Using Generalized Annotated Programs to Solve Social Network Optimization Problems
Paulo Shakarian, V.S. Subrahmanian, Maria Luisa Sapino, 26th Intl.Conference on Logic Programming (tech. communication); July 2010
Reasoning about social networks (labeled, directed, weighted graphs) is be-
coming increasingly important and... more
Reasoning about social networks (labeled, directed, weighted graphs) is be-
coming increasingly important and there are now models of how certain phenomena (e.g.
adoption of products/services by consumers, spread of a given disease) \diuse" through
the network. Some of these diusion models can be expressed via generalized annotated
programs (GAPs). In this paper, we consider the following problem: suppose we have a
given goal to achieve (e.g. maximize the expected number of adoptees of a product or
minimize the spread of a disease) and suppose we have limited resources to use in trying
to achieve the goal (e.g. give out a few free plans, provide medication to key people in the
SN) - how should these resources be used so that we optimize a given objective function
related to the goal? We dene a class of social network optimization problems (SNOPs)
that supports this type of reasoning. We formalize and study the complexity of SNOPs
and show how they can be used in conjunction with existing economic and disease diusion
models.
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Seen by:Large Social Networks can be Targeted for Viral Marketing with Small Seed Sets
Paulo Shakarian, Damon Paulo, IEEE/ACM ASONAM 2012
In a "tipping" model, each node in a social network, representing an individual, adopts a behavior if a... more
In a "tipping" model, each node in a social network, representing an individual, adopts a behavior if a certain number of his incoming neighbors previously held that property. A key problem for viral marketers is to determine an initial "seed" set in a network such that if given a property then the entire network adopts the behavior. Here we introduce a method for quickly finding seed sets that scales to very large networks. Our approach finds a set of nodes that guarantees spreading to the entire network under the tipping model. After experimentally evaluating 31 real-world networks, we found that our approach often finds such sets that are several orders of magnitude smaller than the population size. Our approach also scales well - on a Friendster social network consisting of 5.6 million nodes and 28 million edges we found a seed sets in under 3.6 hours. We also find that highly clustered local neighborhoods and dense network-wide community structure together suppress the ability of a trend to spread under the tipping model.
Complex Systems Modeling: Using Metaphors From Nature In Simulation and Scientific Models
by Luis Rocha
Rocha, Luis M. [1999]. BITS: Computer and Communications News. Computing, Information, and Communications Division. Los Alamos National Laboratory. November 1999.
Emergence And Self-Organization In Urban Structures
Authors:
Al-Sayed K,Turner A
Research on urban growth divides into two strands that barely come together. The first strand is aligned to the view... more Research on urban growth divides into two strands that barely come together. The first strand is aligned to the view that an understanding of cities as socio-spatial phenomena is indispensable for any sensible modelling approach. The second strand is established on assumption-based computational modelling with the perspective that without testing our understanding by reconstructing the phenomena we cannot verify our theoretical propositions about it. It is clear to our understanding that by bridging these two strands we can subject the explanatory models of cities to experimental testing. We acknowledge however the need to explain urban dynamics as a process rather than as an end product for us to base our assumptions on solid evidence. In search for evidence on laws that capture urban dynamics we outline invariants in the historical evolution of two urban structures. The invariants indicate to two processes that govern city growth; a generative process that contributes to structural differentiation and a process of self-organisation that is seen to resemble reaction-diffusion systems famously known in chemistry and biology. At this stage, we cannot verify whether these invariants constitute spatial laws in themselves or whether these invariants are a side effect of another more implicit process. Nonetheless, we assume that the presence of these invariants is conditional for a grid structure to be admitted to the class of natural urban systems. In that, the invariants serve as measures for the characterisation of urban pattern recognition.
