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Given a finite and noisy dataset generated with a closed-form mathematical model, when is it possible to learn the true generating model from the data alone? This is the question we investigate here. We show that this model-learning problem displays a transition from a low-noise phase in which the true model can be learned, to a phase in which the observation noise is too high for the true model to be learned by any method. Both in the low-noise phase and in the high-noise phase, probabilistic model selection leads to optimal generalization to unseen data. This is in contrast to standard machine learning approaches, including artificial neural networks, which in this particular problem are limited, in the low-noise phase, by their ability to interpolate. In the transition region between the learnable and unlearnable phases, generalization is hard for all approaches including probabilistic model selection.

Many studies have shown that there are regularities in the way human beings make decisions. However, our ability to obtain models that capture such regularities and can accurately predict unobserved decisions is still limited. We tackle this problem in the context of individuals who are given information relative to the evolution of market prices and asked to guess the direction of the market. We use a networks inference approach with stochastic block models (SBM) to find the model and network representation that is most predictive of unobserved decisions. Our results suggest that users mostly use recent information (about the market and about their previous decisions) to guess. Furthermore, the analysis of SBM groups reveals a set of strategies used by players to process information and make decisions that is analogous to behaviors observed in other contexts. Our study provides and example on how to quantitatively explore human behavior strategies by representing decisions as networks and using rigorous inference and model-selection approaches.

Background Mobile apps provide an accessible way to test new health-related methodologies. Tobacco is still the primary preventable cause of death in industrialized countries, constituting an important public health issue. New technologies provide novel opportunities that are effective in the cessation of smoking tobacco. Objective This paper aims to evaluate the efficacy and usage of a mobile app for assisting adult smokers to quit smoking. Methods We conducted a cluster randomized clinical trial. We included smokers older than 18 years who were motivated to stop smoking and used a mobile phone compatible with our mobile app. We carried out follow-up visits at 15, 30, and 45 days, and at 2, 3, 6, and 12 months. Participants of the intervention group had access to the Tobbstop mobile app designed by the research team. The primary outcomes were continuous smoking abstinence at 3 and 12 months. Results A total of 773 participants were included in the trial, of which 602 (77.9%) began the study on their D-Day. Of participants in the intervention group, 34.15% (97/284) did not use the app. The continuous abstention level was significantly larger in the intervention group participants who used the app than in those who did not use the app at both 3 months (72/187, 38.5% vs 13/97, 13.4%; P Conclusions Regular use of an app for smoking cessation is effective in comparison with standard clinical practice. Trial Registration Clinicaltrials.gov NCT01734421; https://clinicaltrials.gov/ct2/show/NCT01734421

Jutge.org is an open educational online programming judge designed for students and instructors, featuring a repository of problems that is well organized by courses, topics, and difficulty. Internally, Jutge.org uses a secure and efficient architecture and integrates modern verification techniques, formal methods, static code analysis, and data mining. Jutge.org has exhaustively been used during the last decade at the Universitat Politecnica de Catalunya to strengthen the learning-by-doing approach in several courses. This paper presents the main characteristics of Jutge.org and shows its use and impact on a wide range of courses covering basic programming, data structures, algorithms, artificial intelligence, functional programming, and circuit design.

BACKGROUND Mobile apps provide an accessible way to test new health-related methodologies. Tobacco is still the primary preventable cause of death in industrialized countries, constituting an important public health issue. New technologies provide novel opportunities that are effective in the cessation of smoking tobacco. OBJECTIVE This paper aims to evaluate the efficacy and usage of a mobile app for assisting adult smokers to quit smoking. METHODS We conducted a cluster randomized clinical trial. We included smokers older than 18 years who were motivated to stop smoking and used a mobile phone compatible with our mobile app. We carried out follow-up visits at 15, 30, and 45 days, and at 2, 3, 6, and 12 months. Participants of the intervention group had access to the Tobbstop mobile app designed by the research team. The primary outcomes were continuous smoking abstinence at 3 and 12 months. RESULTS A total of 773 participants were included in the trial, of which 602 (77.9%) began the study on their D-Day. Of participants in the intervention group, 34.15% (97/284) did not use the app. The continuous abstention level was significantly larger in the intervention group participants who used the app than in those who did not use the app at both 3 months (72/187, 38.5% vs 13/97, 13.4%; PP=.01). Participants in the intervention group who used the app regularly and correctly had a higher probability of not being smokers at 12 months (OR 7.20, 95% CI 2.14-24.20; P=.001) than the participants of the CG. CONCLUSIONS Regular use of an app for smoking cessation is effective in comparison with standard clinical practice. CLINICALTRIAL Clinicaltrials.gov NCT01734421; https://clinicaltrials.gov/ct2/show/NCT01734421

