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Mitigation strategies against cascading failures within a project activity network

Complexity Digest - Sat, 07/03/2021 - 14:18

Christos Ellinas, Christos Nicolaides & Naoki Masuda 
Journal of Computational Social Science (2021)

Successful on-time delivery of projects is a key enabler in resolving major societal challenges, such as wasted resources and stagnated economic growth. However, projects are notoriously hard to deliver successfully, partly due to their interconnected and temporal complexity which makes them prone to cascading failures. Here, we develop a cascading failure model and test it on a temporal activity network, extracted from a large-scale engineering project. We evaluate the effectiveness of six mitigation strategies, in terms of the impact of task failure cascading throughout the project. In contrast to theoretical arguments, our results indicate that in the majority of cases, the temporal properties of the activities are more relevant than their structural properties in preventing large-scale cascading failures. In practice, these findings could stimulate new pathways for designing and scheduling projects that naturally limit the extent of cascading failures.

Read the full article at: link.springer.com

Early lock-in of structured and specialised information flows during neural development

Complexity Digest - Sat, 07/03/2021 - 08:12

David P. Shorten, Viola Priesemann, Michael Wibral, Joseph T. Lizier

The brains of many organisms are capable of complicated distributed computation underpinned by a highly advanced information processing capacity. Although substantial progress has been made towards characterising the information flow component of this capacity in mature brains, there is a distinct lack of work characterising its emergence during neural development. This lack of progress has been largely driven by the lack of effective estimators of information processing operations for the spiking data available for developing neural networks. Here, we leverage recent advances in this estimation task in order to quantify the changes in information flow during development. We find that the quantity of information flowing across these networks undergoes a dramatic increase across development. Moreover, the spatial structure of these flows is locked-in during early development, after which there is a substantial temporal correlation in the information flows across recording days. We analyse the flow of information during the crucial periods of population bursts. We find that, during these bursts, nodes undertake specialised computational roles as either transmitters, mediators or receivers of information, with these roles tending to align with their spike ordering either early, mid or late in the bursts. That the nodes identified as information flow mediators tend to spike mid burst aligns with conjecture that nodes spiking in this position play an important role as brokers of neuronal communication. Finally, it was found that the specialised computational roles occupied by nodes during bursts tend to be locked-in early.

Read the full article at: www.biorxiv.org

Genomic windows into ancient epidemics

Complexity Digest - Sat, 07/03/2021 - 06:12

Ornob Alam

Population genomic analyses of ancient and modern individuals are providing unprecedented resolution in our view of past human demography and adaptive episodes. Here, I highlight three recent studies in the field that investigated ancient human encounters with major pathogens.

Read the full article at: natureecoevocommunity.nature.com

Synchronizing billion-scale automata

Complexity Digest - Thu, 07/01/2021 - 14:46

Mustafa Kemal Taş, Kamer Kaya, Hüsnü Yenigün

Information Sciences
Volume 574, October 2021, Pages 162-175

  • Existing synchronization heuristics do not scale due to quadratic space complexity.
  • We propose a simple approach to avoid memory usage thanks to massive parallelism.
  • We use different parallelization approaches on CPUs and GPUs, in a hybrid way.
  • A different treatment of parallelism is useful at different phases of the algorithm.
  • Our algorithms can synchronize a billion-state automaton in around 4 mins.

Read the full article at: www.sciencedirect.com

Decentralized Edge-to-Cloud Load Balancing: Service Placement for the Internet of Things

Complexity Digest - Thu, 07/01/2021 - 10:23

Zeinab Nezami; Kamran Zamanifar; Karim Djemame; Evangelos Pournaras

IEEE Access ( Volume: 9)

