Abstract: Inspired by challenges of environmental change and resource limitations experienced by modern society, recent decades have seen an increased interest in understanding and modelling the long-term development of past societies. A particular focus in this analysis has been on mechanisms that can cause collapse or enable the long term sustainability of a society. A widespread framework to model societal evolution has been dynamical systems theory, which can naturally capture the interaction of multiple sub-systems. Modelling efforts so far have focussed on single, isolated societies, while interactions, or networks of multiple coupled socio-environmental systems have not been considered. In this talk we propose a model of societal evolution that describes the dynamics of a population that harvests renewable resources and manufactures products that can be accumulated. Applying the model to Easter Island gives a good fit to the archaeological record. Collapse is driven by a bifurcation that occurs when the rate of extraction of natural resources is increased beyond a critical point. We present numerical and analytical analysis of the bifurcation diagram. In the second part of the talk we analyse the diffusion and targeted movement of wealth, people, and resources between two societies. Specifically, we investigate how a diffusive coupling and a wealth-driven coupling change the population levels and their distribution across the two societies compared to uncoupled, independent societies. We find that the region of parameter space in which societies can stably survive in the long term is significantly enlarged when coupling occurs in both social and environmental variables. The objective of the talk is to show how the phenomenon of societal collapse can be quantified and how the interaction of multiple coupled socio-environmental systems can be modelled. Attendees will gain insights into novel applications of dynamical system theory and network science.

Abstract: Social media are transforming global communication and coordination and provide unprecedented opportunities for studying socio-technical domains. Here we study global dynamical patterns of communication on Twitter across multiple scales. In particular, we study collective activities across geographical scales, from areas smaller than one square kilometer up to the global scale. Underlying the observed patterns is both the diurnal rotation of the earth, day and night, and the synchrony required for contingency of actions between individuals. We find that urban areas show a cyclic contraction and expansion that resembles heartbeats linked to social rather than natural cycles, mainly determined by daily routines of work, rest and recreation. Different urban areas have characteristic signatures of daily collective activities, varying the shape and location of peaks and valleys of activity. We show that the differences detected are consistent with a new emergent global synchrony that couples behavior in distant regions across the world, in part due to the communication power provided by social media. Although local synchrony is the major force that shapes the collective behavior in cities, a larger-scale synchronization is beginning to occur. Alfredo J. Morales, Vaibhav Vavilala, Rosa M. Benito and Yaneer Bar-Yam Global Patterns of Human Synchronization, arXiv:1602.06219, 2016

Abstract: Rabies is a fatal zoonosis caused by the RABV virus and characterized by a complex epidemiological situation that remains a serious public health problem in developing countries. Rabies incidence is largely attributed to the growth of domestic dog population considered as the most important vector for human exposure. Phylogenetic and virological analysis of isolates collected in Bangui, the capital city of the Central African Republic, indicate the presence of sequentially circulating subtypes and a reproductive number close to one. None of these subtypes seem to persist in the sole population of Bangui and mechanisms underlying virus persistence remain unknown. Two main factors may be at play: the spatial fragmentation of the host population, favoring the importation of isolates from outside the city, and the heterogeneous incubation period. To study the role and interplay of these different layers of complexity on the RABV epidemiology, we built a spatially explicit stochastic metapopulation epidemic model, inferring dog settlements from human demographic data and considering realistic long-tail distributions for incubation and infectious periods. By exploring different epidemic scenarios, we found that the virus can persist even for very low transmissibilities maintaining a stable dog population, and producing invasion cycles in agreement with empirical observations. Interestingly, no persistence would be observed for the same parameters once an exponentially distributed incubation periods is considered. Our findings illustrate how multiple layers of complexity (both ecological and epidemiological) are needed to sustain virus persistence and identify the factors to target for epidemic control.