Qualitative research; Mobile apps; Smoking cessation; Social support Recerca qualitativa; Aplicacions mòbils; Deshabituació del tabac; Suport social Investigación cualitativa; Aplicaciones móviles; Deshabituación del tabaco; Apoyo social Background: Smoking is one of the most significant factors contributing to low life expectancy, health inequalities, and illness at the worldwide scale. Smoking cessation attempts benefit from social support. Mobile phones have changed the way we communicate through the use of freely available message-oriented apps. Mobile app-based interventions for smoking cessation programs can provide interactive, supportive, and individually tailored interventions. Objective: This study aimed to identify emotions, coping strategies, beliefs, values, and cognitive evaluations of smokers who are in the process of quitting, and to analyze online social support provided through the analysis of messages posted to a chat function integrated into a mobile app. Methods: In this descriptive qualitative study, informants were smokers who participated in the chat of Tobbstop. The technique to generate information was documentary through messages collected from September 2014 through June 2016, specifically designed to support a smoking cessation intervention. A thematic content analysis of the messages applied 2 conceptual models: the Lazarus and Folkman model to assess participant's experiences and perceptions and the Cutrona model to evaluate online social support. Results: During the study period, 11,788 text messages were posted to the chat by 101 users. The most frequent messages offered information and emotional support, and all the basic emotions were reported in the chat. The 3 most frequent coping strategies identified were physical activity, different types of treatment such as nicotine replacement, and humor. Beliefs about quitting smoking included the inevitability of weight gain and the notion that not using any type of medications is better for smoking cessation. Health and family were the values more frequently described, followed by freedom. A smoke-free environment was perceived as important to successful smoking cessation. The social support group that was developed with the app offered mainly emotional and informational support. Conclusions: Our analysis suggests that a chat integrated into a mobile app focused on supporting smoking cessation provides a useful tool for smokers who are in the process of quitting, by offering social support and a space to share concerns, information, or strategies.

BACKGROUND Tobacco use during pregnancy entails a serious risk to the mother and harmful effects on the development of the child. Europe has the highest tobacco smoking prevalence (19.3%) compared with the 6.8% global mean. Between 20% to 30% of pregnant women used tobacco during pregnancy worldwide. These data emphasize the urgent need for community education and implementation of prevention strategies focused on the risks associated with tobacco use during pregnancy. OBJECTIVE The aim of this study was to investigate the efficacy of an intervention that incorporates a serious game (Tobbstop) to help pregnant smokers quit smoking. METHODS A two-arm randomized controlled trial enrolled 42 women who visited 2 primary care centers in Catalonia, Spain, between March 2015 and November 2016. All participants were pregnant smokers, above 18 years old, attending consultation with a midwife during the first trimester of pregnancy, and had expressed their desire to stop smoking. Participants were randomized to the intervention (n=21) or control group (n=21). The intervention group was instructed to install the game on their mobile phone or tablet and use it for 3 months. Until delivery, all the participants were assessed on their stage of smoking cessation during their follow-up midwife consultations. The primary outcome was continuous tobacco abstinence until delivery confirmed by the amount of carbon monoxide at each visit, measured with a carboxymeter. RESULTS Continuous abstinence until delivery outcome was 57% (12/21) in the intervention group versus 14% (3/21) in the control group (hazard ratio=4.31; 95% CI 1.87-9.97; P=.001). The mean of total days without smoking until delivery was higher in the intervention group (mean 139.75, SD 21.76) compared with the control group (mean 33.28, SD 13.27; P CONCLUSIONS Serious game use is associated with an increased likelihood to maintain abstinence during the intervention period if compared with those not using the game. Pregnancy is an ideal opportunity to intervene and control tobacco use among future mothers. On the other hand, serious games are an emerging technology, growing in importance, which are shown to be a good tool to help quitting smoking during pregnancy and also to maintain this abstinent behavior. However, because of the study design limitations, these outcomes should be interpreted with caution. More research, using larger samples and longer follow-up periods, is needed to replicate the findings of this study. CLINICALTRIAL ClinicalTrials.gov NCT01734421; https://clinicaltrials.gov/ct2/show/NCT01734421 (Archived by WebCite at http://www.webcitation.org/75ISc59pB)

