Complexity Digest

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How social and physical technologies collaborate to create

Sun, 02/16/2020 - 09:47

Doyne Farmer, Fotini Markopoulou, Eric Beinhocker & Steen Rasmussen


Our world is a system, in which physical and social technologies co-evolve. How can we shape a process we don’t control?


Global ecosystem thresholds driven by aridity

Sat, 02/15/2020 - 10:26

Aridity, which is increasing worldwide because of climate change, affects the structure and functioning of dryland ecosystems. Whether aridification leads to gradual (versus abrupt) and systemic (versus specific) ecosystem changes is largely unknown. We investigated how 20 structural and functional ecosystem attributes respond to aridity in global drylands. Aridification led to systemic and abrupt changes in multiple ecosystem attributes. These changes occurred sequentially in three phases characterized by abrupt decays in plant productivity, soil fertility, and plant cover and richness at aridity values of 0.54, 0.7, and 0.8, respectively. More than 20% of the terrestrial surface will cross one or several of these thresholds by 2100, which calls for immediate actions to minimize the negative impacts of aridification on essential ecosystem services for the more than 2 billion people living in drylands.


Language Evolution in Swarm Robotics: A Perspective

Fri, 02/14/2020 - 13:12

Nicolas Cambier, Roman Miletitch, Vincent Frémont, Marco Dorigo, Eliseo Ferrante and Vito Trianni


While direct local communication is very important for the organization of robot swarms, so far it has mostly been used for relatively simple tasks such as signaling robots preferences or states. Inspired by the emergence of meaning found in natural languages, more complex communication skills could allow robot swarms to tackle novel situations in ways that may not be a priori obvious to the experimenter. This would pave the way for the design of robot swarms with higher autonomy and adaptivity. The state of the art regarding the emergence of communication for robot swarms has mostly focused on offline evolutionary approaches, which showed that signaling and communication can emerge spontaneously even when not explicitly promoted. However, these approaches do not lead to complex, language-like communication skills, and signals are tightly linked to environmental and/or sensory-motor states that are specific to the task for which communication was evolved. To move beyond current practice, we advocate an approach to emergent communication in robot swarms based on language games. Thanks to language games, previous studies showed that cultural self-organization—rather than biological evolution—can be responsible for the complexity and expressive power of language. We suggest that swarm robotics can be an ideal test-bed to advance research on the emergence of language-like communication. The latter can be key to provide robot swarms with additional skills to support self-organization and adaptivity, enabling the design of more complex collective behaviors.


CCS2020: Conference on Complex Systems. Palma de Mallorca, Oct 19-23

Fri, 02/14/2020 - 10:22

Complexity, understood as the emergence of new macro properties from the interactions of basic components, is a pervasive characteristic in natural, artificial and social systems. The Conference on Complex Systems (CCS) is the biggest and most important annual meeting of the international complex systems community. It comes under the auspices of the Complex Systems Society. This edition, after successful events in Singapore , Thessaloniki (Greece) , Cancun (Mexico) and Amsterdam (Netherlands), will take place in the Mediterranean island of Mallorca, Spain, organized by IFISC (CSIC-UIB).


Self-reported willingness to share political news articles in online surveys correlates with actual sharing on Twitter

Thu, 02/13/2020 - 09:26

Mohsen Mosleh, Gordon Pennycook, David G. Rand 


There is an increasing imperative for psychologists and other behavioral scientists to understand how people behave on social media. However, it is often very difficult to execute experimental research on actual social media platforms, or to link survey responses to online behavior in order to perform correlational analyses. Thus, there is a natural desire to use self-reported behavioral intentions in standard survey studies to gain insight into online behavior. But are such hypothetical responses hopelessly disconnected from actual sharing decisions? Or are online survey samples via sources such as Amazon Mechanical Turk (MTurk) so different from the average social media user that the survey responses of one group give little insight into the on-platform behavior of the other? Here we investigate these issues by examining 67 pieces of political news content. We evaluate whether there is a meaningful relationship between (i) the level of sharing (tweets and retweets) of a given piece of content on Twitter, and (ii) the extent to which individuals (total N = 993) in online surveys on MTurk reported being willing to share that same piece of content. We found that the same news headlines that were more likely to be hypothetically shared on MTurk were also shared more frequently by Twitter users, r = .44. For example, across the observed range of MTurk sharing fractions, a 20 percentage point increase in the fraction of MTurk participants who reported being willing to share a news headline on social media was associated with 10x as many actual shares on Twitter. We also found that the correlation between sharing and various features of the headline was similar using both MTurk and Twitter data. These findings suggest that self-reported sharing intentions collected in online surveys are likely to provide some meaningful insight into what content would actually be shared on social media.


