVID-19 pandemic has led to an intense debate about whether interventions should prioritize public health or the economy, leading to a surge of studies analyzing the health and economic costs of various response strategies. Here we show that describing disease transmission in a self-similar (fractal) manner across multiple geographic scales allows for the design of multi-scale containment measures that substantially reduce both these costs. We characterize response strategies using multi-scale reproduction numbers -- a generalization of the basic reproduction number $R_0$ -- that describe pandemic spread at multiple levels of scale and provide robust upper bounds on disease transmission. Stable elimination is guaranteed if there exists a scale such that the reproduction number among regions of that scale is less than $1$, even if the basic reproduction number $R_0$ is greater than $1$. We support our theoretical results using simulations of a heterogeneous SIS model for disease spread in the United States constructed using county-level commuting, air travel, and population data.

q-bio.PE↗

arXiv2023

Quantitative errors in the Cochrane review on "Physical interventions to interrupt or reduce the spread of respiratory viruses"

The COVID-19 pandemic has heightened the urgency to understand and prevent pathogen transmission, specifically regarding infectious airborne particles. Extensive studies validate the understanding of larger (droplets) and smaller (aerosols) particles in disease transmission. Similarly, N95 respirators, and other forms of respiratory protection, have proven efficacy in reducing the risk of infection across various environments. Even though multiple studies confirm their protective effect when adopted in healthcare and public settings for infection prevention, studies on their adoption over the last several decades in both clinical trials and observational studies have not provided as clear an understanding. Here we show that the standard analytical equations used in the analysis of these studies do not accurately represent the random variables impacting study results. By correcting these equations, it is demonstrated that conclusions drawn from these studies are heavily biased and uncertain, providing little useful information. Despite these limitations, we show that when outcome measures are properly analyzed, existing results consistently point to the benefit of N95 respirators over medical masks, and masking over its absence. Correcting errors in widely reported meta-analyses also yields statistically significant estimates. These findings have important implications for study design and using existing evidence for infection control policy guidelines.

physics.soc-ph↗

arXiv2022

Modeling complex systems: A case study of compartmental models in epidemiology

Compartmental epidemic models have been widely used for predicting the course of epidemics, from estimating the basic reproduction number to guiding intervention policies. Studies commonly acknowledge these models' assumptions but less often justify their validity in the specific context in which they are being used. Our purpose is not to argue for specific alternatives or modifications to compartmental models, but rather to show how assumptions can constrain model outcomes to a narrow portion of the wide landscape of potential epidemic behaviors. This concrete examination of well-known models also serves to illustrate general principles of modeling that can be applied in other contexts.

q-bio.PE↗

arXiv2021

Was India saved by staying below the critical travel threshold and was lockdown and travel restriction the most important public health intervention?

Indian response to the pandemic has been described from "India is in denial about the covid-19 crisis" or "India staring at corona virus disaster", to "The mystery of India's plummeting covid-19 cases". These responses have been far from being backed scientifically and appear ignorant of India's capabilities of leveraging its strengths to mitigate the impact of the pandemic. Backed by a swift Government action of restricting/regulating movement to increasing public health capacity to meet the increasing demands of the pandemic, India seems to have done enough to emerge successful. India is doing well, if not guaranteed for the future, but at least for now. Here we review these measures and point to their consistency with analysis of the role of intercommunity transmission and within community action to stop localized outbreaks. In particular, severe restrictions on travel, stopping gatherings, targeted localized lockdowns, school closures, effective public communication, improvements in case identification, rapid ramping of industrial production of masks and other personal protective equipment (PPE) and testing capacity, as well as intensive measures in high density areas of urban deprivation have placed India in a regime of declining cases and outbreak control. It is time to recognize the scientific basis of India's success and give it its due. With the number of new cases in India leveling recently, the urgency is great to complete the eliminiation process so that a new surge does not occur.

