Abstract: We introduce a network valuation model (hereafter NEVA) for the ex-ante valuation of claims among financial institutions connected in a network of liabilities. Similar to previous work, the new framework allows to endogenously determine the recovery rate on all claims upon the default of some institutions. In addition, it also allows to account for ex-ante uncertainty on the asset values, in particular the one arising when the valuation is carried out at some time before the maturity of the claims. The framework encompasses as special cases both the ex-post approaches of Eisenberg and Noe and its previous extensions, as well as the ex-ante approaches, in the sense that each of these models can be recovered exactly for special values of the parameters. We characterize the existence and uniqueness of the solutions of the valuation problem under general conditions on how the value of each claim depends on the equity of the counterparty. Further, we define an algorithm to carry out the network valuation and we provide sufficient conditions for convergence to the maximal solution.

Abstract: In multi-layer networks with directed links, introducing measures of dependency between different layers requires more than a straightforward extension of the multiplexity measures that have been developed for undirected multiplexes. In particular, one should take into account the effects of reciprocity, i.e. the tendency of pairs of vertices to establish mutual connections. In single-layer networks, reciprocity is a crucial structural property affecting several dynamical processes. Here we extend it to multiplexes and introduce the notion of multireciprocity, defined as the tendency of links in one layer to be reciprocated by links in a different layer. While ordinary reciprocity reduces to a scalar quantity, multireciprocity requires a square matrix generated by all the possible pairs of layers.

Abstract: The Japanese banking crisis in the late 1990s has been considered a significant turning point in the history of the Japanese banking system. This period has attracted researchers' interest in studying the increase of bad debt on Japanese banks? balance sheets, which led to the crisis of the 1990s. Here we investigate the risk propagating through a bipartite banking network consisting of two kinds of nodes: assets on one hand and banks on the other. Using a Cascading Failure Model (CFM), originally proposed by Huang et al. [1], to describe the propagation of failures in the network, we attempt to understand the main culprit provoking the crisis and the systemic conditions that amplified or repressed the ?chain reaction? of bankruptcies

Abstract: Bipartite networks are currently regarded as providing a major insight into the organization of real-world systems, unveiling the mechanisms shaping the interactions occurring between distinct groups of nodes. One of the major problems encountered when dealing with bipartite networks is obtaining a (monopartite) projection over the layer of interest which preserves as much as possible the information encoded into the original bipartite structure. In the present paper we propose an algorithm to obtain statistically-validated monopartite projections of bipartite networks, which implements a entropy-based null-model We analyze a social network (i.e. the MovieLens dataset, a bipartite network of users and rated movies) and an economic network (i.e. the countries-products World Trade Web representation): while, in the first case, projecting Movie- Lens on the films layer allows clusters of movies belonging to similar genres to be detected, in the second case, projecting the World Trade Web on the countries layer reveals a modular structure of similarly-industrialized clusters of nations.

Abstract: We introduce two models of inclusion hierarchies: random graph hierarchy (RGH) and limited random graph hierarchy (LRGH). In both models a set of nodes at a given hierarchy level is connected randomly, as in the Erdos-Renyi random graph, with a fixed average degree equal to a system parameter c. Clusters of the resulting network are treated as nodes at the next hierarchy level and they are connected again at this level and so on, until the process cannot continue. In the RGH model we use all clusters, including those of size 1, when building the next hierarchy level, while in the LRGH model clusters of size 1 stop participating in further steps. We find that in both models the number of nodes at a given hierarchy level h decreases approximately exponentially with h. The height of the hierarchy H, i.e. the number of all hierarchy levels, increases logarithmically with the system size N, i.e. with the number of nodes at the first level. The height H decreases monotonically with the connectivity parameter c in the RGH model and it reaches a maximum for a certain c(max) in the LRGH model. The distribution of separate cluster sizes in the LRGH model is a power law with an exponent about -1.25. The above results follow from approximate analytical calculations and have been confirmed by numerical simulations.

Abstract: Information diffusion and epidemics outbreaks share a similar non linear dynamics which is studied in many scientific fields, including biology, engineering and physics.?The problem of controlling such types of dynamics in complex systems has recently been studied in different contexts. We shall provide an overview of recent? results obtained both in the case of control, i.e., when a central decision can be?operated and agents comply to the central controller, and strategic versions of the same?problems, where some of the agents may comply or not, or to a certain degree,? depending on their own utility, both in the case of cooperative behaviors and in the? case of competitive ones.?

Abstract: Worldwide firms interact with each other to form complex networks of financial relations. These financial interactions are partly captured by the ownership relations between them. We constructed the weighted and directed network of 800,000 companies forming multinational firms, with respect to their 1.2 million ownership relations (UNIL-GeoDivercity-Orbis, 2013) and extracted the corresponding directed network of the cities that harbor these firms. The firms, and therefore the cities, are then classified into five categories according to their main field of activity, i.e. high-tech, low-tech, knowledge-intensive services, low knowledge-intensive services and other (OECD, 2009). We employed this classification to divide the network into activity-specific relation networks that are connected through the cities they have in common, thus constructing a weighted and directed, multi-layered, partial-multiplex network of cities.

Abstract: Urban mobility can be modeled as a multilayer network, where each layer represents different modalities such as pedestrians, cyclists, buses, trams, metros, taxis, transportation network companies, private, logistics, freight, and emergency vehicles. The technology required to integrate different transportation modes already exists. However, there are several challenges beyond technology for achieving truly integrated transportation networks. Even within a single transportation mode, different actors may fail to coordinate for different reasons: economic, social, or political. These reasons extrapolate and limit the coordination between layers. After reviewing the limitations, I suggest possible scenarios which could overcome them. As an incentive for doing so, I outline a way of quantifying the benefits on integrated transportation networks.

Abstract: The problem of identifying natural, socio-economically defined neighborhoods arises in applied contexts including Census reporting, measurement of segregation, and dimension reduction in urban computing. This problem is also of interest for urban theory, since the difficulty of identifying neighborhoods may be viewed as a measure of socio-spatial complexity. We develop a rigorous, information-theoretic approach to this topic, using open data on race in American cities as a case study. First, we formulate the mean local information J(X, Y ) as a localization of the mutual information between spatial and socio-economic variables. The measure J(X, Y ) is closely related to the Fisher information of the underlying joint distribution, and is therefore a measure of the intrinsic spatial complexity of an urban phenomenon. Unlike standard global information measures, the mean local information clearly distinguishes between cities like Detroit?which is dominated by a few huge, monoracial superclusters?and cities like Philadelphia?which is an intricate patchwork of small, racially-distinct neighborhoods. Second, we provide a practical algorithm for identifying natural neighborhoods through greedy information maximization, and relate this algorithm?s behavior to the mean local information. Questions raised by this work include the social, economic, and policy determinants of socio-spatial complexity in cities, and the potential use of spatial information measures in quantifying temporal changes in socio-economic structure, on time scales ranging from days to decades.

Abstract: Everyday massive amounts of geolocated data are passively generated by individuals devices like smart phones, credit cards, GPSs, RFIDs or remote sensing devices. This deluge of information growing at an astonishing rate represents an unprecedented opportunity for researchers, to solve challenging problems and unveil fundamental insights on our society. Many disciplines are concerned, ranging from mathematics, physics and computer science for the analysis and management of research data, to applications in astronomy, medicine, geography and social science. Although data passively generated by the use of Information and Communication Technologies (ICT) have the advantage of the large size of the samples (millions of observations) with a high spatio-temporal resolution, they raise many new challenging issues, related to their storage, transport, management and processing. In particular, they may suffer from hidden biases, and therefore observing the world through the lens of big datasets can lead to possible distortions which may lead to erroneous conclusions. It is thus crucial to develop statistical tools and methods to assess the uncertainty in ICT data, notably by comparing the results obtained with different data sources. In the following we present two examples of such uncertainty analysis on results obtained with mobile phone data recorded in Senegal in 2013. We concentrate on two information-retrieval tasks: first, we evaluate the uncertainty when inferring land use from the rhythms of human activity, and second, we study the uncertainty when identifying individuals? most frequented locations. We conclude by mentioning possible future steps to clearly assess the relevance of various ICT data sources for studying different phenomena.

Abstract: As social beings, we create structures that ensure that interactions between and within communities take place. These structures have been changing over time, but they have left footprints that can be identified as patterns that translate into hierarchies of regions and social divisions that are the outcome of a historical process. In this work we use different clustering methodologies and percolation theory to uncover the different communities that can be identified as the outcome of this process, and as the emergence of new kinds of interactions

Abstract: The distribution of population in urban settlements has been extensively characterised in the literature by using Zipf's law but there exist well known deviations from this power-law distribution in the upper and lower tails of the spectrum. In this work we use the definition of cities proposed in a previous paper using a percolation approach on the road network, and show that the same type of power-law distribution exists as well for the number of intersections of a city and that this distribution is fitted with a greater precision presenting less deviations from the theoretical power-law. We also show that it is possible to derive the population of a settlement from this number of intersections and that the existing variability within this approximation can be partially explained by quantifying the position in the regional hierarchy of each settlement. This result gives us another insight into why some cities are over/under-populated with respect to its expected position in the Zipf's law and at the same time renders possible to extract an approximated population of each settlement having as sole source of data the road network of the system. Furthermore, we show that by combining both distributions we can find a clear cut between large and small settlements that can be used to quantitatively define a threshold between urban/rural settlements.

Abstract: The integration of Information and communications technology (ICT) data sources to generate activity-travel information opens interesting opportunities for feeding agent-based transportation models, whose practical implementation is often hindered by the lack of sufficient data. In this talk we present a module developed to generate activity-travel diaries needed to feed the MATSim simulation framework. Activity diaries are generated by merging data from mobile phone records and census data. We discuss of the process followed for the creation of synthetic agents, from the extraction of mobility patterns to the expansion of the sample data to the total population: the dataset provided by the mobile network operator provides the age and gender of the users, while residence location and daily trips are obtained by analysing the mobile phone records; the information obtained from mobile phone records is then expanded to cover the whole population by using census data at census tract level. Finally, some lines of future research for the improvement of the current methodology are discussed. The resulting synthetic population is validated with the EMEF survey results for Barcelona, The comparison shows a good fit in number of trips by gender and age and major discrepancies in trip pourpose assignation. The resulting activity diaries are used to Feed and Calibrate the MATSim simulation model.