Anatomical Connectivity Influences both Intra- and Inter-Brain Synchronizations
Dumas G, Chavez M, Nadel J, Martinerie J (2012) PLoS ONE 7(5): e36414. doi:10.1371/journal.pone.0036414
Recent development in diffusion spectrum brain imaging combined to functional simulation has the potential to further... more Recent development in diffusion spectrum brain imaging combined to functional simulation has the potential to further our understanding of how structure and dynamics are intertwined in the human brain. At the intra-individual scale, neurocomputational models have already started to uncover how the human connectome constrains the coordination of brain activity across distributed brain regions. In parallel, at the inter-individual scale, nascent social neuroscience provides a new dynamical vista of the coupling between two embodied cognitive agents. Using EEG hyperscanning to record simultaneously the brain activities of subjects during their ongoing interaction, we have previously demonstrated that behavioral synchrony correlates with the emergence of inter-brain synchronization. However, the functional meaning of such synchronization remains to be specified. Here, we use a biophysical model to quantify to what extent inter-brain synchronizations are related to the anatomical and functional similarity of the two brains in interaction. Pairs of interacting brains were numerically simulated and compared to real data. Results show a potential dynamical property of the human connectome to facilitate inter-individual synchronizations and thus may partly account for our propensity to generate dynamical couplings with others.
10 views
Seen by:Topological measures for the analysis of wireless sensor networks
V. Labatut & A. Özgövde, 3rd International Conference on Ambient Systems, Networks and Technologies, Niagara Falls, CA, 2012.
Concepts such as energy dependence, random deployment, dynamic topological update, self-organization, varying large... more Concepts such as energy dependence, random deployment, dynamic topological update, self-organization, varying large number of nodes are among many factors that make WSNs a type of complex system. However, when analyzing WSNs properties using complex network tools, classical topological measures must be considered with care as they might not be applicable in their original form. In this work, we focus on the topological measures frequently used in the related field of Internet topological analysis. We illustrate their applicability to the WSNs domain through simulation experiments. In the cases when the classic metrics turn out to be incompatible, we propose some alternative measures and discuss them based on the WSNs characteristics.
2 views
Seen by:Generative Structures in Cities
Co-authored with Alasdair Turner and Sean Hanna
Research in the area of Space syntax tends to be centred on static representations of the built environment and its... more Research in the area of Space syntax tends to be centred on static representations of the built environment and its embedded social logic. Lacking for the most part the element of time, this synchronous representation cannot capture the evolutionary dynamics of urban systems. In this paper, we argue that the abstract values of space-time as a dual dimension play a key role as generators of city systems. Hence, we explore the driving forces that help reproduce growing spatial networks and yet preserve their structural properties. In two case studies; Manhattan and Barcelona, synchronic states of the growing systems are analysed. The states are separated by a certain radius of time. The analysis leads to regularities that may outline a generative model embedded in the pattern of growth and marked by alternating periods of expansion and pruning. In periods of expansion, a positive feedback process operates and takes the form of exponential addition of elements. The emergence of patches on the edges follows high values of choice and is subject to the temporal configurations of the grid. Once we observe the long-term time dimensionality, we note a change in the trend of the system as it reaches its maximum boundary. Following this change, another process of reinforcing feedback is introduced to the spatial network. This process involves intensifying sparse grid structures that have witnessed high gains in centrality in prior states and a process of pruning of poorly integrated elements. Both processes aim to differentiate the spatial structure of a city hence matching that of an organic grid. The findings yield that even at events of large scale planning interventions; cities adapt the local configurations of the new uniform parts to deform in such a way as to reproduce natural growth. In this manner, cities embody the intelligent collective minds of individuals. They are trade-off products of individuals’ decisions and they adapt their behaviour by prioritising a maximum parts-whole relationship that optimises access in the spatial network. We introduce these feedback processes under a framework of a plausible generative model to simulate city growth. The model is expected to both provide a better understanding of city growth and to aid design decision making on urban and regional scales.
