Statistical Mechanics for Modeling and Prediction of Human Behavior

Micro-dynamics, model selection and lab-in-the-field experiments

Description

StatMech2Pred is an ambitious project which intends to understand human behavior at individual and society scales by developing new prediction and observational techniques based on statistical mechanics. During the past 15 years we have witnessed a remarkable increase in both the scale and scope of social and behavioral data available. Such wealth of data has not only opened the possibilities to understand social systems in an unprecedented manner but also has also emphasized the need for new ways to observe, model and predict human behavior at micro and macro scales. From this perspective, tools built upon the principles of statistical mechanics are natural drivers to capture human­ related phenomena.

Importantly, most of the recent advances in this area have been descriptive, and although many models have been proposed, the tools and data behind these models lack the accuracy to be predictive and prescriptive. Current models can neither anticipate the behavior of individuals nor the dynamics of the system as a whole. Therefore, to hypothesize about different scenarios is still an arduous task. For example, information diffusion is usually modelled by mechanistic models borrowed from biology, which do not describe the complex and context­-dependent dynamics of information sharing. For models to become predictive we need: (i) better understanding of the micro­dynamics of human behavior and its non­trivial connection to contextual factors, (ii) better model selection and statistical inference tools, and (iii) better design of experiments and data collection.

StatMech2Pred aims precisely at developing the complex systems tools necessary to infer predictive models of human behavior from empirical data in different contexts, and to carry out experiments to investigate and model aspects of human behavior that are not covered by existing datasets. Our cross­disciplinary approach will allow not only to have more accurate models of human behavior, but also to respond to important problems at macro level like financial markets stability, economic growth or social inequalities.

On the methodological side we will develop tools combining network and non­network inference and model­selection approaches, the theory of critical phenomena, and stochastic processes. Specifically, we will focus on the use of statistical mechanics to: (i) develop models of human micro­dynamics, and (ii) create better model selection tools from empirical data. On the experimental side, we want to combine big data from online sources with data from experiments by for instance using social dilemmas. Through such experiments we will gather controlled data to either validate findings in data from online sources, or to answer specific fundamental questions related to human actions.

Finally, we will analyze empirical data and develop predictive and grounded models considering the relationship between actions and contextual factors. Specifically, we will address: (i) the human decision making process in controlled settings,(ii) the impact of human guesses on market price changes, and (iii) the relationship between human behavior shifts and socio­economic indicators. x

StatMech2Pred will draw from our previous experiences in the development of mathematical tools, data analysis and the setup of controlled experiments to go beyond the current understanding of human actions while creating tools and experimental frameworks to be used as references in future human behavior studies.

Highlights

Complex decision-making strategies in a stock market experiment explained as the combination of few simple strategies

Poux-Medard, G Cobo-Lopez, S Duch, J Guimera, R Sales-Pardo, M

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 inf...

Journal

Universal resilience patterns in labor markets

Moro, E Frank, MR Pentland, A Rutherford, A Cebrian, M Rahwan, I

Cities are the innovation centers of the US economy, but technological disruptions can exclude workers and inhibit a middle class. Therefore, urban policy must promote the jobs and skills that increase worker pay, create employment, and foster economic resilience. In this paper, we model labor ma...

Journal

Telegraphic Transport Processes and Their Fractional Generalization: A Review and Some Extensions

Masoliver, J

We address the problem of telegraphic transport in several dimensions. We review the derivation of two and three dimensional telegrapher's equations-as well as their fractional generalizations-from microscopic random walk models for transport (normal and anomalous). We also present new results on...

