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Does Spending More Always Ensure Higher Cooperation? An Analysis of Institutional Incentives on Heterogeneous Networks

Mon, 01/23/2023 - 13:01

Theodor Cimpeanu, Francisco C Santos, The Anh Han
Humans have developed considerable machinery used at scale to create policies and to distribute incentives, yet we are forever seeking ways in which to improve upon these, our institutions. Especially when funding is limited, it is imperative to optimise spending without sacrificing positive outcomes, a challenge which has often been approached within several areas of social, life and engineering sciences. These studies often neglect the availability of information, cost restraints, or the underlying complex network structures, which define real-world populations. Here, we have extended these models, including the aforementioned concerns, but also tested the robustness of their findings to stochastic social learning paradigms. Akin to real-world decisions on how best to distribute endowments, we study several incentive schemes, which consider information about the overall population, local neighbourhoods, or the level of influence which a cooperative node has in the network, selectively rewarding cooperative behaviour if certain criteria are met. Following a transition towards a more realistic network setting and stochastic behavioural update rule, we found that carelessly promoting cooperators can often lead to their downfall in socially diverse settings. These emergent cyclic patterns not only damage cooperation, but also decimate the budgets of external investors. Our findings highlight the complexity of designing effective and cogent investment policies in socially diverse populations.

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Thoughts on complex systems: an interview with Giorgio Parisi

Sun, 01/22/2023 - 12:31

The Nobel Laureate Giorgio Parisi is interviewed by JPhys Complexity Editor-in-Chief, Ginestra Bianconi, on themes related to the 2021 Nobel Prize in Physics awarded to him for research on complex systems.

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Complex systems in the spotlight: next steps after the 2021 Nobel Prize in Physics

Sun, 01/22/2023 - 11:11

Ginestra Bianconi et al 2023 J. Phys. Complex. 4 010201

The 2021 Nobel Prize in Physics recognized the fundamental role of complex systems in the natural sciences. In order to celebrate this milestone, this editorial presents the point of view of the editorial board of JPhys Complexity on the achievements, challenges, and future prospects of the field. To distinguish the voice and the opinion of each editor, this editorial consists of a series of editor perspectives and reflections on few selected themes. A comprehensive and multi-faceted view of the field of complexity science emerges. We hope and trust that this open discussion will be of inspiration for future research on complex systems.

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Strong Emergence Arising from Weak Emergence

Sat, 01/21/2023 - 11:59

Thomas Schmickl

Complexity Volume 2022 | Article ID 9956885
Predictions of emergent phenomena, appearing on the macroscopic layer of a complex system, can fail if they are made by a microscopic model. This study demonstrates and analyses this claim on a well-known complex system, Conway’s Game of Life. Straightforward macroscopic mean-field models are easily capable of predicting such emergent properties after they have been fitted to simulation data in an after-the-fact way. Thus, these predictions are macro-to-macro only. However, a micro-to-macro model significantly fails to predict correctly, as does the obvious mesoscopic modeling approach. This suggests that some macroscopic system properties in a complex dynamic system should be interpreted as examples of phenomena (properties) arising from “strong emergence,” due to the lack of ability to build a consistent micro-to-macro model, that could explain these phenomena in a before-the-fact way. The root cause for this inability to predict this in a micro-to-macro way is identified as the pattern formation process, a phenomenon that is usually classified as being of “weak emergence.” Ultimately, this suggests that it may be in principle impossible to discriminate between such distinct categories of “weak” and “strong” emergence, as phenomena of both types can be part of the very same feedback loop that mainly governs the system’s dynamics.