On Accuracy of Community Structure Discovery Algorithms
G. K. Orman, V. Labatut & H. Cherifi, Journal of Convergence Information Technology, 6(11):283-292, 2011.
Community structure discovery in complex networks is a quite challenging problem spanning many applications in various... more Community structure discovery in complex networks is a quite challenging problem spanning many applications in various disciplines such as biology, social network and physics. Emerging from various approaches numerous algorithms have been proposed to tackle this problem. Nevertheless little attention has been devoted to compare their efficiency on realistic simulated data. To better understand their relative performances, we evaluate systematically eleven algorithms covering the main approaches. The Normalized Mutual Information (NMI) measure is used to assess the quality of the discovered community structure from controlled artificial networks with realistic topological properties. Results show that along with the network size, the average proportion of intra-community to inter-community links is the most influential parameter on performances. Overall, “Infomap” is the leading algorithm, followed by “Walktrap”, “SpinGlass” and “Louvain” which also achieve good consistency.
An Empirical Study of the Relation Between Community Structure and Transitivity
G. K. Orman, V. Labatut and H. Cherifi, 3rd Workshop on Complex Networks, Melbourne, US-FL, 2012.
(English, extended version of the paper "Relation entre transitivité et structure de communauté dans les réseaux complexes". This version is also slightly longer than the one published in the proceedings)
One of the most prominent properties in real-world networks is the presence of a community structure, i.e. dense and... more One of the most prominent properties in real-world networks is the presence of a community structure, i.e. dense and loosely interconnected groups of nodes called communities. In an attempt to better understand this concept, we study the relationship between the strength of the community structure and the network transitivity (or clustering coefficient). Although intuitively appealing, this analysis was not performed before. We adopt an approach based on random models to empirically study how one property varies depending on the other. It turns out the transitivity increases with the community structure strength, and is also affected by the distribution of the community sizes. Furthermore, increasing the transitivity also results in a stronger community structure. More surprisingly, if a very weak community structure causes almost zero transitivity, the opposite is not true and a network with a close to zero transitivity can still have a clearly defined community structure. Further analytical work is necessary to characterize the exact nature of the identified relationship.
3 views
Seen by:A complex systems approach to the evolutionary dynamics of human history: the case of the Late Medieval World Crisis
Working Paper for the European Meetings on Cybernetics and Systems Research (EMCSR) 2012, Vienna, University Campus, April 10th 2012 (http://www.emcsr.net/symposium-b-evolution-throughout-the-sciences-and
„There are few theoretical approaches to which historian respond so negatively as to the explanation of historical... more
„There are few theoretical approaches to which historian respond so negatively as to the explanation of historical processes by such theories“, the German historian Rainer Waltz states most accurately in his study on „Theories of Social Evolution and History“; there he also presents two main causes for this rejection: a moral one, the perversion of evolutionary thinking in so-called Social Darwinist theories in the 19th and 20th centuries, and a scientific one, the fear of a biologistic interpretation of human history by adopting evolutionary models (Walz, 2004). This distinguishes historical studies from other social sciences and humanities such as anthropology or sociology and even other historical disciplines such as archaeology, where evolutionary models have become part of the methodological toolkit (Renfrew & Bahn, 2008; for a rare example from the field of history of literature cf. Moretti, 2009).
Although most historians are reluctant to adopt evolutionary models (yet alone in their mathematized or sociobiologist form) for the interpretation of human past (respectively the larger or smaller period of time they are specialised in), terms such as “evolution” and concepts of evolutionary thinking such as “adaption” or “selection” are used in numerous descriptions of historical events and processes, albeit often in a metaphorical way (Walz, 2004). At the same time it is evident that major developments in human history such as the emergence of the human kind itself, of human culture and of complex social structures such as states as well as phenomena of long duration (up to the scale of “Big History” from the Big Bang until present times as it has been attempted in the last decades, Spier 2010) cannot be explained without the help of evolutionary concepts (cf. Blute, 2010; Voland, 2009); but again, these subjects refer mainly to the fields of evolutionary biologists and psychologists, anthropologists, sociologists or (prehistoric) archaeologists (cf. Yoffee, 2004). Some specialists from these disciplines have also tried to adapt such concepts for the entire human history beyond its “beginnings”, but have equally found mixed reception among historians, especially if they try to demonstrate some kind of progress in the development of humanity as for instance Steven Pinker has done most recently in his study on “Why Violence has declined” (Pinker, 2011; see also Atran, 2002; Boyd & Richerson, 2005; Morris, 2010).