Abstract: The coexistence of species in large ecosystems has been a longstanding problem in ecology. Since the seminal work by May, we have tools and methods to study local asymptotic stability in terms of random matrices. This results can be generalized to include the effect of empirical network structures. Stability is not the whole part of the issue. It is related to perturbation of population abundances, while feasibility (or structural stability) is related to perturbations of growth rates and to the volume of the domain of growth rates leading to positive population. Feasibility and stability, are different but not independent properties. In this talk, I will show how feasibility can be connected to different properties of interactions networks. [S. Allesina, J. Grilli, G. Barab\'as, S. Tang, J. Aljadeff and A. Maritan. Predicting the stability of large structured food webs. Nature Communications. 6:7842. 2015] [J. Grilli, M. Adorisio, S. Suweis, G. Barab\'as, J.R. Banavar, S. Allesina and A. Maritan. The geometry of coexistence in large ecosystems. arxiv 1507.05337]

Abstract: We present an analysis of the economic dynamics in a floodplain fishery system, focused on the connection between fishery governance and economic inequality. The use of one individual fishing technique – fishing canals – has been growing exponentially in our study area. We observe that this expansion is associated with increasing inequality in fishing incomes, which is consistent with a lognormal distribution. Studying the current revenues of canals of different age, we show that the onset of this dynamics (intensified canal use, increasing inequality) occurred during a transition period in the 1970s. In the region of analysis, this decade was characterized by a combination of ecological stress (Sahelian drought and increasing population) and liberalization of the fishery through the breakdown of traditional oligarchic rule. We argue that, while this change in governance brought fairer opportunities to floodplain fishermen, its combination with ecological stress has led to an increase in economic inequality.

Abstract: Social-ecological systems are prototypical complex adaptive systems. Ecosystem services such as water purification, atmospheric regulation, and food production emerge from the interactions of ecological components occurring at smaller scales. At any focal scale, the diversity of ecosystem services available for production is constrained by the number of unique combinations of component aggregation patterns. For example, a patch can be managed to support high rates of denitrification under wetland cover or high outputs of food production as intensive corn cover, but not both simultaneously in time. At a larger time scale, or in an ecosystem with multiple patches, both types of ecosystem services can be accommodated. At increasingly smaller scales, however, the opposite is true. Social processes, such as management interventions intended to optimize certain ecosystem service process rates, interact with ecological components by changing patterns of aggregation and potentially leading to the development of cross-scale feedbacks. These cross-scale feedbacks can eventually contribute to the loss of relationships among ecological components, including those that support the desired ecosystem service. Our complex systems approach to analyzing ecosystem services provides a way to examine ecosystem service tradeoffs at multiple focal scales and potential cross scale interactions that could result in unexpected, non-linear system behavior. Specifically, we show how a surprising, sudden loss of ecosystem services can emerge from the interactions between management decisions and ecological components, and provide a framework for avoiding these losses.

Abstract: In the study of complex systems, one of the main assets of statistical physics consists in the postulation of simple models capable to reproduce one given relevant property of the system under consideration. This approach allows to simplify the study, by focusing on the property under scrutiny, independently of other complicating factors. In the case of static complex networks, the configuration model fulfills this role. In the field of temporal networks, the non-Poissonian activity driven (NoPAD) model fills this niche, providing a simple model characterized by an arbitrary inter-event time distribution, which assumes any form, in particular that dictated by empirical evidence. In this paper, we present a detailed mathematical study of the properties of the time-integrated networks emerging from the dynamics of the NoPAD model. We focus in two main issues: The topological properties of the integrated networks, and their percolation behavior, as determined by the time Tp at which a giant connected component, spanning a finite fraction of the total number of nodes, first emerges. These two properties are determined as functions of the model’s parameters, namely the exponent of the distribution of the waiting time between two consecutive activations of an agent, and the exponent of the agents’ heterogeneity distribution. The topological properties and the percolation dynamics also depend on the time window of the integration process [ta , ta + t], and are determined by applying a mapping of the network’s construction algorithm to the hidden variables class of models. The NoPAD model represents a minimal model of temporal networks with long tailed inter-event time distribution. As such, it has a wide potential to serve as a synthetic controlled environment to check both numerically and analytically several properties of these networks, and in particular their effect on dynamical processes.

