How can systems in which individuals’ inner workings are very similar to each other, as neural networks or ant colonies, produce so many qualitatively different behaviors, giving rise to roles and specialization? In this work, we bring new perspectives to this question by focusing on the underlying network that defines how individuals in these systems interact. We applied a genetic algorithm to optimize rules and connections of cellular automata in order to solve the density classification task, a classical problem used to study emergent behaviors in decentralized computational systems. The networks used were all generated by the introduction of shortcuts in an originally regular topology, following the small-world model. Even though all cells follow the exact same rules, we observed the existence of different classes of cells’ behaviors in the best cellular automata found—most cells were responsible for memory and others for integration of information. Through the analysis of structural measures and patterns of connections (motifs) in successful cellular automata, we observed that the distribution of shortcuts between distant regions and the speed in which a cell can gather information from different parts of the system seem to be the main factors for the specialization we observed, demonstrating how heterogeneity in a network can create heterogeneity of behavior.
Godoy A, Tabacof P, Von Zuben FJ (2017) The role of the interaction network in the emergence of diversity of behavior. PLoS ONE 12(2): e0172073. doi:10.1371/journal.pone.0172073
Following the financial crisis of 2007–2008, a deep analogy between the origins of instability in financial systems and complex ecosystems has been pointed out: in both cases, topological features of network structures influence how easily distress can spread within the system. However, in financial network models, the details of how financial institutions interact typically play a decisive role, and a general understanding of precisely how network topology creates instability remains lacking. Here we show how processes that are widely believed to stabilize the financial system, that is, market integration and diversification, can actually drive it towards instability, as they contribute to create cyclical structures which tend to amplify financial distress, thereby undermining systemic stability and making large crises more likely. This result holds irrespective of the details of how institutions interact, showing that policy-relevant analysis of the factors affecting financial stability can be carried out while abstracting away from such details.
Pathways towards instability in financial networks Marco Bardoscia, Stefano Battiston, Fabio Caccioli & Guido Caldarelli Nature Communications 8, Article number: 14416 (2017) doi:10.1038/ncomms14416
One hallmark of cognitive complexity is the ability to manipulate objects with a specific goal in mind. Such “tool use” at one time was ascribed to humans alone, but then to primates, next to marine mammals, and later to birds. Now we recognize that many species have the capacity to envision how a particular object might be used to achieve an end. Loukola et al. extend this insight to invertebrates. Bumblebees were trained to see that a ball could be used to produce a reward. These bees then spontaneously rolled the ball when given the chance.
The role of species interactions in controlling the interplay between the stability of ecosystems and their biodiversity is still not well understood. The ability of ecological communities to recover after small perturbations of the species abundances (local asymptotic stability) has been well studied, whereas the likelihood of a community to persist when the conditions change (structural stability) has received much less attention. Our goal is to understand the effects of diversity, interaction strengths and ecological network structure on the volume of parameter space leading to feasible equilibria. We develop a geometrical framework to study the range of conditions necessary for feasible coexistence. We show that feasibility is determined by few quantities describing the interactions, yielding a nontrivial complexity–feasibility relationship. Analysing more than 100 empirical networks, we show that the range of coexistence conditions in mutualistic systems can be analytically predicted. Finally, we characterize the geometric shape of the feasibility domain, thereby identifying the direction of perturbations that are more likely to cause extinctions.
The foraging task is one of the canonical testbeds for cooperative robotics, in which a collection of robots has to search and transport objects to specific storage point(s). In this paper, we investigate the Multi-Agent Foraging (MAF) problem from several perspectives that we analyze in depth.
First, we define the Foraging Problem according to literature definitions. Then we analyze previously proposed taxonomies, and propose a new foraging taxonomy characterized by four principal axes: Environment, Collective, Strategy and Simulation, summarize related foraging works and classify them through our new foraging taxonomy. Then, we discuss the real implementation of MAF and present a comparison between some related foraging works considering important features that show extensibility, reliability and scalability of MAF systems
Finally we present and discuss recent trends in this field, emphasizing the various challenges that could enhance the existing MAF solutions and make them realistic.
Multi-Agent Foraging: state-of-the-art and research challenges Ouarda Zedadra, Nicolas Jouandeau, Hamid Seridi and Giancarlo Fortino Complex Adaptive Systems Modeling 2017 5:3 DOI: 10.1186/s40294-016-0041-8
Coastal hypoxia is a growing problem worldwide, but economic consequences for fisheries are largely unknown. We provide evidence that hypoxia causes economic effects on a major fishery that was once the most valuable fishery in America. Our analysis is also a breakthrough in causal inference for coupled human-natural systems. Although establishing causality with observational data is always challenging, feedbacks across the human and natural systems amplify these challenges and explain why linking hypoxia to fishery losses has been elusive. We offer an alternative approach using a market counterfactual that is immune to contamination from feedbacks in the coupled system. Natural resource prices can thus be a means to assess the significance of an ecological disturbance.
