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Functional observability and target state estimation in large-scale networks

Complexity Digest - Sat, 01/08/2022 - 13:50

Arthur N. Montanari, Chao Duan, Luis A. Aguirre, and Adilson E. Motter
PNAS January 4, 2022 119 (1) e2113750119;

Observing the states of a network is fundamental to our ability to explore and control the dynamics of complex natural, social, and technological systems. The problem of determining whether the system is observable has been addressed by network control researchers over the past decade. Progress on the further problem of actually designing and implementing efficient algorithms to infer the states from limited measurements has been hampered by the high dimensionality of large-scale networks. Noting that often only a small number of state variables in a network are essential for control, intervention, and monitoring purposes, this work develops a graph-based theory and highly scalable methods that achieve accurate estimation of target variables of network systems with minimal sensing and computational resources.

Read the full article at: www.pnas.org

The Meaning and Origin of Goal-Directedness: A Dynamical Systems Perspective

Complexity Digest - Fri, 01/07/2022 - 14:58

Francis Heylighen

This paper attempts to clarify the notion of goal-directedness, which is often misunderstood as being inconsistent with standard causal mechanisms. We first note that goal-directedness does not presuppose any mysterious forces, such as intelligent design, vitalism, conscious intention or backward causation. We then review attempts at defining goal-directedness by means of more operational characteristics: equifinality, plasticity, persistence, concerted action and negative feedback. We show that all these features can be explained by interpreting a goal as a far-from-equilibrium attractor of a dynamical system. This implies that perturbations that make the system deviate from its goal-directed trajectory are automatically compensated—at least as long as the system stays within the same basin of attraction. We argue that attractors and basins with the necessary degree of resilience tend to self-organize in complex reaction networks, thus producing self-maintaining “organizations”. These can be seen as an abstract model of the first goal-directed systems, and thus of the origin of life.

Read the full article at: researchportal.vub.be

Network traits predict ecological strategies in fungi

Complexity Digest - Fri, 01/07/2022 - 13:49

C. A. Aguilar-Trigueros, L. Boddy, M. C. Rillig & M. D. Fricker 
ISME Communications volume 2, Article number: 2 (2022)

Colonization of terrestrial environments by filamentous fungi relies on their ability to form networks that can forage for and connect resource patches. Despite the importance of these networks, ecologists rarely consider network features as functional traits because their measurement and interpretation are conceptually and methodologically difficult. To address these challenges, we have developed a pipeline to translate images of fungal mycelia, from both micro- and macro-scales, to weighted network graphs that capture ecologically relevant fungal behaviour. We focus on four properties that we hypothesize determine how fungi forage for resources, specifically: connectivity; relative construction cost; transport efficiency; and robustness against attack by fungivores. Constrained ordination and Pareto front analysis of these traits revealed that foraging strategies can be distinguished predominantly along a gradient of connectivity for micro- and macro-scale mycelial networks that is reminiscent of the qualitative ‘phalanx’ and ‘guerilla’ descriptors previously proposed in the literature. At one extreme are species with many inter-connections that increase the paths for multidirectional transport and robustness to damage, but with a high construction cost; at the other extreme are species with an opposite phenotype. Thus, we propose this approach represents a significant advance in quantifying ecological strategies for fungi using network information.

Read the full article at: www.nature.com

Transition Therapy: Tackling the Ecology of Tumor Phenotypic Plasticity

Complexity Digest - Thu, 01/06/2022 - 15:52

Guim Aguadé-Gorgorió, Stuart Kauffman & Ricard Solé 

Bulletin of Mathematical Biology volume 84, Article number: 24 (2022)

Phenotypic switching in cancer cells has been found to be present across tumor types. Recent studies on Glioblastoma report a remarkably common architecture of four well-defined phenotypes coexisting within high levels of intra-tumor genetic heterogeneity. Similar dynamics have been shown to occur in breast cancer and melanoma and are likely to be found across cancer types. Given the adaptive potential of phenotypic switching (PHS) strategies, understanding how it drives tumor evolution and therapy resistance is a major priority. Here we present a mathematical framework uncovering the ecological dynamics behind PHS. The model is able to reproduce experimental results, and mathematical conditions for cancer progression reveal PHS-specific features of tumors with direct consequences on therapy resistance. In particular, our model reveals a threshold for the resistant-to-sensitive phenotype transition rate, below which any cytotoxic or switch-inhibition therapy is likely to fail. The model is able to capture therapeutic success thresholds for cancers where nonlinear growth dynamics or larger PHS architectures are in place, such as glioblastoma or melanoma. By doing so, the model presents a novel set of conditions for the success of combination therapies able to target replication and phenotypic transitions at once. Following our results, we discuss transition therapy as a novel scheme to target not only combined cytotoxicity but also the rates of phenotypic switching.

