Emergence of complex social networks from spatial structure and rules of thumb: a modelling approach

Individual-based computer models show that simple heuristic governing individuals’ behavior may suffice to generate complex patterns of social behavior at the group level such as those observed in animal societies. ‘GrooFiWorld’ is an example of such kind of computer models. In this model, self-organization and simple behavioral rules generate complex patterns of social behavior like those described in tolerant and intolerant societies of macaques. Social complexity results from the socio-spatial structure of the group, the nature of which is, in turn, a side-effect of intensity of aggression. The model suggests that a similar mechanism may give rise to complex social structures in macaques. It is, however, unknown if the spatial structure of the model and that of macaques are indeed similar. Here we used social networks analysis as a proxy for spatial structure of the group. Our findings show that the social networks of the model share similar qualitative features with those of macaques. As group size increases, the density and the average individual eigenvector centrality decrease and the modularity and centralization of the network increase. In social networks emerging from simulations resembling intolerant societies the density is lower, the modularity and centralization are higher, and the individuals ranking higher in the dominance hierarchy are more central than in the social networks emerging from simulations resembling egalitarian societies. Given the qualitative similarity between the social networks of the model and that of empirical data, our results suggest that the spatial structure of macaques is similar to that of the model. It seems thus plausible that, as in the model, the spatial structure combined with simple behavioral rules plays a role in the emergence of complex social networks and complex social behavior in macaques.     

The changing criteria of social networks in a Cajun Community

The Zhi Mä Funeral Ceremony of the Na‐Khi of Southwest China. Joseph F. Rock. Studia Instituti Anthropos, Vol. 9. St. Gabriel's Mission Press, Vienna‐Mödling, 1955. 228 pp., 10 pls.

Mythen und Mysterien. Magie und Religian der Tibeter. M. Hermanns. Verlag Balduin Pick, Köln, 1956. 400 pp., 49 figs., 1 map.

Oracles and Demons of Tibet. René de Nebesky‐Wojkowitz. Mouton &; Co., ‘s‐Gravenhage, 1956. 666 pp., 10 pls., 25 figs.

Die Lamaistische Kunst in der Umwelt von Tibet. Siegbert Hummel. Otto Harrassowitz, Leipzig, 1955. 149 pp., 134 figs., 1 map.

El Monte. Igbo Finda, Ewe Orisha, Vititinfinda. Notas sobre la religión, la magía, las supersticiones y el folklore de los negros criollos y del pueblo de Cuba. Lydia Cabrera. Ediciones C. R., Habana 1954. 564 pp., 25 pls.

Die Hieroglyphen der Maya‐Handschriften. Günter Zimmermann. Universität Hamburg, Abhandlungen aus dem Gebiet der Auslandskunde, Band 62, Reihe B: Völkerkunde, Kulturgeschichte und Sprachen, Band 34. Verlag Cram, de Gruyter &; Co., Hamburg, 1956. 174 pp., 8 pls., ca. 2000 text ill.

Das Indianerbuch. Geb. F. A. Brockhaus Verlag. Leipzig 1956. Eva Lips. 443 PP., 32 pls., 11 text ill., 6 maps. Cloth bound DM 10.80.

Das doppelte Geschlecht. Ethnologische Studien zur Bisexualität in Ritus und Mythos. Hermann Baumann. Berlin 1955. 420 pp., 5 maps.

Von Fremden Völkern und Kulturen. Beiträge zur Völkerkunde. Hans Plischke zum 65. Geburtstage gewidmet von seinen Kollegen und Freunden, Schülern und Mitarbeitern. Herausgegeben von W. Lang, W. Nippold und G. Spannaus. Droste‐Verlag, Düsseldorf, 1955. 284 pp., 30 figs., 2 maps.

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Das Problem des Völkertodes. Eine Studie zur historischen Bevölkerungsbiologie. Ilse Schwidetzky. Ferdinand Enke Verlag, Stuttgart 1954. 165 pp. This and the following review have been held over from former times, when these contributions were apt to prove rather exhaustive. Reviews are now heavily cut down irrespective of the significance of the work presented.     

