Emergence through Selection: The Evolution of a Scientific Challenge

One of the most interesting scientific challenges nowadays deals with the analysis and the understanding of complex networks' dynamics and how their processes lead to emergence according to the interactions among their components. In this paper we approach the definition of new methodologies for the visualization and the exploration of the dynamics at play in real dynamic social networks. We present a recently introduced formalism called TVG (for time-varying graphs), which was initially developed to model and analyze highly-dynamic and infrastructure-less communication networks such as mobile ad-hoc networks, wireless sensor networks, or vehicular networks. We discuss its applicability to complex networks in general, and social networks in particular, by showing how it enables the specification and analysis of complex dynamic phenomena in terms of temporal interactions, and allows to easily switch the perspective between local and global dynamics. As an example, we chose the case of scientific communities by analyzing portion of the ArXiv repository (ten years of publications in physics) focusing on the social determinants (e.g. goals and potential interactions among individuals) behind the emergence and the resilience of scientific communities. We consider that scientific communities are at the same time communities of practice (through co-authorship) and that they exist also as representations in the scientists' mind, since references to other scientists' works is not merely an objective link to a relevant work, but it reveals social objects that one manipulates, select and refers to. In the paper we show the emergence/selection of a community as a goal-driven preferential attachment toward a set of authors among which there are some key scientists (Nobel prizes)

Interacting Agents and Continuous Opinions Dynamics

We present a model of opinion dynamics in which agents adjust continuous opinions as a result of random binary encounters whenever their difference in opinion is below a given threshold. High thresholds yield convergence of opinions towards an average opinion, whereas low thresholds result in several opinion clusters. The model is further generalised to network interactions, threshold heterogeneity, adaptive thresholds and binary strings of opinions.Comment: 21 pages, 13 figures. http://www.lps.ens.fr/~weisbuch/contopidyn/contopidyn.htm

Dynamics of Transformation from Segregation to Mixed Wealth Cities

We model the dynamics of the Schelling model for agents described simply by a continuously distributed variable - wealth. Agents move to neighborhoods where their wealth is not lesser than that of some proportion of their neighbors, the threshold level. As in the case of the classic Schelling model where segregation obtains between two races, we find here that wealth-based segregation occurs and persists. However, introducing uncertainty into the decision to move - that is, with some probability, if agents are allowed to move even though the threshold level condition is contravened - we find that even for small proportions of such disallowed moves, the dynamics no longer yield segregation but instead sharply transition into a persistent mixed wealth distribution. We investigate the nature of this sharp transformation between segregated and mixed states, and find that it is because of a non-linear relationship between allowed moves and disallowed moves. For small increases in disallowed moves, there is a rapid corresponding increase in allowed moves, but this tapers off as the fraction of disallowed moves increase further and finally settles at a stable value, remaining invariant to any further increase in disallowed moves. It is the overall effect of the dynamics in the initial region (with small numbers of disallowed moves) that shifts the system away from a state of segregation rapidly to a mixed wealth state. The contravention of the tolerance condition could be interpreted as public policy interventions like minimal levels of social housing or housing benefit transfers to poorer households. Our finding therefore suggests that it might require only very limited levels of such public intervention - just sufficient to enable a small fraction of disallowed moves, because the dynamics generated by such moves could spur the transformation from a segregated to mixed equilibrium.Comment: 12 pages, 7 figure

French Roadmap for complex Systems 2008-2009

This second issue of the French Complex Systems Roadmap is the outcome of the Entretiens de Cargese 2008, an interdisciplinary brainstorming session organized over one week in 2008, jointly by RNSC, ISC-PIF and IXXI. It capitalizes on the first roadmap and gathers contributions of more than 70 scientists from major French institutions. The aim of this roadmap is to foster the coordination of the complex systems community on focused topics and questions, as well as to present contributions and challenges in the complex systems sciences and complexity science to the public, political and industrial spheres

The ANTENATAL multicentre study to predict postnatal renal outcome in fetuses with posterior urethral valves: objectives and design