Read the full article at: link.springer.com

Magnitude-sensitivity: rethinking decision-making

Complexity Digest - Thu, 01/06/2022 - 13:44
The cover of the January issue of the magazine Trends in Cognitive Sciences shows a human brain composed of a honeybee swarm. The artwork depicts two seemingly distant biological systems that present striking similarities in decision dynamics and properties of information processing. Inspired by the study of house-hunting honeybees, recent research has established that performance in decision-making is affected in predictable ways by the overall goal-relevant magnitude of the alternatives. Magnitude-sensitivity has been observed in humans performing a wide variety of tasks and in organisms as diverse as non-human primates and aneural slime molds. Angelo Pirrone and colleagues review the literature and highlight how prominent accounts of theoretical, descriptive, and normative decision-making had to be revisited to explain magnitude-sensitivity. A. Pirrone, A. Reina, T. Stafford, J.A.R. Marshall, F. Gobet. Magnitude-sensitivity: rethinking decision-making. Trends in Cognitive Sciences 26(1), 2022. https://doi.org/10.1016/j.tics.2021.10.006

Read the full article at: www.sciencedirect.com

The Great 1976 Tangshan Earthquake: Learning from the 1966-1976 Chinese Prediction Program

Complexity Digest - Wed, 01/05/2022 - 14:57

Euan Mearns and Didier Sornette

From 1966 to 1976, four large earthquakes shook the Bohai Bay rift basin of Northeast China. This prompted the Chinese to launch one of the world’s largest social and science experiments into earthquake prediction that would engage tens of thousands of common people. The climax of this came in February 1975 where a prediction was made hours before the Haicheng earthquake struck. Evacuation of the city of Yingkou and some rural districts saved thousands of lives. The Chinese were jubilant, believing they had cracked the earthquake prediction conundrum. Eighteen months later, however, on the 28th July, 1976, jubilation turned to despair when a great earthquake flattened the large industrial city of Tangshan resulting in 250,000 to 650,000 casualties. This book describes the geological, technical, political and sociological backgrounds to the Haicheng prediction success and the Tangshan prediction failure.
Ahead of the Tangshan earthquake, Chinese seismologists had accumulated significant information that suggested an earthquake was imminent and came close to making a prediction. With improved knowledge and vastly improved ability to accumulate, consolidate and analyse data, this book suggests that Tangshan could have been predicted today using techniques developed in China in that epic decade of discovery. Building on these insights, it also offers a viable future pathway towards earthquake predictions that combines the insights and organisation of the 1966-1976 Chinese prediction program with modern technologies, in order to facilitate data gathering, interpretation and sharing.

More at: www.cambridgescholars.com

The dynamics of political polarization

Complexity Digest - Sun, 12/19/2021 - 15:31

The dynamics of political polarization
Simon A. Levin, Helen V. Milner, and Charles Perrings

PNAS December 14, 2021 118 (50) e2116950118;

A number of trends in national and international politics greatly affect our capacity to achieve the cooperation that will be necessary to address the challenges facing society over the coming decades. These involve the interplay among partisanship and party loyalties within countries, populism, and polarization within and among nations. The trends are widespread and seem to be reshaping politics across the globe. They are inherently systems-level phenomena, involving interactions among multiple component parts and the emergence of broader-scale features; yet, they have been inadequately explored from that perspective.

To make progress in understanding these issues, political-science research stands to benefit from insights from other disciplines, including evolutionary biology, systems science, and the disciplines concerned with the fair and efficient provision of public goods of all kinds, but especially those affecting the shared environment and public health. These other disciplines, in turn, stand to gain equally from the perspective developed in political science. In viewing political systems as complex adaptive systems, we can gain a new understanding of the forces that shape current trends, and how that knowledge might affect governance strategies going forward. Extreme polarization is a dangerous phenomenon that requires greater scientific attention to address effectively.

This Special Feature of PNAS draws on this relatively new interdisciplinary field, featuring original joint research from collaborating political scientists and complex systems theorists. Each paper is a true partnership among the different disciplines and illustrates the benefits of closer ties between complex systems and social science. The papers explore the emergence of patterns and structures in societies and the linkages among individual behaviors and societal benefits across scales of space, time, and organizational complexity. The COVID-19 pandemic provides the most recent examples of how patterns of polarization in societies interact with our abilities to solve societal challenges.