In contrast to this (non)-use of evolutionary concepts for historical studies, we intend to demonstrate the benefit of a complex evolutionary approach for the analysis of a specific period of late medieval/early modern history between 1200 and 1500 CE, which has been attributed central importance for the so-called “Rise of the West”, since it saw the beginning of European overseas expansion at its end (cf. Goldstone, 2009; Morris, 2010).
In the “calamitous” 14th century, as Barbara Tuchman called it (1978), the medieval world entered a period of severe crisis in demography, economy, politics and religion. This crisis took hold in all regions, ranging from China in the East to England in the West. Even before the catastrophic pandemic of the Black Death (1346-1352), deteriorating climatic conditions had ended the period of demographic and economic expansion that began in the 10th century (Behringer, 2007; Atwell, 2001; Benedictow, 2004; Brook, 2010).
The local and regional impacts and consequences of these general crisis-laden conditions may have differed; outcomes ranged from actual societal collapse to the emergence of powerful new polities. But these conditions provide a framework for global perspective on this period and allow us to use the 14th century-crisis as a field of “natural experiments of history”, as Jared Diamond and James A. Robinson have called them (Diamond & Robinson, 2011); accordingly, we analyse how similar crisis phenomena influenced the development of societies with different (or similar) traditions, religions, institutions, geographies or ecologies (cf. also Borsch, 2005). In particular, we will analyse and compare five polities in the “Old World”, England, Hungary, Byzantium, Egypt and China, of which three disappeared around the end of this period due to the expansion of the most successful newly emerged Ottoman Empire (Byzantium in 1453, Mamluk Egypt in 1517, Hungary in 1526/1541; cf. also Preiser-Kapeller, 2011).
In order to be able to capture variations and complexities within this sample, we adopt concepts and tools provided by the field of complexity science. We understand complex systems as large networks of individual components, whose interactions at the microscopic level produce “complex” changing patterns of behaviour of the whole system on the macroscopic level. In the last decades, historians and social scientists also tried to use concepts of complexity theory for the description of phenomena in their own fields, but again often only in a “metaphoric” way (Gaddis, 2002; Hatcher & Bailey, 2001). Less frequently, though, historians have tried to make use of the mathematical foundations of complexity theory or of quantitative tools provided by this field (Kiel & Elliott, 1997; Preiser-Kapeller, 2012). Recent scholarship has implemented some of these tools especially for the construction of macro-models of socio-economic development (Goldstone, 1991; Turchin, 2003; Turchin & Nefedov, 2009).
In addition, we combine complexity theory with the analytical framework of “systems theory” developed by the German sociologist Niklas Luhmann (1927-1998) in order to capture the interdependencies between politics, economy and religion within a polity and with the political, economic and ecological environment (Luhmann, 1997; Becker & Reinhardt-Becker, 2001; Becker, 2004). Luhmann´s theory is valuable for our analysis in various aspects; it makes us aware of the reduction of environmental and social complexity which is reflected in our historical sources, and it provides a framework to approach complex mechanisms within and the dependencies between various social spheres and their environment. Its evolutionary aspects have also been analysed by Walz (2004). In addition, we employ methods and tools of network analysis, which allow us to capture, analyse and model linkages and cause-effect correlations in society, economy, politics and religion on the macro- and micro-level down to groups and individuals (Gould, 2003; Lemercier, 2005).