The Internet of Things (IoT) requires a new processing paradigm that inherits the scalability of the cloud while minimizing network latency using resources closer to the network edge. On the one hand, building up such flexibility within the edge-to-cloud continuum consisting of a distributed networked ecosystem of heterogeneous computing resources is challenging. On the other hand, IoT traffic dynamics and the rising demand for low-latency services foster the need for minimizing the response time and a balanced service placement. Load-balancing for fog computing becomes a cornerstone for cost-effective system management and operations. This paper studies two optimization objectives and formulates a decentralized load-balancing problem for IoT service placement: (global) IoT workload balance and (local) quality of service (QoS), in terms of minimizing the cost of deadline violation, service deployment, and unhosted services. The proposed solution, EPOS Fog, introduces a decentralized multi-agent system for collective learning that utilizes edge-to-cloud nodes to jointly balance the input workload across the network and minimize the costs involved in service execution. The agents locally generate possible assignments of requests to resources and then cooperatively select an assignment such that their combination maximizes edge utilization while minimizes service execution cost. Extensive experimental evaluation with realistic Google cluster workloads on various networks demonstrates the superior performance of EPOS Fog in terms of workload balance and QoS, compared to approaches such as First Fit and exclusively Cloud-based. The results confirm that EPOS Fog reduces service execution delay up to 25% and the load-balance of network nodes up to 90%. The findings also demonstrate how distributed computational resources on the edge can be utilized more cost-effectively by harvesting collective intelligence.

Read the full article at: ieeexplore.ieee.org

Stewardship of global collective behavior

Complexity Digest - Sun, 06/27/2021 - 12:28

Joseph B. Bak-Coleman, et al.

PNAS July 6, 2021 118 (27) e2025764118

Collective behavior provides a framework for understanding how the actions and properties of groups emerge from the way individuals generate and share information. In humans, information flows were initially shaped by natural selection yet are increasingly structured by emerging communication technologies. Our larger, more complex social networks now transfer high-fidelity information over vast distances at low cost. The digital age and the rise of social media have accelerated changes to our social systems, with poorly understood functional consequences. This gap in our knowledge represents a principal challenge to scientific progress, democracy, and actions to address global crises. We argue that the study of collective behavior must rise to a “crisis discipline” just as medicine, conservation, and climate science have, with a focus on providing actionable insight to policymakers and regulators for the stewardship of social systems.

Read the full article at: www.pnas.org

Swarm Robotics: Past, Present, and Future

Complexity Digest - Sat, 06/26/2021 - 13:21

Marco Dorigo; Guy Theraulaz; Vito Trianni

Proceedings of the IEEE ( Volume: 109, Issue: 7, July 2021)

Swarm robotics deals with the design, construction, and deployment of large groups of robots that coordinate and cooperatively solve a problem or perform a task. It takes inspiration from natural self-organizing systems, such as social insects, fish schools, or bird flocks, characterized by emergent collective behavior based on simple local interaction rules [1] , [2] . Typically, swarm robotics extracts engineering principles from the study of those natural systems in order to provide multirobot systems with comparable abilities. This way, it aims to build systems that are more robust, fault-tolerant, and flexible than single robots and that can better adapt their behavior to changes in the environment.

Read the full article at: ieeexplore.ieee.org

Rethinking cognition: From animal to minimal

Complexity Digest - Fri, 06/25/2021 - 17:54

Lucia Regolin and Giorgio Vallortigara

Biochemical and Biophysical Research Communications
Volume 564

In its current use, cognition refers to all activities and processes dealing with the acquisition, storage, retrieval, and processing of information, and this seems to imply the involvement of a relatively complex nervous system. The term “relatively complex” usually refers to a direct comparison with the human or primate brain. And most research on comparative cognition and its neural bases has been restricted to a limited range of species within the vertebrate taxonomic groups. In the last 20 years, however, comparative research has been accumulating a huge bulk of scientific evidence for a wide range of processes in a variety of distantly related species, that seem to imply cognitive phenomena. Intriguing evidence of sophisticated behaviour has come from models which are extremely distant from primates, sometimes organisms with miniature brains. Great attention has attracted the (unexpected by many) evidence of cognitive behaviour in invertebrates and even in organisms classified outside of the Animal Kingdom. In 1980s Humberto Maturana suggested that: “Living systems are cognitive systems, and living as a process is a process of cognition”, extending this statement to all organisms “with or without a nervous system” [1]. This was of course anticipated by the famous statement by Konrad Lorenz according to whom “Life itself is a process of acquiring knowledge” [2].

