The difficulties that many rich countries now face are not the result of the inexorable laws of economics, to which people simply must adjust, as they would to a natural disaster. On the contrary, the decline in most households' income over the past three decades, particularly in the US, is the result of flawed policies.
The conference is organized with the contribution of the SimulPast project(www.simulpast.es), a 5-year exploratory research project funded by the SpanishGovernment (MICINN CSD2010-00034) that aims at developing an innovative andinterdisciplinary methodological framework to model and simulate ancient societies andtheir relationship with environmental transformations. To achieve these aims, SimulPastintegrates knowledge from diverse fields covering humanities, social, computationaland ecological sciences within a national and international network.
The conference intention is to showcase the result of the SimulPast project together withcurrent international research on the methodological and theoretical aspects of computersimulation in archaeological and historical contexts. The conference will bring togetherscholars from different disciplinary backgrounds (history, ecology, archaeology,anthropology, sociology, computer science and complex systems) in order to promotedeeper understanding and collaboration in the study of past human behavior and history
We are delighted to welcome the 6th International Conference on Social Informatics (SocInfo 2014) to Barcelona, Spain, from November 10th to November 13th.SocInfo is an interdisciplinary venue for researchers from Computer Science, Informatics, Social Sciences and Management Sciences to share ideas and opinions, and present original research work on studying the interplay between socially-centric platforms and social phenomena. The ultimate goal of Social Informatics is to create better understanding of socially-centric platforms not just as a technology, but also as a set of social phenomena. To that end, we are inviting interdisciplinary papers, on applying information technology in the study of social phenomena, on applying social concepts in the design of information systems, on applying methods from the social sciences in the study of social computing and information systems, on applying computational algorithms to facilitate the study of social systems and human social dynamics, and on designing information and communication technologies that consider social context.
2014 Interdisciplinary Symposium on Complex Systems (ISCS'14)
Center for the Study of Complex Systems (CSDC) Department of Physics and Astronomy University of Florence, Florence, Italy September 15 - 18, 2014
The main aim of the 2014 Interdisciplinary Symposium on Complex Systems is to bring together researchers working on complex systems. We invite scientists, philosophers, researchers, engineers, and young students to submit their works, attend, or register for tutorials.The main theme of this year is "How Nature Works".
The human brain makes predictions by finding similarities between the patterns in recent sensory inputs and previous experiences stored in its vast memory. The same process is now perfectly feasible for those engaged in promoting economic development.
Over the last decade online education has emerged as a way for students and faculty to collaborate more freely, attain greater flexibility, and utilize new media to learn. The burning debate lies in whether online educational options are harmful to traditional education or offer endless benefits necessary to accommodate a 21st century learner. Supporters of virtual learning environments suggest that 21st century learners require the construction and creation capabilities offered through Web 2.0 to succeed while critics suggest that asynchronous interactions are not engaging and rigorous enough for higher education. A balanced online environment should provide a blend of both asynchronous and synchronous opportunities, which promote communication and collaboration among classmates and instructors.
For good or ill, big data and networks have taken over our lives and, unfortunately, they too often run amok. From the Arab Spring, mediated on Twitter and Facebook, to the NSA spying scandal, to the 2008 financial crash, big data and networks are causing wrenching changes but very rarely can we piece together why, how, or what do to about the problem. Alex “Sandy” Pentland and his team have created a new data science that not only describes how networks of people behave but also creates actionable intelligence from that understanding. Called “Social Physics,” it encapsulates social, analytical, computer, and managerial sciences into a synthesis that allows us to build more resilient and creative societies while at the same time providing greater protection for personal privacy and resistance to cyber attack. Pentland’s new book, SOCIAL PHYSICS: How Good Ideas Spread—The Lessons from a New Science, is a landmark tour of this new science, offering revolutionary insights into the mysteries of collective intelligence and social influence.
A system is said to be meritocratic if the compensation and power available to individuals is determined by their abilities and merits. A system is topocratic if the compensation and power available to an individual is determined primarily by her position in a network. Here we introduce a model that is perfectly meritocratic for fully connected networks but that becomes topocratic for sparse networks-like the ones in society. In the model, individuals produce and sell content, but also distribute the content produced by others when they belong to the shortest path connecting a buyer and a seller. The production and distribution of content defines two channels of compensation: a meritocratic channel, where individuals are compensated for the content they produce, and a topocratic channel, where individual compensation is based on the number of shortest paths that go through them in the network. We solve the model analytically and show that the distribution of payoffs is meritocratic only if the average degree of the nodes is larger than a root of the total number of nodes. We conclude that, in the light of this model, the sparsity and structure of networks represents a fundamental constraint to the meritocracy of societies.