Adoption Dynamics and Societal Impact of AI Systems in Complex Networks 

Wed, 02/12/2020 - 15:03

Pedro M. Fernandes, Francisco C. Santos, Manuel Lopes

AIES ’20: Proceedings of the AAAI/ACM Conference on AI, Ethics, and SocietyFebruary 2020 Pages 258–264


We propose a game-theoretical model to simulate the dynamics of AI adoption in adaptive networks. This formalism allows us to understand the impact of the adoption of AI systems for society as a whole, addressing some of the concerns on the need for regulation. Using this model we study the adoption of AI systems, the distribution of the different types of AI (from selfish to utilitarian), the appearance of clusters of specific AI types, and the impact on the fitness of each individual. We suggest that the entangled evolution of individual strategy and network structure constitutes a key mechanism for the sustainability of utilitarian and human-conscious AI. Differently, in the absence of rewiring, a minority of the population can easily foster the adoption of selfish AI and gains a benefit at the expense of the remaining majority.


Artificial Life—Next Generation Perspectives: Echoes from the 2018 Conference in Tokyo

Wed, 02/12/2020 - 12:57

Olaf Witkowski, Takashi Ikegami, Nathaniel Virgo, Mizuki Oka and Hiroyuki Iizuka


Artificial life is a research field devoted to the theoretical study of features of living systems, such as evolution and the brain. The field has developed philosophical concepts such as autopoiesis and emergence, alongside a large range of computational and experimental setups, from evolutionary simulations to robotics and chemical experiments.

The complexity and diversity of the artificial life field is crucial to its community. Many researchers consider the community as a real source of creativity and free-minded exchange of ideas on important questions. For ideas that donʼt fit neatly into a single “mainstream” field of science, there is value in examining and discussing them in a context free from departmental or disciplinary constraints, with the purpose of reaching a better knowledge of the fundamental mechanisms that govern living systems.


Ecosystem antifragility: beyond integrity and resilience

Tue, 02/11/2020 - 06:34

We review the concept of ecosystem resilience in its relation to ecosystem integrity from an information theory approach. We summarize the literature on the subject identifying three main narratives: ecosystem properties that enable them to be more resilient; ecosystem response to perturbations; and complexity. We also include original ideas with theoretical and quantitative developments with application examples. The main contribution is a new way to rethink resilience, that is mathematically formal and easy to evaluate heuristically in real-world applications: ecosystem antifragility. An ecosystem is antifragile if it benefits from environmental variability. Antifragility therefore goes beyond robustness or resilience because while resilient/robust systems are merely perturbation-resistant, antifragile structures


Equihua M, Espinosa Aldama M, Gershenson C, López-Corona O, Munguía M, Pérez-Maqueo O, Ramírez-Carrillo E. 2020. Ecosystem antifragility: beyond integrity and resilience. PeerJ 8:e8533


Dynamics of a birth–death process based on combinatorial innovation

Mon, 02/10/2020 - 15:20

Mike Steel, Wim Hordijk, Stuart A. Kauffman

Journal of Theoretical Biology


A feature of human creativity is the ability to take a subset of existing items (e.g. objects, ideas, or techniques) and combine them in various ways to give rise to new items, which, in turn, fuel further growth. Occasionally, some of these items may also disappear (extinction). We model this process by a simple stochastic birth–death model, with non-linear combinatorial terms in the growth coefficients to capture the propensity of subsets of items to give rise to new items. In its simplest form, this model involves just two parameters (P, α). This process exhibits a characteristic ‘hockey-stick’ behaviour: a long period of relatively little growth followed by a relatively sudden ‘explosive’ increase. We provide exact expressions for the mean and variance of this time to explosion and compare the results with simulations. We then generalise our results to allow for more general parameter assignments, and consider possible applications to data involving human productivity and creativity.



Climate risk and response

Mon, 02/10/2020 - 11:19

How could Earth’s changing climate impact socioeconomic systems across the world in the next three decades? A yearlong, cross-disciplinary research effort at McKinsey & Company provides some answers.