physics.soc-ph↗

arXiv2021

Unmasking the mask studies: why the effectiveness of surgical masks in preventing respiratory infections has been underestimated

Face masks have been widely used as a protective measure against COVID-19. However, pre-pandemic empirical studies have produced mixed statistical results on the effectiveness of masks against respiratory viruses. The implications of the studies' recognized limitations have not been quantitatively and statistically analyzed, leading to confusion regarding the effectiveness of masks. Such confusion may have contributed to organizations such as the WHO and CDC initially not recommending that the general public wear masks. Here we show that when the adherence to mask-usage guidelines is taken into account, the empirical evidence indicates that masks prevent disease transmission: all studies we analyzed that did not find surgical masks to be effective were under-powered to such an extent that even if masks were 100% effective, the studies in question would still have been unlikely to find a statistically significant effect. We also provide a framework for understanding the effect of masks on the probability of infection for single and repeated exposures. The framework demonstrates that more frequently wearing a mask provides super-linearly compounding protection, as does both the susceptible and infected individual wearing a mask. This work shows (1) that both theoretical and empirical evidence is consistent with masks protecting against respiratory infections and (2) that nonlinear effects and statistical considerations regarding the percentage of exposures for which masks are worn must be taken into account when designing empirical studies and interpreting their results.

q-bio.QM↗

arXiv2021

Lowest-cost virus suppression

Analysis of policies for managing epidemics require simultaneously an economic and epidemiological perspective. We adopt a cost-of-policy framework to model both the virus spread and the cost of handling the pandemic. Because it is harder and more costly to fight the pandemic when the circulation is higher, we find that the optimal policy is to go to zero or near-zero case numbers. Without imported cases, if a region is willing to implement measures to prevent spread at one level in number of cases, it must also be willing to prevent the spread with at a lower level, since it will be cheaper to do so and has only positive other effects. With imported cases, if a region is not coordinating with other regions, we show the cheapest policy is continually low but nonzero cases due to decreasing cost of halting imported cases. When it is coordinating, zero is cost-optimal. Our analysis indicates that within Europe cooperation targeting a reduction of both within country transmission, and between country importation risk, should help achieve lower transmission and reduced costs.

econ.GN↗

arXiv2020

Comment on: A systematic review and meta-analysis of published research data on COVID-19 infection-fatality rates

The infection fatality rate (IFR) of COVID-19 is one of the measures of disease impact that can be of importance for policy making. Here we show that many of the studies on which these estimates are based are scientifically flawed for reasons which include: nonsensical equations, unjustified assumptions, small sample sizes, non-representative sampling (systematic biases), incorrect definitions of symptomatic and asymptomatic cases (identified and unidentified cases), typically assuming that cases which are asymptomatic at the time of testing are the same as completely asymptomatic (never symptomatic) cases. Moreover, a widely cited meta-analysis misrepresents some of the IFR values in the original studies, and makes inappropriate duplicate use of studies, or the information from studies, so that the results that are averaged are not independent from each other. The lack of validity of these research papers is of particular importance in view of their influence on policies that affect lives and well-being in confronting a worldwide pandemic.

q-bio.PE↗

arXiv2020

Strategizing COVID-19 Lockdowns Using Mobility Patterns

During the COVID-19 pandemic, governments have tried to keep their territories safe by isolating themselves from others, limiting non-essential travel and imposing mandatory quarantines for travelers. While large-scale quarantine has been the most successful short-term policy, it is unsustainable over long periods as it exerts enormous costs on societies. As a result, governments which have been able to partially control the spread of the disease have been deciding to reopen businesses. However, the WHO has warned about the risks of re-opening prematurely, as is playing out in some countries such as Spain, France and various states in the US such as California, Florida, Arizona, and Texas. Thus, it is urgent to consider a flexible policy that limits transmission without requiring large scale and damaging quarantines. Here, we have designed a multi-level quarantine process based on the mobility patterns of individuals and the severity of COVID-19 contagion in the US. By identifying the natural boundaries of social mobility, policymakers can impose travel restrictions that are minimally disruptive to social and economic activity. The dynamics of social fragmentation during the COVID-19 outbreak are analyzed by applying the Louvain method with modularity optimization to weekly mobility networks. In a multi-scale community detection process, using the locations of confirmed cases, natural break points as well as high risk areas for contagion are identified. At the smaller scales, for communities with a higher number of confirmed cases, contact tracing and associated quarantine policies is increasingly important and can be informed by the community structure.