Abstract: Residential location choices are influenced by a series of factors whose influence varies across space. We aim to improve one of Land Use and Transport Integrated (LUTI) models MARS model introducing the different impact of those factors on residential choice by computing local coefficients. In particular, this research explores the methodology of integrating the public choice model into the MARS (Metropolitan Activity Relocation Simulator) model using a general accessibility indicator, thus creating a new approach to estimate the coefficients of each public service section with the use of Geographically Weighted Regression (GWR). The MARS model includes a transport model which simulates the travel behaviour of the population related to their housing and workplace location, a housing development model, a household location choice model, a workplace development model, a workplace location choice model. The method to embed the public services location model into MARS is to re-develop the accessibility indicator which is the key connection between the transport submodel and housing, workplace loction submodel, not only considering the capability to reach workplaces but also involving the ability to access certain public services. Accessibility plays a major role to influence where people to live and work (Wang, et al., 2015). It is one of the outputs of the transport sub-model in year n as well as the input to the land use sub-models in the year n+1. The new accessibility indicator is calculated by integrating a series of travel motives, and then weighting each of them using the results from a GWR. In this manner, the accessibility is evaluated as the key location factor to express the level of public services in land use, which in turn attracts travel demand. We applied GWR (Fotheringham et al., 2002) to generate local models in which specific coefficients are computed for each observation (i.e. spatial unit) and for each significant variable. The calculation of these local coefficients is based on the values of the corresponding variable in nearby locations, giving more weight to close locations, thus establishing an inverse distance relationship. An origindestination matrix through the road network was computed with ArcGIS Network Analyst extension. This matrix was used as input in GWmodel R package in order to consider network distance in local correlations and GWR. The model update and extension of MARS are all based on the Region of Madrid, Spain. The external scenario update is based on the zoning of MARS, which aggregates the 199 municipalities of Madrid Region into 90 modelling zones. The zoning was carried out following homogeneity parameters of socioeconomic characteristics and mobility, plus correspondence with transport zones and regional rings. Data collected at different levels were aggregated to the MARS zoning. The GWR was based on the most disaggregated spatial units that were available (i.e. census tracts). Data sources include INE, Nomecalles, DUAE and TomTom. We computed local correlation statistics between population data and the highest correlated variables for each topic. Based on previous work, we applied a 5km bandwidth to incorporate the values of nearby locations using a Gaussian function. We then performed a model selection process based on the spatial relationships of the observations within 5 km, after which we estimated the best bandwidth in terms of distance (fixed) and number of neighbors (adaptive) both for distance along the road network and for private motor vehicle travel time. In all cases, a 5-neighbor adaptive bandwidth provided the best fitted models, with network distance performing better than travel time. The best-fitted model is the one considering the number of workplaces, education centres and retail. Figure 1 shows the standardized local coefficients once aggregated at the MARS zoning. The intensity and sign of the relationship between each factor and population location have a great variety across space, especially in the case of education centres. Population figures predicted with our model are consistent with real figures, with discrepancies below +- 0.5 standard deviations in the most populated areas.

Abstract: The last few years have seen the emergence of the so-called sharing economy (also known as collaborative consumption) that has been driven by the development of Internet platforms that facilitate peer-to-peer relations. One of the fields in which collaborative consumption has burst onto the scene with greater intensity is that of tourism, both in the travel sector (car-sharing) and that of accommodation. Airbnb is the most successful P2P platform in the field of accommodation, offering more than 2,000,000 listings in 190 countries. The potential impacts of Airbnb on local economies are complex and difficult to measure. The results of the study by Fang et al. (2006) suggest that the entry of sharing economy benefits the entire tourism industry by generating new job positions as more tourists would come due to the lower accommodation cost. From the perspective of the spatial distribution of the Airbnb impacts within the cities, it has been argued that Airbnb listings are more scattered than hotels, so Airbnb guests may be especially likely to disperse their spending in neighbourhoods that do not typically receive many tourists (see Guttentag, 2014). Nevertheless, this possible dispersion may be compatible with a particular concentration of listings in the central areas of the cities, including areas not covered by hotels. This fact could aggravate the problems of crowding and tourism gentrification that some of these areas have to support in certain heritage cities (Russo, 2002; Neuts and Nijkamp, 2012). This article analyses the spatial patterns of Airbnb in Barcelona and compares them with hotels and sightseeing spots.

Abstract: Face-to-face contacts between individuals play an important role in social interactions and can also determine the potential transmission routes of infectious diseases, in particular of respiratory pathogens. An accurate description of these patterns is therefore of interest in order to identify contagion pathways, to inform models of epidemic spread, and to design and evaluate control measures such as the targeting of specific groups of individuals with appropriate prevention strategies or interventions.Many datasets describing contacts between individuals in different contexts have been obtained using either surveys or wearable sensors. Both techniques yield however incomplete or biased data, due on the one hand to population sampling (in both cases), and to underreporting or bad estimation of contact durations (for surveys). Here I will use data describing contacts gathered by both techniques and show how these issues might bias the outcome of simulation of spreading processes, leading to an underestimation of the epidemic risk. I will then describe methods to compensate for such biases.

Abstract: The growing complexity of global mobility is a key challenge for the understanding of the worldwide spread of emergent infectious diseases and the design of effective containment strategies. Despite global connectivity, containment policies are often based on national, regional and ?egocentric? assessments of outbreak situations that are no longer effective or meaningful, as demonstrated by 2014 Ebola outbreak in West Africa where months passed before a concerted, international effort followed. Despite the importance of the matter, optimal strategies in highly connected non-local settings are poorly understood. In this work we investigate a model for the optimal resource distribution for outbreak mitigation in a network of interacting countries. Each node can exercise a limited amount of resources among all nodes in the network to mitigate an outbreak. We define the cost of each node as a combination of invested resources and the cost incurred by the disease. In this game theoretical framework nodes explore the strategy space until the system reaches its equilibrium. Contrary to common belief, we observe that purely selfish and cooperative actions do not differ considerably in single outbreak scenarios, i.e. purely selfish behavior tends to invest resources at the outbreak location. However, in a scenario with multiple outbreak locations different patterns can be observed. When resources are abundant, we observe a behavior similar to single outbreak scenario, in which the network is split into two regions allocating the resources to the nearest infected node. In a setting where resources are limited a third region emerges in which the available resources are commited to the investing nodes themselves. In all cases the topological distance to the infected node determines the strategy: proximity to one of two infected nodes implies investment into the infected node whereas in an equidistant scenario, self-investment is rational on average. Inhomogenious distribution of the resources is not changing the findings described above. Furthermore, we find that the degree of the infected node affects the behavior of its neighbors in a non trivial manner. (Presenter: Olga Baranov)

Abstract: Network-based infectious disease models have been highly effective in elucidating the role of contact structure in the spread of infection. As such, pair- and neighbourhood-based approximation models have played a key role in linking findings from network simulations to standard (random-mixing) results. Recently, for SIR-type infections (that produce one epidemic in a closed population) on locally tree-like networks, these approximations have been shown to be exact. Network models are, however, ideally suited for Sexually Transmitted Infections (STIs) due to the greater level of detail available for sexual contact networks, and these diseases have SIS-type dynamics. Here, using the most testing network constructions, we consider the accuracy of three systematic approximations that can be applied to arbitrary disease dynamics (and in one case arbitrary network topology). By examining how and when these approximation models converge we generate insights into the role of network structure in the infection dynamics of STIs.

Abstract: Contagion processes in structured populations, on networks or in space have attracted much attention in the past. Yet, the great majority of studies focus on the spread of single agents, e.g. single pathogens in epidemic context, single pieces of information in social contagion or single species in coupled eco-systems. Situations in which multiple, interacting agents spread simultaneously are less well understood, particularly when two agents cooperate during the spreading process. We investigate contagion dynamics of two cooperatively interacting agents. We show that cooperation between transmissible agents changes the threshold behavior of the system in a substantial and qualitative way. We show that as a function of the basic reproduction ratio of the system, discontinuous transition to an endemic state emerge in which both agents prevail. Furthermore, the system exhibits hysteresis, i.e. threshold for outbreaks and elimination differ. Finally we discuss unexpected and counterintuitive properties of cooperatively interacting contagion processes in spatially extended systems. The presented work may serve as a starting point for further investigations of interacting contagion processes in populations.

Abstract: Most aspects of real world social networks, e.g., clustering [1,2] and community structure [3], and of human behaviour, e.g., social distancing and increased hygiene, will slow disease spread. Here, we consider a model where individuals with essential societal roles--such as teachers, first responders, health-care workers, etc.-- who fall ill are replaced with healthy individuals. We refer to this process as relational exchange. Relational exchange is also a behaviour, but one whose effect on disease transmission is less obvious. By incorporating this behaviour into a dynamic network model, we demonstrate that replacing individuals can accelerate disease transmission. Furthermore, we find that the effects of this process are trivial when considering a standard mass-action model, but dramatic when considering network structure: featuring accelerating spread, discontinuous transitions, and hysteresis loops. This result highlights another critical shortcoming in mass-action models, namely their inability to account for many behavioural processes. Lastly, using empirical data, we find that this mechanism parsimoniously explains observed patterns across 17 influenza outbreaks at the U.S.A. national-level, 25 years of influenza data at the U.S.A. state-level, and 19 years of dengue virus data from Puerto Rico. We anticipate that our findings will advance the emerging field of disease forecasting, improve our capacity to model the physics of complex behaviours on networks, and will better inform public health decision making during outbreaks.

Abstract: The health and treatment of patients in health care institutions is threatened by the spread of hospital-associated (HA) pathogens, particularly antimicrobial resistant micro-organisms, that take advantage of this susceptible population. Although the responsibility for preventing and controlling the spread of such pathogens lies with individual hospitals, they are by far isolated entities. Patients exchanged between hospitals offer pathogens the opportunity to spread from one hospital to another, thus forming an epidemiological contact between them. The complete hospital network formed by these exchanged patients influences the chance of a hospital encounter HA-pathogens as well as the within-hospital prevalence of such pathogens. However, patient movement patterns may change over time as a result of policy decisions. Hospitals may for instance be driven to specialise and stop offering certain procedures, forcing patients to seek medical treatment elsewhere. The hospital network may therefore change over time, which could have its bearing on the spread of HA-pathogens. Using data from the NHS hospital episode statistics, we tracked the changes in the hospital network in England between 2004 and 2014, and identified both global and local changes in the network that affected the networks susceptibility to the spread of HA-pathogens. Overall, the number of admissions and re-admissions increased, with a pronounced increase in the number of patients moving between hospitals. The increasing number of admissions seems to be driven by an aspiration to reduce the length of stay. Despite this reduced length of stay, the probability of pathogens spreading between hospitals increased over the years. Changes in the network were not uniform over all links, while some pairs of hospitals increased their number of exchanged patients, others almost severed their link completely. These preferential changes affected the community structure of the hospital network, while some of them moved the hospitals in the network closer together overall, increasing the risk of nation-wide outbreaks. Although some of the changes we observed could be the result of a natural evolution of the hospital network, for instance because of an ageing population, many of the observed changes seem to be policy driven. As these policies are often aiming for higher efficiency on the hospital level, the unintended consequences they have on the spread of HA-pathogens are often overseen. The extra cost required to control the spread of HA-pathogens, or treat the increasing number of affected patients, should be taken into account when considering the cost-effectiveness of changes in health care policy.

Abstract: Diseases affecting farmed cattle compromise both human and animal health and welfare, and represent a major cause of loss in economic revenue. Their spread is known to be driven, or at least facilitated, by animal displacements among livestock holdings, both within and across countries. As a result, studying the networks of animal movements is a key step in devising new prevention and containment strategies. Past works have already analyzed cattle networks in several European countries, highlighting complex interactions between topology, function and dynamics at different spatial and time resolutions. A comprehensive study, showing the impact of country-specific driving factors on network evolution and topology, is however still missing. By using data from several European countries, and focusing on the features relevant for the spread of infections, we perform a comparative analysis to highlight both general and country-specific patterns. We find that coarse-graining the structure into statistical distributions of centrality measures is an effective way to highlight the properties shared by all networks, which represent the fingerprint of a livestock market. The situation dramatically changes when we zoom in to the microscopic structure, as we find several country-specific characteristics, especially in temporal evolution. This twofold behavior suggests that on one hand it is possible to identify several global patterns in the ways animal disease spread, which can then be applied to countries for which data are unavailable, or incomplete. On the other hand, resolved country-specific data are needed when devising tailored and targeted intervention strategies.

