Introduced in its contemporary form in 1946 (...) the gravity law is the prevailing framework with which to predict population movement cargo shipping volume and inter-city phone calls, as well as bilateral trade flows between nations. Despite its widespread use, it relies on adjustable parameters that vary from region to region and suffers from known analytic inconsistencies. Here we introduce a stochastic process capturing local mobility decisions that helps us analytically derive commuting and mobility fluxes that require as input only information on the population distribution. The resulting radiation model predicts mobility patterns in good agreement with mobility and transport patterns observed in a wide range of phenomena, from long-term migration patterns to communication volume between different regions. Given its parameter-free nature, the model can be applied in areas where we lack previous mobility measurements, significantly improving the predictive accuracy of most of the phenomena affected by mobility and transport processes.
A universal model for mobility and migration patterns
Filippo Simini, Marta C. González, Amos Maritan & Albert-László Barabási Nature 484, 96–100 (05 April 2012) doi:10.1038/nature10856
Networks are characterized by a variety of topological features and dynamics. Classifying nodes into communities, community structure, is important when exploring networks. This paper explores the community detection metric called modularity. The theoretical definitions of modularity are connected with intuitive insights into the compositions of communities. Local modularity costs/benefits are explored and an efficient stochastic algorithm, Bloom, is introduced, based on growing communities using local improvement measures. Three extensions of Bloom are presented that build upon the basic version. A numerical analysis compares Bloom with the popular fast-greedy algorithm and demonstrates the successful performance of the three modifications of Bloom.
On the global social media stage, it's not so much the message but rather network structure and competition for attention that determine whether a meme becomes popular and shows staying power or whether it falls by the wayside, research led by Indiana University has determined.
Expand your Python skills by working with data structures and algorithms in a refreshing context—through an eye-opening exploration of complexity science. You’ll work with graphs, algorithm analysis, scale-free networks, and cellular automata, using advanced features that make Python such a powerful language. Ideal as a text for courses on Python programming and algorithms, Think Complexity will also help self-learners gain valuable experience with topics and ideas they might not encounter otherwise.
Developing the ability to comprehensively study infections in small populations enables us to improve epidemic models and better advise individuals about potential risks to their health. We currently have a limited understanding of how infections spread within a small population because it has been difficult to closely track an infection within a complete community. The paper presents data closely tracking the spread of an infection centered on a student dormitory, collected by leveraging the residents' use of cellular phones. The data are based on daily symptom surveys taken over a period of four months and proximity tracking through cellular phones. We demonstrate that using a Bayesian, discrete-time multi-agent model of infection to model real-world symptom reports and proximity tracking records gives us important insights about infections in small populations.
Many models in natural and social sciences are comprised of sets of inter-acting entities whose intensity of interaction decreases with distance. This often leads to structures of interest in these models composed of dense packs of entities. Fast Multipole Methods are a family of methods developed to help with the calculation of a number of computable models such as described above. We propose a method that builds upon FMM to detect and model the dense structures of these systems.
Human mobility and, in particular, commuting patterns have a fundamental role in understanding socio-economic systems. Analysing and modelling the networks formed by commuters, for example, has become a crucial requirement in studying rural areas dynamics and to help decision-making. This paper presents a simple spatial interaction commuting model with only one parameter. The proposed algorithm considers each individual who wants to commute, starting from their residence to all the possible workplaces. The algorithm decides the location of the workplace following the classical rule inspired from the gravity law consisting of a compromise between the job offers and the distance to the job. The further away the job is, the more important the offer should be to be considered for the decision. Inversely, the quantity of offers is not important for the decision when these offers are close by. The presented model provides a simple, yet powerful approach to simulate realistic distributions of commuters for empirical studies with limited data availability. The paper also presents a comparative analysis of the structure of the commuting networks of the four European regions to which we apply our model. The model is calibrated and validated on these regions. The results from the analysis show that the model is very efficient in reproducing most of the statistical properties of the network given by the data sources.
In modern science, the concept of resilience has had various meanings depending on the context. Given the impact of resilience in a wide spectrum of fields, definitional issues have attracted a lot of interest. In this book , a consortium of researchers (funded by a joint European project) suggested measures to formalize the concept of resilience by following viability theory, which can also be extremely useful to design management policies in different environments.