physics.soc-ph↗

arXiv2020

Negative Representation and Instability in Democratic Elections

The challenge of understanding the collective behaviors of social systems can benefit from methods and concepts from physics [1-6], not because humans are similar to electrons, but because certain large-scale behaviors can be understood without an understanding of the small-scale details [7], in much the same way that sound waves can be understood without an understanding of atoms. Democratic elections are one such behavior. Over the past few decades, physicists have explored scaling patterns in voting and the dynamics of political opinion formation, e.g. [8-13]. Here, we define the concepts of negative representation, in which a shift in electorate opinions produces a shift in the election outcome in the opposite direction, and electoral instability, in which an arbitrarily small change in electorate opinions can dramatically swing the election outcome, and prove that unstable elections necessarily contain negatively represented opinions. Furthermore, in the presence of low voter turnout, increasing polarization of the electorate can drive elections through a transition from a stable to an unstable regime, analogous to the phase transition by which some materials become ferromagnetic below their critical temperatures. Empirical data suggest that United States presidential elections underwent such a phase transition in the 1970s and have since become increasingly unstable.

physics.soc-ph↗

arXiv2020

On Single Point Forecasts for Fat-Tailed Variables

We discuss common errors and fallacies when using naive "evidence based" empiricism and point forecasts for fat-tailed variables, as well as the insufficiency of using naive first-order scientific methods for tail risk management. We use the COVID-19 pandemic as the background for the discussion and as an example of a phenomenon characterized by a multiplicative nature, and what mitigating policies must result from the statistical properties and associated risks. In doing so, we also respond to the points raised by Ioannidis et al. (2020).

physics.soc-ph↗

arXiv2020

Combining PCR and CT testing for COVID

We analyze the effect of using a screening CT-scan for evaluation of potential COVID-19 infections in order to isolate and perform contact tracing based upon a viral pneumonia diagnosis. RT-PCR is then used for continued isolation based upon a COVID diagnosis. Both the low false negative rates and rapid results of CT-scans lead to dramatically reduced transmission. The reduction in cases after 60 days with widespread use of CT-scan screening compared to PCR by itself is as high as $50\times$, and the reduction of effective reproduction rate $R(t)$ is $0.20$. Our results imply that much more rapid extinction of COVID is possible by combining social distancing with CT-scans and contact tracing.

q-bio.PE↗

arXiv2020

An Introduction to Complex Systems Science and its Applications

The standard assumptions that underlie many conceptual and quantitative frameworks do not hold for many complex physical, biological, and social systems. Complex systems science clarifies when and why such assumptions fail and provides alternative frameworks for understanding the properties of complex systems. This review introduces some of the basic principles of complex systems science, including complexity profiles, the tradeoff between efficiency and adaptability, the necessity of matching the complexity of systems to that of their environments, multi-scale analysis, and evolutionary processes. Our focus is on the general properties of systems as opposed to the modeling of specific dynamics; rather than provide a comprehensive review, we pedagogically describe a conceptual and analytic approach for understanding and interacting with the complex systems of our world. With the exception of a few footnotes, this paper assumes only a high school mathematical and scientific background, so that it may be accessible to academics in all fields, decision-makers in industry, government, and philanthropy, and anyone who is interested in systems and society.

