In his 1942 short story 'Runaround', science-fiction writer Isaac Asimov introduced the Three Laws of Robotics — engineering safeguards and built-in ethical principles that he would go on to use in dozens of stories and novels. They were: 1) A robot may not injure a human being or, through inaction, allow a human being to come to harm; 2) A robot must obey the orders given it by human beings, except where such orders would conflict with the First Law; and 3) A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws. Fittingly, 'Runaround' is set in 2015. Real-life roboticists are citing Asimov's laws a lot these days: their creations are becoming autonomous enough to need that kind of guidance. In May, a panel talk on driverless cars at the Brookings Institution, a think tank in Washington DC, turned into a discussion about how autonomous vehicles would behave in a crisis. What if a vehicle's efforts to save its own passengers by, say, slamming on the brakes risked a pile-up with the vehicles behind it? Or what if an autonomous car swerved to avoid a child, but risked hitting someone else nearby?
It is common practice to partition complex workflows into separate channels in order to speed up their completion times. When this is done within a distributed environment, unavoidable fluctuations make individual realizations depart from the expected average gains. We present a method for breaking any complex workflow into several workloads in such a way that once their outputs are joined, their full completion takes less time and exhibit smaller variance than when running in only one channel. We demonstrate the effectiveness of this method in two different scenarios; the optimization of a convex function and the transmission of a large computer file over the Internet.
Partitioning Uncertain Workflows Bernardo A. Huberman, Freddy C. Chua
Cascades in multiplex financial networks with debts of different seniority
The seniority of debt, which determines the order in which a bankrupt institution repays its debts, is an important and sometimes contentious feature of financial crises, yet its impact on systemwide stability is not well understood. We capture seniority of debt in a multiplex network, a graph of nodes connected by multiple types of edges. Here an edge between banks denotes a debt contract of a certain level of seniority. Next we study cascading default. There exist multiple kinds of bankruptcy, indexed by the highest level of seniority at which a bank cannot repay all its debts. Self-interested banks would prefer that all their loans be made at the most senior level. However, mixing debts of different seniority levels makes the system more stable in that it shrinks the set of network densities for which bankruptcies spread widely. We compute the optimal ratio of senior to junior debts, which we call the optimal seniority ratio, for two uncorrelated Erdős-Rényi networks. If institutions erode their buffer against insolvency, then this optimal seniority ratio rises; in other words, if default thresholds fall, then more loans should be senior. We generalize the analytical results to arbitrarily many levels of seniority and to heavy-tailed degree distributions.
We introduce a new kind of percolation on finite graphs called jigsaw percolation. This model attempts to capture networks of people who innovate by merging ideas and who solve problems by piecing together solutions. Each person in a social network has a unique piece of a jigsaw puzzle. Acquainted people with compatible puzzle pieces merge their puzzle pieces. More generally, groups of people with merged puzzle pieces merge if the groups know one another and have a pair of compatible puzzle pieces. The social network solves the puzzle if it eventually merges all the puzzle pieces. For an Erdős–Rényi social network with n vertices and edge probability p_n, we define the critical value p_c(n) for a connected puzzle graph to be the p_n for which the chance of solving the puzzle equals 1/2. We prove that for the n-cycle (ring) puzzle, p_c(n)=Θ(1/log n), and for an arbitrary connected puzzle graph with bounded maximum degree, p_c(n)=O(1/log n) and ω(1/n^b)for any b>0. Surprisingly, with probability tending to 1 as the network size increases to infinity, social networks with a power-law degree distribution cannot solve any bounded-degree puzzle. This model suggests a mechanism for recent empirical claims that innovation increases with social density, and it might begin to show what social networks stifle creativity and what networks collectively innovate.
Brummitt, Charles D.; Chatterjee, Shirshendu; Dey, Partha S.; Sivakoff, David. Jigsaw percolation: What social networks can collaboratively solve a puzzle?. Ann. Appl. Probab. 25 (2015), no. 4, 2013--2038. doi:10.1214/14-AAP1041.http://projecteuclid.org/euclid.aoap/1432212435.