The complex structure of hunter-gatherer social networks

In nature, many different types of complex system form hierarchical, self-similar or fractal-like structures that have evolved to maximize internal efficiency. In this paper, we ask whether hunter-gatherer societies show similar structural properties. We use fractal network theory to analyse the statistical structure of 1189 social groups in 339 hunter-gatherer societies from a published compilation of ethnographies. We show that population structure is indeed self-similar or fractal-like with the number of individuals or groups belonging to each successively higher level of organization exhibiting a constant ratio close to 4. Further, despite the wide ecological, cultural and historical diversity of hunter-gatherer societies, this remarkable self-similarity holds both within and across cultures and continents. We show that the branching ratio is related to density-dependent reproduction in complex environments and hypothesize that the general pattern of hierarchical organization reflects the self-similar properties of the networks and the underlying cohesive and disruptive forces that govern the flow of material resources, genes and non-genetic information within and between social groups. Our results offer insight into the energetics of human sociality and suggest that human social networks self-organize in response to similar optimization principles found behind the formation of many complex systems in nature.     

Population networks: a large-scale framework for modelling cortical neural networks

Artificial neural networks are usually built on rather few elements such as activation functions, learning rules, and the network topology. When modelling the more complex properties of realistic networks, however, a number of higher-level structural principles become important. In this paper we present a theoretical framework for modelling cortical networks at a high level of abstraction. Based on the notion of a population of neurons, this framework can accommodate the common features of cortical architecture, such as lamination, multiple areas and topographic maps, input segregation, and local variations of the frequency of different cell types (e.g., cytochrome oxidase blobs). The framework is meant primarily for the simulation of activation dynamics; it can also be used to model the neural environment of single cells in a multiscale approach. Received: 9 January 1996 / Accepted in revised form: 24 July 1996     

An epidemiological framework for modelling fungicide dynamics and control

Defining appropriate policies for controlling the spread of fungal disease in agricultural landscapes requires appropriate theoretical models. Most existing models for the fungicidal control of plant diseases do not explicitly include the dynamics of the fungicide itself, nor do they consider the impact of infection occurring during the host growth phase. We introduce a modelling framework for fungicide application that allows us to consider how "explicit" modelling of fungicide dynamics affects the invasion and persistence of plant pathogens. Specifically, we show that "explicit" models exhibit bistability zones for values of the basic reproductive number ([Formula: see text]) less than one within which the invasion and persistence threshold depends on the initial infection levels. This is in contrast to classical models where invasion and persistence thresholds are solely dependent on [Formula: see text]. In addition if initial infection occurs during the growth phase then an additional "invasion zone" can exist for even smaller values of [Formula: see text]. Within this region the system will experience an epidemic that is not able to persist. We further show that ideal fungicides with high levels of effectiveness, low rates of application and low rates of decay lead to the existence of these bistability zones. The results are robust to the inclusion of demographic stochasticity.     

Hypnosis modelling in neural networks

In the framework of the neural network theory effects similar to hypnotic displays are constructed. They are based on the associative paradigm involving non-linear interaction of excitatory and inhibitory channels with synaptic memory. The non-linearity of long-term memorizing processes may cause effects exhibited by blind spots, which are interpreted as the first stage of hypnosis. More complicated phenomena are discussed in terms of a two-layer network.     

Mesoscopic structure conditions the emergence of cooperation on social networks

Background

We study the evolutionary Prisoner''s Dilemma on two social networks substrates obtained from actual relational data.

Methodology/Principal Findings

We find very different cooperation levels on each of them that cannot be easily understood in terms of global statistical properties of both networks. We claim that the result can be understood at the mesoscopic scale, by studying the community structure of the networks. We explain the dependence of the cooperation level on the temptation parameter in terms of the internal structure of the communities and their interconnections. We then test our results on community-structured, specifically designed artificial networks, finding a good agreement with the observations in both real substrates.

Conclusion

Our results support the conclusion that studies of evolutionary games on model networks and their interpretation in terms of global properties may not be sufficient to study specific, real social systems. Further, the study allows us to define new quantitative parameters that summarize the mesoscopic structure of any network. In addition, the community perspective may be helpful to interpret the origin and behavior of existing networks as well as to design structures that show resilient cooperative behavior.     

