# Financial Networks and Policy Applications from Systemic Risk to Sustainability  (FNPA) Session 1

## Chair: Stefano Battiston

 4019 Introduction - "Policy Applications of Financial Networks" Stefano Battiston, FINEXUS - Univ. of Zurich 4020 Contributed Ignite Talks Session (see Satellite webpage http://www.dolfinsproject.eu/index.php/ccs16) 4021 t.b.a. Marten Scheffer, Wageningen University and Research Centre, Netherlands 4022 Bubbles and crashes in large group asset market experiments Cars Hommes, CeNDEF - Univ. of Amsterdam 4023 New Metrics for Economic Complexity: Measuring the Intangible Growth Potential of Countries Luciano Pietronero, University of Rome Sapienza and Institute of Complex Systems, ISC-CNR, Rome, Italy 4024 t.b.a. Doyne Farmer, Oxford Univ. and Institute for New Economic Thinking, UK 4025 Panel Discussion - "Policy Applications of Global System Science: From Systemic Risk to Sustainability." Moderator: Stefano Battiston. Panelists: Marten Scheffer, Cars Hommes, Luciano Pietronero, Doyne Farmer

# UrbanNet 2016: Smart Cities, Complexity and Urban Networks  (U2SC) Session 2

## Chair: Oliva Garcia Cantu / Fabio Lamanna

 14008 Electric vehicle charging as complex adaptive system - information geometric approach [abstract] Abstract: In all major cities in the Netherlands, charging points for electric vehicles seem to spring up like mushrooms. In the city of Amsterdam alone, for example, there were 231 charging points by the end of 2012 in comparison with 1, 185 today, and roughly two new charging stations added every week. Over the same period of time, the average number of charging sessions per week went up from 550 to 8, 000. All charging sessions in the Netherlands are recorded by the service providers and those from Amsterdam, Rotterdam, Utrecht, The Hague and provinces of Northern Holland, Flevoland and Utrecht are made available for research through the respective municipalities to the Urban Technology research program at the University of Applied Sciences Amsterdam1. The dataset of charging sessions, which is the largest of its kind in the world, currently holds more than 3.3 million records, containing information about duration, location and a unique identifier of the users [1]. The tremendous growth in electric vehicle adoption, in combination with the existence of this large and rich dataset, creates a unique opportunity to study many aspects of electric mobility and infrastructure in the context of complex social systems. The question we focus on is the following: if we consider the e-mobility system as complex and adaptive, what is its phase structure? Are there regime changes in the system? And, could we define distinct states of the dynamics of the system at hand? The framework in which we study these questions is that of information geometry [2]. To construct the framework we first define observables of interest from the data. We then estimate the probability distributions of these observables, as a function of time or other parameters of the system. As the system evolves, the shape of the probability distributions might change. We say that a regime shift has occurred when a large and persistent change in the probability distributions has happened. To define a large change in the probability distribution we use Fisher information [3]. Our approach is based on an analogy with the theory of phase transitions in statistical physics, especially second order or ?critical? transitions. In statistical physics one can study the information geometry of the Gibbs distribution and show that at second order phase transitions and on the spinodal curve the curvature of the statistical manifold diverges [4]. Taking it a step further, Prokopenko et al. showed that one can use the Fisher information matrix directly to serve as an order parameter [5]. Following these results, a maximum of the Fisher information matrix is used as a definition of criticality in complex systems, e.g. in [6]. The application of our approach is particularly challenging in the charging infrastructure system since 1) it is an open system (the number of users and charging points changes over time), and, 2) it is an irreversible system (the municipalities gain experience in deploying charging points, the users of the system optimize their usage of the charging point infrastructure, and policies and user support systems change). All this indicates that there is no straightforward notion of phase space for this system, which would allow for a Gibbs-like distribution to be defined. Our previous work, which was applying this framework to a non-linear reaction-diffusion system (the Gray-Scott model), is encouraging since we were also able to detect regime changes based on a macroscopic distribution of observables, independent of the microscopic dynamics of the system [7]. These challenges, however, are typical of complex adaptive social systems and therefore finding a satisfactory solution to them might allow for a generalization of the method to different social systems. In the talk we will present the results of pursuing this line of investigation. We will discuss different observables 1http://www.idolaad.nl 1 we tried and insights we gained into the system from our work. Understanding the phase structure of electric vehicle charging, and hence the dynamics of charging, can have large implications on our understanding of the dynamics of neighborhoods, on planning and policy implementation and on the study of Urban science in general. Close Omri Har-Shemesh 14009 Residential Flows and the Stagnation of Social Mobility with the Economic Recession of 2008 [abstract] Abstract: The movement of people within a city is a driver for the growth, development, and culture of the city. Understanding such movements in more detail is important for a range of diverse issues, including the spread of diseases, city planning, traffic engineering and now-casting economic well-being [1, 3]. Residential environment characteristics have been shown to be strongly associated with changes in individual socioeconomic status, making residential relocation a potential determinant of social mobility [2]. Examining residential mobility flows therefore offers an opportunity to better understand the determinants of social mobility. By using a novel dataset, recording the movement of people within the city of Madrid (Spain) over a time period of 10 years (2004-2014), we studied how residential flows changed during the economic recession of 2008. Here we present preliminary results from these investigations. In particular, we found that the crisis had a profound impact on social change, reducing the social mobility within the city as a whole, thus leading to a ?social stagnation? phenomenon. Methods: We used data from a continuous administrative census of the entire Spanish population (the ?Padron?) that includes universal information on all residential relocations. Using this data, we can assess the mobility within and in and out of the city of Madrid, stratified by age, education and country of origin. For analysis involving property value and unemployment, the granularity of our analysis is on the level of neighborhood ( 20,000 people each, n=128 in Madrid). For all other analysis, our granularity is on the level of census section ( 1,500 people each, n 2400 in Madrid), providing a very fine grained perspective on the residential flows within the city. To examine changes in residential mobility flows, we categorized these into the following: any mobility (any change of residential location), mobility within the city of Madrid, and mobility within the city but to a different area. We further divided these last type of flow into upward (from poorer to richer) or downward (from richer to poorer) mobility. Figure 1 (left) shows an example of the geographical delineations and the associated residential mobility flows. Figure 1: (Left) A data overlay of a section of Madrid. Red outlines correspond to neighborhoods, colored by quintile of property value for 2004 (red areas indicate the highest property value quintile). Black outlines correspond to census sections, and arrows represent residential mobility flows. In particular, white arrows indicate movement to areas of higher property value, black to lower, and blue to areas of equal value. (Right) The total movement (in-flow + out-flow) within each census