Overall, our analytical approach allows us to capture the “diversité véritable” without losing track of essential commonalities (the “strange parallels”, as Victor Liebermann has called them, 2009) with regard to the transformation of polities and societies and their adaption to this “first world crisis”. Thereby, the value of a framework of evolutionary dynamics for the exploration of human history will be demonstrated
References
Atran, S. (2002). In Gods We Trust. The Evolutionary Landscape of Religion. Oxford: Oxford University Press.
Atwell, W. S. (2001). Volcanism and Short-Term Climatic Change in East Asian and World History, c. 1200–1699. Journal of World History 12/1, 29-98.
Becker, F. & Reinhardt-Becker, E. (2001). Systemtheorie. Eine Einführung für die Geschichts- und Kulturwissenschaften. Frankfurt, New York: Campus Verlag.
Becker, F. (Ed.). (2004). Geschichte und Systemtheorie. Exemplarische Fallstudien. Frankfurt, New York: Campus Verlag.
Behringer, W. (2007). Kulturgeschichte des Klimas. Von der Eiszeit bis zur globalen Erwärmung. Munich: C. H. Beck.
Benedictow, O. J. (2004). The Black Death 1346–1353. The Complete History. Woodbridge: Boydell & Brewer Inc.
Blute, M. (2010). Darwinian Sociocultural Evolution. Solutions to Dilemmas in Cultural and Social Theory. Cambridge: Cambridge University Press.
Borsch, St. J. (2005). The Black Death in Egypt and England. A Comparative Study. Austin: University of Texas Press.
Boyd, R. & Richerson, P. J. (2005). The Origin and Evolution of Cultures. Oxford: Oxford University Press.
Brook, T. (2010). The troubled Empire. China in the Yuan and Ming Dynasties. Cambridge (Mass.), London: Harvard University Press.
Diamond, J. & Robinson, J. A. (Eds.). (2011). Natural Experiments of History. Cambridge (Mass.), London: Harvard University Press.
Gaddis, J. L. (2002). The Landscape of History. How Historians map the Past. Oxford: Oxford University Press.
Goldstone, J. A. (1991). Revolution and Rebellion in the Early Modern World. Berkeley: University of California Press.
Goldstone, J. A. (2009). Why Europe? The Rise of the West in World History, 1500–1850. New York: Mcgraw-Hill Higher Education.
Gould, R. V. (2003). Uses of Network Tools in Comparative Historical Research. In: J. Mahoney & D. Rueschemeyer (Eds.). Comparative Historical Analysis in the Social Sciences (p. 241-269). Cambridge: Cambridge University Press.
Hatcher, J. & Bailey, M. (2001). Modelling the Middle Ages. The History and Theory of England´s Economic Development. Oxford: Oxford University Press.
Kiel, L. D. & Elliott, E. (Eds.). (1997). Chaos Theory in the Social Sciences. Foundations and Applications. Ann Arbor, Michigan: University of Michigan Press.
Lemercier, Cl. (2005). Analyse de réseaux et histoire. Revue d’histoire moderne et contemporaine 52/2, 88-112.
Lieberman, L. (2009). Strange Parallels. Southeast Asia in Global Context, c. 800–1830. Vol. 2: Mainland Mirrors: Europe, Japan, China, South Asia, and the Islands. Cambridge: Cambridge University Press.
Luhmann, N. (1997). Die Gesellschaft der Gesellschaft. 2 Vols., Frankfurt am Main: Suhrkamp Verlag.
Moretti, F. (2009). Kurven, Karten, Stammbäume. Abstrakte Modelle für die Literaturgeschichte. Frankfurt am Main: Suhrkamp Verlag.
Morris, I. (2010). Why The West Rules For Now: The Patterns of History and what they reveal about the Future. London: Profile Books.
Pinker, S. (2011). The Better Angels of our Nature. Why Violence has declined. London: Viking.