Abstract: A long tradition of empirical research has demonstrated that voters? political opinions are strongly influenced by their media consumption. Today, however, voters are not only consumers of media content. On the Internet, users produce, adjust, and evaluate media content and contribute to its dissemination. It has been warned that these new forms of social influence may generate feedback processes that intensify users? opinions and give rise to unintended macro-processes, such as opinion polarization. However, these warnings are based on anecdotal evidence and debated, formal models of opinion dynamics. We conducted a field experiment in order to test (i) competing micro-assumptions about social influence and (ii) hypotheses about the macro-dynamics resulting from social-influence. On an American news website, participants read a short, controversial article that contained a voting tool allowing them to rate their opinions about the issue of the article and showing them the opinion ratings of other users. In a first set of experimental treatments, participants saw different distributions of other users? opinion ratings, which allowed us to test alternative assumptions about how users? ratings are influenced by information about other users? views. This data was also used to calibrate a formal model of social influence, which we used to derive macro-predictions about the collective dynamics emerging from the observed social-influence processes. With a second set of experimental treatments, we were able to empirically test these macro-predictions. In a nutshell, we found strong support for social influence. However, the observed forms of social influence generate problematic macro-processes, such as extremization and polarization, only under very limited conditions.

Abstract: Money is central in US politics, and most campaign contributions stem from a tiny, wealthy elite. Like other political acts, campaign donations are known to be socially contagious. We study how campaign donations diffuse through a network of more than 50000 elites and examine how connectivity among previous donors reinforces contagion. We find that the diffusion of donations is driven by independent reinforcement contagion: people are more likely to donate when exposed to donors from different social groups than when they are exposed to equally many donors from the same group. Counter-intuitively, being exposed to one side may increase donations to the other side. Although the effect is weak, simultaneous cross-cutting exposure makes donation somewhat less likely. Finally, the independence of donors in the beginning of a campaign predicts the amount of money that is raised throughout a campaign. We theorize that people infer population-wide estimates from their local observations, with elites assessing the viability of candidates, possibly opposing candidates in response to local support. Our findings suggest that theories of complex contagions need refinement and that political campaigns should target multiple communities.?

Abstract: In this talk is concerned with the disscontempt that the very idea of social physics causes some in the humanities and social sciences. I will address the historical origins of social physics, as well as that of its opposition to address the following question: has the data revolution merely resurfaced an old debate in the social sciences, or does data technology necessitate a new understanding of the philosophy of social science?

Abstract: In this contribution, we focus on the fact that, while the use of a language can be clearly described as a property of the interactions between speakers ---link states---, there are certain features intrinsic to these speakers ---node states--- which have a relevant influence on the language they choose for their communications. In particular, the attitude of a speaker towards a given language ---which determines her willingness to use it--- is affected by individual attributes such as her level of competence in that language, her degree of cultural attachment and affinity with the social group using that language, and the strength of her sense of identity or belonging to that group. For simplicity, we consider that all individual properties affecting language choice can be subsumed under the concept of ``preference''. At the same time, the evolution of the speakers' individual preferences is, in turn, affected by the languages used in their respective social neighborhoods. In this manner, the problem of language competition can be studied from the point of view of the intrinsically coupled evolution of language use and language preference. Ultimately, this change of perspective can be regarded as a shift from a paradigm in which language is considered only as a means of communication to one in which its tight entanglement with culture and identity is also taken into account.?

Abstract: Being able to infer the number of people in a specific area is of extreme importance for the avoidance of crowd disasters and to facilitate emergency evacuations. However, existing approaches which rely on human analysts counting samples of the crowd can be time-consuming or costly. Here, we investigate whether data on mobile phone usage and usage of the online social media service Twitter can be used to estimate the number of people in a specific area at a given time. Using a football stadium and an airport as case studies, we present evidence of a strong relationship between the number of people in restricted areas and activity recorded by mobile phone providers and the online service Twittter. Figure 1 depicts the time series corresponding to the communication activities inside the football stadium and the number of attendees at the ten matches that took place during the period of analysis, showing a remarkable similarity between them. As well as being of clear practical value for a range of business and policy stakeholders, our findings suggest that data generated through our interactions with mobile phone networks and the Internet may allow us to gain a valuable measurements of the current state of society.

Abstract: The initial inspiration for this work came from some preliminary results of an analysis of inter-?rm innovation networks in the alpine region of South Tyrol. This analysis con?rmed the importance of network connections in the spreading of innovations, as already reported by other studies, and suggested that the network structure should be explicitly inserted into one of the models most widely employed for the description of innovation diffusion, namely the Bass equation. Our experience with the local network structures also pointed to the importance of trickle-up innovation process, which are absent from the traditional Bass model and have been rarely studied in the literature. Actually, a trickle-up process can be only simulated in a model with a network structure, so we saw here a chance to improve the Bass model under the two respects at once. We have successfully integrated the network structure into the equations of the original model and we have studied in particular the total diffusion time and the partial diffusion times (in the link classes) in dependence on the model parameters. This was done separately in the cases of diffusion originating uniformly in the network, or mainly in the hubs, or mainly at the periphery. Further technical improvements have been the explicit construction of correlation matrices and suitable modi?cations of the differential equations in order to allow negative linear terms (representing sti?er hubs) or stochastic terms (representing random generation of innovation). Results come in the form of accurate time diffusion curves and of numerical values assessing the anticipation effect in the hubs. These results show that even for the traditional trickle-down Bass model, the introduction of the network offers several advantages.?

Abstract: Few people would deny that spending time in areas of beautiful scenery results in a sense of increased wellbeing. Yet, what if scenic environments have an impact on our health??To date, quantifying this relationship has been limited by the impracticality of gathering large-scale data on humans? perception of the environment. However, data generated through our increasing interactions with the Internet allow us to measure human experiences on a scale that was not readily feasible before. Here, we draw on crowdsourced geographic data from the website Scenic-Or-Not (http://scenicornot.datasciencelab.co.uk/) in order to develop a better understanding of how the aesthetics of the environment may impact our health. Scenic-Or-Not allows Internet users to rate the ?scenicness? of photos from all around the United Kingdom. We combine these ratings with geographic data from the 2011 Census for England and Wales capturing respondents? classification of their health. In order to control for socioeconomic characteristics that may be linked with health, we use deprivation data from the 2010 English Indices of Deprivation.

Abstract: A wide number of human activities can be defined as games, in particular when governed by specific rules and leading to the definition of a kind of ranking. The latter can be defined according to several parameters, as the payoff (i.e. prize) gained by individuals, according to factors as the number and the quality of performed actions, the number of received votes, and so on. While for games, like Chess and (Casino) Roulette, the definition of their nature in terms of skill games or gambling, is quite simple, for other games it is really difficult. For instance, classifying the nature of Poker (i.e. as skill game or gambling) seems really hard and it also constitutes a current problem, whose solution has several implications (from laws to healthcare policies). Similar considerations can be done considering Financial Trading. Notably, there are some recent investigations showing that Trading might be seen as a skill game, while others might support the contrary, i.e. that it can interpreted as a form of gambling. Moreover, in the world of financial markets a number of assets are often indicated as more risky than others, like options, derivatives and 'binary options'. Focusing on these two worlds, i.e. Poker (characterized by a number of variants and rules) and Financial Trading (characterized by different assets), in this talk we aim to present with more details this old, but always current and relevant, debate. Notably, we highlight the prominent role that statistical physics might have for facing this problem, i.e. providing a framework for finding a shared solution for classifying the nature of these games.?Remarkably, some results of these investigations show that the intrinsic nature of some games, as Poker, does not depend on their specific rules, but is strongly affected by the human behavior.?

Abstract: Nowadays, millions of people interact on a daily basis on online social media like Facebook and Twitter, where they share and discuss information about a wide variety of topics. However, how information spreads among individuals strongly depends on several factors, being the underlying social structure and the different types of social interactions the most crucial ones. Generally, researchers tend to aggregate or disregard some information to reduce the complexity of the data and of their models. Here we will show that this approach is often not suitable to represent and analyze social systems. In fact, we will show that even the most basic network descriptors, such as nodes' centrality and their mesoscale organization in groups or communities, can be very misleading if the underlying network model is not appropriate. By disentangling interactions in Twitter, we will discuss the most recent advances about multiplex analysis and modeling of empirical social systems, from their complex network representation to their dynamics during exceptional events.

Abstract: Online social media has greatly affected the way in which we communicate with each other. However, little is known about what fundamental mechanisms drive dynamical information flow in online social systems. Here, we introduce a generative model for online sharing behavior that is analytically tractable and that can reproduce several characteristics of empirical micro-blogging data on hashtag usage, such as (time-dependent) heavy-tailed distributions of meme popularity. The presented framework constitutes a null model for social spreading phenomena that, in contrast to purely empirical studies or simulation-based models, clearly distinguishes the roles of two distinct factors affecting meme popularity: the memory time of users and the connectivity structure of the social network.

Abstract: We develop a dynamic network formation model that explains the observed nestedness in email communication networks inside organizations. Utilizing synchronization we enhance K?nig et al. (2014)?s model with dynamic communication patterns. By endogenizing the probability of the removal of agents we propose a theoretical explanation why some agents become more important to a firm?s informal organization than others, despite being ex ante identical. We also propose a theoretical framework for measuring the coherence of internal email communication and the impact of communication patterns on the informal organization structure as agents come and go. In situations with a high agent turnover rate, networks with high hierarchy outperform what we term ?egalitarian? networks (i.e. all agents are of equal degree) for communication efficiency and robustness. In contrast, in situations with a low agent turnover, networks with low hierarchy outperform what we term ?totalitarian? networks for communication efficiency and robustness. We derive a trade-off that accounts for the network communication performance in terms of both measures. Using the example for a consulting firm we show that the model fits real-world email communication networks.

Abstract: In this talk I will present the network effect in the dynamics of Financial instruments with particular emphasis on the quantitative measurements of bankruptcy impact and risk evaluation.

Abstract: In computational social science, it is typical to use electronic data collected from many individuals to address population-level or network-level questions. However, there is a lot of heterogeneity in social systems, and each individual is different from others. Recently, there has been increasing interest in this heterogeneity, and efforts have been put on understanding individual differences and time evolution of behaviors of individuals that form the networks. As an example, electronic records of activity have revealed that individuals have distinct daily activity patterns of communication in their egocentric networks, and these patterns tend to persist in time. An important factor in?determining daily activity patterns is the chronotype of individuals, that is, the propensity to sleep at certain hours of the day. Typically, individuals are categorized into three main chronotypes: morning-active, evening-active, and normative. Here, our target is to study the chronotypes of individuals based on smartphone data, focusing on the alternation of periods of activity with periods of inactivity that can be associated with sleep and to find out whether individuals with different chronotypes have different communication patterns. To this end, we use a rich dataset of electronic records collected from over 800 people for more than a year 3 . We apply Nonnegative Matrix Factorization (NMF) to 48 weeks data of digital activity of users (phone screen on/off events), for extracting the dominant daily patterns in the population. Looking at the emerging ?components?, we can see that they very well match with expected activity pattern of different chronotypes. We identify users of three different chronotypes, based on the NMF component which best describes each user?s activity pattern. Upon identifying individuals of different chronotypes, we divide the data from 48 weeks into 4 different periods which approximately correspond to the four seasons. We look at size of social network (derived from communication data) of users of each chronotype and see that there are distinct seasonal variations in communication patterns and size of social networks for each chronotype as well as across different chronotypes.