In synthetic ecology, a nascent offshoot of synthetic biology, scientists aim to design and construct microbial communities with desirable properties. Such mixed populations of microorganisms can simultaneously perform otherwise incompatible functions (1). Compared with individual organisms, they can also better resist losses in function as a result of environmental perturbation or invasion by other species (2). Synthetic ecology may thus be a promising approach for developing robust, stable biotechnological processes, such as the conversion of cellulosic biomass to biofuels (3). However, achieving this will require detailed knowledge of the principles that guide the structure and function of microbial communities (see the image).
Ecological communities by design James K. Fredrickson
Antarctic biodiversity is much more extensive, ecologically diverse and biogeographically structured than previously thought. Understanding of how this diversity is distributed in marine and terrestrial systems, the mechanisms underlying its spatial variation, and the significance of the microbiota is growing rapidly. Broadly recognizable drivers of diversity variation include energy availability and historical refugia. The impacts of local human activities and global environmental change nonetheless pose challenges to the current and future understanding of Antarctic biodiversity. Life in the Antarctic and the Southern Ocean is surprisingly rich, and as much at risk from environmental change as it is elsewhere.
The changing form of Antarctic biodiversity • Steven L. Chown, Andrew Clarke, Ceridwen I. Fraser, S. Craig Cary, Katherine L. Moon & Melodie A. McGeoch
Geoffrey Hinton has a news bulletin for you: You’re not conscious. OK, you’re conscious as opposed to being unconscious – such as when you fall asleep at night, or when you get knocked out during a boxing match or when a doctor administers a general anesthetic before surgery. But you don’t have some intangible mental quality that worms or daffodils – or toasters, for that matter – lack.
In February 1988, Richard Lenski set up 12 replicate populations of a single genotype of Escherichia coli in a simple nutrient medium. He has been following their evolution ever since. Here, Lenski answers provocative questions from Jeremy Fox about his iconic "Long-Term Evolution Experiment" (LTEE). The LTEE is a remarkable case study of the interplay of determinism and chance in evolution—and in the conduct of science.
Social media and social networking sites have become a global pinboard for exposition and discussion of news, topics, and ideas, where social media users increasingly form their opinion about a particular topic by learning information about it from her peers. In this context, whenever a user posts a message about a topic, we observe a noisy estimate of her current opinion about it but the influence the user may have on other users' opinions is hidden. In this paper, we introduce a probabilistic modeling framework of opinion dynamics, which allows the underlying opinion of a user to be modulated by those expressed by her neighbors over time. We then identify a set of conditions under which users' opinions converge to a steady state, find a linear relation between the initial opinions and the opinions in the steady state, and develop an efficient estimation method to fit the parameters of the model from historical fine-grained opinion and information diffusion event data. Experiments on data gathered from Twitter, Reddit and Amazon show that our model provides a good fit to the data and more accurate predictions than alternatives.
Modeling Opinion Dynamics in Diffusion Networks Abir De, Isabel Valera, Niloy Ganguly, Sourangshu Bhattacharya, Manuel Gomez Rodriguez
Cooperation is ubiquitous in biological and social systems. Previous studies revealed that a preference toward similar appearance promotes cooperation, a phenomenon called tag-mediated cooperation or communitarian cooperation. This effect is enhanced when a spatial structure is incorporated, because space allows agents sharing an identical tag to regroup to form locally cooperative clusters. In spatially distributed settings, one can also consider migration of organisms, which has a potential to further promote evolution of cooperation by facilitating spatial clustering. However, it has not yet been considered in spatial tag-mediated cooperation models. Here we show, using computer simulations of a spatial model of evolutionary games with organismal migration, that tag-based segregation and homophilic cooperation arise for a wide range of parameters. In the meantime, our results also show another evolutionarily stable outcome, where a high level of heterophilic cooperation is maintained in spatially well-mixed patterns. We found that these two different forms of tag-mediated cooperation appear alternately as the parameter for temptation to defect is increased.
Transitions between homophilic and heterophilic modes of cooperation Genki Ichinose, Masaya Saito, Hiroki Sayama, Hugues Bersini
The coexistence of infinitely many attractors is called extreme multistability in dynamical systems. In coupled systems, this phenomenon is closely related to partial synchrony and characterized by the emergence of a conserved quantity. We propose a general design of coupling that leads to partial synchronization, which may be a partial complete synchronization or partial antisynchronization and even a mixed state of complete synchronization and antisynchronization in two coupled systems and, thereby reveal the emergence of extreme multistability. The proposed design of coupling has wider options and allows amplification or attenuation of the amplitude of the attractors whenever it is necessary. We demonstrate that this phenomenon is robust to parameter mismatch of the coupled oscillators.