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Stigmergic coordination and minimal cognition in plants

Fri, 01/20/2023 - 11:54

Ric Sims  and Özlem Yilmaz

Adaptive Behavior

The tricky question in the plant cognition debate is what theory of cognition should be used to fix the reference of cognitive concepts without skewing the debate too much one way or the other. After all, plants are rather different to animals in many respects: they are not motile, do not possess central nervous systems or even neurons, do not exhibit an invariant morphology, interact with the world in a distributed multi-centred manner, and behave through changes in their physiology. Nonetheless, there is a significant strand in the debate that asserts that plants are indeed cognitive. But what theory of cognition makes sense of this claim without baking in prior zoological assumptions? The aim of this paper is to try out a theory of minimal cognition that makes the claim of plant cognition plausible. It is primarily inspired by the distributed cognition literature and the sensorimotor coordination theory of cognition proposed by van Duijn et al. (2006). We take a cognitive system to be a coordinated set of semi-autonomous processes running over the organism and items in its environment. Coordination is characterised in terms of two functional conditions that ensure that the system generates goal-directed action in the world. The system is stigmergic in the sense that the material results of its actions in the environment are a crucial part of the processes that coordinate further actions. The account possesses a degree of scale invariance and helps unify cognitive explanation across microorganisms, plants and animals.

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Maximum entropy network states for coalescence processes

Thu, 01/19/2023 - 14:03

Arsham Ghavasieh, Manlio De Domenico
Complex network states are characterized by the interplay between system’s structure and dynamics. One way to represent such states is by means of network density matrices, whose von Neumann entropy characterizes the number of distinct microstates compatible with given topology and dynamical evolution. In this Letter, we propose a maximum entropy principle to characterize network states for systems with heterogeneous, generally correlated, connectivity patterns and non-trivial dynamics. We focus on three distinct coalescence processes, widely encountered in the analysis of empirical interconnected systems, and characterize their entropy and transitions between distinct dynamical regimes across distinct temporal scales. Our framework allows one to study the statistical physics of systems that aggregate, such as in transportation infrastructures serving the same geographic area, or correlate, such as inter-brain synchrony arising in organisms that socially interact, and active matter that swarm or synchronize.

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Long COVID: major findings, mechanisms and recommendations

Thu, 01/19/2023 - 11:53

Hannah E. Davis, Lisa McCorkell, Julia Moore Vogel & Eric J. Topol 
Nature Reviews Microbiology (2023)

Long COVID is an often debilitating illness that occurs in at least 10% of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. More than 200 symptoms have been identified with impacts on multiple organ systems. At least 65 million individuals worldwide are estimated to have long COVID, with cases increasing daily. Biomedical research has made substantial progress in identifying various pathophysiological changes and risk factors and in characterizing the illness; further, similarities with other viral-onset illnesses such as myalgic encephalomyelitis/chronic fatigue syndrome and postural orthostatic tachycardia syndrome have laid the groundwork for research in the field. In this Review, we explore the current literature and highlight key findings, the overlap with other conditions, the variable onset of symptoms, long COVID in children and the impact of vaccinations. Although these key findings are critical to understanding long COVID, current diagnostic and treatment options are insufficient, and clinical trials must be prioritized that address leading hypotheses. Additionally, to strengthen long COVID research, future studies must account for biases and SARS-CoV-2 testing issues, build on viral-onset research, be inclusive of marginalized populations and meaningfully engage patients throughout the research process.

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Statistical analysis of word flow among five Indo-European languages

Wed, 01/18/2023 - 12:33

Josué Ely Molina, Jorge Flores, Carlos Gershenson, Carlos Pineda
A recent increase in data availability has allowed the possibility to perform different statistical linguistic studies. Here we use the Google Books Ngram dataset to analyze word flow among English, French, German, Italian, and Spanish. We study what we define as “migrant words”, a type of loanwords that do not change their spelling. We quantify migrant words from one language to another for different decades, and notice that most migrant words can be aggregated in semantic fields and associated to historic events. We also study the statistical properties of accumulated migrant words and their rank dynamics. We propose a measure of use of migrant words that could be used as a proxy of cultural influence. Our methodology is not exempt of caveats, but our results are encouraging to promote further studies in this direction.

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The brain-computer analogy—“A special issue”

Tue, 01/17/2023 - 13:33

Giorgio Matassi and Pedro Martinez

Front. Ecol. Evol., 13 January 2023 Sec. Models in Ecology and Evolution

In this review essay, we give a detailed synopsis of the twelve contributions which are collected in a Special Issue in Frontiers Ecology and Evolution, based on the research topic “Current Thoughts on the Brain-Computer Analogy—All Metaphors Are Wrong, But Some Are Useful.” The synopsis is complemented by a graphical summary, a matrix which links articles to selected concepts. As first identified by Turing, all authors in this Special Issue recognize semantics as a crucial concern in the brain-computer analogy debate, and consequently address a number of such issues. What is missing, we believe, is the distinction between metaphor and analogy, which we reevaluate, describe in some detail, and offer a definition for the latter. To enrich the debate, we also deem necessary to develop on the evolutionary theories of the brain, of which we provide an overview. This article closes with thoughts on creativity in Science, for we concur with the stance that metaphors and analogies, and their esthetic impact, are essential to the creative process, be it in Sciences as well as in Arts.