Science of Stories

Sun, 02/09/2020 - 15:30

Stories have the power to shape our identities and worldviews. They can be factual or fictional, text-based or visual and can take many forms—from novels and non-fiction to conspiracy theories, rumors and disinformation. This Collection includes primary research papers that propose innovative, data-driven approaches to understanding stories and their impact, on such topics as the nature of narrative and narrative thinking, methods to extract stories from datasets and datasets from stories, the role of narrative in science communication, and the transformative power of stories.


Friendship paradox biases perceptions in directed networks

Fri, 02/07/2020 - 14:35

Nazanin Alipourfard, Buddhika Nettasinghe, Andrés Abeliuk, Vikram Krishnamurthy & Kristina Lerman 
Nature Communications volume 11, Article number: 707 (2020)


Social networks shape perceptions by exposing people to the actions and opinions of their peers. However, the perceived popularity of a trait or an opinion may be very different from its actual popularity. We attribute this perception bias to friendship paradox and identify conditions under which it appears. We validate the findings empirically using Twitter data. Within posts made by users in our sample, we identify topics that appear more often within users’ social feeds than they do globally among all posts. We also present a polling algorithm that leverages the friendship paradox to obtain a statistically efficient estimate of a topic’s global prevalence from biased individual perceptions. We characterize the polling estimate and validate it through synthetic polling experiments on Twitter data. Our paper elucidates the non-intuitive ways in which the structure of directed networks can distort perceptions and presents approaches to mitigate this bias.


A call for a better understanding of causation in cell biology

Fri, 02/07/2020 - 12:38

Mariano Bizzarri, Douglas E. Brash, James Briscoe, Verônica A. Grieneisen, Claudio D. Stern & Michael Levin 
Nature Reviews Molecular Cell Biology volume 20, pages261–262(2019)


What does it mean to say that event X caused outcome Y in biology? Explaining the causal structure underlying the dynamic function of living systems is a central goal of biology. Transformative advances in regenerative medicine and synthetic bioengineering will require efficient strategies to cause desired system-level outcomes. We present a perspective on the need to move beyond the classical ‘necessary and sufficient’ approach to biological causality.


Multilayer modeling of adoption dynamics in energy demand management

Fri, 02/07/2020 - 09:06

Chaos 30, 013153 (2020);
Iacopo Iacopini, Benjamin Schäfer, Elsa Arcaute, Christian Beck, and Vito Latora


The electricity system is in the midst of large transformations, and new business models have emerged quickly to facilitate new modes of operation of the electricity supply. The so-called demand response seeks to coordinate demand from a large number of users through incentives, which are usually economic such as variable pricing tariffs. Here, we propose a simple mathematical framework to model consumer behaviors under demand response. Our model considers at the same time social influence and customer benefits to opt into and stay within new control schemes. In our model, information about the existence of a contract propagates through the links of a social network, while the geographic proximity of clusters of adopters influences the likelihood of participation by decreasing the likelihood of opting out. The results of our work can help to make informed decisions in energy demand management.


Enhanced Ability of Information Gathering May Intensify Disagreement Among Groups

Thu, 02/06/2020 - 16:28

Hiroki Sayama


Today’s society faces widening disagreement and conflicts among constituents with incompatible views. Escalated views and opinions are seen not only in radical ideology or extremism but also in many other scenes of our everyday life. Here we show that widening disagreement among groups may be linked to the advancement of information communication technology, by analyzing a mathematical model of population dynamics in a continuous opinion space. We adopted the interaction kernel approach to model enhancement of people’s information gathering ability and introduced a generalized non-local gradient as individuals’ perception kernel. We found that the characteristic distance between population peaks becomes greater as the wider range of opinions becomes available to individuals or the greater attention is attracted to opinions distant from theirs. These findings may provide a possible explanation for why disagreement is growing in today’s increasingly interconnected society, without attributing its cause only to specific individuals or events.


Antifragility Predicts the Robustness and Evolvability of Biological Networks through Multi-class Classification with a Convolutional Neural Network

Thu, 02/06/2020 - 14:33

Hyobin Kim, Stalin Muñoz, Pamela Osuna, Carlos Gershenson


Robustness and evolvability are essential properties to the evolution of biological networks. To determine if a biological network is robust and/or evolvable, the comparison of its functions before and after mutations is required. However, it has an increasing computational cost as network size grows. Here we aim to develop a predictor to estimate the robustness and evolvability of biological networks without an explicit comparison of functions. We measure antifragility in Boolean network models of biological systems and use this as the predictor. Antifragility is a property to improve the capability of a system through external perturbations. By means of the differences of antifragility between the original and mutated biological networks, we train a convolutional neural network (CNN) and test it to classify the properties of robustness and evolvability. We found that our CNN model successfully classified the properties. Thus, we conclude that our antifragility measure can be used as a significant predictor of the robustness and evolvability of biological networks.