Advancing our understanding of human behavior hinges on the ability of theories to unveil the mechanisms underlying such behaviors. Measuring the ability of theories and models to predict unobserved behaviors provides a principled method to evaluate their merit and, thus, to help establish which mechanisms are most plausible. Here, we propose models and develop rigorous inference approaches to predict strategic decisions in dyadic social dilemmas. In particular, we use bipartite stochastic block models that incorporate information about the dilemmas faced by individuals. We show, combining these models with empirical data on strategic decisions in dyadic social dilemmas, that individual strategic decisions are to a large extent predictable, despite not being “rational.” The analysis of these models also allows us to conclude that: (i) individuals do not perceive games according their game-theoretical structure; (ii) individuals make decisions using combinations of multiple simple strategies, which our approach reveals naturally.

Fighting against climate change is a global challenge shared by nations with heterogeneous economical resources and individuals with diverse propensity for cooperation. However, we lack a clear understanding of the role of key factors such as inequality of means when diverse agents interact together towards a common goal. Here, we report the results of a collective-risk dilemma experiment in which groups of subjects were initially given either equal or unequal endowments. We found that although the collective goal was always achieved regardless of the initial capital distribution, the effort distribution was highly inequitable. Specifically, participants with fewer resources contributed significantly more to the public goods than the richer -sometimes twice as much. An unsupervised learning algorithm clustered the subjects according to their individual behavior. We found that the poorest participants congregated within the two "generous clusters" whereas the richest were mostly classified into a "greedy cluster". Our findings suggest that future policies would benefit both from reinforcing climate justice actions addressed to most vulnerable people and educating fairness instead of focusing on understanding of generic or global climate consequences, as the latter has not proven to drive equitable contributions., 20 pages and 19 figures, including the SI file

BACKGROUND Smoking is one of the most significant factors contributing to low life expectancy, health inequalities, and illness at the worldwide scale. Smoking cessation attempts benefit from social support. Mobile phones have changed the way we communicate through the use of freely available message-oriented apps. Mobile app–based interventions for smoking cessation programs can provide interactive, supportive, and individually tailored interventions. OBJECTIVE This study aimed to identify emotions, coping strategies, beliefs, values, and cognitive evaluations of smokers who are in the process of quitting, and to analyze online social support provided through the analysis of messages posted to a chat function integrated into a mobile app. METHODS In this descriptive qualitative study, informants were smokers who participated in the chat of Tobbstop. The technique to generate information was documentary through messages collected from September 2014 through June 2016, specifically designed to support a smoking cessation intervention. A thematic content analysis of the messages applied 2 conceptual models: the Lazarus and Folkman model to assess participant’s experiences and perceptions and the Cutrona model to evaluate online social support. RESULTS During the study period, 11,788 text messages were posted to the chat by 101 users. The most frequent messages offered information and emotional support, and all the basic emotions were reported in the chat. The 3 most frequent coping strategies identified were physical activity, different types of treatment such as nicotine replacement, and humor. Beliefs about quitting smoking included the inevitability of weight gain and the notion that not using any type of medications is better for smoking cessation. Health and family were the values more frequently described, followed by freedom. A smoke-free environment was perceived as important to successful smoking cessation. The social support group that was developed with the app offered mainly emotional and informational support. CONCLUSIONS Our analysis suggests that a chat integrated into a mobile app focused on supporting smoking cessation provides a useful tool for smokers who are in the process of quitting, by offering social support and a space to share concerns, information, or strategies.

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Tens of millions of individuals around the world use decentralized content distribution systems, a fact of growing social, economic, and technological importance. These sharing systems are poorly understood because, unlike in other technosocial systems, it is difficult to gather large-scale data about user behavior. Here, we investigate user activity patterns and the socioeconomic factors that could explain the behavior. Our analysis reveals that (i) the ecosystem is heterogeneous at several levels: content types are heterogeneous, users specialize in a few content types, and countries are heterogeneous in user profiles; and (ii) there is a strong correlation between socioeconomic indicators of a country and users behavior. Our findings open a research area on the dynamics of decentralized sharing ecosystems and the socioeconomic factors affecting them, and may have implications for the design of algorithms and for policymaking.

El objetivo de este artA­culo es analizar la evoluciA³n y la relaciA³n de estas denominaciones en la literatura cientA­fica. Para ello, se muestra el proceso de construcciA³n de una herramienta y el diseA±o de una estrategia para la revisiA³n sistemAitica de esta temAitica en los artA­culos publicados en ISI Web of Science entre 2001 y 2010, asA­ como los principales resultados.

