Neural representations of social and non-social uncertainty in human decision making

The social landscape is filled with an intricate web of species-specific desired objects and course of actions. Humans are highly social animals and, as they navigate this landscape, they need to produce adapted decision-making behaviour. Traditionally social and non-social neural mechanisms affecting choice have been investigated using different approaches. Recently, in an effort to unite these findings, two main theories have been proposed to explain how the brain might encode social and non-social motivational decision-making: the extended common currency and the social valuation specific schema (Ruff & Fehr 2014). One way to test these theories is to directly compare neural activity related to social and non-social decision outcomes within the same experimental setting. Here we address this issue by focusing on the neural substrates of social and non-social forms of uncertainty. Using functional magnetic resonance imaging (fMRI) we directly compared the neural representations of reward and risk prediction and errors (RePE and RiPE) in social and non- social situations using gambling games. We used a trust betting game to vary uncertainty along a social dimension (trustworthiness), and a card game (Preuschoff et al. 2006) to vary uncertainty along a non-social dimension (pure risk). The trust game was designed to maintain the same structure of the card game. In a first study, we exposed a divide between subcortical and cortical regions when comparing the way these regions process social and non-social forms of uncertainty during outcome anticipation. Activity in subcortical regions reflected social and non-social RePE, while activity in cortical regions correlated with social RePE and non-social RiPE. The second study focused on outcome delivery and integrated the concept of RiPE in non-social settings with that of fairness and monetary utility maximisation in social settings. In particular these results corroborate recent models of anterior insula function (Singer et al. 2009; Seth 2013), and expose a possible neural mechanism that weights fairness and uncertainty but not monetary utility. The third study focused on functionally defined regions of the early visual cortex (V1) showing how activity in these areas, traditionally considered only visual, might reflect motivational prediction errors in addition to known perceptual prediction mechanisms (den Ouden et al 2012). On the whole, while our results do not support unilaterally one or the other theory modeling the underlying neural dynamics of social and non-social forms of decision making, they provide a working framework where both general mechanisms might coexist.

Computational Analysis of Intelligent Agents: Social and Strategic Settings

The central motif of this work is prediction and optimization in presence of multiple interacting intelligent agents. We use the phrase `intelligent agents' to imply in some sense, a `bounded rationality', the exact meaning of which varies depending on the setting. Our agents may not be `rational' in the classical game theoretic sense, in that they don't always optimize a global objective. Rather, they rely on heuristics, as is natural for human agents or even software agents operating in the real-world. Within this broad framework we study the problem of influence maximization in social networks where behavior of agents is myopic, but complication stems from the structure of interaction networks. In this setting, we generalize two well-known models and give new algorithms and hardness results for our models. Then we move on to models where the agents reason strategically but are faced with considerable uncertainty. For such games, we give a new solution concept and analyze a real-world game using out techniques. Finally, the richest model we consider is that of Network Cournot Competition which deals with strategic resource allocation in hypergraphs, where agents reason strategically and their interaction is specified indirectly via player's utility functions. For this model, we give the first equilibrium computability results. In all of the above problems, we assume that payoffs for the agents are known. However, for real-world games, getting the payoffs can be quite challenging. To this end, we also study the inverse problem of inferring payoffs, given game history. We propose and evaluate a data analytic framework and we show that it is fast and performant.

From social action to business action - A qualitative research on the professionalization process of a blogger: Case P.s. I Love Fashion -blog