section for the year 2004. Red areas indicate high residential flows. ?1 0 1 2005 2007 2009 2011 2013 Year Quintile of Destination minus Origin Neighborhood Social and Residential Mobility in Madrid 0.05 0.10 0.15 0.20 2006 2008 2010 2012 2014 Year Unemployment Rate (%) Unemployment Rate per Neighborhood in Madrid Figure 2: (Left) Time series of social mobility (average change in quintile of property value of all movers in the neighborhood; where a positive number represents upwards mobility and 0 represents no social mobility) in the six neighborhoods with the highest change in social mobility from 2005 to 2014. (Right) Unemployment time series in all neighborhoods of Madrid, with thicker lines for the six neighborhoods pictured in the left. Results: We find that residential mobility peaked in 2007-2008, especially due to the contribution of incoming flows to Northern and Southeastern Madrid. A centrality based analysis of the residential mobility network reveals the intensity of change in the downtown area (Centro) of Madrid (Figure 1, Right). We further assessed the effect of the 2008 financial crisis on residential mobility flows, showing that neighborhoods in the lower end of the socioeconomic spectrum and those that had changed the most during the housing boom of the 2000s were the most affected by the recession (Figure 2, Right). In particular, these neighborhoods showed a decrease in social mobility associated with residential relocation, with a decreasing proportion of people in poorer relocating to neighborhoods with a higher property value (Figure 2, Left). Moreover, there was also a decreasing proportion of people in richer areas relocating to neighborhoods with a lower property value. This lack of upward mobility (from poorer areas) and downward mobility (from richer areas) led to an stagnation of residential mobility in the aftermath of the recession. Discussion: A combination of fine-grained relocation, socioeconomic and property value data has allowed us to detect communities with increased mobility flows, as well as areas of relative residential stability or stagnation. It has further allowed us to explore changes with the economic recession. Our finding that social mobility at the neighborhood level has stagnated is consistent with previous findings of increased economic segregation concurrent with the economic recession of 2008[4]. Close Usama Bilal 14010 title to be confirmed (invited talk) Filippo Simini 14011 Smart Street Sensor [abstract] Abstract: Urban street structures are a snapshot of human mobility and resources, and are an important medium for facilitating human interaction. Previous studies have analyzed the topology and morphology of street structures in various ways; fractal patterns [1], complex spatial networks [2] and so on. Through a functional aspect, it is important to discuss how street networks are used by people. There are studies analyzing the efficiency [3], accessibility[4] and road usage[5] in the street networks too. In those studies, the researchers investigated either empirical travel routes or theoretical travel routes to understand the functionality of the street network. A travel route is a path within the network selected by people or selected under a given condition. Since the determination of a travel route is directly influenced by travel demand and the spatial pattern of the city, including street network and land-use formation, a selected route is a good way to capture complex interactions among the factors which are often hidden. For instance, fastest routes estimate the possible distribution of traffic as well as the street structure in a city. In this study, we analyze the geometric property of routes to understand the street network considering hierarchical property and traffic condition. Although many studies discuss the efficiency of a route or a street network, few people investigate the geometry of a route [6] or study how individual routes are intrinsic to the city structure. Two cities with similar efficiency can have a different geometry of congestion pattern and traffic pattern [7]. Therefore, understanding the geometric feature of routes can link the the existing knowledge of routes and the structure of urban street network. We especially focus on how much a route is skewed into the city center by measuring a new metric, Inness. The inness I of a route is defined as the difference between inner travel area Pinner and outer travel area Pouter as I = Pinner - Pouter. The areas are defined, after a route is divided into inner part and outer part based on the straight line connecting the origin and destination as described in the Fig.1. We measured the inness of the collected optimal routes within 30km radius from the center for 100 global cities including NYC, London, Delhi and so on. In the cities, we identified two competing forces against each other. Due to the agglomeration of businesses and people, street networks grow denser around the center area to meet the demand, and attract traffic toward the interior of the city. On the other hand, many cities deploy arterial roads located outside of the city to help disperse the congestion at urban core. The arterial roads act as the other force pushing traffic toward the exterior of the city. This tendency is well captured by our suggested metric. We analyze two types of optimal routes by minimizing the travel time and distance. While the shortest routes reveal mere road geometric structure, the fastest routes show the geometry in which the road hierarchy is reflected. We systematically select the origin and the destination having different bearings and different radii from the center. Then, we collect the optimal routes of the O-D pairs via the OpenStreetMap API. Our results consist of two parts. We first compare the general average inness of both the shortest and fastest routes of the 100 global cities in order to point out the their fundamental differences. Later, we analyze the inness patterns of individual cities and discuss street layout and the effects of street hierarchy in each city. Close Balamurugan Soundararaj 14012 A Retail Location Choice Model: Measuring the Role of Agglomeration in Retail Activity [abstract] Abstract: The objective of our work is to build a consumers choice model, where consumers choose their retail destinations only based on a retailers? floorspace and the agglomeration with others. In other words, at a very aggregated level, the goal is to describe a retailers success with a model which only takes into account its position, and its floorspace. We define the attractiveness of a retailer r as Ar = f? r +X r0 f? r0e""drr0 (1) where fr is the retailer?s floorspace, drr0 is the distance between r and some other retail unit r0. Eq.(1) states that the composite perceived utility Ar that a consumer attaches to a particular retailer r is equal to its individual utility, quantified as choice and therefore floorspace f? r , and the utility of the shops in its vicinity. In eq.(1), ? controls the extent of the internal economies and " of the external economies of scale. If ? > 1, the relationship between consumer perceived utility of a shop and its size is super-linear and the economies of scale are positive, meaning that a retailer would benefit from larger floorspace. Similarly, low values of ", which translate into a slow decay, would imply a strong dependency of on vicinity to other attractive neighbours, and viceversa. Exploiting eq.(1) we define the probability of consumer i shopping in r as pi!r = Are"#C(dir,$) P r0 Ar0e"#C(dir0,$) (2) where C(dir, #) is the cost function of travelling from i to r, $and # are two parameters. Eq.(2) has been formulated using random utility theory and as once can see in the proposed cross-nested logit model in eq.(2) consumers prefer to shop at larger shops (internal economies of scale) and at locations with higher concentration of retail activity (external economies of scale). In this work we have considered two types of trips, namely work to retail and home to retail. The model is therefore defined by 6 di?erent parameters, two describing the attractiveness of retailers through their internal and external economies (?, "), and two for each kind of trips describing the cost function, ($h, #h), and ($w, #w). Therefore the total modelled turnover will be of the form Yr = Y w r + Y h r = X l ? nw l pw l!r + nh l ph l!r ? (3) the$ and # have been calibrated using the LTDS datasets, as survey that includes 5004 home and retail and 2242 work to retail trips. Having completed the calibration of the distance profiles we can now calculate the modelled turnover estimates for each retailer r for a set of (?, ") parameters, defined in eq.