Journal

People

Roger Guimerà

Universitat Rovira i Virgili - ICREA Research Professor

Contact

roger.guimera@urv.cat

@sees_lab

Site

Marta Sales-Pardo

Universitat Rovira i Virgili - Associate Professor

Contact

marta.sales@urv.cat

@sees_lab

Site

Esteban Moro

Universidad Carlos III de Madrid - Associate Professor

Contact

emoro@math.uc3m.es

@estebanmoro

Site

Josep Perelló

Universtiat de Barcelona - Associate Professor

Contact

josep.perello@ub.edu

@josperello

Site

Jordi Duch

Universitat Rovira i Virgili - Associate Professor

Contact

jordi.duch@urv.cat

@tanisjones

Miquel Montero

Universitat de Barcelona - Associate Professor

Contact

miquel.montero@ub.edu

Site

Jaume Masoliver

Universitat de Barcelona - Professor

Contact

jaume.masoliver@ub.edu

Site

Javier Villarroel

Universidad de Salamanca - Professor

Contact

javier@usal.es

Site

Young-Ho Eom

Universidad Carlos III de Madrid - Experienced Fellow

Contact

yeom@math.uc3m.es

Site

Publications

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.
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Cities are the innovation centers of the US economy, but technological disruptions can exclude workers and inhibit a middle class. Therefore, urban policy must promote the jobs and skills that increase worker pay, create employment, and foster economic resilience. In this paper, we model labor market resilience with an ecologically-inspired job network constructed from the similarity of occupations' skill requirements. This framework reveals that the economic resilience of cities is universally and uniquely determined by the connectivity within a city's job network. US cities with greater job connectivity experienced lower unemployment during the Great Recession. Further, cities that increase their job connectivity see increasing wage bills, and workers of embedded occupations enjoy higher wages than their peers elsewhere. Finally, we show how job connectivity may clarify the augmenting and deleterious impact of automation in US cities. Policies that promote labor connectivity may grow labor markets and promote economic resilience. Recent technological, social, and educational changes are profoundly impacting our work, but what makes labour markets resilient to those labour shocks? Here, the authors show that labour markets resemble ecological systems whose resilience depends critically on the network of skill similarities between different jobs.
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We address the problem of telegraphic transport in several dimensions. We review the derivation of two and three dimensional telegrapher's equations-as well as their fractional generalizations-from microscopic random walk models for transport (normal and anomalous). We also present new results on solutions of the higher dimensional fractional equations.
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The continuous development of improved non-fullerene acceptors and deeper knowledge of the fundamental mechanisms governing performance underpin the vertiginous increase in efficiency witnessed by organic photovoltaics. While the influence of parameters like film thickness and morphology are generally understood, what determines the strong dependence of the photocurrent on the donor and acceptor fractions remains elusive. Here we approach this problem by training artificial intelligence algorithms with self-consistent datasets consisting of thousands of data points obtained by high-throughput evaluation methods. Two ensemble learning methods are implemented, namely a Bayesian machine scientist and a random decision forest. While the former demonstrates large descriptive power to complement the experimental high-throughput screening, the latter is found to predict with excellent accuracy the photocurrent-composition phase space for material systems outside the training set. Interestingly, we identify highly predictive models that only employ the materials band gaps, thus largely simplifying the rationale of the photocurrent-composition space.
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We present a dynamical model for the price evolution of financial assets. The model is based on a two-level approach: In the first stage, one finds an agent-based model that describes the current state of investors' beliefs, perspectives or strategies. The dynamics is inspired by a model for describing predator-prey population evolution: Agents change their mind through self- or mutual interaction, and the decision is adopted on a random basis, with no direct influence of the price itself. One of the most appealing properties of such a system is the presence of large oscillations in the number of agents sharing the same perspective, what may be linked with the existence of bullish and bearish periods in financial markets. In the second stage, one has the pricing mechanism, which will be driven by the relative population in the different groups of investors. The price equation will depend on the specific nature of the species, and thus, it may change from one market to the other: We will present a simple model of excess demand in the first place and then consider a more elaborate liquidity model. The outcomes of both models are analyzed and compared.
