Abstract: In this paper we address the dynamics of strategic actions in a pollution game model where the payoff matrix of the game depends endogenously on the accumulation or depletion of an environmental stock variable described by a non-linear discrete time dynamical system. The model has the structure of a stock-dependent dynamic game, that is a game where the payoffs to the players changes with the evolution of an external state variable, which in turn depends on the action of the players. More specifically, we analyze a two-player stock-dependent dynamic game in which the stage game is a normal form game with myopic players who use Markov stationary strategy, that is they play a stage game Nash equilibrium in each period. A dynamic consequence of stock-dependent payoffs on the strategies played is that the qualitative structure of the solution of the stock-dependent stage game might change as the stock variable evolves. The method is applied to the discrete time dynamic ecological-economic interest conflicts in the lake pollution problem in which by varying the economic parameters we study the pattern of possible changes in the Nash equilibria structure of the stage game played by the farmers at each time period as a result of the change in the payoffs ranking, classify the corresponding economic outcomes and environmental impacts. The qualitative changes in the game dynamic is represented geometrically in the payoffs space utilizing the notion of the bifurcation diagram. The diagram is partitioned into twelve different regions corresponding to different types of the two-by-two games. We also vary the stiffness or responsiveness of the lake by varying and analyze its consequences on the dynamic of the game.

Abstract: We present our work on a multiplex block-structured social network among a statistically sampled population, which we use to induce a time-varying network of physical contacts. We generate up to 8 Mio nodes with age, sex and place of residence etc. and allocate them into blocks on different layers/regimes like households and workplaces based on statistical and demographic data. From survey data we sample social relations among the nodes which result in physical contacts with a predefined periodicity. Sampling of social relations happens either on a per-block and -layer basis or based on additional assortative (wealth and geographic position) and clustering principles. It is clear that sampling of such "high-dimensional" structures involves a great number of heuristics and assumptions (e.g. commuting area for workplaces, ...). On the one hand, we try to assess the influence of some assumptions and identify steps and concepts in the sampling approach which are irrelevant or negligible to the resulting social network structure. Secondly we try to validate the resulting structure with other contact models by investigating classical measures as well as simple epidemic spread simulations on the secondary quasi-dynamic contact network (which also is a main application scenario). Furthermore we try to find network generation algorithms and parameters that deliver the same key characteristics as the data-driven network generation approach.

Abstract: In the standard view of the industrialization process of countries, these have to face a barrier to escape from the poverty trap, which is a monetary threshold defined in terms of average wage (as measured, for example, by GDP per capita, GDPpc in the following) or physical capital. When such a threshold is reached, a self-feeding process quickly brings the country from one state of equilibrium (the poverty trap) to another, catching up with the fully developed countries. During this transition, the growth is mainly due to inputs, that is, to capital investments and increase in the labor force. This view, if correct, should lead to a positive relation between the growth due to inputs and GDPpc for low values of GDPpc, and a negative relation for high values of GDPpc, i.e. to an upside down parabola. Only the first relation is supported by the empirical evidence. In this work we use a non-monetary measure of the economic complexity of a country, called Fitness, and we see that complex economies start to industrialize with a lower threshold. On the contrary, if the Fitness is low, a sustainable growth can be reached only if a higher standard, monetary threshold is reached. As a consequence, we can introduce the concept of a two-dimensional poverty trap: a country will start the industrialization process if it is not complex but rich (following the neo-classical economic theories), or if it is poor but very complex (exploiting this new dimension and laterally escaping from the poverty trap), or a linear combination of the two. This naturally leads to our proposal of a Development Index that, when studied as a function of the growth due to input, shows the same shape of an upside down parabola that one was expecting from the standard economic theories. Reference: arXiv:1511.08622.

Abstract: Overuse and corruption are major obstacles to managing common-pool resources. Although corruption has traditionally been studied in economics, evolutionary game theory offers certain advantages when analyzing corruption in the context of managing common-pool resources. By incorporating the selection process, evolutionary game theory explicitly accounts for the temporal dynamics of the investigated system. This inclusion of the selection process permits the coupling of evolutionary and resource dynamics to create a more general and powerful analytical framework for the management of common-pool resources. Within such a framework, it is possible to study (i) the influence of ecological parameters (e.g. the resource growth rate) on the prospects that a resource will be overused and (ii) the efficiency of decision making in situations when the resource is still in a transient state due to the slow convergence of the resource dynamics. The traits of our analytical framework are: (i) an arbitrary number of harvesters share the responsibility to sustainably exploit a specific part of an ecosystem, (ii) harvesters face three strategic choices for exploiting the resource, (iii) a delegated enforcement system is available if called upon, (iv) enforcers are either honest or corrupt, and (v) the resource abundance reflects the choice of harvesting strategies. The resulting dynamical system is bistable; depending on the initial conditions, it evolves either to cooperative or defecting equilibria. Using the domain of attraction to cooperative equilibria as an indicator of successful management, we find that the more resilient the resource (i.e. the higher the growth rate), the more likely the dominance of corruption suppressing the cooperative outcome. A qualitatively similar result arises when slow resource dynamics relative to the dynamics of decision-making mask the benefit of cooperation. We discuss the implications of these results in the context of managing common-pool resources.

Abstract: Climate analysis is a fast growing field of study with many economic, political and social implications. In the last years many techniques of complex network analysis have found application in climate research. This big data approach consists of embedding patters found in climate variables, as pseudo-periodic changes in temperature, pressure or rainfall into the topology of complex networks by means of appropriate linear and nonlinear measures. These patterns are also found to be acting on various time scales, such as synoptic atmospheric waves in the extra-tropics or longer time scale events in the tropics. The analysis of this topology can yield insight on the actual behaviour of regional or global climate, taking advantage of the well-developed branch of network analysis. In this paper we present ClimNet, a software toolkit for the construction of complex networks’ adjacency matrices from climate time series. Apart from finding linear and nonlinear relationships, ClimNet also provides ways to fine-tune relationships to different time-scales by means of symbolic ordinal analysis. The functions available in the software and their performance on multi-core platforms are introduced in this work.

Abstract: The main function and the essence of the city is to generate interactions in space and time between different individuals. Human settlements leverage interactions by spatially joining its inhabitants. Accordingly Bettencourt, the cities are organized on a network: the mere fact that individuals are spatially close increases the number of potential encounters between them. Theoretically, the probability of encounters and interaction grows as the population and produces reductions in transaction costs and communication society, and other co-location advantages. Socioeconomic related properties of a city seems to grow faster than a linear relationship with the population, called superlinear scaling. Reversely, the larger a city, the more efficient it is in the use of its infrastructure, leading to a sublinear scaling. To build a quantitative theory of cities we must take into account the city's geometry to calculate aggregate amounts that generate social and infrastructure indexes. In the present work we address a simple explanation for the scaling law in cities based on fractal properties of the cities and as well as on the behavior of individuals. We follow the statistical mechanics approach and the result was a very simple model, just to obtain the scaling of social and infrastructure indicators. For that, we introduce a measure of social potential energy which capture the influence of social interaction on the economic performance and the benefits of facilities in the case of infrastructure offered by the city. We assume that the population density depends on the fractal dimension and the individual interaction intensity decay with distance. As a result we obtain a power Law scaling for social indexes and for infrastructure. Those scaling are coherent with empirical data. An agent based model was proposed to make computational experiments and test the hypothesis.