Controlling nosocomial infection based on structure of hospital social networks

Nosocomial infection (i.e. infection in healthcare facilities) raises a serious public health problem, as implied by the existence of pathogens characteristic to healthcare facilities such as methicillin-resistant Staphylococcus aureus and hospital-mediated outbreaks of influenza and severe acute respiratory syndrome. For general communities, epidemic modeling based on social networks is being recognized as a useful tool. However, disease propagation may occur in a healthcare facility in a manner different from that in a urban community setting due to different network architecture. We simulate stochastic susceptible-infected-recovered dynamics on social networks, which are based on observations in a hospital in Tokyo, to explore effective containment strategies against nosocomial infection. The observed social networks in the hospital have hierarchical and modular structure in which dense substructure such as departments, wards, and rooms, are globally but only loosely connected, and do not reveal extremely right-skewed distributions of the number of contacts per individual. We show that healthcare workers, particularly medical doctors, are main vectors (i.e. transmitters) of diseases on these networks. Intervention methods that restrict interaction between medical doctors and their visits to different wards shrink the final epidemic size more than intervention methods that directly protect patients, such as isolating patients in single rooms. By the same token, vaccinating doctors with priority rather than patients or nurses is more effective. Finally, vaccinating individuals with large betweenness centrality (frequency of mediating connection between pairs of individuals along the shortest paths) is superior to vaccinating ones with large connectedness to others or randomly chosen individuals, which was suggested by previous model studies.     

Taking sociality seriously: the structure of multi-dimensional social networks as a source of information for individuals

Understanding human cognitive evolution, and that of the other primates, means taking sociality very seriously. For humans, this requires the recognition of the sociocultural and historical means by which human minds and selves are constructed, and how this gives rise to the reflexivity and ability to respond to novelty that characterize our species. For other, non-linguistic, primates we can answer some interesting questions by viewing social life as a feedback process, drawing on cybernetics and systems approaches and using social network neo-theory to test these ideas. Specifically, we show how social networks can be formalized as multi-dimensional objects, and use entropy measures to assess how networks respond to perturbation. We use simulations and natural 'knock-outs' in a free-ranging baboon troop to demonstrate that changes in interactions after social perturbations lead to a more certain social network, in which the outcomes of interactions are easier for members to predict. This new formalization of social networks provides a framework within which to predict network dynamics and evolution, helps us highlight how human and non-human social networks differ and has implications for theories of cognitive evolution.     

Functional nodes in dynamic neural networks for bioprocess modelling

This contribution presents a novel method for the direct integration of a-priori knowledge in a neural network and its application for the online determination of a secondary metabolite during industrial yeast fermentation. Hereby, existing system knowledge is integrated in an artificial neural network (ANN) by means of 'functional nodes'. A generalized backpropagation algorithm is presented. For illustration, a set of ordinary differential equations describing the diacetyl formation and degradation during the cultivation is incorporated in a functional node and integrated in a dynamic feedforward neural network in a hybrid manner. The results show that a hybrid modelling approach exploiting available a-priori knowledge and experimental data can considerably outperform a pure data-based modelling approach with respect to robustness, generalization and necessary amount of training data. The number of training sets were decreased by 50%, obtaining the same accuracy as in a conventional approach. All incorrect decisions, according to defined cost criteria obtained with the conventional ANN, were avoided.     

Basic networks: Definition and applications

We define basic networks as the undirected subgraphs with minimal number of units in which the distances (geodesics, minimal path lengths) among a set of selected nodes, which we call seeds, in the original graph are conserved. The additional nodes required to draw the basic network are called connectors. We describe a heuristic strategy to find the basic networks of complex graphs. We also show how the characterization of these networks may help to obtain relevant biological information from highly complex protein-protein interaction data.     

Synthetic signaling networks for therapeutic applications

Synthetic signaling networks contain exogenous, modified, or rationally designed components involved in sending, receiving, and processing information from the environment and other cells. Advances in the input, output, and processing elements for such networks hold promise towards developing new therapies and prophylactics for disease. Therapeutic synthetic signaling systems are still in their infancy, but are progressing into mouse models of disease and even into clinical trials. As signaling technology matures, we will see an increase in implanted and ingested cellular therapies capable of autonomously diagnosing and treating disease. These technologies have the potential to reduce some of the burden on both patients and clinicians, contributing to more efficient and eventually personalized medicine.     

