En-route charging stations are essential to ensure the adoption of electric vehicles. However, careful planning is necessary due to high cost in infrastructure and potentially long waiting queues. Existing literature on the placement of charging stations largely disregards competition, sets prices to cover costs and/or disregards queues. In contrast, this work models competing station investors who aim to maximise expected profit, while electric vehicle drivers aim to minimise expected travel costs including queues. Following a game-theoretic approach, investors strategically decide station capacities, locations and charging unit power outputs as well as fees, taking into consideration building and operational costs. Given the complexity of the problem, the solution involves a combination of theoretical and algorithmic techniques to obtain subgame-perfect equilibria of investor and driver choices. Subgame-perfect equilibria are found to be at least 92.85% efficient, for reasonable fluctuations of problem parameters. Furthermore, it is found that charging prices can be up to approximately 5 times higher than marginal cost due to long charging times, and also that better charging technology may not necessarily benefit drivers in the near future. Finally, subsidies towards the purchase of charging units are shown to be beneficial for both drivers and investors, being able to generate up to 14.3% additional value than the cost of the subsidy. In contrast, subsidies on the energy price for stations are found to have small effect and can be abused by investors.

We identify optimal strategies for maximising influence within a social network in competitive settings under budget constraints. While existing work has focussed on simple threshold models, we consider more realistic settings, where (i) states are dynamic, i.e., nodes oscillate between influenced and uninfluenced states, and (ii) continuous amounts of resources (e.g., incentives or effort) can be expended on the nodes.

Eating disorders are complex mental disorders and responsible for the highest mortality rate among mental illnesses. Recent studies reveal that user-generated content on social media provides useful information in understanding these disorders. Most previous studies focus on studying communities of people who discuss eating disorders on social media, while few studies have explored community structures and interactions among individuals who suffer from this disease over social media. In this paper, we first develop a snowball sampling method to automatically gather individuals who self-identify as eating disordered in their profile descriptions, as well as their social network connections with one another on Twitter. Then, we verify the effectiveness of our sampling method by: 1. quantifying differences between the sampled eating disordered users and two sets of reference data collected for non-disordered users in social status, behavioral patterns and psychometric properties; 2. building predictive models to classify eating disordered and non-disordered users. Finally, leveraging the data of social connections between eating disordered individuals on Twitter, we present the first homophily study among eating-disorder communities on social media. Our findings shed new light on how an eating-disorder community develops on social media.

One of the most intriguing questions in evolution is how organisms exhibit suitable phenotypic variation to rapidly adapt in novel selective environments. Such variability is crucial for evolvability, but poorly understood. In particular, how can natural selection favour developmental organisations that facilitate adaptive evolution in previously unseen environments? Such a capacity suggests foresight that is incompatible with the short-sighted concept of natural selection. A potential resolution is provided by the idea that evolution may discover and exploit information not only about the particular phenotypes selected in the past, but their underlying structural regularities: new phenotypes, with the same underlying regularities, but novel particulars, may then be useful in new environments. If true, we still need to understand the conditions in which natural selection will discover such deep regularities rather than exploiting 'quick fixes' (i.e., fixes that provide adaptive phenotypes in the short term, but limit future evolvability). Here we argue that the ability of evolution to discover such regularities is formally analogous to learning principles, familiar in humans and machines, that enable generalisation from past experience. Conversely, natural selection that fails to enhance evolvability is directly analogous to the learning problem of over-fitting and the subsequent failure to generalise. We support the conclusion that evolving systems and learning systems are different instantiations of the same algorithmic principles by showing that existing results from the learning domain can be transferred to the evolution domain. Specifically, we show that conditions that alleviate over-fitting in learning systems successfully predict which biological conditions (e.g., environmental variation, regularity, noise or a pressure for developmental simplicity) enhance evolvability. This equivalence provides access to a well-developed theoretical framework from learning theory that enables a characterisation of the general conditions for the evolution of evolvability.

The RNA World is widely heralded as the leading candidate for a template first vision of the origin of life, yet doubts as to the plausibility of the natural formation of RNA with catalytic function have led to revived interest in the metabolism first paradigm. Recent studies of the evolvability of reflexively autocatalytic sets of polymers have also revealed the nature of limited heredity in compartmentalised reaction networks. In the algorithmic sense, the lack of a meaningful distinction between data storage and functional expression results in a protocell heredity-fitness dichotomy which gives an intrinsic selective advantage to those protocells with limited heredity. An idealised model is used explore a minimal set of dynamical requirements necessary to weaken the dichotomy. This is achieved by explicitly modelling a protocell as an outer compartment ’phenotype’, heritable only indirectly, in which competitive exponential growth may occur without compromising the heritability of previously discounted non-competitive growth autocatalysts in an inner sub-compartment, ’genotype’. Results show that heritable variation can be achieved under simulations of natural selection in populations of such metabolic replicators.