Abstract Background Posterior urethral valves (PUV) account for 17% of paediatric end-stage renal disease. A major issue in the management of PUV is prenatal prediction of postnatal renal function. Fetal ultrasound and fetal urine biochemistry are currently employed for this prediction, but clearly lack precision. We previously developed a fetal urine peptide signature that predicted in utero with high precision postnatal renal function in fetuses with PUV. We describe here the objectives and design of the prospective international multicentre ANTENATAL (multicentre validation of a fetal urine peptidome-based classifier to predict postnatal renal function in posterior urethral valves) study, set up to validate this fetal urine peptide signature. Methods Participants will be PUV pregnancies enrolled from 2017 to 2021 and followed up until 2023 in >30 European centres endorsed and supported by European reference networks for rare urological disorders (ERN eUROGEN) and rare kidney diseases (ERN ERKNet). The endpoint will be renal/patient survival at 2 years postnatally. Assuming α = 0.05, 1–β = 0.8 and a mean prevalence of severe renal outcome in PUV individuals of 0.35, 400 patients need to be enrolled to validate the previously reported sensitivity and specificity of the peptide signature. Results In this largest multicentre study of antenatally detected PUV, we anticipate bringing a novel tool to the clinic. Based on urinary peptides and potentially amended in the future with additional omics traits, this tool will be able to precisely quantify postnatal renal survival in PUV pregnancies. The main limitation of the employed approach is the need for specialized equipment. Conclusions Accurate risk assessment in the prenatal period should strongly improve the management of fetuses with PUV

Exploiting Human Resource Requirements to Infer Human Movement Patterns for Use in Modelling Disease Transmission Systems:An Example from Eastern Province, Zambia

In this research, an agent-based model (ABM) was developed to generate human movement routes between homes and water resources in a rural setting, given commonly available geospatial datasets on population distribution, land cover and landscape resources. ABMs are an object-oriented computational approach to modelling a system, focusing on the interactions of autonomous agents, and aiming to assess the impact of these agents and their interactions on the system as a whole. An A* pathfinding algorithm was implemented to produce walking routes, given data on the terrain in the area. A* is an extension of Dijkstra’s algorithm with an enhanced time performance through the use of heuristics. In this example, it was possible to impute daily activity movement patterns to the water resource for all villages in a 75 km long study transect across the Luangwa Valley, Zambia, and the simulated human movements were statistically similar to empirical observations on travel times to the water resource (Chi-squared, 95% confidence interval). This indicates that it is possible to produce realistic data regarding human movements without costly measurement as is commonly achieved, for example, through GPS, or retrospective or real-time diaries. The approach is transferable between different geographical locations, and the product can be useful in providing an insight into human movement patterns, and therefore has use in many human exposure-related applications, specifically epidemiological research in rural areas, where spatial heterogeneity in the disease landscape, and space-time proximity of individuals, can play a crucial role in disease spread

Anticipation of the evolution of the criminality at the city scale through agent-based simulation

International audienceThe evolution of criminality is a crucial question to design security policies at the city scale but is often left unexplored in main smart-city scenarios or simulators. Together with the ENSP1 and the DCSP2 in the context of the project MEGA, we built an agent-based simulation of the evolution of the criminality of the French city of Montpellier. Such simulation implemented on the Gama platform enables to demonstrate the interest of such a tool in order to explore potential scenarios and help design adapted policies. The simulation was built using existing police data concerning crimes, victims and criminals, that we used to generate an artificial synthetic population of 50 000 potential criminals distributed on the city of Montpellier at the IRIS3 scale. Such synthetic population corresponds to the statistics available from the police and enables as well to limit the population size in focusing on the potentially active part of the population in the simulation. We generated a synthetic social network among agents taking into account rough statistics concerning their location and age. From this population, the behavioral model of the agents leading them to commit crimes in the simulation is structured through three main components. The first one concerns the moral values and the opinion of the agent, influenced through his social network and that corresponds to his own moral position concerning crime (i.e., whether it is totally unacceptable or could be envisaged depending on the circumstances). The second component corresponds to a risk evaluation and captures roughly the risk of being caught and the consequences. The third component corresponds to the crime modalities: if the first two components are positive (pro-crime and low risk), then the agent elaborates a strategy in order to determine where and when to commit crime. Such modular architecture for the criminal behavior can be seen as oversimplistic, however it presents the advantage of capturing a large spectrum of behaviors (from social influence only, to pure risk evaluation or opportunistic behavior). It has also the advantage to enable and test quite different scenarios of actions from the police (from accompanying the criminals (therefore playing on moral/opinion dynamics), to playing on the consequences if found guilty, but also on more spatial strategies of distributing the police resources). Another main advantage of such model is that it is potentially quite easy to calibrate, as we can use different sources of data to calibrate independently each component. Finally using this model on Gama, we tested different scenarios in order to explore the potential of the proposed architecture. The designed scenarios were encouraging concerning the expressivity of the proposed model to envisage very diverse situations. To name a few, we explored scenarios concerning: the police resources and their spatial distribution, the impact of building a new stadium (that created crime opportunities), the support for juvenile offenders… In any case the aim of such scenarios is not to predict the evolution of criminality but rather by comparison with a base scenario to anticipate potential evolutions and help and test corresponding policies