Read the full article at: www.sciencedirect.com

See Special Issue: Rethinking Cognition: From Animal to Minimal

Association between COVID-19 outcomes and mask mandates, adherence, and attitudes

Complexity Digest - Fri, 06/25/2021 - 15:53

Dhaval Adjodah,Karthik Dinakar,Matteo Chinazzi,Samuel P. Fraiberger,Alex Pentland,Samantha Bates,Kyle Staller,Alessandro Vespignani,Deepak L. Bhatt

PLoS ONE 16(6): e0252315.

We extend previous studies on the impact of masks on COVID-19 outcomes by investigating an unprecedented breadth and depth of health outcomes, geographical resolutions, types of mask mandates, early versus later waves and controlling for other government interventions, mobility testing rate and weather. We show that mask mandates are associated with a statistically significant decrease in new cases (-3.55 per 100K), deaths (-0.13 per 100K), and the proportion of hospital admissions (-2.38 percentage points) up to 40 days after the introduction of mask mandates both at the state and county level. These effects are large, corresponding to 14% of the highest recorded number of cases, 13% of deaths, and 7% of admission proportion. We also find that mask mandates are linked to a 23.4 percentage point increase in mask adherence in four diverse states. Given the recent lifting of mandates, we estimate that the ending of mask mandates in these states is associated with a decrease of -3.19 percentage points in mask adherence and 12 per 100K (13% of the highest recorded number) of daily new cases with no significant effect on hospitalizations and deaths. Lastly, using a large novel survey dataset of 847 thousand responses in 69 countries, we introduce the novel results that community mask adherence and community attitudes towards masks are associated with a reduction in COVID-19 cases and deaths. Our results have policy implications for reinforcing the need to maintain and encourage mask-wearing by the public, especially in light of some states starting to remove their mask mandates.

Read the full article at: journals.plos.org

When and how to reopen?

Complexity Digest - Fri, 06/25/2021 - 13:42

If we reopen carelessly, new cases will surge and further lockdowns will be required. From our recent experience, we know how to reopen safely. Why shouldn’t we?

Read the full article at: covidactiongroup.net

Socio-Economic Impact of the Covid-19 Pandemic in the U.S.

Complexity Digest - Thu, 06/24/2021 - 14:00

Jonathan Barlow and Irena Vodenska

This paper proposes a dynamic cascade model to investigate the systemic risk posed by sector-level industries within the U.S. inter-industry network. We then use this model to study the effect of the disruptions presented by Covid-19 on the U.S. economy. We construct a weighted digraph G = (V,E,W) using the industry-by-industry total requirements table for 2018, provided by the Bureau of Economic Analysis (BEA). We impose an initial shock that disrupts the production capacity of one or more industries, and we calculate the propagation of production shortages with a modified Cobb–Douglas production function. For the Covid-19 case, we model the initial shock based on the loss of labor between March and April 2020 as reported by the Bureau of Labor Statistics (BLS). The industries within the network are assigned a resilience that determines the ability of an industry to absorb input losses, such that if the rate of input loss exceeds the resilience, the industry fails, and its outputs go to zero. We observed a critical resilience, such that, below this critical value, the network experienced a catastrophic cascade resulting in total network collapse. Lastly, we model the economic recovery from June 2020 through March 2021 using BLS data.

Read the full article at: www.mdpi.com

Dynamics of Disruption in Science and Technology

Complexity Digest - Wed, 06/23/2021 - 14:42

Michael Park, Erin Leahey, Russell Funk

Although the number of new scientific discoveries and technological
inventions has increased dramatically over the past century, there have also
been concerns of a slowdown in the progress of science and technology. We
analyze 25 million papers and 4 million patents across 6 decades and find that
science and technology are becoming less disruptive of existing knowledge, a
pattern that holds nearly universally across fields. We link this decline in
disruptiveness to a narrowing in the utilization of existing knowledge.
Diminishing quality of published science and changes in citation practices are
unlikely to be responsible for this trend, suggesting that this pattern
represents a fundamental shift in science and technology.