(3) . This will tell us the modelled fraction of population that will end up shopping 1 in each retailer given their attractiveness and distance. Following this, we calculate the correlation level between the modelled turnovers and the observed floorspace rents. For each retailer r, we use the VOA rateable value as an indicator for willingness to pay for floorspace fr. The Rateable Value (a) Correlations (b) Scatter Plot Figure 1: As we can see from this figures the model yields high correlations with the VOA dataset?s rents. In the left panel we show the correlation between the expected turnover Yr(?, ")/fr and the Rateable Value / Size found in the dataset. Cmax ? C(? = 1.3, " = 0.008). These values are in agreement with a superlinear scaling in floorspace and with the observed retail agglomeration. In the right panel we present a scatter plot of the two quantities. is considered a very good indicator of the property value of the respective hereditament. In fig.(1) we compare the results of the models with rent data coming from VSOA. In fig.(1a) we can see how the maximum correlation between the modelled and real rents per squared meters is given by teh set of parameters (?max = 1.3, "max = 0.008). The ? value is in line with super-linear scaling of floorspace and expected earnings, and seems incredibly realistic, while the " values indicates a benefit in agglomeration of retail activities (the sign is positive), and indicates that the vicinity of a retail activity does have a non negligible role in defining an attractiveness. Close Duccio Piovani 14013 Revealing patterns in human spending behavior [abstract] Abstract:  In the last decade big data originating from human activities has given us the opportunity to analyze individual and collective behavior with unprecedented detail. These approaches are radically changing the way in which we can conceive social studies via complex systems methods. Large data, passively collected from mobile phones or social media, have informed us about social interactions in space and time [1], helping us to to understand the laws that govern human mobility [2?4] or to predict wealth in geographic areas [5]. More recently, data from Credit Card Shopping Records (CCSR) has also been explored providing new insights on human economic activities. Ref. [6] has shown that a fingerprint exists in the sequence of individual payment activities which permits the users to be identifiable with only few of their records. The shoppers spending behaviors and visitation patterns are very much related to urban mobility [7]. Both mobility decisions and expenditure behavior are subject to urban and geographical constraints [8] and to economic and demographic conditions [9, 10]. Further understanding consumer behavior is valuable to model the market dynamics, and to depict the differences between income groups [11]. In particular CCSRs have the potential to transform how we conceive the study of social inequality and human behavior within the geographic and socio-economic constraints of cities. Here we present a novel method to exploit CCSRs to provide new insights in the characterization of human spending patterns and how these are related to sociodemographic attributes. We analyze CCSRs of approx. 150, 000 users over a period of 10 weeks. The dataset is anonymized, and for each user the following demographic information is provided: age, gender, zipcode. For all users we have the chronological sequence of their transaction history with the associated shop typology according to the Merchant Category Codes (MCC) [12]. Our analysis of the aggregated CCSR data reveals that the majority of shoppers adopt the credit card payment for twelve types of transactions among the hundreds of possible MCCs. These are: grocery stores, eating places, toll roads, information services, food stores, gas stations, department stores, telecommunication services, ATM use, taxis, fast food restaurants, and computer software stores. These transaction activities are depicted as icons in Fig. 1. Interestingly, the temporal sequence of how these transactions occur are different among individuals. First, we identify the dominant sequences of transactions for each user using the SEQUITUR algorithm [13]. Then we evaluate the significance level of each sequence calculating the z-score with respect to the sequences computed from 100 randomized sequences whilst preserving the number of transactions per type. Each sequence of transactions defines a path in the space of the transaction codes.We define the User Transaction Network (UTN) connecting the codes of most statistical significant sequence (with z-score> 2), preserving the order.We compute the matrix of user similarity (Fig.1 lower left) calculating the Jaccard index between all the users with at least 3 link in their UTN. Applying the Louvain Method [14] for community detection we are able to group users according to their the most significant sequence of payments. Fig. 1 shows our results for the six different behavioral groups detected, with each cluster ordered in appearance from 1 to 6 in the matrix of users similarity. The upper part of the figure describes the most common sequences of transactions for each group, the link value with the error represents the probability for a user of the group to follow that particular transaction order, and the value in parenthesis defines the fraction of users in the group that perform that transaction sequence. The bottom part shows the demographic attributes of each group with respect to the average population in red. In summary, we have uncovered lifestyles groups in the transaction history of the CCSR data that relates to non-trivial demographic groups. We will discuss future applications of these clusters of life styles in the context of adoption of innovations in the city. Close Riccardo Di Clemente 14014 The universal dynamics of urbanization (invited talk) Marc Barthelemy 14015 Identifying and tackling Water Leaks in Mexico through Twitter [abstract] Abstract: As cities became smarter, the amount of daily data generated has become increasingly granular. Sensors, cameras, crowdsourcing, social media sharing, etc., can monitor different aspects in our cities, such as commuter flows, air quality over different time periods or public transport performance. The rise of the ?smart city? has then the potential of through some light into many fundamental urban problems, and pave the way to make cities a more livable and efficient places. Particularly, Twitter has attracted a lot of attention in recent years (Ausserhofer & Maireder, 2013) for its richness in content. People is not only sharing personal information through its closest contacts, but is using Twitter as a social and political platform to inform and disseminate all sort of statements or ideas (Weng & Menczer, 2015; Lu & Brelsford, 2014; Pi?a-Garc?a, Gershenson, & Siqueiros-Garc?a, 2016). Exploring this type of data has is gradually getting more and more important in terms of data collection. In addition, mining urban social signals can provide quick knowledge of a real-world situation (Roy & Zeng, 2014). It should be noted that the enormous volume of Twitter data has given rise to major computational challenges that sometimes result in the loss of useful information embedded in tweets. Apparently, more and more people are relying on Twitter for information. Twitter has been tagged a strong medium for opinion expression and information dissemination on diverse issues (Adedoyin-Olowe, Gaber, Stahl, & Gomes, 2015). Leveraging large-scale public data from Twitter, we are able to analyze and map the spread of information related to water leaks in the street, under the pavement and roads in Mexico (see Fig. 1). We gathered an initial sample of 2000 geolocated tweets posted by 1599 users tweets that contains the Spanish keywords: "fuga de agua" (water leaks). Close Carlos Adolfo Piña García 14016 Estimating nonlinearity in cities' scaling laws [abstract] Abstract: The study of statistical and dynamical properties of cities from a complex-systems perspective is increasingly popular [1]. A celebrated result is the scaling between a city specific observation y (e.g., the number of patents filed in the city) and the population x of the city as [2] y = ?x? , (1) with a non-trivial (? 