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Reducing inequality is essential for sustainable development, yet our understanding of its many dimensions and driving forces is still limited. Here we study the global distribution of 25 environmental burdens encompassing natural resources (water, materials and land use) and air emissions, all related to activities underpinning human welfare. We find large disparities in inequality levels across burdens and a general, yet slow, decline in inequality in the period 1995-2009, explained mostly by the faster economic growth of emerging economies. Acknowledging that allocation issues may hamper greater equality, we propose a framework for an optimal allocation of quotas for environmental burdens respecting a maximum allowable inequality limit while ensuring a safe operation within the Earth's ecological capacity. Our results shed light on the global distribution of environmental burdens and provide a roadmap for achieving a greater environmental equality using systems optimisation. It is hoped that this work will trigger further discussion on the need to address environmental inequality, currently missing in the Sustainable Development Goals, and open up new research avenues on the use of whole-systems approaches in solving global sustainability problems. (C) 2020 Published by Elsevier Ltd.
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Urban income segregation is a widespread phenomenon that challenges societies across the globe. Classical studies on segregation have largely focused on the geographic distribution of residential neighborhoods rather than on patterns of social behaviors and interactions. In this study, we analyze segregation in economic and social interactions by observing credit card transactions and Twitter mentions among thousands of individuals in three culturally different metropolitan areas. We show that segregated interaction is amplified relative to the expected effects of geographic segregation in terms of both purchase activity and online communication. Furthermore, we find that segregation increases with difference in socio-economic status but is asymmetric for purchase activity, i.e., the amount of interaction from poorer to wealthier neighborhoods is larger than vice versa. Our results provide novel insights into the understanding of behavioral segregation in human interactions with significant socio-political and economic implications.
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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<.001) and 12 months (39/187, 20.9% vs 8/97, 8.25%; P=.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.
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High future discounting rates favor inaction on present expending while lower rates advise for a more immediate political action. A possible approach to this key issue in global economy is to take historical time series for nominal interest rates and inflation, and to construct then real interest rates and finally obtaining the resulting discount rate according to a specific stochastic model. Extended periods of negative real interest rates, in which inflation dominates over nominal rates, are commonly observed, occurring in many epochs and in all countries. This feature leads us to choose a well-known model in statistical physics, the Ornstein-Uhlenbeck model, as a basic dynamical tool in which real interest rates randomly fluctuate and can become negative, even if they tend to revert to a positive mean value. By covering 14 countries over hundreds of years we suggest different scenarios and include an error analysis in order to consider the impact of statistical uncertainty in our results. We find that only 4 of the countries have positive long-run discount rates while the other ten countries have negative rates. Even if one rejects the countries where hyperinflation has occurred, our results support the need to consider low discounting rates. The results provided by these fourteen countries significantly increase the priority of confronting global actions such as climate change mitigation. We finally extend the analysis by first allowing for fluctuations of the mean level in the Ornstein-Uhlenbeck model and secondly by considering modified versions of the Feller and lognormal models. In both cases, results remain basically unchanged thus demonstrating the robustness of the results presented.
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Ever since Nikuradse's experiments on turbulent friction in 1933, there have been theoretical attempts to describe his measurements by collapsing the data into single-variable functions. However, this approach, which is common in other areas of physics and in other fields, is limited by the lack of rigorous quantitative methods to compare alternative data collapses. Here, we address this limitation by using an unsupervised method to find analytic functions that optimally describe each of the data collapses for the Nikuradse dataset. By descaling these analytic functions, we show that a low dispersion of the scaled data does not guarantee that a data collapse is a good description of the original data. In fact, we find that, out of all the proposed data collapses, the original one proposed by Prandtl and Nikuradse over 80 years ago provides the best description of the data so far, and that it also agrees well with recent experimental data, provided that some model parameters are allowed to vary across experiments.
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We study the planar motion of telegraphic processes. We derive the two-dimensional telegrapher's equation for isotropic and uniform motions starting from a random walk model which is the two-dimensional version of the multistate random walk with a continuum number of states representing the spatial directions. We generalize the isotropic model and the telegrapher's equation to include planar fractional motions. Earlier, we worked with the one-dimensional version [Masoliver, Phys. Rev. E 93, 052107 (2016)] and derived the three-dimensional version [Masoliver, Phys. Rev. E 96, 022101 (2017)]. An important lesson is that we cannot obtain the two-dimensional version from the three-dimensional or the one-dimensional one from the two-dimensional result. Each dimension must be approached starting from an appropriate random walk model for that dimension.