Viability and Resilience of Complex Systems: Concepts, Methods and Case Studies from Ecology and Society (Understanding Complex Systems)
Deffuant, Guillaume and Gilbert, Nigel (eds.) Springer-Verlag: Berlin, 2011 ISBN 9783642204227 (pb)
For survival and development, autonomous agents in complex adaptive systems involving the human society must compete against or collaborate with others for sharing limited resources or wealth, by using different methods. One method is to invest, in order to obtain payoffs with risk. It is a common belief that investments with a positive risk-return relationship (namely, high risk high return and vice versa) are dominant over those with a negative risk-return relationship (i.e., high risk low return and vice versa) in the human society; the belief has a notable impact on daily investing activities of investors. Here we investigate the risk-return relationship in a model complex adaptive system, in order to study the effect of both market efficiency and closeness that exist in the human society and play an important role in helping to establish traditional finance/economics theories.
We conduct a series of computer-aided human experiments, and also perform agent-based simulations and theoretical analysis to confirm the experimental observations and reveal the underlying mechanism. We report that investments with a negative risk-return relationship have dominance over those with a positive risk-return relationship instead in such a complex adaptive systems. We formulate the dynamical process for the system's evolution, which helps to discover the different role of identical and heterogeneous preferences. This work might be valuable not only to complexity science, but also to finance and economics, to management and social science, and to physics.
Are there any well-known computational models in the literature that you would like to use, but the code isn't readily available? CoMSES Net is soliciting nominations for models that should be prioritized for submission to the CoMSES Computational Model Library (CML), and implementation on up-to-date modeling platforms is needed.
The process by which genes and memes influence behaviour is poorly understood. Genes generally may have a strong influence as predispositions directing individuals towards certain behaviours; whereas memes may have a less direct influence as information inputs to cognitive processes determining behaviour. In certain areas of medical science, knowledge has progressed towards approximate quantification of genetic influences, while social psychology can provide models of mimetic influence as the spread of attitudes. This paper describes a computational model integration of genetic and mimetic influences in a healthcare domain. It models mimetic influences of advertising and health awareness messages in populations with genetic predispositions towards obesity; environmental variables influence both gene expression and mimetic force. Sensitivity analysis using the model with different population network structures is used to investigate the relative force of meme spread and influence.
Alistair Sutcliffe and Di Wang (2012)
Investigating the Relative Influence of Genes and Memes in Healthcare
Journal of Artificial Societies and Social Simulation 15 (2) 1
Common methods of causal inference generate directed acyclic graphs (DAGs) that formalize causal relations between n variables. Given the joint distribution of all these variables, the DAG contains all information about how intervening on one variable would change the distribution of the other n-1 variables. It remains, however, a non-trivial question how to quantify the causal influence of one variable on another one. Here we propose a measure for causal strength that refers to direct effects and measure the "strength of an arrow" or a set of arrows. It is based on a hypothetical intervention that modifies the joint distribution by cutting the corresponding edge. The causal strength is then the relative entropy distance between the old and the new distribution. We discuss other measures of causal strength like the average causal effect, transfer entropy and information flow and describe their limitations. We argue that our measure is also more appropriate for time series than the known ones. Finally, we discuss conceptual problems in defining the strength of indirect effects.
Quantifying causal influences
Dominik Janzing, David Balduzzi, Moritz Grosse-Wentrup, Bernhard Schoelkopf
Simulation is a creative and epistemologically-delicate process that has attracted growing attention since the 1990s, both in the natural and the social sciences. It is the imitation of the operation of a real-world process or system over time. The act of simulating something first requires that a model be developed; this model represents the key characteristics or behavior of the selected physical or abstract system or process. The model represents the system itself, whereas the simulation represents the operation of the system overtime. The crucial role of simulation in theorizing, modelling, and understanding complex systems, and its increasing use for decision-making in concrete problems and/or public policy, has led the theorizing of simulation to an entirely new level of attention. At the same time, a huge community of researchers are utilizing simulation with a set of tools, methods, and concepts, in an intense cross-disciplinary atmosphere, with obvious interest in investigating the conditions for the successful use of simulation. The recognition that progress in the science of simulation must go hand in hand with the semiotic analysis of its epistemological status has been an important motivation for the organization of the Epistemological Perspectives on Simulation (EPOS) workshops since 2004.
The Complex Systems Society is launching a Liquid Journal of Complex Systems. It is an online journal that allows the possibility of the publication to evolve with community feedback until it reaches a state ready for printed publication.
Sharing your scoops to your social media accounts is a must to distribute your curated content. Not only will it drive traffic and leads through your content, but it will help show your expertise with your followers.
How to integrate my topics' content to my website?
Integrating your curated content to your website or blog will allow you to increase your website visitors’ engagement, boost SEO and acquire new visitors. By redirecting your social media traffic to your website, Scoop.it will also help you generate more qualified traffic and leads from your curation work.
Distributing your curated content through a newsletter is a great way to nurture and engage your email subscribers will developing your traffic and visibility.
Creating engaging newsletters with your curated content is really easy.