Variational Principle underlying Scale Invariant Social Systems

MaxEnt's variational principle, in conjunction with Shannon's logarithmic information measure, yields only exponential functional forms in straightforward fashion. In this communication we show how to overcome this limitation via the incorporation, into the variational process, of suitable dynamical information. As a consequence, we are able to formulate a somewhat generalized Shannonian Maximum Entropy approach which provides a unifying "thermodynamic-like" explanation for the scale-invariant phenomena observed in social contexts, as city-population distributions. We confirm the MaxEnt predictions by means of numerical experiments with random walkers, and compare them with some empirical data

Unravelling the size distribution of social groups with information theory on complex networks

The minimization of Fisher's information (MFI) approach of Frieden et al. [Phys. Rev. E {\bf 60} 48 (1999)] is applied to the study of size distributions in social groups on the basis of a recently established analogy between scale invariant systems and classical gases [arXiv:0908.0504]. Going beyond the ideal gas scenario is seen to be tantamount to simulating the interactions taking place in a network's competitive cluster growth process. We find a scaling rule that allows to classify the final cluster-size distributions using only one parameter that we call the competitiveness. Empirical city-size distributions and electoral results can be thus reproduced and classified according to this competitiveness, which also allows to correctly predict well-established assessments such as the "six-degrees of separation", which is shown here to be a direct consequence of the maximum number of stable social relationships that one person can maintain, known as Dunbar's number. Finally, we show that scaled city-size distributions of large countries follow the same universal distribution

Manifesto of computational social science

The increasing integration of technology into our lives has created unprecedented volumes of data on society's everyday behaviour. Such data opens up exciting new opportunities to work towards a quantitative understanding of our complex social systems, within the realms of a new discipline known as Computational Social Science. Against a background of financial crises, riots and international epidemics, the urgent need for a greater comprehension of the complexity of our interconnected global society and an ability to apply such insights in policy decisions is clear. This manifesto outlines the objectives of this new scientific direction, considering the challenges involved in it, and the extensive impact on science, technology and society that the success of this endeavour is likely to bring about.The publication of this work was partially supported by the European Union’s Seventh Framework Programme (FP7/2007–2013) under grant agreement No. 284709, a Coordination and Support Action in the Information and Communication Technologies activity area (‘FuturICT’ FET Flagship Pilot Project). We are grateful to the anonymous reviewers for the insightful comments.Publicad

IMG 305 - PEMBUNGKUSAN MAKANAN NOV.05.

We discuss the use of Agent-based Modelling for the development and testing of theories about emergent social phenomena in marketing and the social sciences in general. We address both theoretical aspects about the types of phenomena that are suitably addressed with this approach and practical guidelines to help plan and structure the development of a theory about the causes of such a phenomenon in conjunction with a matching ABM. We argue that research about complex social phenomena is still largely fundamental research and therefore an iterative and cyclical development process of both theory and model is to be expected. To better anticipate and manage this process, we provide theoretical and practical guidelines. These may help to identify and structure the domain of candidate explanations for a social phenomenon, and furthermore assist the process of model implementation and subsequent development. The main goal of this paper was to make research on complex social systems more accessible and help anticipate and structure the research process

Agents adopting agriculture:Modeling the agricultural transition

Abstract. The question “What drove foragers to farm? ” has drawn answers from many different disciplines, often in the form of verbal models. Here, we take one such model, that of the ideal free distribution, and implement it as an agent-based computer simulation. Populations distribute themselves according to the marginal quality of different habitats, predicting settlement patterns and subsistence methods over both time and space. Our experiments and our analy-ses thereof show that central conclusions of the ideal free distribution model are reproduced by our agent-based simulation, while at the same time offering new insights into the theory’s underlying assumptions. Generally, we demonstrate how agent-based models can make use of empirical data to reconstruct realistic environmental and cultural contexts, enabling concrete tests of the explanatory power of anthropological models put forward to explain historical develop-ments, such as agricultural transitions, in specific times and places.

Herding and Clustering in Economics: The Yule-Zipf-Simon Model

Focusing on the evergreen problem of the size of firms, we discuss the incompatibility between empirical data and Ewens sampling formula. An alternative model is suggested, inspired to Simon’s approaches to the firm size problem. It differs from the Ewens model both in destruction and in creation. In particular the probability of herding is independent on the size of the herd. This very simple assumption destroys the exchangeability of the random partitions, and forbids an analytical solution. Simple computational simulations look to confirm that actually the mean number of clusters of size i (the equilibrium distribution) follows the corresponding Yule distribution. Finally we introduce a Markov chain, that resembles the marginal dynamics of a cluster, which drives the cluster to the right-censored Yule distribution