To Each According to its Degree: The Meritocracy and Topocracy of Embedded Markets J. Borondo, F. Borondo, C. Rodriguez-Sickert & C. A. Hidalgo
This is a short presentation of what you can do with a software called Bayesian Dynamical Systems. It can be used to find patterns in large amounts of data and is the result of a cooperation between Uppsala University and the Institute for Futures Studies in Stockholm.
Here the software is used to study the relation between economic growth and democracy, presented by David Sumpter.
We’re increasingly living in a world of black boxes. We don’t understand the way things work. And open-source software, open data are critical tools. We see this in the field of computer security. People say, “Well, we have to keep this secret.” Well, it turns out that the strongest security protocols are those that are secure even when people know how they work. Secrecy is actually, it turns out, a fairly weak way of being secure. And I think in a similar way, we have to understand who owns the rules, how are they driven, how are they guiding our behavior. And there may be cases where you say, “Well, actually it’s a reasonable trade-off to have some degree of secrecy.” We have this with trade secrets all the time in the commercial world. But there are other areas where we should say, “No, we really need to know how this works.”
We apply measures of complexity, emergence and self-organization to an abstract city traffic model for comparing a traditional traffic coordination method with a self-organizing method in two scenarios: cyclic boundaries and non-orientable boundaries. We show that the measures are useful to identify and characterize different dynamical phases. It becomes clear that different operation regimes are required for different traffic demands. Thus, not only traffic is a non-stationary problem, which requires controllers to adapt constantly. Controllers must also change drastically the complexity of their behavior depending on the demand. Based on our measures, we can say that the self-organizing method achieves an adaptability level comparable to a living system.
Measuring the Complexity of Self-organizing Traffic Lights Dario Zubillaga, Geovany Cruz, Luis Daniel Aguilar, Jorge Zapotecatl, Nelson Fernandez, Jose Aguilar, David A. Rosenblueth, Carlos Gershenson
This is the first in a series of articles recounting the history of the Santa Fe Institute drawn from primary and, in a few cases, secondary sources.
By John German
In George Cowan's telling, the notion for a Santa Fe Institute began to form in the summer of 1956. He had been invited to the Aspen Institute, where prominent intellectuals from the arts, science, and culture gathered for free-form philosophical exchanges. He had just participated as the lone scientist in a discussion of literature. (...)
Animal behavior isn't complicated, but it is complex. Nicolas Perony studies how individual animals -- be they Scottish Terriers, bats or meerkats -- follow simple rules that, collectively, create larger patterns of behavior. And how this complexity born of simplicity can help them adapt to new circumstances, as they arise.
Large-scale networks of human interaction, in particular country-wide telephone call networks, can be used to redraw geographical maps by applying algorithms of topological community detection. The geographic projections of the emerging areas in a few recent studies on single regions have been suggested to share two distinct properties: first, they are cohesive, and second, they tend to closely follow socio-economic boundaries and are similar to existing political regions in size and number. Here we use an extended set of countries and clustering indices to quantify overlaps, providing ample additional evidence for these observations using phone data from countries of various scales across Europe, Asia, and Africa: France, the UK, Italy, Belgium, Portugal, Saudi Arabia, and Ivory Coast. In our analysis we use the known approach of partitioning country-wide networks, and an additional iterative partitioning of each of the first level communities into sub-communities, revealing that cohesiveness and matching of official regions can also be observed on a second level if spatial resolution of the data is high enough. The method has possible policy implications on the definition of the borderlines and sizes of administrative regions.
Sobolevsky S, Szell M, Campari R, Couronné T, Smoreda Z, et al. (2013) Delineating Geographical Regions with Networks of Human Interactions in an Extensive Set of Countries. PLoS ONE 8(12): e81707. http://dx.doi.org/10.1371/journal.pone.0081707
It is generally recognized that life is becoming more complex. This article analyzes the human social environment using the "complexity proﬁle," a mathematical tool for characterizing the collective behavior of a system. The analysis is used to justify the qualitative observation that complexity of existence has increased and is increasing. The increase in complexity is directly related to sweeping changes in the structure and dynamics of human civilization—the increasing interdependence of the global economic and social system, and the instabilities of dictatorships, communism and corporate hierarchies. Our complex social environment is consistent with identifying global human civilization as an organism capable of complex behavior that protects its components (us) and which should be capable of responding eﬀectively to complex environmental demands
Data arising from social systems is often highly complex, involving non-linear relationships between the macro-level variables that characterize these systems. We present a method for analyzing this type of longitudinal or panel data using differential equations. We identify the best non-linear functions that capture interactions between variables, employing Bayes factor to decide how many interaction terms should be included in the model. This method punishes overly complicated models and identifies models with the most explanatory power. We illustrate our approach on the classic example of relating democracy and economic growth, identifying non-linear relationships between these two variables. We show how multiple variables and variable lags can be accounted for and provide a toolbox in R to implement our approach.