Preiser-Kapeller, J. (2012). Complex historical dynamics of crisis: the case of Byzantium. In: A. Suppan (Ed.). Krise und Transformation (in print). Vienna: Austrian Academy Press (pre-print online: http://oeaw.academia.edu/JohannesPreiserKapeller/Papers/506625/Complex_historical_dynamics_of_crisis_the_case_of_Byzantium).
Preiser-Kapeller, J. (2011). (Not so) Distant Mirrors: a complex macro-comparison of polities and political, economic and religious systems in the crisis of the 14th century. In: A. Simon (Ed.). Proceedings of the International Conference "The Angevin Dynasty (14th Century)" in Târgoviște (Romania), October 21st-23rd 2011 (forthcoming). Vienna: Peter Lang (working Paper online: http://oeaw.academia.edu/JohannesPreiserKapeller/Papers/506595/_Not_so_Distant_Mirrors_a_complex_macro-comparison_of_polities_and_political_economic_and_religious_systems_in_the_crisis_of_the_14th_century)
Renfrew, C. & Bahn, P. (2008). Archaeology: Theories, Methods and Practice. London: Thames & Hudson.
Spier, F. (2010). Big History and the Future of Humanity. Chichester: John Wiley & Sons.
Tuchman, B. (1978). A Distant Mirror. The calamitous 14th Century. New York: Alfred A. Knopf.
Turchin, P. & Nefedov, S. A. (2010). Secular cycles. Princeton, Oxford: Princeton University Press.
Turchin, P. (2003). Historical Dynamics. Why States Rise and Fall (Princeton Studies in Complexity). Princeton, Oxford: Princeton University Press.
Voland, E. (2009). Soziobiologie. Die Evolution von Kooperation und Konkurrenz. 3rd ed., Heidelberg: Spektrum Akademischer Verlag.
Walz, R. (2004). Theorien sozialer Evolution und Geschichte. In: F. Becker (Ed.), Geschichte und Systemtheorie. Exemplarische Fallstudien (p. 29-75). Frankfurt, New York: Campus Verlag.
Yoffee, N. (2004). Myths of the Archaic State. Evolution of the Earliest Cities, States, and Civilizations. Cambridge: Cambridge University Press.
204 views
Seen by:GED: The Method for Group Evolution Discovery in Social Networks
by Piotr Bródka
Bródka P., Saganowski P., Kazienko P.: GED: The Method for Group Evolution Discovery in Social Networks, Social Network Analysis and Mining, DOI:10.1007/s13278-012-0058-8
OPEN ACCESS
http://www.ii.pwr.wroc.pl/~brodka/ged.php
The continuous interest in the social network area contributes to the fast development of this field. The new... more
The continuous interest in the social network area contributes to the fast development of this field. The new possibilities of obtaining and storing data facilitate deeper analysis of the entire network, extracted social groups and single individuals as well. One of the most interesting research topic is the dynamics of social groups, it means analysis of group evolution over time. Having appropriate knowledge and methods for dynamic analysis, one may attempt to predict the future of the group, and then manage it properly in order to achieve or change this predicted future according to specific needs. Such ability would be a powerful tool in the hands of human resource managers, personnel recruitment, marketing, etc.
The social group evolution consists of individual events and seven types of such changes have been identified in the paper: continuing, shrinking, growing, splitting, merging, dissolving and forming. To enable the analysis of group evolution a change indicator – inclusion measure was proposed. It has been used in a new method for exploring the evolution of social groups, called Group Evolution Discovery (GED).