We propose and develop a Lexicocalorimeter: an online, interactive instrument for measuring the “caloric content” of social media and other large-scale texts. We do so by constructing extensive yet improvable tables of food and activity related phrases, and respectively assigning them with sourced estimates of caloric intake and expenditure. We show that for Twitter, our naive measures of “caloric input”, “caloric output”, and the ratio of these measures are all strong correlates with health and well-being measures for the contiguous United States. Our caloric balance measure in many cases outperforms both its constituent quantities; is tunable to specific health and well-being measures such as diabetes rates; has the capability of providing a real-time signal reflecting a population’s health; and has the potential to be used alongside traditional survey data in the development of public policy and collective self-awareness. Because our Lexicocalorimeter is a linear superposition of principled phrase scores, we also show we can move beyond correlations to explore what people talk about in collective detail, and assist in the understanding and explanation of how population-scale conditions vary, a capacity unavailable to black-box type methods.
Alajajian SE, Williams JR, Reagan AJ, Alajajian SC, Frank MR, Mitchell L, et al. (2017) The Lexicocalorimeter: Gauging public health through caloric input and output on social media. PLoS ONE 12(2): e0168893. doi:10.1371/journal.pone.0168893
The dominant external forces influencing the rate of change of the Earth System have been astronomical and geophysical during the planet’s 4.5-billion-year existence. In the last six decades, anthropogenic forcings have driven exceptionally rapid rates of change in the Earth System. This new regime can be represented by an ‘Anthropocene equation’, where other forcings tend to zero, and the rate of change under human influence can be estimated. Reducing the risk of leaving the glacial–interglacial limit cycle of the late Quaternary for an uncertain future will require, in the first instance, the rate of change of the Earth System to become approximately zero.
The Anthropocene equation Owen Gaffney, Will Steffen
This year’s ‘Frontiers in biology’ Insight features Reviews on how genomics is helping to uncover the peopling of the world, the interplay between morphogens and morphogenesis in determining organismal shape, the factors that influence the immune response to cancer, advances in single-cell genomics, and the effects of base modifications in messenger RNA.
Once considered provocative, the notion that the wisdom of the crowd is superior to any individual has become itself a piece of crowd wisdom, leading to speculation that online voting may soon put credentialed experts out of business. Recent applications include political and economic forecasting, evaluating nuclear safety, public policy, the quality of chemical probes, and possible responses to a restless volcano. Algorithms for extracting wisdom from the crowd are typically based on a democratic voting procedure. They are simple to apply and preserve the independence of personal judgment. However, democratic methods have serious limitations. They are biased for shallow, lowest common denominator information, at the expense of novel or specialized knowledge that is not widely shared. Adjustments based on measuring confidence do not solve this problem reliably. Here we propose the following alternative to a democratic vote: select the answer that is more popular than people predict. We show that this principle yields the best answer under reasonable assumptions about voter behaviour, while the standard ‘most popular’ or ‘most confident’ principles fail under exactly those same assumptions. Like traditional voting, the principle accepts unique problems, such as panel decisions about scientific or artistic merit, and legal or historical disputes. The potential application domain is thus broader than that covered by machine learning and psychometric methods, which require data across multiple questions.
A solution to the single-question crowd wisdom problem
Dražen Prelec, H. Sebastian Seung & John McCoy
Nature 541, 532–535 (26 January 2017) doi:10.1038/nature21054
Inferring properties of the interaction matrix that characterizes how nodes in a networked system directly interact with each other is a well-known network reconstruction problem. Despite a decade of extensive studies, network reconstruction remains an outstanding challenge. The fundamental limitations governing which properties of the interaction matrix (e.g. adjacency pattern, sign pattern or degree sequence) can be inferred from given temporal data of individual nodes remain unknown. Here, we rigorously derive the necessary conditions to reconstruct any property of the interaction matrix. Counterintuitively, we find that reconstructing any property of the interaction matrix is generically as difficult as reconstructing the interaction matrix itself, requiring equally informative temporal data. Revealing these fundamental limitations sheds light on the design of better network reconstruction algorithms that offer practical improvements over existing methods.
