Violent extremist group ecologies under stress

Violent extremist groups are currently making intensive use of Internet fora for recruitment to terrorism. These fora are under constant scrutiny by security agencies, private vigilante groups, and hackers, who sometimes shut them down with cybernetic attacks. However, there is a lack of experimental and formal understanding of the recruitment dynamics of online extremist fora and the effect of strategies to control them.Here, the authors utilise data on ten extremist fora that we collected for four years to develop a data-driven mathematical model that is the first attempt to measure whether (and how) these external attacks induce extremist fora to self-regulate. The results suggest that an increase in the number of groups targeted for attack causes an exponential increase in the cost of enforcement and an exponential decrease in its effectiveness. Thus, a policy to occasionally attack large groups can be very efficient for limiting violent output from these fora.Authored by Manuel Cebrian, Manuel R. Torres, Ramon Huerta and James H. Fowler

Overcoming Problems in the Measurement of Biological Complexity

In a genetic algorithm, fluctuations of the entropy of a genome over time are interpreted as fluctuations of the information that the genome's organism is storing about its environment, being this reflected in more complex organisms. The computation of this entropy presents technical problems due to the small population sizes used in practice. In this work we propose and test an alternative way of measuring the entropy variation in a population by means of algorithmic information theory, where the entropy variation between two generational steps is the Kolmogorov complexity of the first step conditioned to the second one. As an example application of this technique, we report experimental differences in entropy evolution between systems in which sexual reproduction is present or absent.Comment: 4 pages, 5 figure

Measuring and Optimizing Cultural Markets

Social influence has been shown to create significant unpredictability in cultural markets, providing one potential explanation why experts routinely fail at predicting commercial success of cultural products. To counteract the difficulty of making accurate predictions, "measure and react" strategies have been advocated but finding a concrete strategy that scales for very large markets has remained elusive so far. Here we propose a "measure and optimize" strategy based on an optimization policy that uses product quality, appeal, and social influence to maximize expected profits in the market at each decision point. Our computational experiments show that our policy leverages social influence to produce significant performance benefits for the market, while our theoretical analysis proves that our policy outperforms in expectation any policy not displaying social information. Our results contrast with earlier work which focused on showing the unpredictability and inequalities created by social influence. Not only do we show for the first time that dynamically showing consumers positive social information under our policy increases the expected performance of the seller in cultural markets. We also show that, in reasonable settings, our policy does not introduce significant unpredictability and identifies "blockbusters". Overall, these results shed new light on the nature of social influence and how it can be leveraged for the benefits of the market

Evolution in the Design and Functionality of Rubrics: from “Square” Rubrics to “Federated” Rubrics

The assessment of learning remains one of the most controversial and challenging aspects for teachers. Among some recent technical solutions, methods and techniques like eRubrics emerge in an attempt to solve the situation. Understanding that all teaching contexts are different and there can be no single solution for all cases, specific measures are adapted to contexts where teachers receive support from institutions and communities of practice. This paper presents the evolution of the eRubric service [1] which started from a first experience with paper rubrics, and, with time and after several I+D+R [2] educational projects, has evolved thanks to the support of a community of practice [3] and the exchange of experiences between teachers and researchers. This paper shows the results and functionality of the eRubrics service up to the date of publicationa.) Project I+D+i EDU2010-15432: eRubric federated service for assessing university learning http://erubrica.uma.es/?page_id=434. b.) Centre for the Design of eRubrics. National Distance Education System -Sined- Mexico. [http://erubrica.uma.es/?page_id=389

Optimizing Expected Utility in a Multinomial Logit Model with Position Bias and Social Influence

Motivated by applications in retail, online advertising, and cultural markets, this paper studies how to find the optimal assortment and positioning of products subject to a capacity constraint. We prove that the optimal assortment and positioning can be found in polynomial time for a multinomial logit model capturing utilities, position bias, and social influence. Moreover, in a dynamic market, we show that the policy that applies the optimal assortment and positioning and leverages social influence outperforms in expectation any policy not using social influence

Efficient detection of contagious outbreaks in massive metropolitan encounter networks

Physical contact remains difficult to trace in large metropolitan networks, though it is a key vehicle for the transmission of contagious outbreaks. Co-presence encounters during daily transit use provide us with a city-scale time-resolved physical contact network, consisting of 1 billion contacts among 3 million transit users. Here, we study the advantage that knowledge of such co-presence structures may provide for early detection of contagious outbreaks. We first examine the "friend sensor" scheme --- a simple, but universal strategy requiring only local information --- and demonstrate that it provides significant early detection of simulated outbreaks. Taking advantage of the full network structure, we then identify advanced "global sensor sets", obtaining substantial early warning times savings over the friends sensor scheme. Individuals with highest number of encounters are the most efficient sensors, with performance comparable to individuals with the highest travel frequency, exploratory behavior and structural centrality. An efficiency balance emerges when testing the dependency on sensor size and evaluating sensor reliability; we find that substantial and reliable lead-time could be attained by monitoring only 0.01% of the population with the highest degree.Comment: 4 figure