## 10:00 - 12:30 on 21st Sep 2016

### Chair: Taha Yasseri

 5000 Social influence and opinion polarization on news websites. A field experiment. [abstract]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. Michael Mäs, Bary Pradelski, and Bernhard Clemm von Hohenberg 5001 Complex Contagion of Campaign Donations [abstract]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.? Vincent Antonio Traag 5002 Does social physics exist? [abstract]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? Frederike Kaltheuner 5003 Coupled dynamics of node and link states: A model for language competition [abstract]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.? Adrián Carro, Raúl Toral and Maxi San Miguel 5004 Quantifying crowd size with mobile phone and Twitter data [abstract]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. Federico Botta, Helen Susannah Moat and Tobias Preis 5005 The Bass diffusion model on correlated scale-free networks [abstract]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.? Maria Letizia Bertotti, Johann Brunner & Giovanni Modanese 5006 Quantifying the Impact of Scenic Environments on Wellbeing [abstract]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. Chanuki Seresinhe, Tobias Preis and Suzy Moat 5007 Skill games versus gambling: from Poker to financial markets. An old debate faced by Statistical Physics. [abstract]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.? Marco Alberto Javarone 5008 Disentangling interactions in online social systems using multiplex networks [abstract]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. Manlio de Domenico 5009 Effects of Network Structure, Competition and Memory Time on Social Spreading Phenomena [abstract]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. James Gleeson, Kevin O'Sullivan, Raquel Álvarez and Yamir Moreno 5010 Nestedness in Communication Networks: From Information Exchange to Topology [abstract]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. Alexander Grimm and Claudio Tessone 5011 Economic and Financial Networks [abstract]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. Guido Caldarelli 5012 Communications Patterns of Individuals with Different Chronotypes [abstract]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. Talayeh Aledavood, Ilkka Kivimäki, Sune Lehmann Lehmann and Jari Saramäki 5013 Tracking Protests Using Geotagged Flickr Photographs [abstract]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. Merve Alanyali, Tobias Preis and Suzy Moat

### Chair: Sumit Sourabh

 4014 Introduction- "Challenges in Computational Finance" Dr. Drona Kandhai, ING Bank and University of Amsterdam 4015 Systemic risk and centralized clearing of OTC derivatives: a network approach Dr. Svetlana Barakova, Vrije University, Amsterdam 4016 Criticality and early warning signals in swap markets Dr. Rick Quax, University of Amsterdam 4017 Early warning signals of topological collapse in interbank networks Dr. Diego Garlaschelli, Leiden University 4018 Liquidity Risk in Credit Derivatives Dr. Sumit Sourabh, University of Amsterdam

### Chair: Cyrille Bertelle

 39000 Toward an Integrative Logistics in the CS-DC TIMES flagship [abstract]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. Paul Bourgine (Ecole polytechnique and CS-DC UNRSCO UniTwin, Paris, France) 39001 Coupling effects and bifurcations in network of bistable dynamical systems [abstract]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) Valentina Lanza (LMAH, Normandie Univ., UNIHAVRE, France) 39002 Revisiting urban economics [abstract]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. Marc Barthelemy (Institut de Physique Theorique, CEA-IPhT, CNRS URA 2306, Gif-sur-Yvette, France) 39003 Mode coupling in a nonlinear network [abstract]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. Imene Khames (LMI, Normandie Univ, INSAROUEN, France) 39004 Cluster Synchronization of Complex Networks [abstract]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.? M.A. Aziz-Alaoui, C. Bertelle & J. Zhao (LMAH, LITIS, Normandie Univ, UNIHAVRE, France & Hunan University of Commerce, China )

### Chair: Ana Isabel Barros

 19000 Workshop "Onto the Central Stage: Model-Based Exploration of Refugee Scenarios and Policies under Deep Uncertainty" [abstract]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. Erik Pruyt (Delft University of Technology) 19001 Setting the scene of the complex security environment [abstract]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. Lt Col van Daalen (Dutch Land Warfare Center), Peter Van Scheepstal (TNO) 19002 Complexity, Uncertainty and Planning for the military [abstract]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. Simon Collander-Brown (Dstl) 19003 A Framework for Analysis of Attacker-Defender Interaction in Cyber Systems [abstract]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. Alexander V. Outkin, Brandon K. Eames, Stephen T. Jones, Eric D. Vugrin, , Cynthia A. Phillips, Sarah Walsh, Jacob A. Hobbs, Stephen J. Verzi (Sandia National Laboratories) 19004 Discussion A.I. Barros (TNO)

### Chair: Jonathan F. Donges

 37000 Nonlinear time-series analysis and complex network approach for identifying and characterizing regime transitions [abstract]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) Cristina Masoller (invited talk) 37001 Sustainable use of renewable resources in a stylized social-ecological network model [abstract]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. Wolfram Barfuss, Jonathan Donges, Wolfgang Lucht 37002 Cascading effects of critical transitions in social-ecological systems [abstract]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. Juan Rocha 37003 Modelling social-ecological transformations: an adaptive network proposal [abstract]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. Steven Lade (invited talk) 37004 Transition techniques to look at network phenomena [abstract]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. Michael Tamm (invited talk) 37005 The synchronization between power-grid nodes for the stable electric power system. [abstract]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. Kim Heetae, Sang Hoon Lee, Petter Holme 37006 Understanding the XY model collective behaviours through graph signal analysis [abstract]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. Sarah de Nigris, Paul Expert, Taro Takaguchi and Renaud Lambiotte 37007 Color avoiding percolation as a tool for cyber security [abstract]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. Vinko Zlatic, Michael Danziger, Sebastian Krause 37008 The use of complex network techniques to determine (future) climate transitions [abstract]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. Henk Dijkstra (invited talk), Qingyi Feng

### Chair: Stefan Thurner

 46000 Most Sociable and Most Polite: the Collective Mathematics of Creativity [abstract]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. Simon DeDeo 46001 TBA [abstract]Abstract: TBA Stuart A Kauffman 46002 Adaptive self-organization of? Bali?s ancient rice terraces [abstract]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. Steve Lansing

### Chair: Lourens Waldorp

 41000 Network approaches to psychopathology [abstract]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. Denny Borsboom 41001 Discovering Psychological Dynamics in Longitudinal Data [abstract]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. Sacha Epskamp 41002 Latent variables, there and back again [abstract]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. Maarten Marsman 41003 Dynamic Structural Equation Modeling in Mplus [abstract]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. Ellen Hamaker 41004 Bayesian VAR-modelling: Unraveling emotion dynamics in multivariate, multisubject time series [abstract]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. Casper Albers

### Chair: Timoteo Carletti

 25000 Self Organized Bistability [abstract]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. R. Burioni 25001 Pattern formation and collective dynamics in reaction-diffusion systems on networks [abstract]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. H. Nakao 25002 Recent developments of Turing pattern formation in complex networks [abstract]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. M. Asllani 25007 Stationary patterns on bistable networks: theory and experiments [abstract]Abstract: TBA N. E. Kouvaris