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Don’t follow the leader: Independent thinkers create scientific innovation

Sat, 01/14/2023 - 12:52

Sean Kelty, Raiyan Abdul Baten, Adiba Mahbub Proma, Ehsan Hoque, Johan Bollen, Gourab Ghoshal
Academic success is distributed unequally; a few top scientists receive the bulk of attention, citations, and resources. However, do these “superstars” foster leadership in scientific innovation? We introduce three information-theoretic measures that quantify novelty, innovation, and impact from scholarly citation networks, and compare the scholarly output of scientists who are either not connected or strongly connected to superstar scientists. We find that while connected scientists do indeed publish more, garner more citations, and produce more diverse content, this comes at a cost of lower innovation and higher redundancy of ideas. Further, once one removes papers co-authored with superstars, the academic output of these connected scientists diminishes. In contrast, authors that produce innovative content without the benefit of collaborations with scientific superstars produce papers that connect a greater diversity of concepts, publish more, and have comparable citation rates, once one controls for transferred prestige of superstars. On balance, our results indicate that academia pays a price by focusing attention and resources on superstars.

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Maximum entropy network states for coalescence processes

Fri, 01/13/2023 - 17:46

Arsham Ghavasieh, Manlio De Domenico
Complex network states are characterized by the interplay between system’s structure and dynamics. One way to represent such states is by means of network density matrices, whose von Neumann entropy characterizes the number of distinct microstates compatible with given topology and dynamical evolution. In this Letter, we propose a maximum entropy principle to characterize network states for systems with heterogeneous, generally correlated, connectivity patterns and non-trivial dynamics. We focus on three distinct coalescence processes, widely encountered in the analysis of empirical interconnected systems, and characterize their entropy and transitions between distinct dynamical regimes across distinct temporal scales. Our framework allows one to study the statistical physics of systems that aggregate, such as in transportation infrastructures serving the same geographic area, or correlate, such as inter-brain synchrony arising in organisms that socially interact, and active matter that swarm or synchronize.

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Self-organisation, (M, R)–systems and enactive cognitive science

Fri, 01/13/2023 - 15:44

Tomasz Korbak

Adaptive Behavior 31(1)

The notion of self-organisation plays a major role in enactive cognitive science. In this paper, I review several formal models of self-organisation that various approaches in modern cognitive science rely upon. I then focus on Rosen’s account of self-organisation as closure to efficient cause and his argument that models of systems closed to efficient cause – (M, R) systems – are uncomputable. Despite being sometimes relied on by enactivists this argument is problematic it rests on assumptions unacceptable for enactivists: that living systems can be modelled as time-invariant and material-independent. I then argue that there exists a simple and philosophically appealing reparametrisation of (M, R)–systems that accounts for the temporal dimensions of life but renders Rosen’s argument invalid.

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Phase-separation physics underlies new theory for the resilience of patchy ecosystems

Fri, 01/13/2023 - 13:43

Koen Siteur, Quan-Xing Liu, Vivi Rottschäfer, Tjisse van der Heide, Max Rietkerk, Arjen Doelman, Christoffer Boström, and Johan van de Koppel

PNAS 120 (2) e2202683120

Human-induced environmental changes push ecosystems worldwide toward their limits. Therefore, there is a growing need for indicators to assess the resilience of ecosystems against external changes and disturbances. We highlight a novel class of spatial patterns in ecosystems for which resilience indicators are lacking and introduce a new indicator framework for these ecosystems, akin to the physics of phase separation. Our work suggests that aerial imagery can be used to monitor patchy ecosystems and highlights a link between physics and ecosystem resilience.