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The study explores the expectations and cooperative behaviours of men and women in a lab-in-the-field experiment by means of citizen science practices in the public space. It specifically examines the influence of gender-based pairings on the decisions to cooperate or defect in a framed and discrete Prisoner's Dilemma game after visual contact. Overall, we found that when gender is considered behavioural differences emerge in expectations of cooperation, cooperative behaviours, and their decision time depending on whom the partner is. Men pairs are the ones with the lowest expectations and cooperation rates. After visual contact women infer men's behaviour with the highest accuracy. Also, women take significantly more time to defect than to cooperate, compared to men. Finally, when the interacting partners have the opposite gender they expect significantly more cooperation and they achieve the best collective outcome. Together, the findings suggest that non verbal signals may influence men and women differently, offering novel interpretations to the context-dependence of gender differences in social decision tasks.
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The random walk with hyperbolic probabilities that we are introducing is an example of stochastic diffusion in a one-dimensional heterogeneous media. Although driven by site-dependent one-step transition probabilities, the process retains some of the features of a simple random walk, shows other traits that one would associate with a biased random walk and, at the same time, presents new properties not related to either of them. In particular, we show how the system is not fully ergodic, as not every statistic can be estimated from a single realization of the process. We further give a geometric interpretation for the origin of these irregular transition probabilities.
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Closed-form, interpretable mathematical models have been instrumental for advancing our understanding of the world; with the data revolution, we may now be in a position to uncover new such models for many systems from physics to the social sciences. However, to deal with increasing amounts of data, we need "machine scientists" that are able to extract these models automatically from data. Here, we introduce a Bayesian machine scientist, which establishes the plausibility of models using explicit approximations to the exact marginal posterior over models and establishes its prior expectations about models by learning from a large empirical corpus of mathematical expressions. It explores the space of models using Markov chain Monte Carlo. We show that this approach uncovers accurate models for synthetic and real data and provides out-of-sample predictions that are more accurate than those of existing approaches and of other nonparametric methods.
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Motivation: The analysis of biological samples in untargeted metabolomic studies using LC-MS yields tens of thousands of ion signals. Annotating these features is of the utmost importance for answering questions as fundamental as, e.g. how many metabolites are there in a given sample. Results: Here, we introduce CliqueMS, a new algorithm for annotating in-source LC-MS1 data. CliqueMS is based on the similarity between coelution profiles and therefore, as opposed to most methods, allows for the annotation of a single spectrum. Furthermore, CliqueMS improves upon the state of the art in several dimensions: (i) it uses a more discriminatory feature similarity metric; (ii) it treats the similarities between features in a transparent way by means of a simple generative model; (iii) it uses a well-grounded maximum likelihood inference approach to group features; (iv) it uses empirical adduct frequencies to identify the parental mass and (v) it deals more flexibly with the identification of the parental mass by proposing and ranking alternative annotations. We validate our approach with simple mixtures of standards and with real complex biological samples. CliqueMS reduces the thousands of features typically obtained in complex samples to hundreds of metabolites, and it is able to correctly annotate more metabolites and adducts from a single spectrum than available tools.
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We present a general formulation of the resetting problem which is valid for any distribution of resetting intervals and arbitrary underlying processes. We show that in such a general case, a stationary distribution may exist even if the reset-free process is not stationary, as well as a significant decreasing in the mean first-passage time. We apply the general formalism to anomalous diffusion processes which allow simple and explicit expressions for Poissonian resetting events.