Blogging is one of the most common forms of social media today. Blogs have become a powerful media and bloggers are settled stakeholders to marketers. Commercialization of the blogosphere has enabled an increasing number of bloggers professionalize and blog as a full-time occupation. The purpose of this study is to understand the professionalization process of a blogger from an amateur blogger to a professional actor. The following sub-questions were used to further elaborate the topic: What have been the meaningful events and developments fostering professionalization? What are the prerequisites for popularity in blogging? Are there any key success factors to acknowledge in order being able to make business out of your blog? The theoretical framework of this study was formed based on the two chosen focus areas for professionalization; social drivers and business drivers. The theoretical framework is based on literature from fields of marketing and social sciences, as well as previous research on social media, blogging and professionalization. The study is a qualitative case-study and the research data was collected in a semi-structured interview. The case chosen to this study is a lifestyle-blog. The writer of the case blog has been able to develop her blog to become a full-time professional blogger. Based on the results, the professionalization process of a blogger is not a defined process, but instead comprised of coincidental events as well as considered advancements. Success in blogging is based on the bloggers own motivation and passion for writing and expressing oneself in the form of a blog, instead of a systematic construction of a successful career in blogging. Networking with other bloggers as well as affiliates was seen as an important success factor. Popularity in the blogosphere and a high number of followers enable professionalization, as marketers actively seek to collaborate with popular bloggers with strong personal brands. Bloggers with strong personal brands are especially attractive due to their opinion leadership in their reference group. A blogger can act professionally either as entrepreneur or blogging for a commercial webpage. According to the results of this study, it is beneficial for the blogger’s professional development as well as career progress, to act on different operating models

INFLUÊNCIA DA POSIÇÃO SOCIAL NAS RELAÇÕES MORFOMÉTRICAS DE Araucaria angustifolia

The morphometric relations allow describing dimensions of trees without prior knowledge of the age, it help the forest planning and implementation of silvicultural treatments, especially when needs to make sustainable use of forests. For this purpose, the aim of this study was to model and comparising the morphometric relations araucaria trees in social position dominant, codominant and dominated in native forest remnant, located in Lages, SC. A total of 294 trees distributed in dbh classes were intentionally selected inside of forest. In each tree was measured dbh, total height, bole height, crown diameter by eight radius, as well as the classification of social position. Simple and multiple linear regression models were used to describe the relation h/d, the proportion of the crown and formal crown in function of diameter at breast height with simple transformation, quadratic, cubic, inverse and logarithmic form. The analysis of covariance with dummy variables were used to describe the social position and tested the parallelism and slope of regression indicating need or not of the use independent regressions. The results indicated that even with great variability in the shape and size of the crown due to growth and competition process, the morphometric relations of araucaria can be accurately estimated by regression models. The relation h/d, proportion of the crown and formal crown can be described by individual model for social position dominant, codominant and dominant, or alternatively a single model with the use of dummy variables that differentiate trees group dominated for the relation h/d and formal crown. The proportion of crown presented difference in dimensions of the trees, being necessary to use dummy variable for each social stratus or use the individual models.

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.

Discursos y prácticas culturales durante “La revolución en marcha”. Reforma educativa y cambio social

La República Liberal (1930-1946) se reconoce por los intentos de trasforma­ción social, aunque pueda cuestionarse su profundidad e impacto en la socie­dad. Los propósitos de trasformación intentan adaptar el país a los desafíos del capitalismo industrial y la moder­nidad intelectual. En este marco de cambios, se propone también la modifi­cación de los discursos y prácticas cul­turales para la modernización del país y, con ello, el evidente progreso que significaba, de los anhelos en boga, el avance hacia modelos de organización social. Una reforma estructural de la organización social exigía modificacio­nes en la conciencia de los individuos para que incorporasen los nuevos proyectos. Discurso y práctica cultural buscan, de esta forma, la modificación de las ideas de los individuos para su­marlos a los proyectos y reformas que estaban en marcha.