Measuring synchronization in neuronal networks for biosensor applications

Cultures of neurons can be grown on microelectrode arrays (MEAs), so that their spike and burst activity can be monitored. These activity patterns are quite sensitive to changes in the environment, such as chemical exposure, and hence the cultures can be used as biosensors. One key issue in analyzing the data from neuronal networks is how to quantify the level of synchronization among different units, which represent different neurons in the network. In this paper, we propose a synchronization metric, based on the statistical distribution of unit-to-unit correlation coefficients. We show that this synchronization metric changes significantly when the networks are exposed to bicuculline, strychnine, or 2,3-dioxo-6-nitro-l,2,3,4-tetrahydrobenzoquinoxaline-7-sulphonamide (NBQX). For that reason, this metric can be used to characterize pharmacologically induced changes in a network, either for research or for biosensor applications.     

Mycorrhizal networks: Mechanisms,ecology and modelling

Mycorrhizal networks, defined as a common mycorrhizal mycelium linking the roots of at least two plants, occur in all major terrestrial ecosystems. This review discusses the recent progress and challenges in our understanding of the characteristics, functions, ecology and models of mycorrhizal networks, with the goal of encouraging future research to improve our understanding of their ecology, adaptability and evolution. We focus on four themes in the recent literature: (1) the physical, physiological and molecular evidence for the existence of mycorrhizal networks, as well as the genetic characteristics and topology of networks in natural ecosystems; (2) the types, amounts and mechanisms of interplant material transfer (including carbon, nutrients, water, defence signals and allelochemicals) in autotrophic, mycoheterotrophic or partial mycoheterotrophic plants, with particular focus on carbon transfer; (3) the influence of mycorrhizal networks on plant establishment, survival and growth, and the implications for community diversity or stability in response to environmental stress; and (4) insights into emerging methods for modelling the spatial configuration and temporal dynamics of mycorrhizal networks, including the inclusion of mycorrhizal networks in conceptual models of complex adaptive systems. We suggest that mycorrhizal networks are fundamental agents of complex adaptive systems (ecosystems) because they provide avenues for feedbacks and cross-scale interactions that lead to self-organization and emergent properties in ecosystems. We have found that research in the genetics of mycorrhizal networks has accelerated rapidly in the past 5 y with increasing resolution and throughput of molecular tools, but there still remains a large gap between understanding genes and understanding the physiology, ecology and evolution of mycorrhizal networks in our changing environment. There is now enormous and exciting potential for mycorrhizal researchers to address these higher level questions and thus inform ecosystem and evolutionary research more broadly.     

Advances in satellite remote sensing of pheno-climatic features for epidemiological applications

Geographical Information Systems (GIS) and Remote Sensing (RS) technologies are being used increasingly to study the spatial and temporal patterns of diseases. They can be used to complement conventional ecological monitoring and modelling techniques, and provide a means to portray complex relationships in the ecology of diseases with strong environmental determinants. In particular, satellite technology has been extraordinarily improved during recent years, providing new parameters useful to understand the epidemiology of parasites, such as vegetation indices, land surface temperatures, soil moisture and rainfall indices. In the present review, Normalized Difference Vegetation Index (NDVI) is primarily considered, since it is the index characterizing vegetation that is most used in epidemiological studies. Multi-temporal study of RS data allows collection of bio-climatic information about risk area distribution, along with predictive studies and anticipatory models of diseases, at different geographic scales ranging from global to local. The main physical and technological basis of a mathematical model, effective at different scales, for identification of landscape pheno-climatic features is described in the current paper.     

Communication networks: social environments for receiving and signalling behaviour

Communication and social behaviour are inextricably linked, with communication mediating important social behaviours such as resource defence and mate attraction. However, the social environment in which communication occurs is often ignored in discussions of communication behaviour. We argue that networks of several individuals are the common social environment for communication behaviour. The consequences for receivers and signallers of communicating in a network environment are the main subjects of this review. Eavesdropping is a receiving behaviour that is only possible in the environment of a network and therefore we concentrate on this behaviour. The main effect of communication networks on signallers is to create competition with other signallers for receiver attention. We discuss the consequences of such competition. To conclude, we explore the role of signals and signalling interactions as sources of information that animals exploit to direct their behaviour. Received: 1 December 1999 / Received in revised form: 18 January 2000 / Accepted: 18 January 2000