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Our private connections can be exposed by link prediction algorithms. To date, this threat has only been addressed from the perspective of a central authority, completely neglecting the possibility that members of the social network can themselves mitigate such threats. We fill this gap by studying how an individual can rewire her own network neighborhood to hide her sensitive relationships. We prove that the optimization problem faced by such an individual is NP-complete, meaning that any attempt to identify an optimal way to hide one's relationships is futile. Based on this, we shift our attention towards developing effective, albeit not optimal, heuristics that are readily-applicable by users of existing social media platforms to conceal any connections they deem sensitive. Our empirical evaluation reveals that it is more beneficial to focus on "unfriending" carefully-chosen individuals rather than befriending new ones. In fact, by avoiding communication with just 5 individuals, it is possible for one to hide some of her relationships in a massive, real-life telecommunication network, consisting of 829,725 phone calls between 248,763 individuals. Our analysis also shows that link prediction algorithms are more susceptible to manipulation in smaller and denser networks. Evaluating the error vs. attack tolerance of link prediction algorithms reveals that rewiring connections randomly may end up exposing one's sensitive relationships, highlighting the importance of the strategic aspect. In an age where personal relationships continue to leave digital traces, our results empower the general public to proactively protect their private relationships.
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Topological heterogeneities of social networks have a strong impact on the individuals embedded in those networks. One of the interesting phenomena driven by such heterogeneities is the friendship paradox (FP), stating that the mean degree of one's neighbors is larger than the degree of oneself. Alternatively, one can use the median degree of neighbors as well as the fraction of neighbors having a higher degree than oneself. Each of these reflects on how people perceive their neighborhoods, i.e., their perception models, hence how they feel peer pressure. In our paper, we study the impact of perception models on the FP by comparing three versions of the perception model in networks generated with a given degree distribution and a tunable degree-degree correlation or assortativity. The increasing assortativity is expected to decrease network-level peer pressure, while we find a nontrivial behavior only for the mean-based perception model. By simulating opinion formation, in which the opinion adoption probability of an individual is given as a function of individual peer pressure, we find that it takes the longest time to reach consensus when individuals adopt the median-based perception model compared to other versions. Our findings suggest that one needs to consider the proper perception model for better modeling human behaviors and social dynamics.
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Many real-world complex systems are well represented as multilayer networks; predicting interactions in those systems is one of the most pressing problems in predictive network science. To address this challenge, we introduce two stochastic block models for multilayer and temporal networks; one of them uses nodes as its fundamental unit, whereas the other focuses on links. We also develop scalable algorithms for inferring the parameters of these models. Because our models describe all layers simultaneously, our approach takes full advantage of the information contained in the whole network when making predictions about any particular layer. We illustrate the potential of our approach by analyzing two empirical data sets: a temporal network of e-mail communications, and a network of drug interactions for treating different cancer types. We find that multilayer models consistently outperform their single-layer counterparts, but that the most predictive model depends on the data set under consideration; whereas the node-based model is more appropriate for predicting drug interactions, the link-based model is more appropriate for predicting e-mail communication.
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In this paper we develop a methodology, based on Mutual Information and Transfer of Entropy, that allows to identify, quantify and map on a network the synchronization and anticipation relationships between financial traders. We apply this methodology to a dataset containing 410,612 real buy and sell operations, made by 566 non-professional investors from a private investment firm on 8 different assets from the Spanish IBEX market during a period of time from 2000 to 2008. These networks present a peculiar topology significantly different from the random networks. We seek alternative features based on human behavior that might explain part of those 12,158 synchronization links and 1031 anticipation links. Thus, we detect that daily synchronization with price (present in 64.90% of investors) and the one-day delay with respect to price (present in 4.38% of investors) play a significant role in the network structure. We find that individuals reaction to daily price changes explains around 20% of the links in the Synchronization Network, and has significant effects on the Anticipation Network. Finally, we show how using these networks we substantially improve the prediction accuracy when Random Forest models are used to nowcast and predict the activity of individual investors.
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We investigate the effects of resetting mechanisms on random processes that follow the telegrapher's equation instead of the usual diffusion equation. We thus study the consequences of a finite speed of signal propagation, the landmark of telegraphic processes. Likewise diffusion processes where signal propagation is instantaneous, we show that in telegraphic processes, where signal propagation is not instantaneous, random resettings also stabilize the random walk around the resetting position and optimize the mean first-arrival time. We also obtain the exact evolution equations for the probability density of the combined process and study the limiting cases.