Extreme multistability: Attractor manipulation and robustness Chittaranjan Hens, Syamal K. Dana, Ulrike Feudel Chaos 25, 053112 (2015)
The slower is faster (SIF) effect occurs when a system performs worse when its components try to be better. Thus, a moderate individual efficiency actually leads to a better systemic performance. The SIF effect takes place in a variety of phenomena. We review studies and examples of the SIF effect in pedestrian dynamics, vehicle traffic, traffic light control, logistics, public transport, social dynamics, ecological systems, and adaptation. Drawing on these examples we generalize common features of the SIF effect and suggest possible future lines of research.
When slower is faster Carlos Gershenson, Dirk Helbing
In the fall of 1949, many graduate students at Princeton University were assigned rooms in the Graduate College. In one suite, John Nash inhabited a single room, while I shared the double with Lloyd Shapley. John and Lloyd were the mathematicians and I was the economist, and together we pursued our interest in game theory. John was one of the youngest students at the Graduate College. He was from West Virginia, where his father was an engineer and his mother a Latin teacher. He graduated from the Carnegie Institute of Technology with bachelor's and master's degrees in mathematics, and arrived at the math department in Princeton in 1948.
The high population density in cities confers many advantages, including improved social interaction and information exchange. However, it is often argued that urban living comes at the expense of reducing happiness. The goal of this research is to shed light on the relationship between urban communication and urban happiness. We analyze geo-located social media posts (tweets) within a major urban center (Milan) to produce a detailed spatial map of urban sentiments. We combine this data with high-resolution mobile communication intensity data among different urban areas. Our results reveal that happy (respectively unhappy) areas preferentially communicate with other areas of their type. This observation constitutes evidence of homophilous communities at the scale of an entire city (Milan), and has implications on interventions that aim to improve urban well-being.
Misery loves company: happiness and communication in the city Alshamsi A, Awad E, Almehrezi M, Babushkin V, Chang P, Shoroye Z, Tóth A, Rahwan I EPJ Data Science 2015, 4 :7
The detection and characterization of self-organized criticality (SOC), in both real and simulated data, has undergone many significant revisions over the past 25 years. The explosive advances in the many numerical methods available for detecting, discriminating, and ultimately testing, SOC have played a critical role in developing our understanding of how systems experience and exhibit SOC. In this article, methods of detecting SOC are reviewed; from correlations to complexity to critical quantities. A description of the basic autocorrelation method leads into a detailed analysis of application-oriented methods developed in the last 25 years. In the second half of this manuscript space-based, time-based and spatial-temporal methods are reviewed and the prevalence of power laws in nature is described, with an emphasis on event detection and characterization. The search for numerical methods to clearly and unambiguously detect SOC in data often leads us outside the comfort zone of our own disciplines - the answers to these questions are often obtained by studying the advances made in other fields of study. In addition, numerical detection methods often provide the optimum link between simulations and experiments in scientific research. We seek to explore this boundary where the rubber meets the road, to review this expanding field of research of numerical detection of SOC systems over the past 25 years, and to iterate forwards so as to provide some foresight and guidance into developing breakthroughs in this subject over the next quarter of a century.
25 Years of Self-Organized Criticality: Numerical Detection Methods R.T. James McAteer, Markus J. Aschwanden, Michaila Dimitropoulou, Manolis K. Georgoulis, Gunnar Pruessner, Laura Morales, Jack Ireland, Valentyna Abramenko
Conversational modeling is an important task in natural language understanding and machine intelligence. Although previous approaches exist, they are often restricted to specific domains (e.g., booking an airline ticket) and require hand-crafted rules. In this paper, we present a simple approach for this task which uses the recently proposed sequence to sequence framework. Our model converses by predicting the next sentence given the previous sentence or sentences in a conversation. The strength of our model is that it can be trained end-to-end and thus requires much fewer hand-crafted rules. We find that this straightforward model can generate simple conversations given a large conversational training dataset. Our preliminary suggest that, despite optimizing the wrong objective function, the model is able to extract knowledge from both a domain specific dataset, and from a large, noisy, and general domain dataset of movie subtitles. On a domain-specific IT helpdesk dataset, the model can find a solution to a technical problem via conversations. On a noisy open-domain movie transcript dataset, the model can perform simple forms of common sense reasoning. As expected, we also find that the lack of consistency is a common failure mode of our model.