Read the full article at: arxiv.org

The Ascent of Information: Books, Bits, Genes, Machines, and Life’s Unending Algorithm. Scharf, Caleb

Complexity Digest - Sat, 06/19/2021 - 11:43

One of the most peculiar and possibly unique features of humans is the vast amount of information we carry outside our biological selves. But in our rush to build the infrastructure for the 20 quintillion bits we create every day, we’ve failed to ask exactly why we’re expending ever-increasing amounts of energy, resources, and human effort to maintain all this data.

Drawing on deep ideas and frontier thinking in evolutionary biology, computer science, information theory, and astrobiology, Caleb Scharf argues that information is, in a very real sense, alive. All the data we create—all of our emails, tweets, selfies, A.I.-generated text and funny cat videos—amounts to an aggregate lifeform. It has goals and needs. It can control our behavior and influence our well-being. And it’s an organism that has evolved right alongside us.

This symbiotic relationship with information offers a startling new lens for looking at the world. Data isn’t just something we produce; it’s the reason we exist. This powerful idea has the potential to upend the way we think about our technology, our role as humans, and the fundamental nature of life.

The Ascent of Information offers a humbling vision of a universe built of and for information. Scharf explores how our relationship with data will affect our ongoing evolution as a species. Understanding this relationship will be crucial to preventing our data from becoming more of a burden than an asset, and to preserving the possibility of a human future.

More at: www.amazon.com

Extracting real social interactions from social media: a debate of COVID-19 policies in Mexico

Complexity Digest - Fri, 06/18/2021 - 12:22

Alberto García-Rodríguez, Tzipe Govezensky, Carlos Gershenson, Gerardo G. Naumis, Rafael A. Barrio
A study of the dynamical formation of networks of friends and enemies in social media, in this case Twitter, is presented. We characterise the single node properties of such networks, as the clustering coefficient and the degree, to investigate the structure of links. The results indicate that the network is made from three kinds of nodes: one with high clustering coefficient but very small degree, a second group has zero clustering coefficient with variable degree, and finally, a third group in which the clustering coefficient as a function of the degree decays as a power law. This third group represents ∼2% of the nodes and is characteristic of dynamical networks with feedback. This part of the lattice seemingly represents strongly interacting friends in a real social network.

Read the full article at: arxiv.org

Thermodynamic Efficiency of Interactions in Self-Organizing Systems

Complexity Digest - Wed, 06/16/2021 - 14:40

Ramil Nigmatullin and Mikhail Prokopenko

Entropy 2021, 23(6), 757

The emergence of global order in complex systems with locally interacting components is most striking at criticality, where small changes in control parameters result in a sudden global reorganization. We study the thermodynamic efficiency of interactions in self-organizing systems, which quantifies the change in the system’s order per unit of work carried out on (or extracted from) the system. We analytically derive the thermodynamic efficiency of interactions for the case of quasi-static variations of control parameters in the exactly solvable Curie–Weiss (fully connected) Ising model, and demonstrate that this quantity diverges at the critical point of a second-order phase transition. This divergence is shown for quasi-static perturbations in both control parameters—the external field and the coupling strength. Our analysis formalizes an intuitive understanding of thermodynamic efficiency across diverse self-organizing dynamics in physical, biological, and social domains.

Read the full article at: www.mdpi.com

Too Lazy to Read the Book: Episode 10 with Dashun Wang

Complexity Digest - Tue, 06/15/2021 - 15:36

Dashun is an Associate Professor and the Founding Director of the Center for Science of Science and Innovation at Northwestern University. 

He works on the Science of Science, turning the scientific method upon ourselves, using amazing new datasets and tools from complexity sciences and artificial intelligence.

His research has been published repeatedly in journals like Nature and Science, and has been featured in virtually all major global media outlets. Dashun is a recipient of multiple awards for his research and teaching, including Young Investigator awards, Poets & Quants Best 40 Under 40 Professors, Junior Scientific Award from the Complex Systems Society, Thinkers50 Radar List, and more. 

In this wide-ranging conversation, we talk about his life, career and his new book The Science of Science.