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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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We consider ballistic motion on the line with random velocity which at certain random epochs of time is reset to its starting position. The mobile then restarts from scratch with new velocity, sampled with a certain probability distribution, until the next reset occurs. The distribution for the hitting time to an arbitrary level is obtained. Inversion of the relevant Laplace transform is discussed under particular choices for the distribution of velocities and resets train and classified in terms of the weights of reset and velocities tails. The large time behavior of the running-maximum is considered.
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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.
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Online social media are information resources that can have a transformative power in society. While the Web was envisioned as an equalizing force that allows everyone to access information, the digital divide prevents large amounts of people from being present online. Online social media, in particular, are prone to gender inequality, an important issue given the link between social media use and employment. Understanding gender inequality in social media is a challenging task due to the necessity of data sources that can provide large-scale measurements across multiple countries. Here, we show how the Facebook Gender Divide (FGD), a metric based on aggregated statistics of more than 1.4 billion users in 217 countries, explains various aspects of worldwide gender inequality. Our analysis shows that the FGD encodes gender equality indices in education, health, and economic opportunity. We find gender differences in network externalities that suggest that using social media has an added value for women. Furthermore, we find that low values of the FGD are associated with increases in economic gender equality. Our results suggest that online social networks, while suffering evident gender imbalance, may lower the barriers that women have to access to informational resources and help to narrow the economic gender gap.
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A principled approach to understand network structures is to formulate generative models. Given a collection of models, however, an outstanding key task is to determine which one provides a more accurate description of the network at hand, discounting statistical fluctuations. This problem can be approached using two principled criteria that at first may seem equivalent: selecting the most plausible model in terms of its posterior probability; or selecting the model with the highest predictive performance in terms of identifying missing links Here we show that while these two approaches yield consistent results in most cases, there are also notable instances where they do not, that is, where the most plausible model is not the most predictive. We show that in the latter case the improvement of predictive performance can in fact lead to overfitting both in artificial and empirical settings. Furthermore, we show that, in general, the predictive performance is higher when we average over collections of models that are individually less plausible than when we consider only the single most plausible model.
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Cooperation is one of the behavioral traits that define human beings, however we are still trying to understand why humans cooperate. Behavioral experiments have been largely conducted to shed light into the mechanisms behind cooperation-and other behavioral traits. However, most of these experiments have been conducted in laboratories with highly controlled experimental protocols but with limitations in terms of subject pool or decisions' context, which limits the reproducibility and the generalization of the results obtained. In an attempt to overcome these limitations, some experimental approaches have moved human behavior experimentation from laboratories to public spaces, where behaviors occur naturally, and have opened the participation to the general public within the citizen science framework. Given the open nature of these environments, it is critical to establish the appropriate data collection protocols to maintain the same data quality that one can obtain in the laboratories. In this article we introduce Citizen Social Lab, a software platform designed to be used in the wild using citizen science practices. The platform allows researchers to collect data in a more realistic context while maintaining the scientific rigor, and it is structured in a modular and scalable way so it can also be easily adapted for online or brick-and-mortar experimental laboratories. Following citizen science guidelines, the platform is designed to motivate a more general population into participation, but also to promote engaging and learning of the scientific research process. We also review the main results of the experiments performed using the platform up to now, and the set of games that each experiment includes. Finally, we evaluate some properties of the platform, such as the heterogeneity of the samples of the experiments, the satisfaction level of participants, or the technical parameters that demonstrate the robustness of the platform and the quality of the data collected.
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We conduct the largest ever investigation into the relationship between meteorological conditions and the sentiment of human expressions. To do this, we employ over three and a half billion social media posts from tens of millions of individuals from both Facebook and Twitter between 2009 and 2016. We find that cold temperatures, hot temperatures, precipitation, narrower daily temperature ranges, humidity, and cloud cover are all associated with worsened expressions of sentiment, even when excluding weather-related posts. We compare the magnitude of our estimates with the effect sizes associated with notable historical events occurring within our data.