A Neural Conversational Model Oriol Vinyals, Quoc Le
The oft-repeated claim that Earth’s biota is entering a sixth “mass extinction” depends on clearly demonstrating that current extinction rates are far above the “background” rates prevailing between the five previous mass extinctions. Earlier estimates of extinction rates have been criticized for using assumptions that might overestimate the severity of the extinction crisis. We assess, using extremely conservative assumptions, whether human activities are causing a mass extinction. First, we use a recent estimate of a background rate of 2 mammal extinctions per 10,000 species per 100 years (that is, 2 E/MSY), which is twice as high as widely used previous estimates. We then compare this rate with the current rate of mammal and vertebrate extinctions. The latter is conservatively low because listing a species as extinct requires meeting stringent criteria. Even under our assumptions, which would tend to minimize evidence of an incipient mass extinction, the average rate of vertebrate species loss over the last century is up to 100 times higher than the background rate. Under the 2 E/MSY background rate, the number of species that have gone extinct in the last century would have taken, depending on the vertebrate taxon, between 800 and 10,000 years to disappear. These estimates reveal an exceptionally rapid loss of biodiversity over the last few centuries, indicating that a sixth mass extinction is already under way. Averting a dramatic decay of biodiversity and the subsequent loss of ecosystem services is still possible through intensified conservation efforts, but that window of opportunity is rapidly closing.
Accelerated modern human–induced species losses: Entering the sixth mass extinction Gerardo Ceballos, Paul R. Ehrlich, Anthony D. Barnosky, Andrés García, Robert M. Pringle and Todd M. Palmer
The origin of life can be understood mathematically to be the origin of information that can replicate. The likelihood that entropy spontaneously becomes information can be calculated from first principles, and depends exponentially on the amount of information that is necessary for replication. We do not know what the minimum amount of information for self-replication is because it must depend on the local chemistry, but we can study how this likelihood behaves in different known chemistries, and we can study ways in which this likelihood can be enhanced. Here we present evidence from numerical simulations (using the digital life chemistry "Avida") that using a biased probability distribution for the creation of monomers (the "biased typewriter") can exponentially increase the likelihood of spontaneous emergence of information from entropy. We show that this likelihood may depend on the length of the sequence that the information is embedded in, but in a non-trivial manner: there may be an optimum sequence length that maximizes the likelihood. We conclude that the likelihood of spontaneous emergence of self-replication is much more malleable than previously thought, and that the biased probability distributions of monomers that are the norm in biochemistry may significantly enhance these likelihoods
From Entropy to Information: Biased Typewriters and the Origin of Life Christoph Adami, Thomas LaBar
Big Data on electronic records of social interactions allow approaching human behaviour and sociality from a quantitative point of view with unforeseen statistical power. Mobile telephone Call Detail Records (CDRs), automatically collected by telecom operators for billing purposes, have proven especially fruitful for understanding one-to-one communication patterns as well as the dynamics of social networks that are reflected in such patterns. We present an overview of empirical results on the multi-scale dynamics of social dynamics and networks inferred from mobile telephone calls. We begin with the shortest timescales and fastest dynamics, such as burstiness of call sequences between individuals, and “zoom out” towards longer temporal and larger structural scales, from temporal motifs formed by correlated calls between multiple individuals to long-term dynamics of social groups. We conclude this overview with a future outlook.
From seconds to months: an overview of multi-scale dynamics of mobile telephone calls Jari Saramäki and Esteban Moro
Network analysis — the mathematical analysis of relationships between elements or actors in a complex system — has become popular among transportation planners and spatial analysts, but its use remains relatively limited among architects and urban designers, whose day-to-day work demands more visioning than analysis. Now, researchers at the joint MIT-SUTD International Design Center (IDC) have created a free network analysis plugin for Rhinoceros 3-D modeling software, one of the most popular software platforms among architects and urban designers. The new Urban Network Analysis (UNA) plugin enables urban planners and architects to describe spatial patterns of cities using mathematical network analysis methods.