Fundamental limitations of network reconstruction from temporal data Marco Tulio Angulo, Jaime A. Moreno, Gabor Lippner, Albert-László Barabási, Yang-Yu Liu
Recently, there has been an increasing interest in the control community in studying large-scale distributed systems. Several techniques have been developed to address the main challenges for these systems, such as the amount of information needed to guarantee the proper operation of the system, the economic costs associated with the required communication structure, and the high computational burden of solving for the control inputs for largescale systems.
We are at the historic moment, where we have to decide on the right path—a path that allows us all to benefit from the digital revolution. Therefore, we urge to adhere to the following fundamental principles:
1. to increasingly decentralize the function of information systems;
2. to support informational self-determination and participation;
3. to improve transparency in order to achieve greater trust;
4. to reduce the distortion and pollution of information;
5. to enable user-controlled information filters;
6. to support social and economic diversity;
7. to improve interoperability and collaborative opportunities;
8. to create digital assistants and coordination tools;
9. to support collective intelligence, and
10. to promote responsible behavior of citizens in the digital world through digital literacy and enlightenment.
Will Democracy Survive Big Data and Artificial Intelligence? By Dirk Helbing, Bruno S. Frey, Gerd Gigerenzer, Ernst Hafen, Michael Hagner, Yvonne Hofstetter, Jeroen van den Hoven, Roberto V. Zicari, Andrej Zwitter on February 25, 2017
The identification of critical states is a major task in complex systems, and the availability of measures to detect such conditions is of utmost importance. In general, criticality refers to the existence of two qualitatively different behaviors that the same system can exhibit, depending on the values of some parameters. In this paper, we show that the relevance index may be effectively used to identify critical states in complex systems. The relevance index was originally developed to identify relevant sets of variables in dynamical systems, but in this paper, we show that it is also able to capture features of criticality. The index is applied to two prominent examples showing slightly different meanings of criticality, namely the Ising model and random Boolean networks. Results show that this index is maximized at critical states and is robust with respect to system size and sampling effort. It can therefore be used to detect criticality.
Identifying Critical States through the Relevance Index Andrea Roli, Marco Villani, Riccardo Caprari and Roberto Serra
Thomas Schelling, the distinguished economist, died on 13 December 2016 at his home in Bethesda, Maryland. He was 95 years old. Schelling applied his prolific work in game theory to arms control and deterrence, negotiation strategy, and most recently, global warming. His strategic insights made the world a much safer place.
Thomas Crombie Schelling (1921–2016) Richard Zeckhauser
Science 24 Feb 2017: Vol. 355, Issue 6327, pp. 800 DOI: 10.1126/science.aam9079
Thanks to their widespread usage, mobile devices have become one of the main sensors of human behaviour and digital traces left behind can be used as a proxy to study urban environments. Exploring the nature of the spatio-temporal patterns of mobile phone activity could thus be a crucial step towards understanding the full spectrum of human activities. Using 10 months of mobile phone records from Greater London resolved in both space and time, we investigate the regularity of human telecommunication activity on urban scales. We evaluate several options for decomposing activity timelines into typical and residual patterns, accounting for the strong periodic and seasonal components. We carry out our analysis on various spatial scales, showing that regularity increases as we look at aggregated activity in larger spatial units with more activity in them. We examine the statistical properties of the residuals and show that it can be explained by noise and specific outliers. Also, we look at sources of deviations from the general trends, which we find to be explainable based on knowledge of the city structure and places of attractions. We show examples how some of the outliers can be related to external factors such as specific social events.
Prediction limits of mobile phone activity modelling Dániel Kondor, Sebastian Grauwin, Zsófia Kallus, István Gódor, Stanislav Sobolevsky, Carlo Ratti
Many adaptive systems sit near a tipping or critical point. For systems near a critical point small changes to component behaviour can induce large-scale changes in aggregate structure and function. Criticality can be adaptive when the environment is changing, but entails reduced robustness through sensitivity. This tradeoff can be resolved when criticality can be tuned. We address the control of finite measures of criticality using data on fight sizes from an animal society model system (Macaca nemestrina, n=48). We find that a heterogeneous, socially organized system, like homogeneous, spatial systems (flocks and schools), sits near a critical point; the contributions individuals make to collective phenomena can be quantified; there is heterogeneity in these contributions; and distance from the critical point (DFC) can be controlled through biologically plausible mechanisms exploiting heterogeneity. We propose two alternative hypotheses for why a system decreases the distance from the critical point.