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Despite the huge interest in network resilience to stress, most of the studies have concentrated on internal stress damaging network structure (e.g., node removals). Here we study how networks respond to environmental stress deteriorating their external conditions. We show that, when regular networks gradually disintegrate as environmental stress increases, disordered networks can suddenly collapse at critical stress with hysteresis and vulnerability to perturbations. We demonstrate that this difference results from a trade-off between node resilience and network resilience to environmental stress. The nodes in the disordered networks can suppress their collapses due to the small-world topology of the networks but eventually collapse all together in return. Our findings indicate that some real networks can be highly resilient against environmental stress to a threshold yet extremely vulnerable to the stress above the threshold because of their small-world topology.
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Mental disorders have an enormous impact in our society, both in personal terms and in the economic costs associated with their treatment. In order to scale up services and bring down costs, administrations are starting to promote social interactions as key to care provision. We analyze quantitatively the importance of communities for effective mental health care, considering all community members involved. By means of citizen science practices, we have designed a suite of games that allow to probe into different behavioral traits of the role groups of the ecosystem. The evidence reinforces the idea of community social capital, with caregivers and professionals playing a leading role. Yet, the cost of collective action is mainly supported by individuals with a mental condition - which unveils their vulnerability. The results are in general agreement with previous findings but, since we broaden the perspective of previous studies, we are also able to find marked differences in the social behavior of certain groups of mental disorders. We finally point to the conditions under which cooperation among members of the ecosystem is better sustained, suggesting how virtuous cycles of inclusion and participation can be promoted in a 'care in the community' framework.
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This article describes and analyzes the collaborative design of a citizen science research project through co-creation. Three groups of secondary school students and a team of scientists conceived three experiments on human behavior and social capital in urban and public spaces. The study goal is to address how interdisciplinary work and attention to social concerns and needs, as well as the collective construction of research questions, can be integrated into scientific research. The 95 students participating in the project answered a survey to evaluate their perception about the dynamics and tools used in the co-creation process of each experiment, and the five scientists responded to a semi-structured interview. The results from the survey and interviews demonstrate how citizen science can achieve a "co-created" modality beyond the usual "contributory" paradigm, which usually only involves the public or amateurs in data collection stages. This type of more collaborative science was made possible by the adaptation of materials and facilitation mechanisms, as well as the promotion of key aspects in research such as trust, creativity and transparency. The results also point to the possibility of adopting similar co-design strategies in other contexts of scientific collaboration and collaborative knowledge generation.
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Social networks are made out of strong and weak ties having very different structural and dynamical properties. But what features of human interaction build a strong tie? Here we approach this question from a practical way by finding what are the properties of social interactions that make ties more persistent and thus stronger to maintain social interactions in the future. Using a large longitudinal mobile phone database we build a predictive model of tie persistence based on intensity, intimacy, structural and temporal patterns of social interaction. While our results confirm that structural (embeddedness) and intensity (number of calls) features are correlated with tie persistence, temporal features of communication events are better and more efficient predictors for tie persistence. Specifically, although communication within ties is always bursty we find that ties that are more bursty than the average are more likely to decay, signaling that tie strength is not only reflected in the intensity or topology of the network, but also on how individuals distribute time or attention across their relationships. We also found that stable relationships have and require a constant rhythm and if communication is halted for more than 8 times the previous communication frequency, most likely the tie will decay. Our results not only are important to understand the strength of social relationships but also to unveil the entanglement between the different temporal scales in networks, from microscopic tie burstiness and rhythm to macroscopic network evolution.