6= 1) exponent. Super-linear scaling (? > 1) was observed when y quantifies creative or economical outputs and indicates that the concentration of people in large cities leads to an increase in the percapita production (y/x). Sub-linear scaling (? < 1) was observed when y quantifies resource use and suggests that large cities are more efficient in the per-capita (y/x) consumption. Since its proposal, non-linear scaling has been reported in an impressive variety of different aspects of cities. It has also inspired the proposal of different generative processes to explain its ubiquitous occurrence. Scalings similar to the one in Eq. (1) appear in physical (e.g., phase transitions) and biological (e.g., allometric scaling) systems suggesting that cities share similarities with these and other complex systems (e.g., fractals). More recent results cast doubts on the significance of the ? 6= 1 observations [3, 4, 5]. These results ask for a more careful statistical analysis that rigorously quantifies the evidence for ? 6= 1 in different datasets. We propose a statistical framework based on a probabilistic formulation of the scaling law (1) that allows us to perform hypothesis testing and model comparison. In particular, we quantify the evidence in favor of ? 6= 1 comparing (through the Bayesian Information Criterion, BIC) models with ? 6= 1 to models with ? = 1. The scaling relation in Eq. (1) describes a relation between two quantities y and x. However, the empirical data indicates that this relation can only be fulfilled on average. The statistical analysis we propose is based on the likelihood L of the data being generated by different models. Following Ref. [6], we assume that the index y (e.g. number of patents) of a city of size x is a random variable with probability density P(y | x). We interpret Eq. (1) as the scaling of the expectation of y with x E(y|x) = ?x? . (2) This relation does not specify the shape of P(y | x) , e.g., it does not specify how the fluctuations V(y|x) ? E(y 2 |x) ? E(y|x) 2 of y around E(y|x) scale with x. Here we are interested in models P(y | x) satisfying V(y|x) = ?E(y|x) ? . (3) This choice corresponds to Taylor?s law. It is motivated by its ubiquitous appearance in complex systems, where typically ? ? [1, 2], and by previous analysis of city data which reported non-trivial fluctuations. The fluctuations in our models aim to effectively describe the combination of different effects, such as the variability in human activity and imprecisions on data gathering. In principle, these effects can be explicitly included in our framework by considering distinct models for each of them. We specify different models P(y | x) compatible with Eqs. (2,3): City models are the ones where we assume that each data point yi is an independent realization from the conditional distribution P(y|xi), effectively to each city the same weight when computing the BIC of the model. For this model, we considered two different types of fluctuations, one Gaussian and the other Lognormally distributed, thus choosing a priori a parametric form for P(y | x). Person models are based in the natural interpretation of Eq. (1) that people?s efficiency (or consumption) scale with the size of the city they are living in. This motivates us to consider a generative process in which tokens (e.g. a patent,a dollar of GDP, a mile of road) are produced or consumed by (assigned to) individual persons, which leads to a P(y | x) that effectively weights the observations in of people. 1 100 101 102 103 104 y, Brazil-Aids City Model Person Model Running mean 103 104 105 106 107 x, Population 0.0 0.2 0.4 0.6 0.8 1.0 fraction < x 80% of the cities 75% of the population (A) (B) Figure 1: Comparison of the model of Cities and Persons. (A) Reported deaths by AIDS with respect to cities? population (dots). The lines represent the estimated scaling law giving the same weight to each city (city model, ? = 0.61) and giving the same weight to each person (person model). (B) Cumulative distribution of heavy-tailed distribution of city-sizes in terms of cities and persons, i.e. the fraction of i) cities of size ? x (City Model); and ii) the population in cities of size ? x. We apply this approach to 15 datasets of cities from 5 regions and find that the conclusions regarding ? vary dramatically not only depending on the datasets but also on assumptions of the models that go beyond (1). We argue that the estimation of ? is challenging and depends sensitively on the model because of the following two statistical properties of cities: i The distribution of city-population has heavy tails (Zipf?s law). ii There are large and heterogeneous fluctuations of y as a function of x (Heteroscedasticity). We found that in most cases models are rejected by the data and therefore conclusions can only be based on the comparison between the descriptive power of the different models considered here. Moreover, we found that models which differ only in their assumptions on the fluctuations can lead to different estimations of the scaling exponent ?. In extreme cases, even the conclusion on whether a city index scales linearly ? = 1 or non-linearly ? 6= 1 with city population depends on assumptions on the fluctuations. A further factor contributing to the large variability of ? is the broad city-size distribution which makes models to be dominated either by small or by large cities. In particular, these results show that the usual approach based on least-square fitting is not sufficient to conclude on the existence of non-linear scaling. Recent works focused on developing generative models of urban formation that explain non-linear scalings. Our finding that most models are rejected by the data confirms the need for such improved models. The significance of our results on models with different fluctuations is that they show that the estimation of ? and the development of generative models cannot be done as separate steps. Instead, it is essential to consider the predicted fluctuations not only in the validation of the model but also in the estimation of ?. Close José M. Miotto 14017 Estimating Railway Travel Demand Through Social Media Geo-localised Data [abstract] Abstract: The fundamental four-stage modelling framework on railway planning is highly focused both on modal choice models and on the assignment of passengers' flows over networks. These last steps pursue the achievement of the maximum potential of new policies of transportation modes, constantly running towards more efficient and ecological modes. In Europe we assist at the emergence of several projects that aim to interconnect urban areas within and among countries, both with new or better-performing links and through the developing of rolling stock able to interoperate among national networks characterized by different power-supply infrastructures and signalling/security systems and protocols. Linking demand and supply is therefore a challenge to project, provide and validate better international services that are both reliable and of high quality. Here we develop a new framework able to estimate railway traffic demand through the detection of a set of geo-localised tweets, posted in the last three years, overlapping railway lines in Europe. We scale the data of the potential passengers over a line through the so-called Òpenetration rateÓ, able to get an estimation of the sample we got over the total tweeting population. We compare our data per line with the frequency of the services on several railway branches in order to calibrate our estimations on flows. Our findings provide information about passengers' flows through regions, running over current methodologies that generally constrained data within single countries or administrations. Therefore the potential of the methodology goes towards the interoperability of data through countries, helping planners not only in getting a new source of cross-country demand estimation, but moreover to get a new tool and set of data for the calibration and validation of transportation demand models. Close Fabio Lamanna