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Economic Complexity Theory and Applications – Cesar Hidalgo

Thu, 01/12/2023 - 11:06

Economic complexity methods have become popular tools in economic geography, international development, and innovation studies. Here, I review economic complexity theory and applications, with a particular focus on two streams of literature: the literature on relatedness, which focuses on the evolution of specialization patterns, and the literature on metrics of economic complexity, which uses dimensionality reduction techniques to create metrics of economic sophistication that are predictive of variations in income, economic growth, emissions, and income inequality.

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Postdoc position on “Creating bio-inspired co-evolutionary incentive systems to promote recycling, using Internet of Things technologies” ETH Zurich

Wed, 01/11/2023 - 15:23

You will produce a simulation program demonstrating self-organizing logistic networks that become more circular and sustainable over time. 

You will create novel research breakthroughs and contribute to the ambitious ERC Advanced Investigator Grant on “Co-Evolving City Life” (CoCi) in subject areas connected to smart cities and digital societies. Your research focus will be on “Sustainable Cities and Coordination”. Given recent digital technologies such as the Internet of Things (sensor and communication networks), Artificial Intelligence, and blockchain technology, one can expect that production, logistics, and even waste, are becoming increasingly smart. Ideally, you will study how the convergence of these technologies can be used to fuel new approaches towards more sustainable production and logistics in an urban context. 

The research question we would like to answer is, how the approach of self-organized and federated, learning, networked multi-agent systems can be used to create socio-economic incentives that would promote the emergence of closed loops in a material supply network and could thereby boost the formation of a circular and sharing economy. We want to study, how a multi-dimensional real-time measurement, feedback and coordination system would have to be designed and operated in order to reach this goal. 

Together with our team, you will work on the mechanisms and effects of multi-dimensional real-time coordination, perform related agent-based simulations, and work towards demonstrating the approach in an application project. It will be great to couple the simulation program with a sensor-based environment (Raspberry Pi or Arduino, or other) that responds to measurements, flexibly adapts, and self-organizes. You will be the key researcher addressing these challenges or a subset of them (please specify), collaborating with a highly motivated team.

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There’s Plenty of Room Right Here: Biological Systems as Evolved, Overloaded, Multi-scale Machines

Sun, 01/08/2023 - 12:52

Joshua Bongard, Michael Levin
The applicability of computational models to the biological world is an active topic of debate. We argue that a useful path forward results from abandoning hard boundaries between categories and adopting an observer-dependent, pragmatic view. Such a view dissolves the contingent dichotomies driven by human cognitive biases (e.g., tendency to oversimplify) and prior technological limitations in favor of a more continuous, gradualist view necessitated by the study of evolution, developmental biology, and intelligent machines. Efforts to re-shape living systems for biomedical or bioengineering purposes require prediction and control of their function at multiple scales. This is challenging for many reasons, one of which is that living systems perform multiple functions in the same place at the same time. We refer to this as “polycomputing” – the ability of the same substrate to simultaneously compute different things. This ability is an important way in which living things are a kind of computer, but not the familiar, linear, deterministic kind; rather, living things are computers in the broad sense of computational materials as reported in the rapidly-growing physical computing literature. We argue that an observer-centered framework for the computations performed by evolved and designed systems will improve the understanding of meso-scale events, as it has already done at quantum and relativistic scales. Here, we review examples of biological and technological polycomputing, and develop the idea that overloading of different functions on the same hardware is an important design principle that helps understand and build both evolved and designed systems. Learning to hack existing polycomputing substrates, as well as evolve and design new ones, will have massive impacts on regenerative medicine, robotics, and computer engineering.

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The theoretical foundations of enaction: Precariousness

Sat, 01/07/2023 - 12:59

Randall D.Beer, Ezequiel A.Di Paolo

Volume 223, January 2023, 104823

Enaction is an increasingly influential approach to cognition that grew out of Maturana and Varela’s earlier work on autopoiesis and the biology of cognition. As with any relatively new scientific discipline, the enactive approach would benefit greatly from a careful analysis of its theoretical foundations. Here we initiate such an analysis for one of the core concepts of enaction, precariousness. Specifically, we consider three types of fragility: systemic, processual and thermodynamic. Using a glider in the Game of Life as a toy model, we illustrate each of these fragilities and examine the relationships between them. We also argue that each type of fragility is characterized by which aspects of a system are hardwired into its definition from the outset and which aspects are emergent and hence vulnerable to disintegration without ongoing maintenance.