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We study financial distributions from the perspective of Continuous Time Random Walks with memory. We review some of our previous developments and apply them to financial problems. We also present some new models with memory that can be useful in characterizing tendency effects which are inherent in most markets. We also briefly study the effect on return distributions of fractional behaviors in the distribution of pausing times between successive transactions.
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Leadership positions are still stereotyped as masculine, especially in male-dominated fields (e.g., engineering). So how do gender stereotypes affect the evaluation of leaders and team cohesiveness in the process of team development? In our study participants worked in 45 small teams (4–5 members). Each team was headed by either a female or male leader, so that 45 leaders (33% women) supervised 258 team members (39% women). Over a period of nine months, the teams developed specific engineering projects as part of their professional undergraduate training. We examined leaders’ self-evaluation, their evaluation by team members, and team cohesiveness at two points of time (month three and month nine, the final month of the collaboration). While we did not find any gender differences in leaders’ self-evaluation at the beginning, female leaders evaluated themselves more favorably than men at the end of the projects. Moreover, female leaders were evaluated more favorably than male leaders at the beginning of the project, but the evaluation by team members did not differ at the end of the projects. Finally, we found a tendency for female leaders to build more cohesive teams than male leaders.
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In this paper, we consider a stochastic process that may experience random reset events which relocate the system to its starting position. We focus our attention on a one-dimensional, monotonic continuous-time random walk with a constant drift: the process moves in a fixed direction between the reset events, either by the effect of the random jumps, or by the action of a deterministic bias. However, the orientation of its motion is randomly determined after each restart. As a result of these alternating dynamics, interesting properties do emerge. General formulas for the propagator as well as for two extreme statistics, the survival probability and the mean first-passage time, are also derived. The rigor of these analytical results is verified by numerical estimations, for particular but illuminating examples.
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We derive the three-dimensional telegrapher's equation out of a random walk model. The model is a three-dimensional version of the multistate random walk where the number of different states form a continuum representing the spatial directions that the walker can take. We set the general equations and solve them for isotropic and uniform walks which finally allows us to obtain the telegrapher's equation in three dimensions. We generalize the isotropic model and the telegrapher's equation to include fractional anomalous transport in three dimensions.
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Quantum walks and random walks bear similarities and divergences. One of the most remarkable disparities affects the probability of finding the particle at a given location: typically, almost a flat function in the first case and a bell-shaped one in the second case. Here I show how one can impose any desired stochastic behavior (compatible with the continuity equation for the probability function) on both systems by the appropriate choice of time-and site-dependent coins. This implies, in particular, that one can devise quantum walks that show diffusive spreading without losing coherence as well as random walks that exhibit the characteristic fast propagation of a quantum particle driven by a Hadamard coin.
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We review some extensions of the continuous time random walk first introduced by Elliott Montroll and George Weiss more than 50 years ago [E.W. Montroll, G.H. Weiss, J. Math. Phys. 6, 167 (1965)], extensions that embrace multistate walks and, in particular, the persistent random walk. We generalize these extensions to include fractional random walks and derive the associated master equation, namely, the fractional telegrapher's equation. We dedicate this review to our joint work with George H. Weiss (1930-2017). It saddens us greatly to report the recent death of George Weiss, a scientific giant and at the same time a lovely and humble man.
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Craniosynostosis, the premature fusion of cranial bones, affects the correct development of the skull producing morphological malformations in newborns. To assess the susceptibility of each craniofacial articulation to close prematurely, we used a network model of the skull to quantify the link reliability (an index based on stochastic block models and Bayesian inference) of each articulation. We show that, of the 93 human skull articulations at birth, the few articulations that are associated with non-syndromic craniosynostosis conditions have statistically significant lower reliability scores than the others. In a similar way, articulations that close during the normal postnatal development of the skull have also lower reliability scores than those articulations that persist through adult life. These results indicate a relationship between the architecture of the skull and the specific articulations that close during normal development as well as in pathological conditions. Our findings suggest that the topological arrangement of skull bones might act as a structural constraint, predisposing some articulations to closure, both in normal and pathological development, also affecting the long-term evolution of the skull.
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