Abstract: Here, we analyze the adaptive climate policies that comply with the planetary boundary (Rockström et al., 2009) on climate change in 2100 - recovering a CO2 concentration of 350ppm until 2100 – and the policy implications in terms of CO2 emission reductions and in terms of implementation of geoengineering technologies (negative emissions) under budget constraint. For this purpose, we couple the viability theory and the DICE model for assessing the set of these adaptive climate policies and we analyze the trade-off between increasing CO2 emission reduction and implementing new geoengineering technologies yielding negative emissions. Results show that the objective of 350ppm in 2100 is reached only with carbon neutrality and the effective implementation of innovative geoengineering technologies (10% of negative emissions) before 2060, under the assumption of getting out of the baseline scenario without delay. Then, this trade-off is analyzed according to costs involved in terms of abatement costs and investment in new technologies. The talk will present the main processes of the DICE model as well as viability theory before discussing the main results in terms of adaptive climate policies associated to abatement and investment costs. Reference Rockström J, Steffen W, Noone K, Persson A, Chapin FSIII, Lambin E, Lenton TM, Scheffer M, Folke C, Schellnhuber HJ, Nykvist B, De Wit CA, Hughes T, Van der Leeuw S, Rodhe H, Sörlin S, Snyder PK, Costanza R, Svedin U, Falkenmark M, Karlberg L, Corell RW, Fabry VJ, Hansen J, Walker B, Liverman D, Richardson K, Crutzen P, Foley J (2009) Planetary boundaries: Exploring the safe operating space for humanity. Ecology and Society 14(2):32.

Abstract: Diffusion processes model propagation phenomena on complex networks, such as epidemics, information diffusion, and viral marketing. In many situations, it is critical to suppress an undesired diffusion process by means of dynamic resource allocation, where one needs to decide targeted actions by taking into account the evolving infection state of the network. In the context of continuous-time SIS, and with provided full information regarding the nodes’ state, we consider the scenario where a budget of treatment resources of limited efficiency is available at each time for distribution to infected nodes. Recent results on this particular problem include the Priority Planning approach which computes a linear ordering of the nodes with minimal maxcut, and the optimal greedy approach called Largest Reduction of Infectious Edges (LRIE). The latter is a simple, yet efficient, strategy that computes an intuitive priority score for the infected nodes which combines the notion of node virality (possibility to infect other nodes) and vulnerability (possibility to get reinfected after recovery). In this work we show that the principle of the LRIE score holds for a wide range of SIS-like modeling scenarios. More specifically, we propose the Generalized LRIE (gLRIE) strategy and study the dynamic diffusion control by introducing a two-fold extension to SIS which can model important aspects of social diffusion (e.g. behaviors or habits). The first considers nonlinear functions of infection rates with saturation. On the top that, our second extension considers competition in the sense that the two node states, the infected and the healthy, are both diffusive, though each node can only be in only one of them at a time. In this case, our gLIE control strategy has to align with the healthy diffusion to help it win the competition. Finally, simulations on large-scale real and synthetic networks show the efficiency of gLIE.

Abstract: MERS coronavirus emerged in Arabian Peninsula in 2012 raising great concern for its severity, its international spread, and the many uncertainties characterizing its transmission and ecology. During the first three years after the emergence, the epidemic in the source area showed strong spatiotemporal heterogeneities emerging by the interplay between the zoonotic and the human-to-human transmission routes. Episodes of virus importation in foreign countries had highly variable outcomes, where little if no transmission followed importation except for a large outbreak in South Korea that raised worldwide alert. We aimed at understanding the mechanisms underlying this complex dynamics. We studied MERS spread in Middle East by means of an integrative approach combining dynamical modeling at different spatial scales – regional and international. The resulting multi-scale framework allowed extracting maximal information from the sparse and diverse epidemiological records, thus increasing inference power. It provided estimates of epidemiological parameters and their spatiotemporal variation, showing that human-to-human transmission is more important than expected for the generation of cases, while the observed geographical structure is induced by variations in the zoonotic source. To understand the drivers of global dissemination we modeled imported cases and onward transmission using detailed information on air-travel, along with digital proxies for collective and public health awareness (e.g. Google Trends records). We showed that structure and dynamics of air-transportation network shapes the spatiotemporal pattern of MERS propagation, and we quantified the effect of collective attention on the epidemic response observing that high collective attention is associated to more rapid isolation of imported cases. The study demonstrates the power of dynamical models in interpreting limited epidemiological records in light of the extensive socio-demographic and behavioral information available. Models are thus able to address fundamental questions regarding emerging diseases’ spread, the underling biological mechanisms and the role of human response.