Socially relevant situations that involve strategic interactions are widespread among animals and humans alike. To study these situations, theoretical and experimental works have adopted a game-theoretical perspective, which has allowed to obtain valuable insights about human behavior. However, most of the results reported so far have been obtained from a population perspective and considered one specific conflicting situation at a time. This makes it difficult to extract conclusions about the consistency of individuals' behavior when facing different situations, and more importantly, to define a comprehensive classification of the strategies underlying the observed behaviors. Here, we present the results of a lab-in-the-field experiment in which subjects face four different dyadic games, with the aim of establishing general behavioral rules dictating individuals' actions. By analyzing our data with an unsupervised clustering algorithm, we find that all the subjects conform, with a large degree of consistency, to a limited number of behavioral phenotypes (Envious, Optimist, Pessimist, and Trustful), with only a small fraction of undefined subjects. We also discuss the possible connections to existing interpretations based on a priori theoretical approaches. Our findings provide a relevant contribution to the experimental and theoretical efforts towards the identification of basic behavioral phenotypes in a wider set of contexts without aprioristic assumptions regarding the rules or strategies behind actions. From this perspective, our work contributes to a fact-based approach to the study of human behavior in strategic situations, that could be applied to simulating societies, policy-making scenario building and even for a variety of business applications., Comment: Main text together with Supplementary Information

Collaboration plays an increasingly important role in promoting research productivity and impact. What remains unclear is whether female and male researchers in science, technology, engineering, and mathematical (STEM) disciplines differ in their collaboration propensity. Here, we report on an empirical analysis of the complete publication records of 3,980 faculty members in six STEM disciplines at select U.S. research universities. We find that female faculty have significantly fewer distinct co-authors over their careers than males, but that this difference can be fully accounted for by females’ lower publication rate and shorter career lengths. Next, we find that female scientists have a lower probability of repeating previous co-authors than males, an intriguing result because prior research shows that teams involving new collaborations produce work with higher impact. Finally, we find evidence for gender segregation in some sub-disciplines in molecular biology, in particular in genomics where we find female faculty to be clearly under-represented.

Though the impact of file-sharing of copyrighted content has been discussed for over a decade, only in the past few years have countries begun to adopt legislation to criminalize this behavior. These laws impose penalties ranging from warnings and monetary fines to disconnecting Internet service. While their supporters are quick to point out trends showing the efficacy of these laws at reducing use of file-sharing sites, their analyses rely on brief snapshots of activity that cannot reveal long- and short-term trends. In this paper, we introduce an approach to model user behavior based on a hidden Markov model and apply it to analyze a two-year-long user-level trace of download activity of over 38k users from around the world. This approach allows us to quantify the true impact of file-sharing laws on user behavior, identifying behavioral trends otherwise difficult to identify. For instance, despite an initial reduction in activity in New Zealand when a three-strikes law took effect, after two months activity had returned to the level observed prior to the law being enacted. Given that punishment seems to, at best, result in short-term compliance, we suggest that incentives-based approaches may be more effective at changing user behavior.

Using a database of research projects of the European 6th Framework Programme, we present a methodology to analyze large social data sets based on a new community detection algorithm. As a main advantage, we stress that community determination makes easier the operation of crossing relational data (who is connected to whom) with particular information about each organization.

Energy performance of island tourism has been analyzed in the literature. However, tourist services tend to concentrate in tourist hubs, especially where mass tourism predominates (e.g., Mediterranean), and the energy metabolism of these systems has not yet been assessed. The present paper models and estimates the energy metabolism of tourist hubs in the Menorca Island (Spain) by integrating social, geographical and environmental methods. Mobility (both external and internal) and consumption of lodging services were characterized through surveys to users (tourists) and business managers. An environmental assessment evaluated CO2 emissions, and energy self-sufficiency potential was estimated via GIS data. The results indicate that, on average, a tourist consumes 4756MJ with associated emissions of 277kg of CO2 per stay (20 days on average). Of all the energy flows, external mobility contributes the most to total emissions (77%). For every day spent in a tourist hub, a tourist consumes between 29MJ and 93MJ in lodging services, consumption that could be 100% satisfied by photovoltaic systems, and these systems would result in positive effects for the island. Sustainable tourism management might focus on promoting environmentally friendly transportation, energy efficient practices, and environmental communication through ecolabeling. © 2013 Elsevier Ltd.