Effect of the nature of randomness on quenching dynamics of Ising model on complex networks
by Soham Biswas
Phys. Rev. E 84, 066107 (2011)
Randomness is known to affect the dynamical behavior of many systems to a large extent. In this paper we investigate... more Randomness is known to affect the dynamical behavior of many systems to a large extent. In this paper we investigate how the nature of randomness affects the dynamics in a zero-temperature quench of the Ising model on two types of random networks. In both networks, which are embedded in a one-dimensional space, the first-neighbor connections exist and the average degree is 4 per node. In random model A the second-neighbor connections are rewired with a probability p, while in random model B additional connections between neighbors at a Euclidean distance l (l>1) are introduced with a probability P(l)∝l−α. We find that for both models, the dynamics leads to freezing such that the system gets locked in a disordered state. The point at which the disorder of the nonequilibrium steady state is maximum is located. The behavior of dynamical quantities such as residual energy, order parameter, and persistence are discussed and compared. Overall, the behavior of physical quantities are similar, although subtle differences are observed due to the difference in the nature of randomness.
Luhmann in Byzantium. A systems theory approach for historical network analysis
Working Paper for the Conference "The Connected Past: people, networks and complexity in archaeology and history", March 24-25th 2012, University of Southampton, GB; http://connectedpast.soton.ac.uk/schedule/
The slides of the presentation you will find here: http://oeaw.academia.edu/JohannesPreiserKapeller/Talks/74834/Luhmann_i
While Social Network Analysis (SNA) has become an accepted research tool in historical studies in the last decades,... more While Social Network Analysis (SNA) has become an accepted research tool in historical studies in the last decades, actual theoretical foundations for the approach to depict and analyse past social realities in the form of nodes and ties have remained as many-voiced and sometimes under-determined as in other fields of network analysis. A theoretical framework from which historical network analysis may benefit is the systems theory established by the sociologist Niklas LUHMANN (1927–1998). In Luhmann´s theory, social systems are systems of communication; in modern society, Luhmann identified several differentiated communication systems such as politics, religion or economy. For the analysis of Byzantine society, we combine Luhmannʼs framework with the concepts of SNA: we understand ties between nodes as potential channels of communication which can pertain to any communication system. And while communications between individuals in a specific institutional framework such as state administration or the church may primarily pertain to one system, we have to account for “multiplex” ties of communication and an overlap of various communication systems on the same set of nodes (who, in Luhmannʼs theory, are not per se part of any of these social systems, which only consist of communications). This approach also enables us either to examine communication ties (their density, distribution patterns, etc.) of one system separately or to concentrate on the structural position of individuals within the general social framework. Thus, we demonstrate that Luhmann can provide a coherent and at the same time flexible framework for historical network analysis.
The Transformation of Quantity Into Quality: Critical Mass in the Formation of Customary International
The formation of customary international law has long been criticized for its lack of a clear methodology,... more
The formation of customary international law has long been criticized for its lack of a clear methodology, characterized by an ambivalent relationship with state consent. Although customary international law seems to be entirely a creature of state consent, after all it is based on actual practice, in reality the fit with state consent is loose at best. Customary international law only awkwardly bridges the gap between a descriptive and prescriptive norm. Unable to move forward, the study of the formation of customary international law appears to have largely reached an impasse. Yet, states still appear to support and apply customary international law as a source of law, so we are faced with the situation of embracing a source of law that we do not understand well and where the applicable law is often vague.
This article is an attempt to bring into international law a perspective from the hard and soft sciences for discussing the formation of customary international law, specifically the study of critical mass in collective group behavior. This language is not entirely new to discussions on customary international law. Where it has been mentioned, the implications of critical mass theory have not been fully explored. Critical mass can be a loose concept to simply describe the accumulation of small actions that result in large shifts in collective behavior; however, it is also an empirically-based scientific study that attempts to assess how those changes come about. This article seeks to delve more deeply into critical mass and apply the insights from this study to the formation of customary international law.
Following a very brief introduction with background on customary international law, the paper will describe how the social sciences have embraced the critical mass theoretical perspective in the study of collective decision-making. Three primary elements of social change will be identified: (1) the importance of the content of the norm, (2) the role of influence through networks, and (3) the role of key individuals, “opinion leaders” and “opinion diffusors”.