Network science or graph theory has its roots in the first half of the 18th century when Leonhard Euler, a Prussian mathematician, analyzed the problem of finding a route through the city of Königsberg that crossed every one of the city's seven major bridges once and only once. Ever since then, network theory has attracted the interest of mathematicians like the famous Paul Erdős, who (together with Alfréd Rényi [2] and independently Gilbert [3]) was first to define random graphs in the late 1950s. Later, social scientists joined in founding social network theory as a new branch of the field. In the last two decades, an explosion of research into network science has been stimulated by the recent introduction of the small-world [4] and scale-free [1] network paradigms. As a result, the field has become ever more multidisciplinary, and concepts from network theory have become standard knowledge in many areas of the natural and social sciences. Examples of fields where network approaches have almost become standard tools are computer science, systems biology, engineering, and physics, just to name a few. At the same time, the knowledge about network theory has become somewhat fragmented: Different terminology is used in different fields, and often solutions to problems that have been developed for one set of applications are not readily accessible to practitioners in other areas.With his new book Networks—An Introduction, Mark Newman aims at bridging this gap. Using a consistent terminology, the book provides a summary of the knowledge about the empirical analysis and theory of networks that has been accumulated in this diverse set of disciplines over many years. The book is very successful in this pursuit and gives an excellent introduction to the topic of network theory. Not only this, gradually working its way up from introductory content to more advanced topics, the book also provides a neat collection of relevant material for the more advanced reader. A set of problems at the end of most chapters allows the reader to check his or her understanding of the presented material. Altogether, I highly recommend this book, not only for the non-specialist reader who wants to understand what network theory is all about, but also for the specialist who wants to use the book as a quick reference or as the basis for an undergraduate or more advanced course taught at a university.After so much praise, let us have a brief look at the content of the book. It basically consists of five parts. The first part highlights the interdisciplinary nature of network applications and discusses examples from four different areas of research: infrastructure networks, social networks, networks linked to the transmission of information, and biological networks. In each of these broad application areas the author explains the most prominent examples and illustrates the relevance of network approaches.The second part of the book is composed of an introduction to the fundamentals of network theory. Chapter II.6 sets out with a series of definitions of various types of networks, followed by the introduction of various metrics generally used to classify networks. Chapter II.8 discusses small-worldness, degree distributions, clustering, and assortative mixes of degrees, and finishes by giving a classification of networks in terms of their macroscopic appearance.Having defined network metrics, the next logical step is to think about efficient computer algorithms to evaluate them for a given network. Accordingly, the third part of the book gives a very useful summary of some frequently applied computer algorithms. Again the chapter starts with a readily accessible introduction to some basics, such as various methods of storing a networks in computer memory. The author then quickly progresses to algorithms for the efficient computation of graph metrics, while leaving a last large chapter at the end of the part to the topics of graph partitioning and modularity. It is this part that makes the book particularly useful for readers who want to employ network approaches in their own fields of work.Part IV of the book gives an overview of the most well-known models of networks. This includes the topics of random graphs and—more generally—random graphs with general degree distributions. The book then proceeds to models of network formation that give rise to power law degree distributions. In the last sections of the part the small-world model and exponential random graphs find their place.The last part of the book brings the reader close to topics of very active current research. It is devoted to processes on networks and is composed of topics such as percolation and issues of network robustness; it also discusses epidemics on networks via various models of disease spread. The part also includes a general chapter on dynamical systems on networks and synchronization. A chapter on various aspects of the problem of search on networks concludes the book.Network approaches are a very fashionable topic in the scientific literature at the moment, and a word of caution is necessary. In some literature, researchers have not gone much beyond identifying questionable “power law” patterns in obscure data sets. By contrast, the present book is to be recommended for its sober approach, very careful selection of included topics, and firm grounding in relevant applications. I think this is one of the best among the recently published books about network theory.

We investigate networks whose evolution is governed by the interaction of a random assembly process and an optimization process. In the first process, new nodes are added one at a time and form connections to randomly selected old nodes. In between node additions, the network is rewired to minimize its path length. For time scales at which neither the assembly nor the optimization processes are dominant, we find a rich variety of complex networks with power law tails in the degree distributions. These networks also exhibit nontrivial clustering, a hierarchical organization, and interesting degree-mixing patterns.

We study the evolution of cooperation in the prisoner's dilemma game on time-invariant heterogeneous payoff landscapes on regular and heterogeneous networks. Correlations in the landscape structure and their implications for the evolution of cooperation are investigated. On regular networks we find that negatively and neutrally correlated payoff landscapes strongly enhance cooperation, while positively correlated landscapes may suppress the evolution of cooperation. On heterogeneous networks, cooperation is facilitated if payoff stochasticity is positively correlated with network heterogeneity and may be suppressed otherwise.