Read the full article at: www.pnas.org

Why We Forgive Humans More Readily Than Machines

Complexity Digest - Sat, 12/18/2021 - 15:35

When things go wrong, flexible moral intuitions cause us to judge computers more severely

Read the full article at: www.scientificamerican.com

The rise and fall of rationality in language

Complexity Digest - Sat, 12/18/2021 - 13:37

Marten Scheffer, Ingrid van de Leemput, Els Weinans, and Johan Bollen

PNAS December 21, 2021 118 (51) e2107848118

The post-truth era has taken many by surprise. Here, we use massive language analysis to demonstrate that the rise of fact-free argumentation may perhaps be understood as part of a deeper change. After the year 1850, the use of sentiment-laden words in Google Books declined systematically, while the use of words associated with fact-based argumentation rose steadily. This pattern reversed in the 1980s, and this change accelerated around 2007, when across languages, the frequency of fact-related words dropped while emotion-laden language surged, a trend paralleled by a shift from collectivistic to individualistic language.

Read the full article at: www.pnas.org

Tina Eliassi-Rad on Democracies as Complex Systems

Complexity Digest - Sat, 12/18/2021 - 12:31

This week on Complexity, we speak with SFI External Professor Tina Eliassi-Rad, Professor of Computer Science at Northeastern University, about her complex systems research on democracy, what forces stabilize or upset democratic process, and how to rigorously study the relationships between technology and social change.

Read the full article at: complexity.simplecast.com

The geometry of decision-making in individuals and collectives

Complexity Digest - Fri, 12/17/2021 - 15:37

Vivek H. Sridhar, Liang Li, Dan Gorbonos, Máté Nagy, Bianca R. Schell, Timothy Sorochkin, Nir S. Gov, and Iain D. Couzin

PNAS December 14, 2021 118 (50) e2102157118

Almost all animals must make decisions on the move. Here, employing an approach that integrates theory and high-throughput experiments (using state-of-the-art virtual reality), we reveal that there exist fundamental geometrical principles that result from the inherent interplay between movement and organisms’ internal representation of space. Specifically, we find that animals spontaneously reduce the world into a series of sequential binary decisions, a response that facilitates effective decision-making and is robust both to the number of options available and to context, such as whether options are static (e.g., refuges) or mobile (e.g., other animals). We present evidence that these same principles, hitherto overlooked, apply across scales of biological organization, from individual to collective decision-making.

Read the full article at: www.pnas.org

What Does It Mean for AI to Understand?

Complexity Digest - Fri, 12/17/2021 - 12:28

Melanie Mitchell

Language models can generate uncannily humanlike prose (and poetry!) and seemingly perform sophisticated linguistic reasoning. How can we test if these machines actually understand what they’re doing?

Read the full article at: www.quantamagazine.org

Paradigms of Computational Agency

Complexity Digest - Fri, 12/17/2021 - 12:26

Srinath Srinivasa, Jayati Deshmukh
Agent-based models have emerged as a promising paradigm for addressing ever increasing complexity of information systems. In its initial days in the 1990s when object-oriented modeling was at its peak, an agent was treated as a special kind of “object” that had a persistent state and its own independent thread of execution. Since then, agent-based models have diversified enormously to even open new conceptual insights about the nature of systems in general. This paper presents a perspective on the disparate ways in which our understanding of agency, as well as computational models of agency have evolved. Advances in hardware like GPUs, that brought neural networks back to life, may also similarly infuse new life into agent-based models, as well as pave the way for advancements in research on Artificial General Intelligence (AGI).

Read the full article at: arxiv.org

Infodemics: A new challenge for public health

Complexity Digest - Thu, 12/16/2021 - 15:40

Infodemics: A new challenge for public health
Cell Volume 184, Issue 25, 9 December 2021, Pages 6010-6014

The COVID-19 information epidemic, or “infodemic,” demonstrates how unlimited access to information may confuse and influence behaviors during a health emergency. However, the study of infodemics is relatively new, and little is known about their relationship with epidemics management. Here, we discuss unresolved issues and propose research directions to enhance preparedness for future health crises.