Abstract: In the last years, scientists coming from different communities investigated several socio-economic and biological phenomena under the lens of Evolutionary Game Theory (EGT). In general, studying the evolution of a population and identifying strategies that trigger cooperative behaviors constitute some of the major aims in this field. In particular, the emergence of cooperation becomes really interesting when agent interactions are based on games having a Nash equilibrium of defection, as in the Public Goods Game (PGG). The latter is analyzed by adding a viral spreading process based on the Susceptible-Infected-Susceptible (SIS) model. Notably, we consider a virus, with a spreading rate $\lambda$, whose effect is turning cooperators to defectors. In doing so, we can merge the two dynamics, i.e. the PGG and the epidemic spreading, in order to study the equilibria reached by the population. In particular, we analyze the relation between the spreading rate $\lambda$ (epidemic process) and the synergy factor (PGG). The proposed model aims to represent complex competitive processes, as the emergence of tumors. Notably, the latter, on a quality level, can be interpreted as the emergence of defection among the cells of an organism. Since some forms of tumors seem to be triggered by viruses, as the Papilloma Virus (PV), we deem that our investigations might shed some light on these complex phenomena, even if studied by a theoretical approach. Results of investigations can be of interest for those researchers interested in interdisciplinary applications of mathematical and physical models in biology, and for those interested in theoretical biology. To conclude, beyond to show results of our work we want to highlight the link between EGT and Biology.

Abstract: Human decisions affect and are affected by ecological systems in multiple ways. Natural resource modeling has commonly focused on decisions of resource users or strategic planners in one way. We argue that the dynamics of social-ecological systems (SES), however, emerge from multiple social-ecological interactions that are the result of decisions from different actors. We exemplify this with the case of lake restoration, i.e. the ecological regime shift from a turbid to a clear water state which is influenced by decisions from lake managers (governance) and individual households (beneficiaries + polluters). House owners can affect the nutrient inflow, the main driver for the lakes state, through their choices of sewage treatment. The management challenge, in this case, stems from the temporal and spatial decoupling between lake use activities by beneficiaries and the activities from distant actors eventually polluting the lake. Beneficiaries are those that enjoy ecosystem services such as drinking water, fish and recreation provided by the lake. We developed a coupled agent-based and system dynamics model to explore different pathways of managing the activities affecting the lake state back towards the clear state. Hereby, we discriminate between the timing of regulation measures (institutional level), pathways of rule enforcement (individual-institutional link), and the households initial attitude (individual level) in their effects on lake restoration time lags. By our incremental approach, we build a faceted understanding of how sensitive lake restoration is on the macro level to individual actor traits in the human-decision model on the micro level. Concluding, we reflect on the importance of the empirical as well as theoretical basis for human-decision modeling to increase its relevance for model-based learning.

Abstract: Recognizing the Earth System as a coupled complex social-ecological co-evolutionary system is important to enrich the discourse on global sustainability. However, it is an open question how to meaningfully formalize social dynamics in the context of mathematical social-ecological systems modeling. Existing models of social-ecological interactions often use a system dynamics approach of aggregated quantities, thereby not being able to account for complex social network effects, social stratification and inequalities - all presumably central issues for global sustainability; other types of models incorporate these ideas, but put their focus rather on regional, case specific social-ecological systems and tend not to use a "first-principle" approach. In this work we combine the concept of reinforcement learning (RL) with a co-evolutionary social-ecological systems perspective by providing the social agents with RL decision methods, making them capable of dealing with complex environments. Analytical calculations and computer simulations were performed to explore this scheme. It offers a promising view on a "first principle" method on agent behavior capable of dealing with unknown, possibly nonlinear environments to reveal potential counter intuitive traps and boundaries hindering social and ecological sustainability.

Abstract: Networks within cities have become a feature in environmental governance, in particular in relation to dealing with climate change. Previous research has shown that the initiatives, such as C40, have created learning opportunities globally. Quantitative analyses have shown that connections have been formed and cities are learning from each other. However, less is known about what kinds of information is being shared through these networks. The concepts of adaptation and mitigation fundamentally advocate change. How they are conceptualised affects the way the climate change is addressed in practice. Previous research has shown that adaptation can be conceptualised as adjusting to the changing climate conditions (adjustment-based adaptation), as transforming the structures of society causing vulnerability (transformational adaptation), or as a combination of the two (reformist adaptation), and a similar classification of degree of change can also be found for mitigation. In this paper, our aim is to find out the degree of change as stated in the adaptation and mitigation strategies the C40 network and its members advocate. We approach the governance of urban adaptation as a complex system and ask how these concepts are defined in the documents produced by the C40 network and in the strategies of its member cities. We conduct an analysis of documents produced by C40 network and its member cities’ climate strategies with a computer assisted method to get the general overview of how far the documents support change. The result is controlled and deepened by close-reading of a representative sample of documents. Our findings reveal the concepts behind the climate strategies of C40 and its member cities that search to be the world leaders in addressing climate change. This gives context to the best practices promoted by the C40 network and its member cities and makes it possible to analyse them more profoundly.