Control of finite critical behaviour in a small-scale social system Bryan C. Daniels, David C. Krakauer & Jessica C. Flack Nature Communications 8, Article number: 14301 (2017) doi:10.1038/ncomms14301
Computer experiments, testing features proposed to explain the evolution of sexual recombination, show that this evolution is better described as a network of interactions between possible sexual forms, including diploidy, thelytoky, facultative sex, assortation, bisexuality, and division of labor, rather than a simple transition from parthenogenesis to sexual recombination. Results show that sex is an adaptation to manage genetic complexity in evolution; that bisexual reproduction emerges only among anisogamic diploids with a synergistic division of reproductive labor; and that facultative sex is more likely to evolve among haploids practicing assortative mating. Looking at the evolution of sex as a complex system explains better the diversity of sexual strategies known to exist in nature. The paper shows that Analytical mathematics used in theoretical biology has limitations in tackling complex problems. Switching to algorithmic mathematics, such as ABM, will be important in advancing our understanding of complex issues. More sophisticated models will enlighten more aspects of this complex dynamics with implications for the understanding biological and cultural evolution, intelligence, and complex systems in general.
Synergy from reproductive division of labor and complexity drive the evolution of sex Klaus Jaffe
An immune system inspired Artificial Immune System (AIS) algorithm is presented, and is used for the purposes of automated program verification. Relevant immunological concepts are discussed and the field of AIS is briefly reviewed. It is proposed to use this AIS algorithm for a specific automated program verification task: that of predicting shape of program invariants. It is shown that the algorithm correctly predicts program invariant shape for a variety of benchmarked programs. Program invariants encapsulate the computability of a particular program, e.g. whether it performs a particular function correctly and whether it terminates or not. This work also lays the foundation for applying concepts of theoretical incomputability and undecidability to biological systems like the immune system that perform robust computation to eliminate pathogens.
This is a worrying time for those who believe government policies should be based on the best evidence. Pundits claim we've entered a postfactual era. Viral fake news stories spread alternative facts. On some issues, such as climate change and childhood vaccinations, many scientists worry their hard-won research findings have lost sway with politicians and the public, and feel their veracity is under attack. But just how should evidence shape policy? And why does it sometimes lose out? Those are just some of the questions tackled in this special section on evidence-based policymaking.
A matter of fact David Malakoff Science 10 Feb 2017: Vol. 355, Issue 6325, pp. 562-563 DOI: 10.1126/science.355.6325.562
Empirically validated mathematical models show that a combination of intraspecific competition between subterranean social-insect colonies and scale-dependent feedbacks between plants can explain the spatially periodic vegetation patterns observed in many landscapes, such as the Namib Desert ‘fairy circles’.
A theoretical foundation for multi-scale regular vegetation patterns
Corina E. Tarnita, Juan A. Bonachela, Efrat Sheffer, Jennifer A. Guyton, Tyler C. Coverdale, Ryan A. Long & Robert M. Pringle
Nature 541, 398–401 (19 January 2017) doi:10.1038/nature20801
Faced with ever-changing products, consumers can benefit from trying new items. But data collected over almost five years show that, the longer shoppers have been buying a favourite product, the more likely they are to stick with it.
Human behaviour: Shoppers like what they know Peter M. Todd Nature 541, 294–295 (19 January 2017) doi:10.1038/nature21114
Land degradation results in declining biodiversity and the disruption of ecosystem functioning worldwide, particularly in the tropics1. Vegetation restoration is a common tool used to mitigate these impacts and increasingly aims to restore ecosystem functions rather than species diversity2. However, evidence from community experiments on the effect of restoration practices on ecosystem functions is scarce3. Pollination is an important ecosystem function and the global decline in pollinators attenuates the resistance of natural areas and agro-environments to disturbances4. Thus, the ability of pollination functions to resist or recover from disturbance (that is, the functional resilience)5, 6 may be critical for ensuring a successful restoration process7. Here we report the use of a community field experiment to investigate the effects of vegetation restoration, specifically the removal of exotic shrubs, on pollination. We analyse 64 plant–pollinator networks and the reproductive performance of the ten most abundant plant species across four restored and four unrestored, disturbed mountaintop communities. Ecosystem restoration resulted in a marked increase in pollinator species, visits to flowers and interaction diversity. Interactions in restored networks were more generalized than in unrestored networks, indicating a higher functional redundancy in restored communities. Shifts in interaction patterns had direct and positive effects on pollination, especially on the relative and total fruit production of native plants. Pollinator limitation was prevalent at unrestored sites only, where the proportion of flowers producing fruit increased with pollinator visitation, approaching the higher levels seen in restored plant communities. Our results show that vegetation restoration can improve pollination, suggesting that the degradation of ecosystem functions is at least partially reversible. The degree of recovery may depend on the state of degradation before restoration intervention and the proximity to pollinator source populations in the surrounding landscape5, 8. We demonstrate that network structure is a suitable indicator for pollination quality, highlighting the usefulness of interaction networks in environmental management6, 9.
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