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The Physics Principle That Inspired Modern AI Art

Fri, 01/06/2023 - 16:56

Diffusion models generate incredible images by learning to reverse the process that, among other things, causes ink to spread through water.

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Mass testing to end the COVID-19 public health threat

Fri, 01/06/2023 - 12:20

Cecile Philippe, Yaneer Bar-Yam, Stephane Bilodeau. Carlos Gershenson, Sunil K.Raina, Shu-Ti Chiou, Gunhild A. Nyborg, Matthias F.Schneider

The Lancet Regional Health – Europe
Volume 25, February 2023, 100574

After a period where many countries have let the SARS-CoV-2 virus spread more or less freely, individuals and communities are now grappling with the many negative health effects and economic ramifications from high levels of illness over long periods. As evidence of the detrimental long-term effects of the virus mount, it is increasingly clear that the policy vacuum comes at an unacceptable price both in the short and long term; its only justification would be if there was no other alternative that did not come at an even greater cost. Entering the cold season, the number of infections will most likely increase significantly in Europe (≈ one – two order of magnitude in 2021). While the world awaits and hopes for new and more effective vaccines, we need tools in the toolbox that can effectively control transmission of rapidly spreading new variants, especially if more pathogenic. Otherwise, we may face significant disruptions and enormous costs due to repeated waves of illness, with each wave increasing the numbers of workers thrown out of the workforce from long term health effects. Lockdowns, due to their social restrictions and high short-term economic costs, are no longer the best available option. We here point out that mass testing (regular asymptomatic screening of the general population) is an alternative approach that can dramatically reduce cases and quickly restore economic and social activity.

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PhD opportunity at Sorbonne University: Transfer learning to inform the spread of other respiratory viruses : Application to Influenza using COVID19 and drug sales

Fri, 01/06/2023 - 12:08

In high-income countries, the COVID-19 pandemic fostered the generation of surveillance data at spatial and temporal resolution unseen before, providing comprehensive and accurate estimates of cases, detection capability, hospitalizations and deaths. At the same time, data describing behavioral response, mobility, mixing and compliance to public health measures have also become available with similar level of detail. Such an exhaustive picture of the unfurling of a pandemic was a first in human history, made possible because we live in the digital age. It does not imply that epidemiological surveillance will remain this way in the future. As COVID-19 becomes less virulent with vaccination and acquired immunity, political pressure is shifting away from comprehensive detection of cases, and individual willingness to get tested may also be declining. At the same time, corporate commitment to make proprietary data on human behavior available to scientific research (e.g., mobile phone data) is waning. This underpins the main scientific goal of this project: can we use the experience of “wartime” COVID-19 surveillance during years2020-2022 to improve epidemic understanding in the future “peacetime” period ? Typical data available for surveillance in peacetime is scarcer, for example syndromic surveillance for influenza and other respiratory viruses as reported in networks of general practitioners (GP), with limited virological confirmation. Other data sources, including participatory surveillance and drug sales, may complement such reports, but are less specific. Importantly, during the first 2 years of COVID-19, the aforementioned high-resolution data and the scarcer traditional data sources were observed together. We wish to exploit this overlap to build statistical and mathematical models that will extract more and better information from peacetime surveillance data. Specifically, we aim at generating estimates of incidence, severe cases, reproductive number that are better than those previously available in terms of spatial resolution, temporal resolution, predictive power (ability to make short-term forecasts and mid-term projections of epidemic activity). We will make use of AI/ML techniques to come up with models with which transfer of knowledge, for example from the dynamics of COVID-19 to that of Influenza, or from drug sales data to influenza, from mobility to infectious spread will make it possible to improve accurate estimation of influenza incidence and short term prediction. The impact of this project will be thus twofold. First, we will improve the knowledge and predictability of seasonal epidemic waves of airborne, directly transmitted pathogens. Second, we will provide with policymakers with new tools to inform public health response to seasonal acute respiratory illness.

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