Abstract: Recent years have witnessed waves of protests sweeping across countries and continents, which in some cases resulting in political and governmental change. Much media attention has been focused on the increasing usage of social media to coordinate and provide instantly available reports on these protests. In this talk, I will describe recent research in which we explore whether the data created through such widespread usage of online services may offer a valuable new source for measurements of behaviour during protests. We analyse a large corpus of 25 million geotagged photographs taken and uploaded to Flickr in 2013. For each week and each of the 244 countries and regions, we determine how many photographs were uploaded with the word ?protest? in 34 different languages in the photograph title, description or tag. In order to determine whether there is a link between the number of protest tagged photos and the number of protest outbreaks, we use data from newspaper reports as a proxy for ground truth. For each of the 244 countries and regions, we determine the weekly number of protest related online articles in The Guardian from 2013. We find that higher proportions of protest tagged photos in a given area and week correspond to greater numbers of protest related articles about that area in The Guardian. Our results are in line with the hypothesis that data on photographs uploaded to Flickr may contain signs of protest outbreaks (Alanyali, M., Preis, T. and Moat, H.S., 2016. Tracking protests using geotagged Flickr photographs. PLOS ONE, 11(3), p.e0150466). These findings illustrate the potential value of photographs uploaded to the Internet as a source of global, cheap and rapidly available measurements of human behaviour in the real world.

Abstract: The talk is presenting what could be an Integrative Logistics, as a new integrative and predictive science, in the framework of the CS-DC TIMES flagship. This flagship aims at creating a global e-ecosystem to give the same equality of chance for any territory to become a ??smart?? territory by using a global market for open responsible innovations linked to the global scientific and technological revolution. This global e-ecosystem is using the 2nd?internet revolution for sharing in a trustable way the big data including the ones tracing the current logistic activities between territories at all levels. The main challenge of Integrative Logistics is, through dynamical deep learning, to bring multilevel logistic models toward a "smart" multilevel logistics. ?Such goal has to involve?all of these ? scientists of any discipline or experts from territorial governments, NGOs, firms, start-ups as well as ordinary citizens ? wanting to jointly increase social wellbeing, improve the relationship with Nature and to change the relations between science, engineering, politics and ethics.

Abstract: Oscillatory networks have been one of the most used paradigms in order to mimic repetitive dynamical processes taking place in complex systems. The leading feature of oscillatory networks is the emergence of synchronized oscillations among their elements, i.e. it is observed that quite a lot of oscillators tune their rhythms so that numerous groups of them exhibit highly correlated behavior. From a mathematical point of view, the emergence of such coordinated behavior corresponds to the existence of global periodic oscillations, that can arise, for example, through Hopf bifurcations. Understanding how the interactions among the oscillators influence the appearance of properties such as global oscillations may be crucial in order to characterize complex dynamics in oscillatory networks. Moreover, it is well known in literature that in case of multistability in a single element of the network, new equilibrium configurations arise due to the coupling. In this work, we consider directed acyclic networks of bistable units and we investigate the coupling effects on the Hopf bifurcations occurrence and on the number of equilibria of the entire network. (joint work with N. Corson and N. Verdi?re)

Abstract: Always more data about cities are available which allows to build and to test theories and models. In particular, many urban economics models were developed to describe how cities are organized and I will discuss here their predictions about urban mobility. I will illustrate on various examples such as the total commuting length in cities, or the variation of the commuting length with income, how empirical data force us to reconsider these models in order to reach conclusions that are in agreement with empirical observations.

Abstract: We consider a graph wave equation with a cubic defocusing non-linearity on a general network. This ?well-posed model is close to the $\phi^4$ model in condensed matter physics. Using the normal modes of the graph Laplacian as a basis, we derive amplitude equations and define resonance conditions that relate the graph structure to the dynamics.

Abstract: Cluster synchronization is an interesting issue in complex dynamical networks?with community structure.?We study this phenomenon, in which?individuals in the same cluster are?identical,?while those in different clusters are not. Some sufficient conditions?that ensure?cluster synchronization of complex networks are provided.?The increase?of coupling strength inside clusters is very useful to achieve cluster?synchronization, however, the coupling among different clusters?is an?obstacle.?

Abstract: During this interactive workshop, we will use simulation models about (potential) refugee crises. First, we will show how an exploratory system dynamics model on the Syrian-European refugee crisis (esp. the Balkan route) can be used interactively to provide high-level policy advice. The model and map-based animations we will be using were developed and used in November 2015. Going back to November 2015, we will use different techniques to assess and analyse effects of scenarios and potential policy options under deep uncertainty. Second, we will do this for potential future refugee crises. Sets of scenarios proposed by groups of workshop participants will be included into a simulation model to generate ensembles of plausible scenarios. We will visualize the effects of exemplar scenarios from these ensembles on maps and test policy options suggested by the workshop participants across all scenarios. Finally, we will use group model building techniques to develop a better understanding of the essence of migration as a security issue, and find structural policies.

Abstract: Military forces operate in an environment consisting of many intertwined influences, factors and actors that affect one another, complicating the situation and impacting stability. This presentation will discuss several concepts in order to make sense of this complex security environment and aims at challenging researchers to develop practical approaches that enable combining these concepts. We will discuss how the operational environment can be defined as a dynamic ecosystem consisting of flows between actors and factors that create the forces that influence the conflict. We will also introduce the concept of fighting in three landscapes, meaning that flows can be tackled in the physical landscape, the information landscape (cyber) and the human landscape (identity and beliefs that determine behaviour). In order to restore security, or enhance resilience before security deteriorates, we need to sufficiently understand and monitor these complex ecosystems. Furthermore to be effective in this complex environment a whole of society approach is needed in which military assets are only one of the effectors. Coordination in such a joint international, multi-agency, public (JIMP) setting creates many challenges of its own.

Abstract: The world is a complex adaptive system, that is to say a system of many component parts where the behaviour of the system cannot be inferred from the behaviour of the components in isolation. This is particularly true of combat when two or more intelligent parties are each trying to achieve advantage in a dynamic situation. These situations have been traditionally examined by the use of wargames, which require humans to make the decisions or simulations often based on scripts or simple rule sets to define the behaviour. Both of these methods mean that only a small subset of the possible ?phase space? of reasonable courses of action for each side in any given situation are explored. An imperfect but useful analogy would be that it?s like trying to understand chess from examining a small number of games. In order to address this, Dstl have been developing a tool ?Mission Planner? which will automatically generate courses of action for all sides within a simulation and respond to the perceived enemy responses. This should allow the automatic generation of a wide variety of courses of action for both sides given the same starting conditions and thus provide insight in to the range of outcomes possible. The talk will discuss the progress of the Mission Planner, describe its strengths and weaknesses, its future use and development.

Abstract: The complexity of our growing dependency on cyber systems increases the need to understand them in multi-faceted mathematical terms. Without a comprehensive and methodical understanding of these systems, unintended outcomes can be large and impactful. This creates a need for simple, analytically tractable yet practically insightful models for understanding these systems and their security. In this discussion, we start by building upon an existing model called FlipIt, extending it into a scenario involving a probabilistic attacker and defender playing for control over a resource. We then present an incomplete information game-theoretic model of the attacker-defender interaction. Using the martingale-based approach, we analytically solve the model for defender strategies. Afterward, we compare the analytical solution to a simulation and extend the simulation for cases that cannot be treated analytically. Finally, we compare and contrast with existing approaches based on Stackelberg equilibria.

Abstract: To better understand the flows of ideas or information through social and biological systems, researchers develop maps that reveal important patterns in network flows. In practice, network flow models have implied memoryless first-order Markov chains, but recently researchers have introduced higher-order Markov chain models with memory to capture patterns in multi-step pathways. Higher-order models are particularly important for effectively revealing actual, overlapping community structure, but higher-order Markov chain models suffer from the curse of dimensionality: their vast parameter spaces require exponentially increasing data to avoid overfitting and therefore make mapping inefficient already for moderate-sized systems. To overcome this problem, we introduce an efficient cross-validated mapping approach based on network flows modeled by sparse Markov chains. To illustrate our approach, we present a map of citation flows in science with research fields that overlap in multidisciplinary journals. Compared with currently used categories in science of science studies, the research fields form better units of analysis because the map more effectively captures how ideas flow through science.

Abstract: A challenging problem in the study of complex systems is that of resolving, without prior information, the emergent, mesoscopic organization determined by groups of units whose dynamical activity is more strongly correlated internally than with the rest of the system. The existing techniques to filter correlations are not explicitly oriented towards identifying such modules and can suffer from an unavoidable information loss. A promising alternative is that of employing community detection techniques developed in network theory. Unfortunately, this approach has focused predominantly on replacing network data with correlation matrices, a procedure that we show to be intrinsically biased because of its inconsistency with the null hypotheses underlying the existing algorithms. Here, we introduce, via a consistent redefinition of null models based on random matrix theory, the appropriate correlation-based counterparts of the most popular community detection techniques. Our methods can filter out both unit-specific noise and system-wide dependencies, and the resulting communities are internally correlated and mutually anticorrelated. We also implement multiresolution and multifrequency approaches revealing hierarchically nested subcommunities with ?hard? cores and ?soft? peripheries. We apply our techniques to financial and brain time series and identify robust patterns providing nontrivial information about these systems. Ref: Mel MacMahon and Diego Garlaschelli, Phys. Rev. X 5, 021006 (2015).

Abstract: The identification and control of ties, which are transmitting information in complex networks can provide effective ways to hinder and postpone outbreaks of spreading phenomena. In this work we introduce a new method by using diffusion processes to define transmission centrality for links, which quantifies their importance in disseminating information in the network structure. We demonstrate that this measure can precisely identify weak ties in the network structure and furthermore estimates the betweenness centrality of each link in a computationally efficient way. By testing various link removal strategies in three human communication networks we show that the combined measure of transmission centrality and link overlap provides the best way to identify links keeping the global structure connected. We also explored their role in control of spreading processes through SIR model simulations. This method allows us to select the optimal but minimal set of links to gain maximum control on spreading processes.

Abstract: We understand multiplex networks as a non-linear superposition of complex networks, where components, being them social actors, genes, devices, or physical locations, interacts through a variety of different relationships and communication channels, which we conceptualize as different layers of the multiplex network. Because of the structure of a multiplex network, it is natural to try to aggregate the interaction pattern of each layer in a single network somehow. An operation that we call dimensionality reduction, whereas the result of such operation leads to what we call an aggregate network. Several candidates for the aggregate network have been proposed in the literature. We claim that the natural definition of an aggregate network is given by the notion of quotient network. In addiction, in the quotient network framework, we are able to introduce in a symmetric way another reduced network, the network of layers, that encodes the connectivity pattern between layers. We give the relations between the Laplacian and the adjacency spectra of the original multiplex network and that of its coarse-grained representations, and we show that a number of the structural and dynamical properties of the original multiplex are inherited by the coarse-grained representations. Finally, we show the relation between some walk-based structural metrics (e.g. the clustering coefficient) calculated on the original multiplex and the same metric calculated on the aggregate network.