REFUGEES AND RESETTLEMENT: A QUALITATIVE ANALYSIS OF REFUGEE INTEGRATION THROUGH SOCIAL & SUPPORT SERVICES

This dissertation studies refugee resettlement in the United States utilizing the Integration Indicator’s framework developed by Ager and Strang for the U.S. context. The study highlights the U.S. refugee admissions program and the policies in the states of Maryland and Massachusetts while analyzing the service delivery models and its effects on refugee integration in these locations. Though immigration policy and funding for refugee services are primarily the domain of the federal government, funds are allocated through and services are delivered at the state level. The Office of Refugee Resettlement (ORR), which operates under the Department of Health and Human Services, was established after the Refugee Act of 1980 to deliver assistance to displaced persons. The ORR provides funds to individual states primarily through The Refugee Social Service and Targeted Assistance Formula Grant programs. Since the inauguration of the ORR three primary models of refugee integration through service delivery have emerged. Two of the models include the publicly/privately administered programs, where resources are allocated to the state in conjunction with private voluntary agencies; and the Wilson/Fish Alternative programs, where states sub-contract all elements of the resettlement program to voluntary agencies and private organizations —in which they can cease all state level participation and voluntary agencies or private organizations contract directly from the ORR in order for all states to deliver refugee services where the live. The specific goals of this program are early employment and economic self-sufficiency. This project utilizes US Census, state, and ORR data in conjunction with interviews of refugee resettlement practitioners involved in the service delivery and refugees. The findings show that delivery models emphasizing job training, English instruction courses, institutional collaboration, and monetary assistance, increases refugee acclimation and adaptation, providing insight into their potential for integration into the United States.

A Fuzzy Decision Strategy for Topic Identification and Dynamic Selection of Language Models

International audience

The Role of Attachment in a Social Cognitive Model of Social Domain Satisfaction in College Students

The study examined a modified social cognitive model of domain satisfaction (Lent, 2004). In addition to social cognitive variables and trait positive affect, the model included two aspects of adult attachment, attachment anxiety and avoidance. The study extended recent research on well-being and satisfaction in academic, work, and social domains. The adjusted model was tested in a sample of 454 college students, in order to determine the role of adult attachment variables in explaining social satisfaction, above and beyond the direct and indirect effects of trait positive affect. Confirmatory factor analysis found support for 8 correlated factors in the modified model: social domain satisfaction, positive affect, attachment avoidance, attachment anxiety, social support, social self-efficacy, social outcome expectations, and social goal progress. Three alternative structural models were tested to account for the ways in which attachment anxiety and attachment avoidance might relate to social satisfaction. Results of model testing provided support for a model in which attachment avoidance produced only an indirect path to social satisfaction via self-efficacy and social support. Positive affect, avoidance, social support, social self-efficacy, and goal progress each produced significant direct or indirect paths to social domain satisfaction, though attachment anxiety and social outcome expectations did not contribute to the predictive model. Implications of the findings regarding the modified social cognitive model of social domain satisfaction were discussed.

ADVERTISEMENT ALLOCATION AND TRUST MECHANISMS DESIGN IN SOCIAL NETWORKS

Social network sites (SNS), such as Facebook, Google+ and Twitter, have attracted hundreds of millions of users daily since their appearance. Within SNS, users connect to each other, express their identity, disseminate information and form cooperation by interacting with their connected peers. The increasing popularity and ubiquity of SNS usage and the invaluable user behaviors and connections give birth to many applications and business models. We look into several important problems within the social network ecosystem. The first one is the SNS advertisement allocation problem. The other two are related to trust mechanisms design in social network setting, including local trust inference and global trust evaluation. In SNS advertising, we study the problem of advertisement allocation from the ad platform's angle, and discuss its differences with the advertising model in the search engine setting. By leveraging the connection between social networks and hyperbolic geometry, we propose to solve the problem via approximation using hyperbolic embedding and convex optimization. A hyperbolic embedding method, \hcm, is designed for the SNS ad allocation problem, and several components are introduced to realize the optimization formulation. We show the advantages of our new approach in solving the problem compared to the baseline integer programming (IP) formulation. In studying the problem of trust mechanisms in social networks, we consider the existence of distrust (i.e. negative trust) relationships, and differentiate between the concept of local trust and global trust in social network setting. In the problem of local trust inference, we propose a 2-D trust model. Based on the model, we develop a semiring-based trust inference framework. In global trust evaluation, we consider a general setting with conflicting opinions, and propose a consensus-based approach to solve the complex problem in signed trust networks.