# Computational Social Science: Social Contagion, Collective Behaviour, and Networks  (CSS) Session 2

## Chair: Taha Yasseri

 99999 TBC [abstract] Abstract: TBC Close TBC

# Dynamics of Multilevel Complex Systems  (DMC) Session 2

## Chair: Guido Caldarelli

 18007 Color Avoiding Percolation [abstract] Abstract: When assessing the security or robustness of a complex system, including the fact that many nodes may fail together is essential. Though complex network studies typically assume that nodes are identical with respect to their vulnerability to failure or attack, this is often inaccurate.Surprisingly, this heterogeneity can be utilized to improve the system's functionality using a new color-avoiding percolation'' theory.We illustrate this with a new topological approach to cybersecurity.If there are many eavesdroppers, each tapping many nodes, we propose to split the message, and transmit each piece on a path that avoids all the nodes which are vulnerable to one of the eavesdroppers.Our theory determines which nodes can securely communicate and is applicable to a wide range of systems, from economic networks to epidemics Close Vinko Zlatic 18008 On topological characterization of behavioural properties: the TOPDRIM approach to the dynamics of complex systems [abstract] Abstract: The project TOPDRIM envisioned a new mathematical and computational framework based on topological data analysis for probing data spaces and extracting manifold hidden relations (patterns) that exist among data. While pursuing this objective, a general program aiming to construct an innovative methodology to perform data analytics has been devised. This program proposes the realization of a Field Theory of Data starting from topological data analysis, passing through field theory and returning an automaton as a recognizer of the data language. TOPDRIM contributed mainly to the first stage of the program by giving evidence that topological data analysis is a viable tool to tame the wild collection of data and to detect changes in complex networks. However, TOPDRIM already went beyond the concept of networks by considering instead simplicial complexes, which allow the study of n-dimensional objects (n>=2). An alternative approach to machine learning has been put forward, where data mining starts without receiving any initial input. Close Emanuela Merelli 18009 The effect of spatiality on multiplex networks [abstract] Abstract: Multilayer infrastructure is often interdependent, with nodes in one layer depending on nearby nodes in another layer to function. The links in each layer are often of limited length, due to the construction cost of longer links. Here, we model such systems as a multiplex network, in which each layer has links of characteristic geographic length. This is equivalent to a system of interdependent spatially embedded networks in which the connectivity links are constrained in length but varied while the length of the dependency links is always zero. We find two distinct percolation transition behaviors depending on the characteristic length of the links. When this value is longer than a certain critical value, abrupt, first-order transitions take place, while for shorter values the transition is continuous. Close MIchael Danzinger 18010 When and how multiplex really matters? [abstract] Abstract: In this talk, we will give a topological characterization of functional and behavioural features of complex systems. In particular we propose an interpretation of languages of regular expressions as the outcome of global topological features of the space intrinsically generated by the formal representation of processes constrained over the space. Our goal is a new scheme, (in the sense of Grothendieck) allowing for a new characterization of regular expressions and the study of a different axiomatic structure, analogous to Kleene algebras, but encompassing non-deterministic process interpretation. Close Vito Latora 18011 Predictive Models and Hybrid, Data Based Simulation Concepts for Smart Cities [abstract] Abstract: Developing, and managing predictive, causal models for smart cities must involve stakeholders with conflicting requirements, limited available data, limited knowledge and different ?city-subsystems? which interacts. Challenges can be summarized: (1) Present initiatives mostly focus on closed sets of topics leading to a narrow domain view. (2) Current simulations rely on small-scale, isolated models of real-world environments, where changes and migration of simulation results to real-world must be carried out manually. (3) Predictive causal models have to prove additional benefit by including smart cities ?behaviour? e.g. dynamic feedback loops of domains. Interdisciplinary, holistic approaches should integrate big static and dynamic data, the city emits from sources including IoT, documents or citizens. Data must be managed to provide the fundament for hybrid simulation models operated by multi-domain experts. This provides decision support for governance stakeholders, industry and citizens to influence the city. Close NIcholas Popper 18012 Can Twitter sentiment predict Earning Announcements returns? [abstract] Abstract: Social media are increasingly reflecting and influencing behavior of other complex systems. We investigate the relations between Twitter and stock market, in particular the Dow Jones Industrial Average contituents. In our previous work we adapted the well-known \event study" from economics to the analysis of Twitter data. We defined \events" as peaks of Twitter activity, and automatically classified sentiment in Twitter posts. During the Twitter peaks, we found significant dependence between the Twitter sentiment and stock returns: the sentiment polarity implies the direction of Cumulative Abnormal Returns Close Igor Mozetic 18013 The temporal dimension of multiplex networks [abstract] Abstract: Social interactions are composite, involve different communication layers and evolve in time. However, a rigorous analysis of the whole complexity of social networks has been hindered so far by lack of suitable data. Here we consider both the multi-layer and dynamic nature of social relations by analysing a diverse set of empirical temporal multiplex networks. We focus on the measurement and characterization of inter-layer correlations to investigate how activity in one layer affects social acts in another layer. We define observables able to detect when genuine correlations are present in empirical data, and single out spurious correlation induced by the bursty nature of human dynamics. We show that such temporal correlations do exist in social interactions where they act to depress the tendency to concentrate long stretches of activity on the same layer and imply some amount of potential predictability in the connection patterns between layers. Our work sets up a general framework to measure temporal correlations in multiplex networks, and we anticipate that it will be of interest to researchers in a broad array of fields. Close Romualdo Pastor-Satorras 18014 Topological and functional b-cells networks: a biological paradigm of self-organised dynamics [abstract] Abstract: Most of complex physical systems are characterised by an emergent behaviour arising from the interaction of many particles dynamics. The resulting patterns at the macroscopic level can thus be linked to functional states of the system, which strongly depend on the topological features of the connections, on the single node intrinsic dynamics and environmental inputs. Nowadays these concepts are generalised and applied to a plethora of fields, including social dynamics, epidemics spreading, information flows and, in this particular case, also to the physiology of excitable biological media. In this perspective, we analysed emergent dynamics of the endocrine b-cells in the pancreas, as a typical example of biological electrically-coupled oscillators which release insulin in response to appropriate blood glucose levels. The primary focus was to establish a link between the underlying physical connectivity of the nodes and the functional state of the global network, modulated by specific operating conditions. A functional state was determined by looking at the robustness of the emergent electrical oscillations and the synchronisation patterns, investigated through a functional network approach. Indeed, a deep bond exists between the original physical network and the induced functional network. The possibile presence of multiplex via connections with other networks will also be discussed. Close Simonetta Filippi