Are living beings extended autopoietic systems? An embodied reply

Wed, 02/05/2020 - 16:13

Mario Villalobos, Pablo Razeto-Barry

Adaptive Behavior Vol 28, Issue 1, 2020


Building on the original formulation of the autopoietic theory (AT), extended enactivism argues that living beings are autopoietic systems that extend beyond the spatial boundaries of the organism. In this article, we argue that extended enactivism, despite having some basis in AT’s original formulation, mistakes AT’s definition of living beings as autopoietic entities. We offer, as a reply to this interpretation, a more embodied reformulation of autopoiesis, which we think is necessary to counterbalance the (excessively) disembodied spirit of AT’s original formulation. The article aims to clarify and correct what we take to be a misinterpretation of AT as a research program. AT, contrary to what some enactivists seem to believe, did not (and does not) intend to motivate an extended conception of living beings. AT’s primary purpose, we argue, was (and is) to provide a universal individuation criterion for living beings, these understood as discrete bodies that are embedded in, but not constituted by, the environment that surrounds them. However, by giving a more explicitly embodied definition of living beings, AT can rectify and accommodate, so we argue, the enactive extended interpretation of autopoiesis, showing that although living beings do not extend beyond their boundaries as autopoietic unities, they do form part, in normal conditions, of broader autopoietic systems that include the environment.


This is a Target Article. See Also: Opinions and Reply

OSoMe Research Scientist Wanted

Wed, 02/05/2020 - 14:17

We are looking for a research scientist to help run the Observatory on Social Media (OSoMe, pronounced awe•some) at Indiana University Bloomington (IUB). The official title of the position is Senior Project Coordinator (SPC). The Senior Project Coordinator will join the OSoMe senior management team — director Filippo Menczer, co-directors for research Betsi Grabe and Alessandro Flammini, co-directors for education Elaine Monaghan and John Paolillo, Dean James Shahahan, and associate director for technology Val Pentchev. The mission of the Observatory, which recently received a $6 million investment from the John S. and James L. Knight Foundation and Indiana University, is to study the media and technology networks that drive the online diffusion of dis/mis/information. OSoMe offers access to data and tools for researchers worldwide to uncover the vulnerabilities of the media ecosystem and develops methods for increasing the resilience of citizens and democratic systems to manipulation.


A First Course in Network Science

Tue, 02/04/2020 - 15:17

The book A First Course in Network Science by CNetS faculty members Filippo Menczer and Santo Fortunato and CNetS PhD graduate Clayton A. Davis was recently published by Cambridge University Press. This textbook introduces the basics of network science for a wide range of job sectors from management to marketing, from biology to engineering, and from neuroscience to the social sciences. Extensive tutorials, datasets, and homework problems provide plenty of hands-on practice. The book has been endorsed as “Rigorous” (Alessandro Vespignani), “comprehensive… indispensable” (Olaf Sporns), “with remarkable clarity and insight” (Brian Uzzi), “accessible” (Albert-László Barabási), “amazing… extraordinary” (Alex Arenas), and “sophisticated yet introductory… an excellent introduction that is also eminently practical” (Stephen Borgatti). It was ranked by Amazon #1 among new releases in mathematical physics.


Mining social media data for biomedical signals and health-related behavior

Tue, 02/04/2020 - 09:35

Rion Brattig Correia, Ian B. Wood, Johan Bollen, Luis M. Rocha


Social media data has been increasingly used to study biomedical and health-related phenomena. From cohort level discussions of a condition to planetary level analyses of sentiment, social media has provided scientists with unprecedented amounts of data to study human behavior and response associated with a variety of health conditions and medical treatments. Here we review recent work in mining social media for biomedical, epidemiological, and social phenomena information relevant to the multilevel complexity of human health. We pay particular attention to topics where social media data analysis has shown the most progress, including pharmacovigilance, sentiment analysis especially for mental health, and other areas. We also discuss a variety of innovative uses of social media data for health-related applications and important limitations in social media data access and use.