VOX POPULI: THREE ESSAYS ON THE USE OF SOCIAL MEDIA FOR VALUE CREATION IN SERVICES

Prior research shows that electronic word of mouth (eWOM) wields considerable influence over consumer behavior. However, as the volume and variety of eWOM grows, firms are faced with challenges in analyzing and responding to this information. In this dissertation, I argue that to meet the new challenges and opportunities posed by the expansion of eWOM and to more accurately measure its impacts on firms and consumers, we need to revisit our methodologies for extracting insights from eWOM. This dissertation consists of three essays that further our understanding of the value of social media analytics, especially with respect to eWOM. In the first essay, I use machine learning techniques to extract semantic structure from online reviews. These semantic dimensions describe the experiences of consumers in the service industry more accurately than traditional numerical variables. To demonstrate the value of these dimensions, I show that they can be used to substantially improve the accuracy of econometric models of firm survival. In the second essay, I explore the effects on eWOM of online deals, such as those offered by Groupon, the value of which to both consumers and merchants is controversial. Through a combination of Bayesian econometric models and controlled lab experiments, I examine the conditions under which online deals affect online reviews and provide strategies to mitigate the potential negative eWOM effects resulting from online deals. In the third essay, I focus on how eWOM can be incorporated into efforts to reduce foodborne illness, a major public health concern. I demonstrate how machine learning techniques can be used to monitor hygiene in restaurants through crowd-sourced online reviews. I am able to identify instances of moral hazard within the hygiene inspection scheme used in New York City by leveraging a dictionary specifically crafted for this purpose. To the extent that online reviews provide some visibility into the hygiene practices of restaurants, I show how losses from information asymmetry may be partially mitigated in this context. Taken together, this dissertation contributes by revisiting and refining the use of eWOM in the service sector through a combination of machine learning and econometric methodologies.

Procedimento para estudo da escolha modal em viagens realizadas por estudantes de instituições de ensino médio, mediado pela psicologia social

Tese (doutorado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Civil e Ambiental, 2016.

COLLEGE STUDENTS’ ATTITUDES TOWARD SEEKING PROFESSIONAL HELP: THE ROLE OF SOCIAL CLASS, CLASSISM, AND STIGMA

Research on attitudes toward seeking professional help among college students has examined the influence of social class and stigma. This study tested 4 theoretically and empirically derived structural equation models of college students’ attitudes toward seeking counseling with a sample of 2230 incoming university students. The models represented competing hypotheses regarding the manners in which objective social class, subjective social class, classism, public stigma, stigma by close others, and self-stigma related to attitudes toward seeking professional help. Findings supported the social class direct and indirect effects model, as well as the notion that classism and stigma domains could explain the indirect relationships between social class and attitudes. Study limitations, future directions for research, and implications for counseling are discussed.

Teachers' legitimacy: Effects of justice perception and social comparison processes

Background. Teachers’ legitimacy is central to school functioning. Teachers’ justice, whether distributive or procedural, predicts teachers’ legitimacy. Aims. What is still do be found, and constitutes the goal of this paper, is whether unjust treatment by a teacher affects the legitimacy of the teacher differently when the student knows that the teacher was fair to a peer (comparative judgement) or when the student does not have that information (autonomous judgement). Samples. A total of 79 high school students participated in Study 1; 75 high school students participated in Study 2. Methods. Two experimental studies with a 2 justice valence (just, unjust) 9 2 social comparison processes (autonomous judgements, comparative judgements) betweenparticipants design were conducted. Study 1 addressed distributive justice and Study 2 addressed procedural justice. The dependent variable was teachers’ legitimacy. Results. In both studies, situations perceived as just led to higher teachers’ legitimacy than situations perceived as unjust. For the distributive injustice conditions, teachers’ legitimacy was equally lower for autonomous judgement and comparative judgement conditions. For procedural injustice, teachers’ legitimacy was lower when the peer was treated justly and the participant was treated unfairly, compared with the condition when the participants did not know how the teacher treated the peer. Conclusions. We conclude that teachers’ injustice affects teachers’ legitimacy, but it does it differently according to the social comparisons involved and the type of justice involved. Moreover, these results highlight that social comparisons are an important psychological process and, therefore, they should be taken into account in models of justice.