Scientific journals are the repositories of the gradually accumulating knowledge of mankind about the world surrounding us. Just as our knowledge is organised into classes ranging from major disciplines, subjects and fields to increasingly specific topics, journals can also be categorised into groups using various metrics. In addition to the set of topics characteristic for a journal, they can also be ranked regarding their relevance from the point of overall influence. One widespread measure is impact factor, but in the present paper we intend to reconstruct a much more detailed description by studying the hierarchical relations between the journals based on citation data. We use a measure related to the notion of m-reaching centrality and find a network which shows the level of influence of a journal from the point of the direction and efficiency with which information spreads through the network. We can also obtain an alternative network using a suitably modified nested hierarchy extraction method applied to the same data. The results are weakly methodology-dependent and reveal non-trivial relations among journals. The two alternative hierarchies show large similarity with some striking differences, providing together a complex picture of the intricate relations between scientific journals.
Hierarchical networks of scientific journals Gergely Palla, Gergely Tibély, Enys Mones, Péter Pollner, Tamás Vicsek
Social media are used as main discussion channels by millions of individuals every day. The content individuals produce in daily social-media-based micro-communications, and the emotions therein expressed, may impact the emotional states of others. A recent experiment performed on Facebook hypothesized that emotions spread online, even in absence of non-verbal cues typical of in-person interactions, and that individuals are more likely to adopt positive or negative emotions if these are over-expressed in their social network. Experiments of this type, however, raise ethical concerns, as they require massive-scale content manipulation with unknown consequences for the individuals therein involved. Here, we study the dynamics of emotional contagion using Twitter. Rather than manipulating content, we devise a null model that discounts some confounding factors (including the effect of emotional contagion). We measure the emotional valence of content the users are exposed to before posting their own tweets. We determine that on average a negative post follows an over-exposure to 4.34% more negative content than baseline, while positive posts occur after an average over-exposure to 4.50% more positive contents. We highlight the presence of a linear relationship between the average emotional valence of the stimuli users are exposed to, and that of the responses they produce. We also identify two different classes of individuals: highly and scarcely susceptible to emotional contagion. Highly susceptible users are significantly less inclined to adopt negative emotions than the scarcely susceptible ones, but equally likely to adopt positive emotions. In general, the likelihood of adopting positive emotions is much greater than that of negative emotions.
Measuring Emotional Contagion in Social Media Emilio Ferrara, Zeyao Yang
In the past decades many definitions of complexity have been proposed. Most of these definitions are based either on Shannon's information theory or on Kolmogorov complexity; these two are often compared, but very few studies integrate the two ideas. In this article we introduce a new measure of complexity that builds on both of these theories. As a demonstration of the concept, the technique is applied to elementary cellular automata and simulations of the self-organization of porphyrin molecules.
Complexity Measurement Based on Information Theory and Kolmogorov Complexity Leong Ting Lui, Germán Terrazas, Hector Zenil, Cameron Alexander, Natalio Krasnogor Artificial Life Spring 2015, Vol. 21, No. 2: 205–224.
In complex environments, there are costs to both ignorance and perception. An organism needs to track fitness-relevant information about its world, but the more information it tracks, the more resources it must devote to memory and processing. Rate-distortion theory shows that, when errors are allowed, remarkably efficient internal representations can be found by biologically-plausible hill-climbing mechanisms. We identify two regimes: a high-fidelity regime where perceptual costs scale logarithmically with environmental complexity, and a low-fidelity regime where perceptual costs are, remarkably, independent of the environment. When environmental complexity is rising, Darwinian evolution should drive organisms to the threshold between the high- and low-fidelity regimes. Organisms that code efficiently will find themselves able to make, just barely, the most subtle distinctions in their environment.
The evolution of lossy compression Sarah E. Marzen, Simon DeDeo
Random walk is a fundamental concept with applications ranging from quantum physics to econometrics. Remarkably, one specific model of random walks appears to be ubiquitous across many fields as a tool to analyze transport phenomena in which the dispersal process is faster than dictated by Brownian diffusion. The Lévy-walk model combines two key features, the ability to generate anomalously fast diffusion and a finite velocity of a random walker. Recent results in optics, Hamiltonian chaos, cold atom dynamics, biophysics, and behavioral science demonstrate that this particular type of random walk provides significant insight into complex transport phenomena. This review gives a self-consistent introduction to Lévy walks, surveys their existing applications, including latest advances, and outlines further perspectives.
Lévy walks V. Zaburdaev, S. Denisov, and J. Klafter Rev. Mod. Phys. 87, 483
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