Read the full article at: www.sciencedirect.com

Autopoiesis: Foundations of Life, Cognition, and Emergence of Self/Other – Call for papers – BioSystems

Complexity Digest - Tue, 12/14/2021 - 15:44

The special issue “Autopoiesis: Foundations of Life, Cognition, and Emergence of Self/Other” is devised to host an interdisciplinary forum on scientific research based on autopoiesis and its role for undestanding life, cognition, the emergence of self/other, and related issues. It is open to various approaches, targets, and goals, all having autopoiesis as common denominator, sharing and applying its core concepts to face novel problems, perspectives, and activities.

We suggest interested Authors to manifest their interest by contacting the special issue Editors, providing a title and (preferably) an extended abstract (around 500 words) about the topic they intend to approach and other methodological details before May 31, 2022.

More at: www.journals.elsevier.com

Systemic liquidity contagion in the European interbank market

Complexity Digest - Tue, 12/14/2021 - 15:40

Valentina Macchiati, Giuseppe Brandi, Tiziana Di Matteo, Daniela Paolotti, Guido Caldarelli & Giulio Cimini 

Journal of Economic Interaction and Coordination (2021)

Systemic liquidity risk, defined by the International Monetary Fund as “the risk of simultaneous liquidity difficulties at multiple financial institutions,” is a key topic in financial stability studies and macroprudential policy-making. In this context, the complex web of interconnections of the interbank market plays the crucial role of allowing funding liquidity shortages to propagate between financial institutions. Here, we introduce a simple yet effective model of the interbank market in which liquidity shortages propagate through an epidemic-like contagion mechanism on the network of interbank loans. The model is defined by using aggregate balance sheet information of European banks, and it exploits country and bank-specific risk features to account for the heterogeneity of financial institutions. Moreover, in order to obtain the European-wide topology of the interbank network, we define a block reconstruction method based on the exchange flows between the various countries. We show that the proposed contagion model is able to estimate systemic liquidity risk across different years and countries. Results suggest that our effective contagion approach can be successfully used as a viable alternative to more realistic but complicated models, which not only require more specific balance sheet variables with high time resolution but also need assumptions on how banks respond to liquidity shocks.

Read the full article at: link.springer.com

Complexity–GAINs International Summer School | Santa Fe Institute

Complexity Digest - Tue, 12/14/2021 - 15:29

The Santa Fe Institute, together with five European complexity science institutions, will offer Ph.D. students a two-week, residential advanced training opportunity focused on the disintegration of society. The Complexity–GAINs International Summer School will focus on topics that are critical to our world today, including democracy, justice, inequality, sustainability, and more.

Ph.D. students from any of the natural and social sciences, mathematics, and computation are welcome to apply. Students need not be working in the social sciences to benefit from the program; students wishing to explore new research directions or applications of quantitative skills from other disciplines will find the curriculum valuable. There is no tuition, and the program aims to be no- or low-cost to all applicants who are accepted. Applications are due by January 18, 2022, and the program will be held July 4-15, 2022.

More at: www.santafe.edu

Quantifying the Robustness of Complex Networks with Heterogeneous Nodes

Complexity Digest - Thu, 12/09/2021 - 10:44

Prasan Ratnayake, Sugandima Weragoda, Janaka Wansapura, Dharshana Kasthurirathna and Mahendra Piraveenan

Mathematics 2021, 9(21), 2769;

The robustness of a complex network measures its ability to withstand random or targeted attacks. Most network robustness measures operate under the assumption that the nodes in a network are homogeneous and abstract. However, most real-world networks consist of nodes that are heterogeneous in nature. In this work, we propose a robustness measure called fitness-incorporated average network efficiency, that attempts to capture the heterogeneity of nodes using the ‘fitness’ of nodes in measuring the robustness of a network. Further, we adopt the same measure to compare the robustness of networks with heterogeneous nodes under varying topologies, such as the scale-free topology or the Erdős–Rényi random topology. We apply the proposed robustness measure using a wireless sensor network simulator to show that it can be effectively used to measure the robustness of a network using a topological approach. We also apply the proposed robustness measure to two real-world networks; namely the CO2 exchange network and an air traffic network. We conclude that with the proposed measure, not only the topological structure, but also the fitness function and the fitness distribution among nodes, should be considered in evaluating the robustness of a complex network.

Read the full article at: www.mdpi.com

ALife 2022

Complexity Digest - Tue, 12/07/2021 - 13:32

The 2022 Conference on Artificial Life will be held in Trento, 18-22 July 2022. Given the unfolding COVID situation, the conference will likely be held virtually. The organizers are however considering the possibility of organizing a small live venue.

More at: alifetrento2022.wixsite.com


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