# Fundamentals of Networks  (FON) Session 1

## Chair: Remco van der Hofstad

 49003 Scaling Limits for Stochastic Networks [abstract] Abstract: In this talk I will sketch a body of recent results obtained in the context of stochastic networks of dependently operating resources. These could be thought of to represent real-life networks of all sorts, such as traffic or communication networks, but I?ll point out that this setup is also highly relevant in economic and biological applications. The underlying model can be thought of as a network of interacting resources, which can be modeled in a discrete state-space context through coupled queues, and in a continuous state-space context through specific systems of stochastic differential equations; the individual resources operate dependently as they react to the same environmental process. For such large networks, one would typically like to describe their dynamic behavior, and to devise procedures that can deal with various undesired events (link failures, sudden overload, etc.). I?ll show how for systems that do not allow explicit analyses, various parameter scalings help shedding light on their behavior. More specifically, I'll discuss situations in which the time-scale corresponding to the fluctuations of the available resources differs from that of the fluctuations of the customer's demand, leading to various appealing limit results. Close Michel Mandjes 49005 Rumor spread and competition on scale-free random graphs [abstract] Abstract: Empirical findings have shown that many real-world networks share fascinating features. Indeed, many real-world networks are small worlds, in the sense that typical distances are much smaller than the size of the network. Further, many real-world networks are scale-free in the sense that there is a high variability in the number of connections of the elements of the networks, making these networks highly inhomogeneous. Such networks are typically modeled using random graphs with power-law degree sequences. In this lecture, we will investigate the behavior of competition processes on scale-free random graphs with finite-mean, but infinite-variance degrees. Take two vertices uniformly at random, or at either side of an edge chosen uniformly at random, and place an individual of two distinct types at these two vertices. Equip the edges with traversal times, which could be different for the two types. Then let each of the two types invade the graph, such that any other vertex can only be occupied by the types that gets there first. Let the speed of the types be the inverse of the expected traversal times of an edge by that types. We distinguish two cases. When the traversal times are exponential, we see that one (not necessarily the faster) types will occupy almost all vertices, while the losing types only occupied a bounded number of vertices, i.e., the winner takes it all, the loser's standing small. In particular, no asymptotic coexistence can occur. On the other hand, for deterministic traversal times, the fastest types always gets the majority of the vertices, while the other occupies a subpolynomial number. When the speeds are the same, asymptotic coexistence (in the sense that both types occupy a positive proportion of the vertices) occurs with positive probability. This lecture is based on joint work with Mia Deijfen, Julia Komjathy and Enrico Baroni, and builds on earlier work with Gerard Hooghiemstra, Shankar Bhamidi and Dmitri Znamenski. Close Remco van der Hofstad 49002 Networks with strong homogeneous clustering are geometric [abstract] Abstract: Two common features of many large real networks are that they are sparse and that they have strong clustering, i.e., large number of triangles homogeneously distributed across all nodes. In many growing networks for which historical data is available, the average degree and clustering are roughly independent of the growing network size. Recently, latent-space random graph models, also known as (soft) random geometric graphs, have been used successfully to model these features of real networks, to predict missing and future links in them, and to study their navigability, with applications ranging from designing optimal routing in the Internet, to identification of the information-transmission skeleton in the human brain. Yet it remains unclear if latent-space models are indeed adequate models of real networks, as these models may have properties that real networks do not have, or vice versa. We show that maximum-entropy random graphs in which the expected numbers of edges and triangles at every node are fixed to constants, are approximately soft random geometric graphs on the real line. The approximation is exact in the limit of standard random geometric graphs with a sharp connectivity threshold and strongest clustering. This result implies that a large number of triangles homogeneously distributed across all vertices is not only necessary but also a sufficient condition for the presence of a latent/effective metric space in large sparse networks. Strong clustering, ubiquitously observed in real networks, is thus a reflection of their latent geometry. Close Dmitri Krioukov 49000 Breaking of Ensemble Equivalence in Networks [abstract] Abstract: It is generally believed that, in the thermodynamic limit, the microcanonical description as a function of energy coincides with the canonical description as a function of temperature. However, various examples of systems for which the microcanonical and canonical ensembles are not equivalent have been identified. A complete theory of this intriguing phenomenon is still missing. Here we show that ensemble nonequivalence can manifest itself also in random graphs with topological constraints. We find that, while graphs with a given number of links are ensemble equivalent, graphs with a given degree sequence are not. This result holds irrespective of whether the energy is nonadditive (as in unipartite graphs) or additive (as in bipartite graphs). In contrast with previous expectations, our results show that (1) physically, nonequivalence can be induced by an extensive number of local constraints, and not necessarily by long-range interactions or nonadditivity, (2) mathematically, nonequivalence is determined by a different large-deviation behavior of microcanonical and canonical probabilities for a single microstate, and not necessarily for almost all microstates. The latter criterion, which is entirely local, is not restricted to networks and holds in general. (joint work with Tiziano Squartini, Joey de Mol and Frank den Hollander) Close Diego Garlaschelli 49001 Mixing times of random walks on dynamic configuration models [abstract] Abstract: The mixing time of a Markov chain is the time it needs to approach its stationary distribution. For random walks on graphs, the characterisation of the mixing time has been the subject of intensive study. One of the motivations is the fact that the mixing time gives information about the geometry of the graph. In the last few years, much attention has been devoted to the analysis of mixing times for random walks on random graphs, which poses interesting challenges. Many real-world networks are dynamic in nature. It is therefore natural to study random walks on dynamic random graphs. In this talk we investigate what happens for simple random walk on a dynamic version of the configuration model in which, at each unit of time, a fraction $\alpha_n$ of the edges is randomly relocated, where $n$ is the number of nodes. For degree distributions that converge and have a second moment that is bounded in $n$, we show that the mixing time is of order $1/\sqrt{\alpha_n}$, provided $\lim_{n\to\infty} \alpha_n(\log n)^2=\infty$. We identify the sharp asymptotics of the mixing time when we additionally require that $\lim_{n\to\infty} \alpha_n=0$, and relate the relevant proportionality constant to the average probability of escape from the root by a simple random walk on an augmented Galton-Watson tree that is obtained by taking a Galton-Watson tree whose offspring distribution is the size-biased version of the limiting degree distribution and attaching to its root another Galton-Watson tree with the same offspring distribution. Proofs are based on a randomised stopping time argument in combination with coupling estimates. [Joint work with Luca Avena (Leiden), Hakan Guldas (Leiden) and Remco van der Hofstad (Eindhoven)] Close Frank den Hollander

