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.