physics.soc-ph↗

arXiv2020

Eliminating COVID-19: The Impact of Travel and Timing

We analyze the spread of COVID-19 by considering the transmission of the disease among individuals both within and between regions. A set of regions can be defined as any partition of a population such that travel/social contact within each region far exceeds that between them. COVID-19 can be eliminated if the region-to-region reproductive number---i.e. the average number of other regions to which a single infected region will transmit the virus---is reduced to less than one. We find that this region-to-region reproductive number is proportional to the travel rate between regions and exponential in the length of the time-delay before region-level control measures are imposed. Thus, reductions in travel and the speed with which regions take action play decisive roles in whether COVID-19 is eliminated from a collection of regions. If, on average, infected regions (including those that become re-infected in the future) impose social distancing measures shortly after active spreading begins within them, the number of infected regions, and thus the number of regions in which such measures are required, will exponentially decrease over time. Elimination will in this case be a stable fixed point even after the social distancing measures have been lifted from most of the regions.

q-bio.PE↗

arXiv2020

Segregated interactions in urban and online space

Urban income segregation is a widespread phenomenon that challenges societies across the globe. Classical studies on segregation have largely focused on the geographic distribution of residential neighborhoods rather than on patterns of social behaviors and interactions. In this study, we analyze segregation in economic and social interactions by observing credit card transactions and Twitter mentions among thousands of individuals in three culturally different metropolitan areas. We show that segregated interaction is amplified relative to the expected effects of geographic segregation in terms of both purchase activity and online communication. Furthermore, we find that segregation increases with difference in socio-economic status but is asymmetric for purchase activity, i.e., the amount of interaction from poorer to wealthier neighborhoods is larger than vice versa. Our results provide novel insights into the understanding of behavioral segregation in human interactions with significant socio-political and economic implications.

cs.SI↗

arXiv2019

Segregation Dynamics with Reinforcement Learning and Agent Based Modeling

Societies are complex. Properties of social systems can be explained by the interplay and weaving of individual actions. Incentives are key to understand people's choices and decisions. For instance, individual preferences of where to live may lead to the emergence of social segregation. In this paper, we combine Reinforcement Learning (RL) with Agent Based Models (ABM) in order to address the self-organizing dynamics of social segregation and explore the space of possibilities that emerge from considering different types of incentives. Our model promotes the creation of interdependencies and interactions among multiple agents of two different kinds that want to segregate from each other. For this purpose, agents use Deep Q-Networks to make decisions based on the rules of the Schelling Segregation model and the Predator-Prey model. Despite the segregation incentive, our experiments show that spatial integration can be achieved by establishing interdependencies among agents of different kinds. They also reveal that segregated areas are more probable to host older people than diverse areas, which attract younger ones. Through this work, we show that the combination of RL and ABMs can create an artificial environment for policy makers to observe potential and existing behaviors associated to incentives.

physics.soc-ph↗

arXiv2019

U.S. Social Fragmentation at Multiple Scales

Despite global connectivity, societies seem to be increasingly polarized and fragmented. This phenomenon is rooted in the underlying complex structure and dynamics of social systems. Far from homogeneously mixing or adopting conforming views, individuals self-organize into groups at multiple scales, ranging from families up to cities and cultures. In this paper, we study the fragmented structure of the American society using mobility and communication networks obtained from geo-located social media data. We find self-organized patches with clear geographical borders that are consistent between physical and virtual spaces. The patches have multi-scale structure ranging from parts of a city up to the entire nation. Their significance is reflected in distinct patterns of collective interests and conversations. Finally, we explain the patch emergence by a model of network growth that combines mechanisms of geographical distance gravity, preferential attachment, and spatial growth. Our observations are consistent with the emergence of social groups whose separated association and communication reinforce distinct identities. Rather than eliminating borders, the virtual space reproduces them as people mirror their offline lives online. Understanding the mechanisms driving the emergence of fragmentation in hyper-connected social systems is imperative in the age of the Internet and globalization.

physics.soc-ph↗