# Coarse-graining of Complex Systems  (CCS) Session 2

## Chair: Mauro Faccin

 20006 Coarse graining and data aggregation techniques in location-based services [abstract] Abstract: Location-based services have become a popular subject of research over the past decade thanks to their significance as a novel source of geo-referenced data that provide solutions for a variety of research problems in a host of disciplines. Despite their richness in terms of the multiple data layers that become available, these datasets are often sparse and are characterised by skewed distributions which make the application of classical statistical frameworks and machine learning algorithms in this context a challenge. In this talk, we will review a wider range of data aggregation and coarse graining techniques that enable useful characterisations of complex location-based systems and find applicability in many real world applications. Close Anastasios Noulas 20007 Modularity and the spread of perturbations in complex dynamical systems [abstract] Abstract: Many complex systems are modular, in that their components are organized in tightly-integrated subsystems that are weakly-coupled to one another. Modularity has been argued to be important for evolvability, state-space exploration, subsystem specialization, and many other important functions. The problem of decomposing a system into weakly-coupled modules, which has been studied extensively in graphs, is here considered in the domain of multivariate dynamics, a commonly-used framework for modeling complex physical, biological and social systems. We propose to decompose dynamical systems using the idea that modules constrain the spread of localized perturbations. We find partitions of system variables that maximize a novel measure called perturbation modularity', defined as the auto-covariance of a coarse-grained description of perturbed trajectories. Our approach effectively separates the fast intra-modular from the slow inter-modular dynamics of perturbation spreading (in this respect, it is a generalization of the Markov stability method of community detection). Perturbation modularity can capture variation of modular organization across different system states, time scales, and in response to different kinds of perturbations. We argue that our approach offers a principled alternative to detecting graph communities in networks of statistical dependency between system variables (e.g. relevance networks', 'functional networks', and other networks based on correlation or information-transfer measures). Using coupled logistic maps, we demonstrate that the method uncovers hierarchical modular organization encoded in a system's coupling matrix. Additionally, we use it to identify the onset of self-organized modularity in certain parameter regimes of homogeneously-coupled map lattices (originally popularized by Kaneko). Our approach offers a powerful and novel tool for exploring the modular organization of complex dynamical systems. Close Artemy Kolchinsky, Alexander Gates, Luis Rocha 20008 Automatic identification of relevant concepts in scientific publications [abstract] Abstract: In recent years, the increasing availability of publication records has attracted the attention of the scientific community. In particular, many efforts have been devoted to the study of the organization and evolution of science by exploiting the textual information extracted from the title and abstract of the articles. However, lesser attention has been devoted to the core of the article, i.e., its body. The access to the entire text, instead, paves the way to a better comprehension of the relations of similarity between articles. In the present work, concepts are extracted from the body of the scientific articles available on the ScienceWISE platform, and are used to build a network of similarity between articles. The resulting weighted network possesses a considerably high edge density, spoiling any attempt of associating communities of papers to specific topics. This happens because not all the concepts inside an article are truly informative and, even worse, they may not be useful to discriminate articles with different contents. Moreover, the presence of generic concepts'' with a loose meaning implies that a considerable amount of connections is made up by spurious similarities. To eliminate generic concepts, we introduce a method to evaluate the concepts' relevance according to an information-theoretic approach. The significance of a concept $c$ is defined in terms of the distance between its maximum entropy distribution, $S_{max}$, and the empirical one, $S_c$, calculated using the frequency of occurrence inside papers. By evaluation such distance, generic concepts are automatically identified as those with an entropy closer to the maximum. The progressive removal of generic concepts retaining only the meaningful'' ones has a twofold effect: it decreases sensibly the density of the network and reinforce meaningful relations. By applying different filtering thresholds,'' we unveil a refined topical organization of science in a coarse-grained way. Close Andrea Martini, Alessio Cardillo, Paolo De Los Rios 20009 Tensorial Stochastic Block Models for layered data [abstract] Abstract: In this talk I will discuss the problem of developing predictive ?models for layered data. Time-resolved networks are a typical example of layered data, since each time window results in a specific pattern of connections. I will present stochastic tensorial block models as a valid approach to predict missing information in network data with different layers of information. I will discuss results for two cases: a temporally resolved e-mail communication network and a drug-drug interaction network in different cell lines. Close Marta Sales-Pardo 20010 The dynamics of community sentiments on Twitter [abstract] Abstract: We study a large evolving network obtained from Twitter created by a sample of users @-mentioning each other. We find that people who have potentially the largest communication reach (according to a dynamic centrality measure) use sentiment differently than the average user: for example they use positive sentiment more often and negative sentiment less often. Furthermore, we use several algorithms for community detection based on structure of the network and users' sentiment levels to identify several communities. These communities are structurally stable over a period of months. Their sentiment levels are also stable, and sudden changes in daily community sentiment in most cases can be traced to external events affecting the community. Based on our findings, we create and calibrate a simple agent-based model that is capable of reproducing measures of emotive response comparable to those obtained from the observed data. Close Danica Vukadinovic Greetham, Nathaniel Charlton, Colin Singleton 20011 Probabilistic and flux-based analysis of metabolic graphs [abstract] Abstract: We present a framework for the construction and analysis of directed metabolic reaction graphs that can be tailored to reflect different environmental conditions. In the absence of information about the environmental context, we propose a Probabilistic Flux Reaction Graph (PRG) in which the weight of a connection between two reactions is the probability that a randomly chosen metabolite is produced by the source and consumed by the target. Using context-dependent flux distributions from Flux Balance Analysis (FBA), we produce a Flux-Balance Graph (FBG) with weighted links representing the amount of metabolite flowing from a source reaction to a target reaction per unit time. The PRG and FBG graphs are analyzed with tools from network theory to reveal salient features of metabolite flows in each biological context. We illustrate our approach with the directed network of the central carbon metabolism of Escherichia coli, and study its properties in four relevant biological scenarios. Our results show that both flow and network structure depend drastically on the environment: graphs produced from the same metabolic model in different contexts have different edges, components, and flow communities, capturing the biological re-routing of metabolic flows inside the cell. By integrating probabilistic and FBA-based analysis with tools from network science, our results provide a framework to interrogate cellular metabolism beyond standard pathway descriptions that are blind to the environmental context. Close Mariano Beguerisse Diaz, Mauricio Barahona, Gabriel Bosque Chacón, Diego Oyarzún and Jesús Picó