Abstract: Patterns are widespread in nature and appear in large plethora of different conformations.From chemistry to biology,passing through physics,ecology,social systems or engineered ones, just to name few.Beautiful spatially extended motifs are found which spontaneously emerge from an ensemble made of interacting microscopic actors. The proto-typical approach to patterns formation in reaction-diffusion processes dates back to Turing?s seminal paper on morphogenesis[1].Under specific conditions,diffusion drives an instability by perturbing a homogeneous stable fixed point, via an activator-inhibitor mechanism.As the perturbation grows,nonlinear reactions balance the diffusion terms,yielding the asymptotic, spatially inhomogeneous, steady state. Reaction diffusion models are often defined on a regular lattice, either continuous or discrete.In many cases of interest, it is however more natural to schematize the system as a complex network.Building on the pioneering work of Othmer and Scriven[2], Nakao and Mikhailov developed[3] the theory of Turing patterns on random static undirected network, highlighting the peculiarities that stem from the embedding graph structure.The dispersion relation, from which the instability conditions ultimately descend, is related to the spectrum of the discrete Laplacian associated to the embedding network.Laplacian eigenvalues determine hence the spatial characteristics of the emerging patterns. Evidence suggests that in many applications networks evolve in time,new nodes can enter or other can leave the system,new links can emerge or disappear[4-6].Linear processes such as diffusion have been studied on temporal networks[7-8].The goal of this paper is to propose a first study of a generic non-linear process running on top of a complex temporal network and determine the conditions for the onset of Turing patterns.We will provide examples where patterns emerge in the temporal network while they cannot develop in each time-snapshot network, namely for any fixed time. [1]A.M.Turing,PTR.Soc.London,Ser.B 237,37(1952) [2]H.G.Othmer,L.E.J.Scriven,Theor.Biol. 32,507(1971);43,83(1974) [3]H.Nakao,A.S.Mikhailov, NatPhys 6,544(2010) [4]V. Kostakos,PhysicaA 388,1007(2009) [5]P. Holme,J.Saram?ki,PhysRep 519,97(2012) [6]P.Holme,EPJB 88, 234(2015) [7]J.-C.Delvenne,R.Lambiotte,L.E.C.Rocha,NatComm 6,7366(2015) [8]R. Lambiotte,L.Tabourier,J.-C.Delvenne,EPJB 86,320(2013)

Abstract: Usual coarse-graining techniques require a full-knowledge of the network and its dynamics. This assumption is increasingly irrealistic as we consider larger and larger networks, due to both incomplete data and lack computational resources. We propose a theory of local coarse graining where a set of nodes is reduced to a single node without knowledge of the rest of the network, and with limited impact on the resulting dynamics. This allows us to identify interesting structures in a network, including, but not limited to, local communities. The methodology is illustrated on social, biological and power networks. Joint work with YW Yu, S Yaliraki, M Barahona

Abstract: Climate change is causing substantial disruption of socio-economic systems around the world. Climate mitigation and adaptation measures are vital and require careful analysis of economic costs and benefits. Integrated Assessment Models (IAMs) are widely applied for these purposes in an attempt to support policy decisions. Despite their recent advancements, IAMs still suffer from substantial methodological limitations risking to mislead policy makers (Stern, 2016). It is argued that IAMs should further improve on the following assumptions: (i) a rough assessment of impacts on broad aggregated regions assuming a representative economic agent, (ii) a focus primarily on the reduction of economic output due to climate impacts ignoring destruction of livelihoods, loss of life and migration, (iii) assuming an incremental change in the GDP loss as temperatures increase ignoring the presence of potential tipping points, (iv) absence of learning, (v) simplified representation of technological change in the energy sector (Pindyck, 2013; Farmer et.al. 2015; Burke et.al. 2016). Together with the traditional problems with intergenerational discounting these issues form a significant challenge for the scientific community. Significant research effort is being invested currently in improving (iv) and (v), which both alter the estimation of costs of mitigation. Estimation of benefits of mitigation ? i.e. avoided losses (i-iii) ? did not yet received enough attention. The main issue here is that adverse impacts of climate change are quite unevenly distributed between regions and various socio-economic groups within them. Global damage estimates are likely to be significantly different if this heterogeneity, interactions between various entities, endogenous learning and non-linearity in economies reactions to climate is accounted for. While a myriad of local empirical studies provides an ample evidence that it matters for the overall damage assessment, the task of aggregating and finding general patterns and processes in this body of knowledge is not easy. We discuss the potential ways to address this problem.Firstly, we review the general approaches to estimate climate-driven economic impacts and damages in IAMs on the macro level. Then we outline how regional and local studies approach the same issue accounting for micro-level processes. We finally discuss few alternative approaches to bridge the two. To what extent can this gap be covered by adjusting the existing IAMs design? Should a completely different approach be taken by constructing the entire global economy from the bottom up? What are the risks and advantages of using a suit of models or nested model as a compromise solution? During the conference we would like to discuss these issues and corresponding pros and cons of each of the alternatives. References:Burke, M., et.al. Opportunities for advances in climate change economics. Science, 2016. 352(6283): p. 292-293.Farmer, J.D., C. Hepburn, P. Mealy, and A. Teytelboym, A Third Wave in the Economics of Climate Change. Environmental Resource Economics, 2015. 62: p. 329?357.Pindyck, R.S., Climate Change Policy: What Do the Models Tell Us? Journal of Economic Literature, 2013. 51(3): p. 860?872.Stern, N., Current climate models are grossly misleading. Nature, 2016. 530: p. 407-409.

Abstract: Human agency is a main determinant of land use and global land use change emerges from individual land use decisions in response to driving factors operating across multiple spatial and temporal scales. However, in global integrated models used for climate assessments, land use change is either presented by simplified aggregate decision making using profit optimization assumptions or by heuristics accounting for location suitability. There is a common understanding that there is a need to better represent (variation in) human agency in such large scale land use assessments (Rounsevell et al., 2014; Verburg et al., 2016).In this presentation I will discuss how a systematic review of case-studies on land use change is used to characterize different human land use decision mechanisms and how these are linked to contextual conditions. Based on this empirical knowledge land change models may vary their behavioral assumptions across time and space to better represent variation and evolvement of human agency. In the context of climate change a representation of human agency should capture feedbacks between land use change and climate change beyond the physical responses, i.e. in terms of mitigation and adaptation behavior. A first idea on including such feedbacks and constraints in model implementation is discussed.References:Rounsevell MDA, Arneth A, Alexander P, Brown DG, De Noblet-Ducoudr? N, Ellis E, Finnigan J, Galvin K, Grigg N, Harman I, Lennox J, Magliocca N, Parker D, O'Neill BC, Verburg PH, Young O. 2014. Towards decision-based global land use models for improved understanding of the Earth system. Earth Syst. Dynam., 5, 117-137. http://dx.doi.org/10.5194/esd-5-117-2014Verburg PH, Dearing J, Dyke J, Leeuw Svd, Seitzinger S, Steffen W, Syvitski J. 2015. Methods and Approaches to Modelling the Anthropocene. Global Environmental Change doi: 10.1016/j.gloenvcha.2015.08.007

Abstract: Until recently, projections of future climate have been obtained by first projecting the evolution of society ? in particular human energy and land-use activities ? and then examining the implications of those activities on climate and ecosystems within Earth system models.Typically, these assessments have not considered feedbacks from climate and ecosystem change onto the evolution of society and therefore energy and land-use, which are the primary anthropogenic drivers of climate change. Here we present the rationale for and theoretical basis of a coupled model framework known as the Integrated Earth System Model (iESM) that combines the physical representations of an Earth System Model with the human system representations of an Integrated Assessment Model. We demonstrate a previously unexplored feedback whereby future climatic, biological, and geochemical changes influence the productivity of land resources and therefore the evolution of land-use, agriculture, bioenergy use, and fossil fuel emissions. We further decompose this effect into the separate influences of climate change and the elevated CO2 concentrations, and we examine the role of climate mitigation policies in affecting the magnitude and nature of this feedback. The synchronous coupling of human and Earth systems in the iESM is enabled by a generalized software framework that could in principle accommodate a wide variety of human and Earth system models with varying levels of spatial and temporal resolution, as well as process detail. We discuss key lessons learned in building such an architecture and highlight the theoretical and practical challenges encountered in coupling models that represent linked processes at fundamentally different scales.

Abstract: This paper presents an agent based model for the study of coupled economic and climate dynamics that endogenously co-evolve across a range of different scenarios. The model offers a flexible laboratory to test various combinations of macroeconomic, industrial and climate policies both in the context of long run economic growth and medium run transition towards a greener economy. Furthermore, we propose a stochastic description of the feedbacks stemming from a warming and more volatile climate and study how such negative shocks propagate through the economy. For this reason, the model is particularly well suited for the study of extreme climate events, which are usually forgotten by standard integrated assessment models.

Abstract: We analyzed the interactivity time, defined as the duration between two consecutive tasks such as sending emails, collecting friends and followers and writing comments in online social networks (OSNs).The distributions of these times are heavy tailed and often described by a power-law distribution. However, power-law distributions usually only fit the heavy tail of empirical data and ignore the information in the smaller value range. We argue that the durations between writing emails or comments, adding friends and receiving followers are likely to follow a lognormal distribution, discussing the similarities between power-law and lognormal distributions, and show that binning of data can deform a lognormal to a power-law distribution. An explanation for the appearance of lognormal interactivity times will be discussed and the influence of non-Markovian infection spread on the Susceptible-Infected-References:- C. Doerr, N. Blenn, and P. Van Mieghem. Lognormal Infection Timesof Online Information Spread. PLoS ONE, 8(5):e64349, 05 2013.- E. Cator, R. van de Bovenkamp, and P. Van Mieghem.Susceptible-infected-infection and cure times. Physical Review E, 87:062816, Jun 2013.- P. Van Mieghem and R. van de Bovenkamp. Non-Markovian InfectionSpread Dramatically Alters the Susceptible-Infected-Threshold in Networks. Physical Review Letters, 110:108701, Mar 2013.

Abstract: A number of human activities exhibit a bursty pattern, namely periods of very high activity that are followed by rest periods. Records of these processes generate time series of events whose inter- event times follow a probability distribution that displays a fat tail. The grounds for such phenomenon are not yet clearly understood. In this talk I will present some results concerning the use the freely available Wikipedia?s editing records to unravel some features of this phenomenon. We show that even though the probability to start editing is conditioned by the circadian 24 hour cycle, the conditional probability for the time interval between successive edits at a given time of the day is independent from the latter. We confirm our findings with the activity of posting on the social network Twitter. Our result suggests there is an intrinsic humankind scheduling pattern: after overcoming the encumbrance to start an activity, there is a robust distribution of new related actions, which does not depend on the time of day. Some simulations were performed in order to understand the effect of circadian patterns in the activity, in particular in the stationarity of the power-law inter-event distributions.