Listen at: toolazy.buzzsprout.com

Unifying Themes in Complex Systems X

Complexity Digest - Tue, 06/15/2021 - 11:48

Read the full book at: link.springer.com

Towards an engineering theory of evolution

Complexity Digest - Tue, 06/15/2021 - 11:45

Simeon D. Castle, Claire S. Grierson & Thomas E. Gorochowski
Nature Communications volume 12, Article number: 3326 (2021)

Biological technologies are fundamentally unlike any other because biology evolves. Bioengineering therefore requires novel design methodologies with evolution at their core. Knowledge about evolution is currently applied to the design of biosystems ad hoc. Unless we have an engineering theory of evolution, we will neither be able to meet evolution’s potential as an engineering tool, nor understand or limit its unintended consequences for our biological designs. Here, we propose the evotype as a helpful concept for engineering the evolutionary potential of biosystems, or other self-adaptive technologies, potentially beyond the realm of biology. Effective biological engineering requires the acknowledgement of evolution and its consideration during the design process. In this perspective, the authors present the concept of the evotype to reason about and shape the evolutionary potential of natural and engineered biosystems.

Read the full article at: www.nature.com

Ranking online social users by their Influence

Complexity Digest - Mon, 06/14/2021 - 12:42

Anastasios Giovanidis, Bruno Baynat, Clémence Magnien & Antoine Vendeville
IEEE/ACM Transactions on Networking ( Early Access ) (2021)

DOI: 10.1109/TNET.2021.3085201

Date of Publication: 08 June 2021

This work introduces an original mathematical model to analyze the diffusion of posts within a generic online social platform. The main novelty is that each user is not simply considered as a node on the social graph, but is further equipped with his/her own Wall and Newsfeed, and has his/her own individual self-posting and re-posting activity. As a main result using the developed model, the probabilities that posts originating from a given user are found on the Wall and Newsfeed of any other can be derived in closed form. These are the solution of a linear system of equations, which can be resolved iteratively. In fact, the new model is very flexible with respect to the modeling assumptions. Using the probabilities derived from the solution, a new measure of per-user influence over the entire network is defined, named the Ψ-score, which combines the user position on the graph with user (re-)posting activity. In the homogeneous case where all users have the same activity rates, it is shown that a variant of the Ψ-score is equal to PageRank. Furthermore, the new model and its Ψ-score are compared against the empirical influence measured from very large data traces (Twitter, Weibo). The results illustrate that these new tools can accurately rank influencers with asymmetric (re-)posting activity for such real world applications.

Read the full article at:  ieeexplore.ieee.org

Shrunken Social Brains? A Minimal Model of the Role of Social Interaction in Neural Complexity

Complexity Digest - Mon, 06/14/2021 - 11:39

Georgina Montserrat Reséndiz-Benhumea, Ekaterina Sangati, Federico Sangati, Soheil Keshmiri and Tom Froese

The social brain hypothesis proposes that enlarged brains have evolved in response to the increasing cognitive demands that complex social life in larger groups places on primates and other mammals. However, this reasoning can be challenged by evidence that brain size has decreased in the evolutionary transitions from solitary to social larger groups in the case of Neolithic humans and some eusocial insects. Different hypotheses can be identified in the literature to explain this reduction in brain size. We evaluate some of them from the perspective of recent approaches to cognitive science, which support the idea that the basis of cognition can span over brain, body, and environment. Here we show through a minimal cognitive model using an evolutionary robotics methodology that the neural complexity, in terms of neural entropy and degrees of freedom of neural activity, of smaller-brained agents evolved in social interaction is comparable to the neural complexity of larger-brained agents evolved in solitary conditions. The nonlinear time series analysis of agents’ neural activity reveals that the decoupled smaller neural network is intrinsically lower dimensional than the decoupled larger neural network. However, when smaller-brained agents are interacting, their actual neural complexity goes beyond its intrinsic limits achieving results comparable to those obtained by larger-brained solitary agents. This suggests that the smaller-brained agents are able to enhance their neural complexity through social interaction, thereby offsetting the reduced brain size.

Read the full article at: www.frontiersin.org

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