# Santa Fe Institute Workshop  (SFIW) Session 2

## Chair: Stefan Thurner

 46003 Predicting the evolution of technology [abstract] Abstract: Technological progress is the ultimate driver of economic growth, and forecasting technological progress is one of the pivotal issues for climate mitigation. While there is a rich anecdotal literature for technological change, there is still no overarching theory. Technology evolves under descent with variation and selection, but under very different rules than in biology. The data available to study technology are also very different: On one hand we have historical examples giving the performance of a few specific technologies over spans of centuries; on the other hand, the collection of information is much less systematic than it is for fossils. There is no good taxonomy, so in a sense the study of technological evolution is pre-Linnaean. This may be due to the complexities of horizontal information transfer, which plays an even bigger role for technology than it does for bacteria. There are nonetheless empirical laws for predicting the performance of technologies, such as Moore’s law and Wright’s law, that can be used to make quantitative distributional forecasts and address questions such as “What is the likelihood that solar energy will be cheaper than nuclear power 20 years from now?”. I will discuss the essential role of the network properties of technology, and show how 220 years of US patent data can be used as a "fossil record” to identify technological eras. Finally I will discuss new approaches for understanding technological progress that blend ideas from biology and economics. Close J Doyne Farmer 46004 Understanding of power laws in path dependent processes [abstract] Abstract: Where do power laws come from? There exist a handful of famous mechanisms that dynamically leadto scaling laws in complex dynamical systems, including preferential attachment processes and self-organized criticality. One extremely simple and transparent mechanism has so-far been overlooked. We present a mathematical theorem that states that every stochastic process that reduces its number of possible outcomes over time, leads to power laws in the frequency distributions of that so-called sample-space-reducing process (SSR). We show that targeted diffusion on networks is exactly such a SSR process, and we can thus understand the origin of power law visiting distributions that are ubiquitous in nature. We further comment on several examples where SSR processes can explain the origin of observed scaling laws including search processes, language formation and fragmentation processes. Close Stefan Thurner 46005 TBA Geoffrey West

# Complexity in personalised dynamical networks for mental health  (CPDN) Session 2

## Chair: Lourens Waldorp

 41005 Changing Dynamics, Changing Networks [abstract] Abstract: A network captures how components in a system interact. Take humans as an example. Humans are complex dynamic systems, whose emotions, cognitions, and behaviors constantly fluctuate and interact over time. Networks in this case represent, for example, the interaction or dynamics between emotions over time. However, in time the dynamics of a process are themselves prone to change. Consider, for example, external factors like stress, which can lower the self-predictability and interaction of emotions and thus change the dynamics. In this case, there should not be a single network or static figure of the emotion dynamics, but a network movie representing the evolution of the network over time. We have developed a new data-driven model that can explicitly model the change in temporal dependency within an individual without pre-existing knowledge of the nature of the change: the semi-parametric time-varying vector autoregressive method (TV-VAR). The TV-VAR proposed here is based on the easy applicable and well-studied generalized additive modeling techniques (GAM), available in the software R. Using the semi-parametric TV-VAR one can detect and model changing dynamics or network movies for a single individual or system. Close Laura F. Bringmann 41006 Mental disorders as complex systems: empirical tests [abstract] Abstract: Background: Mental disorders are influenced by such a complex interplay of factors that it is extremely difficult to develop accurate predictive models. Complex dynamical system theory may provide a new route to assessment of personalized risk for transitions in depression. In complex systems early warning signals (EWS), signaling critical slowing down of the system, are found to precede critical transitions. Experience Sampling Methodology (ESM) may help to empirically test whether principals of complex dynamical systems also apply to mental disorders. Method: ESM techniques were employed to examine whether EWS can be detected in intra-individual change patterns of affect. Previously reported EWS are rising autocorrelation, variance and strength of associations between elements in the system. Results: Empirical findings support the idea that higher levels of autocorrelation, variance and connection strength may indeed function as EWS for mood transitions. Results will be visualized in network models during the presentation. Conclusion: Empirical findings, as obtained with ESM, suggest that transitions in mental disorders may behave according to principles of complex dynamical system theory. This may change our view upon mental disorders and yield novel possibilities for personalized assessment or risk for transition. Close Marieke Wichers 41007 Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data [abstract] Abstract: Graphical models have become a popular way to abstract complex systems and gain insights into relational patterns among observed variables. For temporally evolving systems, time-varying graphical models offer additional insights as they provide information about organizational processes, information diffusion, vulnerabilities and the potential impact of interventions. In many of these situations the variables of interest do not follow the same type of distribution, for instance, one might be interested in the relations between physiological and psychological measures (continuous) and the type of drug (categorical) in a medical context. We present a novel method based on generalized covariance matrices and kernel smoothed neighborhood regression to estimate time-varying mixed graphical models in a high-dimensional setting. In addition to our theory, we present a freely available software implementation, performance benchmarks in realistic situations and an illustration of our method using a dataset from the field of psychopathology. Close Jonas Haslbeck 41008 Mean field dynamics of graphs: Evolution of probabilistic cellular automata on different types of graphs and an empirical example. [abstract] Abstract: We describe the dynamics of networks using one-dimensional discrete time dynamical systems theory obtained from a mean field approach to (elementary) probabilistic cellular automata (PCA). Often the mean field approach is used on a regular graph (a grid or torus) where each node has the same number of edges and the same probability of becoming active. We consider finite elementary PCA where each node has two states (two-letter alphabet): ?active? or ?inactive? (0/1). We then use the mean field approach to describe the dynamics of the PCA on a random, and a small world graph. We verified the accuracy of the mean field by means of a simulation study. Results showed that the mean field accurately estimates the percentage of active nodes (density) across various simulation conditions, and thus performs well when non-regular network structures are under consideration. The application we have in mind is that of psychopathology. The mean field approach then allows possible explanations of ?jumping? behaviour in depression, for instance. We show with an extensive time-series dataset how the mean field is applied and how the risk for phase transitions can be assessed. Close Jolanda Kossakowski 41009 Cinematic theory of cognition and cognitive phase transitions modeled by random graphs and networks [abstract] Abstract: The Cinematic Theory of Cognition (CTC) postulates that cognition is a dynamical process manifested in the sequence of complex metastable patterns. Each consecutive pattern can be viewed as a frame in a movie, while the transition from one frame to the other acts as the movie shutter. Experimental evidence indicates that each pattern is maintained for about 100-200 ms (theta rate), while the transition from one pattern to the other is rapid (10-20 ms). This talk will address the following issues: 1. Experimental evidence of the CTC. Experiments involve intracranial ECoG of animals and human patients in preparation to epilepsy surgery, as well as noninvasive scalp EEG of human volunteers. 2. Dynamical systems theory of cognition and neurodynamics. Accordingly, the brain is viewed as a complex system with a trajectory exploring a high-dimensional attractor landscape. Experimentally observed, metastable patterns represent brain states corresponding to the meaning of the environmental inputs, while the transitions signify the ?aha? moment of deep insights and decision. 3. Modeling of the sequence of metastable patterns as phase transitions in the brain networks and a large-scale graph. Synchronization-desynchronization transitions with singular dynamics are described in the brain graph as a dissipative system. The hypothesis is made that the observed long-range correlations are neural correlates of cognition (NCC). Close Robert Kozma