Abstract: On many economic, political, social, and religious agendas, disagreement among individuals is pervasive. For example, the following are or have been highly debated: whether abortion, gay marriage, or death penalty should be legalized or not; the scientic standing of evolution; whether taxes/social subsidies/unemployment benefits/(lower bounds on) wages should be increased or decreased; the effectiveness of alternative (or `standard') medicine such as homeopathy. In the field of so-called "opinion dynamics", long-run disagreement among individuals is sometimes considered a challenge, since a large class of models such as the famous model of DeGroot learning (DeGroot, 1974; Golub and Jackson, 2010) predict long-run opinion consensus as long as individuals form a connected group. However, there have recently been several models suggested which include a mode of `anti-conformity' or `opposition' that predict disagreement even among connected interacting agents. Here, we present another such model of negative relationships among interacting agents, which extends the classical model of DeGroot for opinion dynamics. Our contributions are that we provide precise game-theoretic motivations of individuals' behavior as well as mathematically rigorous results on long-run disagreement in connected societies. Our game-theoretic motivation is that agents wish to coordinate with their friends (their 'in-group') and anti-coordinate with their enemies (their 'out-group'). Such behavior is well-documented in social psychology, both within the laboratory (see, e.g., Taifel 1978; Fehrler and Kosfeld, 2013) and outside. Our mathematical results include very general conditions for persistent disagreement among connected agents as well as an exhaustive graph-theoretical classification of long-run opinions in certain special cases. We find that persistent disagreement 'easily' obtains under the presence of negative relationships. As a consequence, crowd wisdom, the condition when all individuals learn the true state of nature or come close to it, is likely to fail.

Abstract: How does people behave when dealing with situations pervaded by thresholds? Imagine you’re a fisherman whose livelihoods depend on a resource on the brink to collapse, what would you do? and what do you think others will do? Here we report results form a field experiment with fishermen from four coastal communities in the Colombian Caribbean. A dynamic game with 256 fishermen helped us investigate behavioural responses to the existence of thresholds (probability =1 ), risk (threshold with a climate event with known probability of 0.5) and uncertainty (threshold with an unknown probability climate event). Communication was allowed during the game and the social dilemma was confronted in groups of 4 fishermen. We found that fishermen facing thresholds presented a more conservative behaviour on the exploration of the parameter space of resource exploitation. Some groups that crossed the threshold managed to recover to a regime of high fish reproduction rate. However, complementary survey data reveals that groups that collapsed the resource in the game come often from communities with high livelihood diversification, lower resource dependence and strongly exposed to infrastructure development. We speculate that the later translates on higher noise levels on resource dynamics which decouples or mask the relationship between fishing efforts and stock size encouraging a more explorative behaviour of fishing effort in real life. This context is brought to our artificial game and leaves statistical signatures on resource exploitation patterns. In general, people adopt a precautionary behaviour when dealing with common pool resource dilemmas with thresholds. However, stochasticity can trigger the opposite behaviour.

Abstract: Sustainable farming based on the self-organization of ecosystem is an important alternative for smallholders. In contrast to conventional monoculture system based on a strong control and load to environment, complex systems perspective can provide biodiversity-based polyculture, in which various emergent ecosystem functions serve as a principal source of productivity and resilience. This article investigates the statistics of vegetation cover in ecological optimum with a simple model of niche differentiation, and discuss the origin of power law observed in field data. We also focus on farming application and analyze the yield in response to crop diversity. Power-law distribution of crops diverges the mean yield of single crop into large fluctuation, therefore it requires alternative conception of yield at plant community level. With the use of information entropy as management cost and basic conception of measure integration of niche distribution, basic strategy of adaptive diversification of vegetation portfolio is proposed, in order to assure minimum harvest for food security in changing environment. Statistics of natural vegetation and mixed polyculture of crops are demonstrated for the proof of concept.

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

Abstract: 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.