Advances in Complex Systems

Physics and economics are two disciplines that share the common challenge of linking microscopic and macroscopic behaviors. However, while physics is based on collective dynamics, economics is based on individual choices. This conceptual difference is one of the main obstacles one has to overcome in order to characterize analytically economic models. In this paper, we build both on statistical mechanics and the game theory notion of Potential Function to introduce a rigorous generalization of the physicist's free energy, which includes individual dynamics. Our approach paves the way to analytical treatments of a wide range of socio-economic models and might bring new insights into them. As first examples, we derive solutions for a congestion model and a residential segregation model.

Epidemic modeling has a long tradition in facilitating an understanding of the spread of infectious agents with direct impact on public health. Recent modeling approaches have specifically improved our understanding of the connections between transmission network topology and epidemic spread: Host populations are generally not well mixed and show heterogeneous and dynamic contact behavior. This results in a broad range of epidemic patterns regarding both speed and width of epidemic expansion. However, there is also a mutual influence between transmission network structure, mixing between susceptible and infected hosts and the epidemic process: epidemic control gets more difficult if infected hosts mix well within the susceptible population. We present a new mathematical framework which allows for a study of mixing patterns between susceptible and infected hosts in populations with very general structural features. While clustering of infected hosts is a qualitatively well known phenomenon, our approach allows for its quantification and puts it in relation to the hosts' overall contact behavior. It shows that different degrees of assortativity emerge depending on the transmission network structure and epidemic stage. This method's use is demonstrated for synthetic populations. It can however easily be transferred to more realistic settings in which information about mixing patterns of epidemic groups is vital for improved epidemic control.

This paper, describes an agent based model of the spreading of a population over a territory. The models aims at reproducing a distribution of settlements with statistical and spatial characteristics similar to a historically produced pattern. The model operates on a representation of a real territory, taking into account hydrography and relief. The two main goals are to obtain a rank size distribution of the size of settlements which corresponds to a power law (also known as the Zipf Law of settlements) and to place the settlements in the territory in patterns that are close to the real ones, in zones where settlements were the result of a long historical process. The goal of the project was to demonstrate that a set of relatively simple rules could produce a complex pattern, similar to the result of a long and complex historical process. Therefore, it is an assumed reductionist approach.

Our conclusions show that a simple territorial logic, taking into account the quality of land, accessibility, population growth and migration preferences could reproduce Zipf distributions and interesting patterns of agent flow among the settlements created. However, achieving spatial patterns closer to the historical record needs an extra dimension involving field of sight. The best results were achieved by creating an artifical population which chooses to create settlements in places where a wide field of view exists of quality territory.

Despite the increasing diffusion of the Internet technology, TV remains the principal medium of communication. People's perceptions, knowledge, beliefs and opinions about matters of fact get (in)formed through the information reported on by the media.

However, a single source of information (and consensus) could be a potential cause of anomalies in the structure and evolution of a society.

Hence, as the information available (and the way it is reported) is fundamental for our perceptions and opinions, the definition of conditions allowing for a good information to be disseminated is a pressing challenge. In this paper starting from a report on the last Italian political campaign in 2008, we derive a socio-cognitive computational model of opinion dynamics where agents get informed by different sources of information. Then, a what-if analysis, performed through simulations on the model's parameters space, is shown. In particular, the scenario implemented includes three main streams of information acquisition, differing in both the contents and the perceived reliability of the messages spread. Agents' internal opinion is updated either by accessing one of the information sources, namely media and experts, or by exchanging information with one another. They are also endowed with cognitive mechanisms to accept, reject or partially consider the acquired information.

The Web of Data (WoD) is an Internet-based network of data resources and their relations. It has recently taken flight and combines over a hundred interlinked data sources with more than 15 billion edges. A consequence of this recent success is that a paradigm shift has taken place: up to now the Web of Data could be studied, searched and maintained like a classical database; nowadays it has turned into a Complex System and needs to be studied as such. In this paper, we introduce the Web of Data as a challenging object of study and provide initial results on two network scales: the pure data-layer, and the global connection between groups data items. In this analysis, we show that the "official" abstract representation of the WoD does not fit the real distribution we derive from the lower scale. As interesting as these results are, bigger challenges for analysis await in the form of the highly dynamic character of the WoD, and the typed, and implicit, character of the edges which is, to the best of our knowledge, hitherto unstudied.

In this paper, we develop methods to estimate the network coverage of a TTL-bound query packet undergoing flooding on an unstructured p2p network. The estimation based on the degree distribution of the networks, reveals that the presence of certain cycle-forming edges, that we name as cross and back edges, reduces the coverage of the peers in p2p networks and also generate a large number of redundant messages, thus wasting precious bandwidth. We therefore develop models to estimate the back/cross edge probabilities and the network coverage of the peers in the presence of these back and cross edges. Extensive simulation is done on random, power-law and Gnutella networks to verify the correctness of the model. The results highlight the fact that for real p2p networks, which are large but finite, the percentage of back/cross edges can increase enormously with increasing distance from a source node, thus leading to huge traffic redundancy.

In this work we analyze the topological and dynamical properties of a simple model of complex food webs, namely the niche model. In order to underline competition among species, we introduce "prey" and "predators" weighted overlap graphs derived from the niche model and compare synthetic food webs with real data. Doing so, we find new tests for the goodness of synthetic food web models and indicate a possible direction of improvement for existing ones. We then exploit the weighted overlap graphs to define a competition kernel for Lotka–Volterra population dynamics and find that for such a model the stability of food webs decreases with its ecological complexity.