Abstract: Empirical studies revealed a number of features of bursty time series [1,2]. We implemented a simple task-queuing model, which produces bursty time series due to the non-trivial dynamics of the task list. We give exact results on the asymptotic behaviour of the model and we show that the inter-event time distribution (IETD) is power-law decaying for any kind of input distributions that remain normalizable in the infinite list limit, with exponents tuneable between 1 and 2. The model satisfies a scaling law between the exponents of inter-event time distribution (?) and autocorrelation function (?):???+???=?2. This law is general for renewal processes with power-law decaying inter-event time distribution and a departure from it indicates long-range dependence between the inter-event times [3]. We investigated another model based on self-exciting point processes with variable memory range. We found that in an intermediate range of memory effect the generated correlated bursts are comparable to empirical findings [4]. Empirical studies show that burstiness has major impact on the spreading processes on networks [5]. We solve the SI model on the infinite complete graph and show that fat tailed IETD causes always acceleration [6]. In order to understand the role of these influencing factors we studied the SI model on temporal networks with different aggregated topologies and different IETDs. Based on analytic calculations and numerical simulations, we show that if the stationary bursty process is governed by power-law IETD, the spreading can be slowed down or accelerated as compared to a Poisson process; the speed being determined by the short time behaviour, which in our model is controlled by the exponent. We demonstrate that finite, so called 'locally tree-like' networks, like the Barab?si?Albert networks behave very differently from real tree graphs if the IETD is strongly fat-tailed. Furthermore, non-stationarity of the dynamics has a significant effect on the spreading speed for strongly fat-tailed power-law IETDs [7].References:[1] M?rton Karsai, Kimmo Kaski, Albert-L?szl? Barab?si, J?nos Kert?sz: Universal features of correlated bursty behaviour, Scientific Reports, 2 397 (2012)[2] M?rton Karsai, Kimmo Kaski, J?nos Kert?sz: Correlated dynamics in egocentric communication networks, PloS ONE, 7, e40612 (2012)[3] Szabolcs Vajna, B?lint T?th, J?nos Kert?sz: Modelling bursty time series, New Journal of Physics, 15, 103023 (2013)[4] Hang-Hyun Jo, Juan I Perotti, Kimmo Kaski, J?nos Kert?sz: Correlated bursts and the role of memory range, Physical Review E, 92, 022814 (2015)[5] M?rton Karsai, Mikko Kivel?, Raj Kumar Pan, Kimmo Kaski, J?nos Kert?sz, A-L Barab?si, Jari Saram?ki: Small but slow world: How network topology and burstiness slow down spreading, Phys. Rev. E, 83, 025102 (2011)[6] Hang-Hyun Jo, Juan I Perotti, Kimmo Kaski, J?nos Kert?sz: Analytically solvable model of spreading dynamics with non-Poissonian processes, Phys. Rev. X, 011041 (2014)[7] D?vid X. Horv?th, J?nos Kert?sz: Spreading dynamics on networks: the role of burstiness, topology and non-stationarity, New Journal of Physics, 16, 073037 (2014)

Abstract: A diverse variety of processes?including recurrent disease episodes, neuron firing, and communication patterns among humans?can be described using inter-event time (IET) distributions. Many such processes are ongoing, although event sequences are only available during a finite observation window. Because the observation time window is more likely to begin or end during long IETs than during short ones, the analysis of such data is susceptible to a bias induced by the finite observation period. In this talk, I illustrate how this length bias is born, how it can be corrected, and formulate simple heuristic for determining the severity of the bias. This can be donewithout assuming any particular shape for the IET distribution, but one needs to assume that the event sequences are produced by (stationary) renewal processes. I illustrate the method for several well-known empirical communication networks from the literature. It turns out that in these data sets the resulting bias can lead to systematic underestimates of the variance in the IET distributions and that correcting for the bias can lead to qualitatively different results for the tails of the IET distributions.

Abstract: Given a stationary point process, an intensity burst is defined as a short time period during which the number of counts is larger than the typical count rate. It might signal a local non-stationarity or the presence of an external perturbation to the system. In this paper we propose a novel procedure for the detection of intensity bursts within the Hawkes process framework. By using a model selection scheme we show that our procedure can be used to detect intensity bursts when both their occurrence time and their total number is unknown. Moreover the initial time of the burst can be determined with a precision given by the typical inter-event time. We apply our methodology to the mid-price change in FX markets showing that these bursts are frequent and that only a relatively small fraction is associated to news arrival. We show lead-lag relations in intensity burst occurrence across different FX rates and we discuss their relation with price jumps.

Abstract: When modelling dynamical systems on networks, it is often assumed that the process is Markovian, that is future states depend only upon the present state and not on the sequence of events that preceded it. Examples include diffusion of ideas or diseases on social networks, or synchronisation of interacting dynamical units. In each case, the dynamics is governed by coupled differential equation, where the coupling is defined by the adjacency matrix of the underlying network. The main purpose of this talk is to challenge this Markovian picture. We will argue that non-Markovian models can provide a more realistic picture in the case of temporal networks where edges change in time, or in situations when pathways can be measured empirically. We will focus on the importance of non-Poisson temporal statistics, and show analytically the impact of burstiness on diffusive dynamics, before turning to applications and incorporating memory kernels in predictive models of retweet dynamics.

Abstract: Complex systems often undergo abrupt or gradual transitions to dynamical regimes that canbe safe or dangerous for the system functionality. Examples of dangerous transitions includedesertification, population extinctions, financial crashes, cardiac arrhythmia, epileptic seizures,etc. A precise identification of such transitions is important for preventing harmfulconsequences, and a lot of efforts are nowadays focused on developing reliable diagnostictools that can be applied to observed time-series, which are finite and usually stochastic. Inthis presentation I will discuss our recent work aimed at exploiting network theory andnonlinear time-series analysis tools, for characterizing and quantifying regime transitions indifferent systems. Specifically, I will consider synthetic data generated from a vegetationmodel [1] and empirical data recorded from the output of various laser systems [2-4].References[1] G. Tirabassi, J. Viebahn, V. Dakos, H. A. Dijkstra, C. Masoller, M. Rietkerk, and S.C. Dekker,"Interaction network based early-warning indicators of vegetation transitions", EcologicalComplexity 19, 148 (2014).[2] C. Masoller, Y. Hong, S. Ayad, F. Gustave, S. Barland, A. J. Pons, S. Gomez, and A. Arenas,?Quantifying sudden changes in dynamical systems using symbolic networks?, New Journal ofPhysics 17, 023068 (2015).[3] A. Aragoneses, L. Carpi, N. Tarasov, D. V. Churkin, M. C. Torrent, C. Masoller, and S. K.Turitsyn, ?Unveiling temporal correlations characteristic to phase transition in the intensity offibre laser radiation?, Phys. Rev. Lett. 116, 033902 (2016).[4] C. Quintero-Quiroz, J. Tiana-Alsina, J. Roma, M. C. Torrent, and C. Masoller, ?Characterizinghow complex optical signals emerge from noisy intensity fluctuations?, submitted (2016)

Abstract: Human societies depend on the resources ecosystems provide. Particularly since the last century, human activities have transformed the relationship between nature and society at a global scale. We study this coevolutionary relationship by utilizing a stylized model of regional resource use and preference formation on an adaptive social network. The latter process is based on two social key dynamics beyond economic paradigms: boundedly rational imitation of resource use preferences and homophily in the formation of social network ties. The private and logistically growing resources are harvested either with a sustainable (small) or non-sustainable (large) effort. We show that these social processes can have a profound influence on the environmental state, such as determining whether the private renewable resources collapse from overuse or not. Additionally, we demonstrate that heterogeneously distributed regional resource capacities shift the critical social parameters (social-ecological transition regime) where this resource extraction system collapses. We make these points to argue that, in more advanced coevolutionary models of the planetary social-ecological system, such socio-cultural phenomena as well as regional resource heterogeneities should receive attention in addition to the processes represented in established Earth system and integrated assessment models.

Abstract: Critical transitions in nature and society are likely to occur more often and severe as humans increase they pressure on the world ecosystems. Yet it is largely unknown how these transitions will interact, whether the occurrence of one will increase the likelihood of another, and whether these potential teleconnections (social and ecological) correlate critical transition in distant places. Here we present a framework for exploring three types of potential cascading effects of critical transitions: forks, domino effects and inconvenient feedbacks. Drivers and feedback mechanisms are reduced to a network form that allow us to explore drivers co-occurrence (forks). Sharing drivers is likely to increase correlation in time or space among critical transitions but not necessarily interdependence. Random walks on causal networks allow us to detect and compare communities of common drivers and feedback mechanisms across different critical transitions. Domino effects and inconvenient feedbacks were identified by mapping new circular pathways on coupled networks that have not been previously reported. The method serves as a platform for hypothesis exploration of plausible new feedbacks between critical transitions in social-ecological systems; it helps to scope structural interdependence and hence an avenue for future modelling and empirical testing of regime shifts coupling.

Abstract: Transformations to create more sustainable social-ecological are urgently needed. Agency and dynamics are frequently cited as key components for understanding transformational change, but structural change is a third central feature in transformations of social-ecological systems that has received relatively little attention. Here, we propose a framework for conceptualising and modelling sustainability transformations based on adaptive networks. Adaptive networks focus attention on the interplay between the structure of a social-ecological system and the dynamics of individual entities within it. Adaptive networks have the potential to progress transformations research by: 1) focusing research on structure, a neglected aspect of social-ecological transformation; 2) providing quantitative modelling tools in an area of study dominated by qualitative methods; 3) providing a conceptual framework that clarifies the temporal dynamics of social-ecological transformations compared to the most commonly used heuristic in resilience studies, the ball-and-cup diagram. We illustrate the application of adaptive networks to social-ecological transformations using a case study of illegal fishing in the Southern Ocean. Adaptive network modelling could help drive a renaissance of modelling and conceptual work regarding transformations of social-ecological systems. Insights from these studies, in turn, could help society respond to the need for sustainability transformations in today?s era of global social and environmental change.

Abstract: Study of phase transition and the structure of equilibrium phases plays the central role in statistical mechanics of condensed systems. Indeed, often understanding the symmetries of the phases and proper choice of an order parameter is a crucial step allowing one to get an insight into what is actually going on in the system under consideration. Large equilibrium random networks are, clearly, an object very well suitable to be studied by the methods of statistical mechanics, and importing our insights into phase transition theory can be very instructive. In my talk I will give an overview of several examples where such import of the ideas from the theory of phase transitions turned out to be useful, and then concentrate on a particular example which we have been studying at length lately. Consider a large annealed network with a frozen degree distribution of nodes (that is to say, links between nodes can rewire, but the degree of each node always stays the same), and introduce a three-node interaction in this system which favours creation of closed triangles of bonds. We show that with increasing strength of the interaction, this system undergoes a first order phase transition from a homogeneous phase with locally almost tree-like structure and small concentration of triangles to a "triangle condensate" where the concentration of closed triangles drastically increases. Moreover, the condensed phase has a beautiful microstructure consisting: it is a set of almost fully-connected clicks with very small number of links between them. Such a microstructure formation can be easily understood and is akin to formation of microstructured phases in soft condensed matter.

Abstract: The synchronization between power-grid nodes is one of the essential conditions for the stable electric power system. The synchronization stability changes according to various network parameters corresponding to the amount of power input at each node, damping coefficient, and transmission strength between nodes. Previously, studies have revealed that the synchronization is more stable with smaller power input or larger damping coefficient. In this study we investigate the synchronization stability as a function of the transmission strength. We particularly focus on the transition phenomenon of the synchronization stability measured by basin stability that is numerical Monte Carlo simulation. In order to overcome the computationally costly process, we introduce some techniques to detect and classify various transition patterns. In this talk, we cover the dynamical transition phenomenon of synchronization stability and introduce useful techniques to overcome practical difficulties.