Following this review of the science, the author will draw some implications for customary international law. In particular, the author will re-characterize three major discussions within customary international law into the three key factors of critical mass. The first discussion is that over the qualitative assessment of norms. The second is the growing influence of networks either as transnational governance or international organization rule-making. The final discussion is a proposal to understand the role of the “specially interested” state as a norm entrepreneur. The paper will conclude that study of critical mass can contribute to a better, and more formal, methodology for understanding customary international law.
51 views
Seen by: and 8 moreA Holistic Method for Reliability Performance Assessment and Critical Components Detection in Complex Networks
by Chi Zhang
IIE Transactions, Vol. 43, No. 9, pp. 661-675
Many infrastructures are now considered to be critical for both the economic development and general functioning of... more
Many infrastructures are now considered to be critical for both the economic development and general functioning of modern
societies. Thus, understanding their performance is important as a basis to develop intelligent and cost-effective ways to protect these networks. In this article, a critical infrastructure is modeled as a complex network for which a new metric is defined to understand its reliability. This metric called reliability Pi describes the average reliability between every pair of nodes in a complex network. As such, it is related to the two-terminal reliability concept in the traditional network context. Furthermore, in an effort to identify the most critical components that affect reliability Pi, a multi-objective optimization problem, known as the critical component detection problem, is introduced. The solution to this problem provides two important insights about the behavior of a complex network: (i) an
approximation to the set of optimal solutions that identifies the most critical components; and (ii) a quantitative assessment of how these failures affect the complete complex network.
Effects of sampling completeness on the structure of plant - pollinator networks
Accepted for publication in Ecology
Co-authored with A. Rivera-Hutinel, R.O. Bustamante & V.H. Marin
Plant-animal interaction networks provide important information on community organization. One of the most critical... more Plant-animal interaction networks provide important information on community organization. One of the most critical assumptions of network analysis is that the observed interaction patterns constitute an adequate sample of the set of interactions present in plant-animal communities. In spite of its importance, few studies have evaluated this assumption and in consequence there is no consensus on the sensitivity of network metrics to sampling methodological shortcomings. In this study we examine how variation in sampling completeness influences the estimation of six network metrics frequently used in the literature (connectance, nestedness, modularity, robustness to species loss, path length, and centralization). We analyze data of 186 flowering plants and 336 pollinator species in ten networks from a forest fragmented system in central Chile. Using species-based accumulation curves we estimated the deviation of network metrics in undersampled communities with respect to exhaustively sampled communities and the effect of network size and sampling evenness on network metrics. Our results indicate that: 1) most metrics were affected by sampling completeness, but they differ in their sensitivity to sampling effort, 2) nestedness, modularity, and robustness to species loss were less influenced by insufficient sampling than connectance, path length, and centralization, 3) robustness was mildly influenced by sampling evenness. These results caution studies that summarize information from databases with high, or unknown, heterogeneity in sampling effort per species, and stimulate researchers to report sampling intensity to standardize its effects in the search for broad patterns in plant-pollinator networks.
Spatial embedding and the structure of complex networks
Bullock, S., Barnett, L. and Di Paolo, E. A. (2010) Spatial embedding and the structure of complex networks, Complexity, 16(2): 20 – 28, doi:10.1002/cplx.20338.
We review and discuss the structural consequences of embedding a random network within a metric space such that nodes... more
We review and discuss the structural consequences of embedding a random network within a metric space such that nodes distributed in this space tend to be connected to those nearby. We find that where the spatial distribution of nodes is maximally symmetrical some of the structural properties of the resulting networks are similar to those of random nonspatial networks. However, where the distribution of nodes is inhomogeneous in some way, this ceases to be the case, with consequences for the distribution of neighborhood sizes within the network, the correlation between the number of neighbors of connected nodes, and the way in which the largest connected component of the network grows as the density of edges is increased. We present an overview of these findings in an attempt to convey the ramifications of spatial embedding to those studying real-world complex systems.
Key Words: spatial embedding; networks; random graphs; random geometric graphs