Abstract: In various context, high-dimensional data reside on the vertices of networks, so that the network is the ?natural space? for such systems. Therefore techniques, as Graph Signal Transform, levering the structure of the network, to grasp the main features of the dynamical process upon it are drawing increasing attention. Graph Signal Transform is, by design, well-suited to treat signals in very irregular domains and it allows applications that span from image compression to uncovering network communities [1]. In a nutshell, this technique is reminiscent of Fourier transform, but at the same time it embeds the inhomogeneities of the underlying graph: the time series on the network nodes, i.e. the graph signal, is decomposed in a sum of components on the Laplacian eigenvectors and this decomposition allows to finger the ones which have an high weight, i.e. that are resonant with the dynamics. In this work, we apply Graph Signal Transform to a classical model for magnetized materials, the XY spin model of which we consider the phenomenology on networks. Remarkably, there is recent evidence that a variety of collective responses can be ignited if a complex network connects the spins [2-3]. In particular, we observe the same collective state on different networks through a fine tuning of the network topological parameters: a magnetized regime, displaying a second order phase transition to a non-magnetized phase and, furthermore, a peculiar oscillating phase has been observed where the order parameter is affected by persistent global oscillations. We thus focus on the Graph Signal Transform to benchmark the time series produced by the model in the three aforementioned macroscopic states at equilibrium[4]. Through this benchmarking phase, we retrieve the ?selected network modes? for each macroscopic state on different topologies and this selection points to the sub-structures of the graph relevant for the dynamics. [1]D. Shuman, S. K. Narang, P. Frossard et al., Signal Processing Magazine, IEEE 30, 83 (2013). [2]S. De Nigris and X. Leoncini, Phys. Rev. E 88, 012131 (2013). [3]S. de Nigris and X. Leoncini, Phys. Rev. E 91, 042809 (2015). [4]S. de Nigris, P. Expert, T. Takaguchi and R. Lambiotte, to be submitted.

Abstract: In many complex systems, it may be desirable or even essential to avoid classes of nodes by utilizing multiple paths. The need to avoid a certain set of nodes may be because they are tapped by the same eavesdropper or liable to fail collectively. Thus, secure connectivity is limited to a subset of nodes which can be connected with multiple paths, each avoiding nodes of a given vulnerability. To analyze this secure connectivity, we describe each vulnerable class as a color and develop a ``color-avoiding percolation'' framework. We present an analytic theory for random networks and a numerical algorithm for all networks. Applying our physics-based theory to the Internet, we show how color-avoiding percolation can be used as the basis for new topologically aware secure communication protocols. Beyond applications to cybersecurity, our framework reveals a new layer of hidden structure in a wide range of natural and technological systems. The transition from fully connected system to partially connected system and to a completely disconnected system is systematically analyzed.

Abstract: In recent years much research activity has focused on the application of complex network techniques to problems in climate variability. This has been particularly successful in climate transition problems, where relatively simple topological properties of reconstructed interaction or recurrence networks have provided useful early warning indicators of transitions in the ocean circulation, vegetation patterns and El Nino. In this presentation, a critical evaluation will be given on recent results on such transition problems, with a focus on El Nino occurrences and the collapse of the Atlantic Ocean circulation.

Abstract: A broad range of physical, biological and social phenomena evade the description within the framework of traditional analytic functions. Properties of disordered materials, diffusion in anisotropic systems, stock market crashes, power grid failures, earthquakes or physiological time series are all characterized by long-term memory, spatial heterogeneity, non-stationarity and nonergodicity. Those inherent signatures of complexity require a new approach extending beyond classical models, and the fractional calculus provides one framework for such new way of thinking.?In this talk we will explore reasons why the traditional calculus is not sufficient to capture the full range of dynamics found in natural and man-made processes. We will introduce few definitions fundamental to the field and we will illustrate how fractional calculus approach can provide more natural description of complex phenomena than traditional methods.

Abstract: Many dynamical processes on real world networks display complex temporal patterns as, for instance, a fat-tailed distribution of inter-events times, leading to heterogeneous waiting times between events. In this work, we focus on distributions whose average inter-event time diverges, and study its impact on the dynamics of random walkers on networks. The process can naturally be described, in the long time limit, in terms of Riemann-Liouville fractional derivatives. We show that all the dynamical modes possess, in the asymptotic regime, the same power law relaxation, which implies that the dynamics does not exhibit time-scale separation between modes, and that no mode can be neglected versus another one, even for long times. Our results are then confirmed by numerical simulations.

Abstract: Random evolutions provide a framework for exploring the probability interpretation of fractional operators. In this context, fractional order dynamical systems represent the average behavior of integer order dynamical systems subject to fluctuations from either internal or external sources. Many complex systems consist of inherently noisy elements and cannot be isolated from their surrounding environment without losing practical meaning. Therefore it is important to study random evolutions that lead to fractional macroscopic equations in order to be able to recognize when a fractional calculus description is appropriate. We plan to discuss this issue at an introductory level with illustrations that include the continuous time random walk and the harmonic oscillator with a randomly modulated frequency (e.g., from being coupled to a network of oscillators).

Abstract: A wide variety of systems require filtering of signals by which noise is dampened so that a signal of interest can be extracted. In control systems, iterative filtering of a stochastic process is needed to estimate state variables and apply those measurements to control signals. A celebrated example is the Kalman filter [1], which uses linear approximation and covariance estimates to iteratively improve estimates of random variables. Each iteration of the Kalman filter consists of a prediction step, in which the mean and covariance of the random process is forecast, and an update step, in which a measurement of the mean and covariance is used to correct errors in the prediction. While the Kalman filter is optimal for linear signals with Gaussian noise, and can work effectively for a broader range of signal processing applications, complex systems with nonlinear signals and non-Gaussian noise impose unique challenges. Approaches to these challenges include the extended Kalman filter, which splices linear approximations, and the particle filter, which generalizes filter design to non-parametric distributions. An important improvement was achieved by Zakai [2], who showed that a nonlinear filtering problem can be reduced to a linear stochastic partial differential equation by allowing the distribution to be unnormalized. L?vy processes [3], which are characterized by heavy-tail distributions, are particularly challenging to model because the jumps and ?L?vy flights? have many of the characteristics of deterministic processes despite their stochastic origin. Fractional calculus provides a methodology to design filters for stochastic processes driven by L?vy processes. This is achieved by generalizing the Zakai equation using pseudo differential operators with fractional order. A particularly interesting application of the fractional Zakai equation is the modeling of time-change processes, in which time is also treated as a random variable [4].

Abstract: As the first step in control and optimization the flow of crowds of pedestrians, modeling of dynamic crowd has received increasing attention in recent years. In this talk, the model of crowds of pedestrians with large numbers is derived from agents that interact with neighbors in micro-scale based on the CTRW (continuous time random walk) framework. We also derived the model in macro-scale based on conservation laws. To some extent, the macroscopic models obtained using the above two methods coincide with each other where long-range interactions and short-range interactions have played an important role in deriving the fractional order macroscopic model. Relationship between microscopic model and macroscopic model revealed in this talk from the viewpoint of interactions, which will contribute to the analysis and control of flocking of large crowds so that some tragedies such as stampede could be avoided. For efficient evacuation of crowds in some bounded area, a micro-macro dynamic model of fractional order is studied using fractional mean field games theory and a fractional forward backward model has been constructed in the end. The obtained dynamic models of fractional order in different scales have been tied and connected with each other from the view point of interaction. Some other interesting work that undergoing is also included in this talk such as the boundary control based on initial distribution of pedestrians and boundary control based on the spatial structure.

Abstract: Universal critical behavior in networks is determined by the structure of a network and the symmetry of a given model. Many real networks exhibiting a small-world property, are scale-free. The problem of studying dynamic processes in networks with such universal properties raises a question: do there exist common patterns in the dynamics of various complex systems? We derive an equation of motion for the order parameter in the small world networks with temporal memory and obtain a fractional differential equation for the order parameter. We analyze phase transitions induced by noise in such systems. We discuss the applicability of the obtained results to analyze dynamic processes in the brain network.

Abstract: A framework for studying the vulnerability and the recovery of networks of interdependent networks will be presented.In interdependent networks, when nodes in one network fail, they cause dependent nodes in other networks to also fail. This is also the case when some nodes like certain locations play a role in two networks --multiplex. This may happen recursively and can lead to a cascade of failures and to a sudden fragmentation of the system.I will present analytical solutions for the critical thresholds and the giant component of a network of n interdependent networks.I will show, that the general theory has many novel features that are not present in the classical network theory. When recovery of components is possible global spontaneous recovery of the networks and hysteresis phenomena occur and the theory suggests an optimal repairing strategy for a system of systems.I will also show that interdependent networks embedded in space are significantly more vulnerable compared to non embedded networks. In particular, small localized attacks of zero fraction may lead to cascading failures and catastrophic consequences.Thus, analyzing real data and realistic models of network of networks is highly required to understand the system vulnerability.

Abstract: In this talk we study the impact that mobility patterns have on the onset of epidemics when different classes of individuals coexist within a metapopulation. Each class of individuals show different mobility patterns between the patches composing the metapopulation. Thus, the presence of M different types of individuals turns the usual networked metapopulation into a multiplex metapopulation of M layers, where each layer encodes the mobility network of each type of individuals. We will develop a Markovian formulation for calculating the time evolution of the infected individuals in each patch and compare our model with MonteCarlo agent based simulations. In addition, we focus on two particular datasets from the cities of Medell?n and Bogot?, both in Colombia. Although mobility patterns in these two cities are similar from those typically found for large cities, these datasets provide additional information about the socioeconomic status of the individuals. This information is particularly important when the level of inequality in a society is large, as it is the case in Colombia. Thus, taking advantage of this additional information we will analyze how the different mobility pattern of each socioeconomic class affect the onset of epidemics.

Abstract: Understanding the evolution of cooperation is one of the most fascinating challenges in many disciplines. There is a large amount of literature analysing the mechanisms for cooperation to emerge and to be sustained, both from theoretical and experimental studies. Another way to understand the evolution of cooperation in human societies consist in deciphering the cooperative behaviour in ancient communities from historical records. In a previous work we studied cooperation in the Yamana society that inhabited Argentina and observed that the emergence of an informal network of vigilance promoted cooperation. Several field studies have found evidence of humans exposing a pro-social behaviour when being observed by others and also under the presence of subtle cues of being watched. The observability effect (the increase of cooperation under vigilance) seems to be driven by our reputational concerns, bringing the indirect reciprocity mechanism into play. This work explores the effect of vigilance on cooperation in networked systems, in the framework of the Prisoners? Dilemma game. We study the bidirectionally-coupled vigilance and game dynamics. We quantify the impact of the topological structure of the network, and the interplay between vigilance and behaviour, on the outcome of cooperation. Moreover, we study the impact of vigilance on cooperation when the individuals have to afford a cost to become vigilant actors. We also analyse the influence of network multiplexity, i.e. the interconnection of different topological structures for the vigilance and the game networks, and the impact of layer-degree correlations, i.e. when node degrees of the multiplex layers are not randomly distributed but correlated. Our results show that vigilant actors can significantly affect the levels of cooperation, not only by enhancing cooperation in regions of the phase diagram where cooperation is expected to hold, but also by altering the critical point for the emergence of cooperation.

Abstract: Recent studies of multimodal urban transportation networks have used the framework of multiplex network analysis to illuminate their structure and make analytic or simulation-based inferences about their dynamics. However, these systems are intrinsically sociotechnical, and their dynamical behavior depends not only on structure, but also on user behavior--particularly travel demand. We present the first study of an urban transportation system combining multiplex network analysis and validated Origin-Destination travel demand, using Riyadh's planned metro as a case study. First, we develop methods for analyzing the impact of additional transportation layers on existing dynamics, and show that the structure of demand plays a key role in determining qualitative system behavior. We then demonstrate the existence of fundamental limits to changes in traffic dynamics due to the intrinsic geometry of the metro layer. Finally, we develop a simple analytical framework for informing the planning of additional feeder layers (such as buses) to maximize the impact of the metro on global congestion. The techniques we present are computationally practical, easily extensible to arbitrary numbers of transportation layers, and implemented in open-source software.

Abstract: In multi-layer networks with directed links, introducing measures of dependency between different layers requires more than a straightforward extension of the multiplexity measures that have been developed for undirected multiplexes. In particular, one should take into account the effects of reciprocity, i.e. the tendency of pairs of vertices to establish mutual connections. In single-layer networks, reciprocity is a crucial structural property affecting several dynamical processes. Here we extend it to multiplexes and introduce the notion of multireciprocity, defined as the tendency of links in one layer to be reciprocated by links in a different layer. While ordinary reciprocity reduces to a scalar quantity, multireciprocity requires a square matrix generated by all the possible pairs of layers. We introduce multireciprocity metrics valid for both binary and weighted multiplexes and provide an empirical analysis of the World Trade Multiplex, representing the import-export relationships between world countries in different products. We show that several pairs of layers exhibit strong multiplexity, an effect which is however largely encoded into the degree or strength sequences of individual layers. We also find that most pairs of commodities are characterised by positive multireciprocity, and that such values are significantly lower than the usual reciprocity measured on the aggregated network. Moreover, layers with low (high) internal reciprocity are embedded within groups of layers with low (high) mutual multireciprocity. We finally identify robust empirical patterns showing that joint multi-layer connection probabilities can be reconstructed from marginal ones via the multireciprocity matrix. Therefore the latter can bridge the gap between single-layer properties and truly multiplex information.

Abstract: Focusing on the dynamics of multiplex networks, we discuss some examples where multiplexity gives rise to the emergence of novel dynamical behaviors, otherwise unobserved in the dynamics of single-layer networks.

Abstract: Bayesian models of cognition have been extremely successful at describing human behavior in the laboratory. Yet they can neither predict nor explain our most advanced forms of human communication, from political debate to free markets. Optimal agents will neither trade nor attempt to persuade. Without a rigorous mathematical account of collective reasoning, however, we are unable to imagine new social systems, or to know what is of value in the ones we wish to repair. I show how standard Bayesian models are undermined by the need to explore an indefinitely large problem space. I then present an alternative account of human rationality based on sociability, rather than computation. This framework predicts a central role for reciprocal conversation among equals, bounded conflict, and non-aligned incentives, in discovering new solutions. I conclude with recent empirical evidence for these models, drawn from collaborative research into scientific creativity, parliamentary debate, and play.

Abstract: TBA

Abstract: Spatial patterning often occurs in ecosystems as a self-organizing process caused by feedback between organisms and the physical environment. Here we show that the spatial patterns observable in centuries-old Balinese rice terraces are also created by feedback between farmer’s decisions and the ecology of the paddies, which triggers a transition from local to global-scale control by groups of farmers. An evolutionary game based on this model predicts spatial patterning that closely matches multispectral image analysis of Balinese rice terraces extending over five orders of magnitude. The model shows for the first time that adaptation in a coupled human-natural system can trigger self-organized criticality (SOC). In standard SOC models, the driver is exogenous, scale invariance of patch distributions occurs across a wide range of parameter values, adaptation plays no role and nothing is optimized. In contrast, adaptive SOC is a self-organizing process of local adaptations that drive parameter settings to a very narrow range at the phase transition, approaching local and global optima.

Abstract: In the network approach to psychopathology, disorders are sets of causally connected symptoms. This conceptualization offers novel perspectives on the theoretical status of mental disorders: instead of cleanly separable categories that reflect central neural or psychological deficits, disorders are tightly connected regions in a symptom network. This conceptual framework suggests novel approaches to both the analysis of research data and the organization of treatment interventions, primarily through the application of network analysis: a set of techniques that offers powerful tools to study the dynamics of interconnected systems, to analyze the architecture of networks involving large numbers of entities (e.g., neurons, people, genes, variables), and to visualize connectivity structures in such networks. In the present talk, I will give an overview of the most important insights and results that have arisen from the network approach.

Abstract: In this presentation, I will present an out-of-the-box methodology applicable to longitudinal psychological data for exploratory discovery of relationships between observed measures. This framework takes the form of the well-known (multi-level) vector-autoregression model (VAR), but extends the emphasis beyond the temporal coefficients typically interpreted as a directed network. The VAR model can be seen to extend the increasingly popularly used Gaussian Graphical Model (GGM)---a network of partial correlation coefficients---to data in which cases are not independent. In addition to temporal networks, the methodology also returns a contemporaneous network and a between-subjects network, both in the form of a GGM. During this presentation, I will discuss the plausibility of assumptions made and the potential causal interpretation of contemporaneous and between-subject network structures. In addition, two R packages will be introduced for estimating these structures: graphicalVAR, which uses LASSO regularisation to estimate the temporal and contemporaneous network of a single subject, and mlVAR, which uses multi-level modeling to estimate the temporal, contemporaneous and between-subjects networks of multiple subjects. I will show empirical examples of both methods.

Abstract: The most dominant approach to model observable behaviour in psychology makes use of latent variables. That is, it is assumed that the observable behaviour is caused or governed by an unobserved attribute (e.g., `intelligence', `ability', `disorder', etc.). Even though it is a highly successful approach, the theoretical status of latent variables is often challenged. Recently, it was proposed to model observable behaviour directly using network models, instead of using latent variables. That is, it was suggested that observed variables directly influence each other (e.g., `sleep problems' --- `concentration problems'). Such models have been applied successfully to psychopathology and personality data in the last years, for instance. Since psychometric (latent variable) models are formally related to network models, it follows that both types of models show promise when applied to the same question. In this respect, the latent variable and the network model approach can be seen as being two sides of the same coin, allowing us to gain new insights in to existing problems, and new problems for existing insights. One area where latent variable and network models have been less successful is in modelling qualitative inter-individual differences of psychological processes. For example, models have difficulties explaining why specific people who experiencing the same stressor (e.g., an adverse life-event) develop a depressive episode while others do not. Here we propose that such processes can be better explained using random cluster models, where each individual comes with their own network. That is, we propose that the network structure itself is a latent variable.

Abstract: Due to technological developments (e.g., smartphones), there is an enormous increase in studies based on daily diaries, ecological momentary assessments, ambulatory assessments, and experience sampling methods. The intensive longitudinal data stemming from these studies provide us with the unique opportunity to investigate the dynamics of psychological processes as they are unfolding over time. This can be done by using single-subject time series models, or by using new multilevel models where level 1 is formed by a time series model, while at level 2 individual differences in the time series parameters are modeled. Currently, the software package Mplus is being extended with Dynamic Structural Equation Modeling (DSEM), which will allow for N=1 time series modeling, as well as its multilevel extensions. Furthermore, it will also allow for regime-switching processes. In this talk I will provide a bird?s eye view of these exciting new developments. I will briefly present the general DSEM framework and show a few applications consisting of multilevel vector autoregressive models and latent multilevel autoregressive models. Additionally, I will touch upon some of the major challenges in this rapidly developing area, including how to standardize parameters in these models to allow for meaningful comparisons among them, and whether ESM data should be considered as 2-level or 3-level data.

Abstract: Emotion dynamic research typically aims at revealing distinct information on affective functioning and regulation. Herewith, one distinguishes various elementary emotion dynamic features (EDFs), which are studied using intensive longitudinal data. Typically, each EDF is quantified separately, which seriously hampers the study of relationships between various features. We propose a Bayesian vector autoregressive model (VAR) and apply it to emotion data. The model encompasses all six emotion dynamic features central in emotion research at once, and can be applied with relatively short time series, including missing data. The model can be applied to both univariate and multivariate time series, allowing to model the relationships between emotions. Further, it may model multiple individuals jointly as well as external variables and non-Gaussian observed data, and can deal with missing data. We illustrate the usefulness of the model with an empirical example using relatively short time series of three emotions, with missing time points within the series, measured for three individuals. Finally, we demonstrate that the model can easily deal with measurements that are not equally spaced in time.

Abstract: Multistability?understood as the existence of diverse stationary states under a fixed set of conditions?is ubiquitous in physics and in biology, it and leads to interesting spatial and temporal patterns. Motivated by several empirical observation of bimodal distributions of activity, we propose and analyze a theory for the self-organization to the point of phase-coexistence in systems exhibiting a first-order phase transition. It explains the emergence of regular avalanches patterns with attributes of scale-invariance which coexist with huge anomalous ones, with realizations in many fields.

Abstract: Since Turing?s seminal work, reaction-diffusion models have played a central role in the analysis of various self-organized spatio-temporal patterns in nature. As pointed out by Othmer and Scriven already in 1971, it is straightforward to generalize the reaction-diffusion models to networks, which gives us a wider perspective on pattern formation. In this talk, several topics on pattern formation and collective dynamics in reaction-diffusion models on random networks will be discussed. We consider formation of Turing patterns in activator-inhibitor systems on networks, where difference in diffusivity of chemical species leads to destabilization of uniform states and formation of patterns. It is shown that, for networks with degree heterogeneity, simple mean-field approximation of the network can account for backbones of the developed patterns. We will also see that essentially the same mechanism, called Benjamin-Feir instability, destabilizes uniformly synchronized state and leads to collective dynamics in coupled oscillators on networks. More general types of diffusion-induced instabilities in reaction-diffusion systems with three chemical species or in directed networks will also be discussed. Some related unsolved issues, such as self-consistency analysis of developed patterns, bifurcation analysis of instability, and localization properties of Laplacian eigenmodes on networks, will also be mentioned.

Abstract: Pattern formation has attracted the interest of the scientific communities of several fields since the Turing seminal paper on morphogenesis [1] first appeared. Recently, patterns emergence has been studied in complex networks [2], where a spontaneous differentiation of nodes in activator(inhibitor)-rich and activator(inhibitor)-poor nodes was observed in a two species reaction-diffusion system. From then, several extensions and generalizations have followed. In this talk we aim reviewing the main framework of our research on the pattern formation theory from the network prospective. Starting from the Turing instability mechanism we prove that the spontaneous segregation of the nodes in different groups extends far beyond Turing original conditions. In particular the network topology plays an active role in the initialization of the self-organization process as it happens for the directed networks [3]. In other cases the peculiarities of the network structure in layers (multiplex [4]) or product of sub-networks (Cartesian network [5]) explain why motifs are more likely to appear in such networks than others or how they emerge as a collective property of networks. Different applications from ecology to neuroscience can rise.

Abstract: TBA