Does a peer model’s task proficiency influence children’s solution choice and innovation?

The current study investigated whether 4- to 6-year-old children’s task solution choice was influenced by the past proficiency of familiar peer models and the children’s personal prior task experience. Peer past proficiency was established through behavioral assessments of interactions with novel tasks alongside peer and teacher predictions of each child’s proficiency. Based on these assessments, one peer model with high past proficiency and one age-, sex-, dominance-, and popularity-matched peer model with lower past proficiency were trained to remove a capsule using alternative solutions from a three-solution artificial fruit task. Video demonstrations of the models were shown to children after they had either a personal successful interaction or no interaction with the task. In general, there was not a strong bias toward the high past-proficiency model, perhaps due to a motivation to acquire multiple methods and the salience of other transmission biases. However, there was some evidence of a model-based past-proficiency bias; when the high past-proficiency peer matched the participants’ original solution, there was increased use of that solution, whereas if the high past-proficiency peer demonstrated an alternative solution, there was increased use of the alternative social solution and novel solutions. Thus, model proficiency influenced innovation.

Paradigms that guide the models of attention to the health in Brazil: an analytic essay

SILVA, Flávio César Bezerra da ; COSTA, Francisca Marta de Lima; ANDRADE, Hamilton Leandro Pinto de; FREIRE, Lúcia de Fátima; MACIEL, Patrícia Suerda de Oliveira; ENDERS, Bertha Cruz ; MENEZES, Rejane Maria Paiva de. Paradigms that guide the models of attention to the health in Brazil: an analytic essay. Revista de Enfermagem UFPE On Line., Recife, v.3,n.4, p.460-65. out/dez. 2009. Disponível em < http://www.ufpe.br/revistaenfermagem/index.php/revista/search/results >.

The relationship between personal cognition, social context, and knowledge sharing in global communities of practice

The purpose of this study was to identify the structural pathways of personal cognition and social context as they influence knowledge sharing behaviors in communities of practice. Based on the existing literature, ten hypotheses and a conceptual model built on the basis of the social cognitive theory were developed regarding the interrelationships of the five constructs: self-efficacy for knowledge sharing, outcome expectations, sense of community, leadership of a community, and knowledge sharing. The data were collected through an online questionnaire from the employees who have participated in communities of practice in a Fortune 100 corporation. A total of 438 usable questionnaires were collected. Overall, three analyses were conducted in order to prove the given hypotheses: (a) hypothesized measurement model fit, (b) relational and influential associations among the constructs, and (c) structural equation model analysis (SEM). In addition, open-ended responses were analyzed. The results presented that (a) hypothesized measurement models were valid and reliable, (b) personal cognitive factors, self-efficacy and outcome expectations for knowledge sharing, were found to be significant predictors of community members’ sense of community and knowledge sharing behaviors, (c) sense of community had the most significant impact on the knowledge sharing, (d) as the perceived social context, sense of community mediated the effects of personal cognition on knowledge sharing behaviors, and (e) personal cognition and social context jointly contributed to knowledge sharing. In brief, all of the hypotheses were positively supported. A conclusive summary is provided along with contributive discussion. Implications and contributions to HRD researchers and practitioners are discussed, and recommendations are provided.

SOCIAL AND ECOLOGICAL FACTORS INFLUENCING COLLECTIVE BEHAVIOR IN GIANT DANIO

A fundamental problem in biology is understanding how and why things group together. Collective behavior is observed on all organismic levels - from cells and slime molds, to swarms of insects, flocks of birds, and schooling fish, and in mammals, including humans. The long-term goal of this research is to understand the functions and mechanisms underlying collective behavior in groups. This dissertation focuses on shoaling (aggregating) fish. Shoaling behaviors in fish confer foraging and anti-predator benefits through social cues from other individuals in the group. However, it is not fully understood what information individuals receive from one another or how this information is propagated throughout a group. It is also not fully understood how the environmental conditions and perturbations affect group behaviors. The specific research objective of this dissertation is to gain a better understanding of how certain social and environmental factors affect group behaviors in fish. I focus on two ecologically relevant decision-making behaviors: (i) rheotaxis, or orientation with respect to a flow, and (ii) startle response, a rapid response to a perceived threat. By integrating behavioral and engineering paradigms, I detail specifics of behavior in giant danio Devario aequipinnatus (McClelland 1893), and numerically analyze mathematical models that may be extended to group behavior for fish in general, and potentially other groups of animals as well. These models that predict behavior data, as well as generate additional, testable hypotheses. One of the primary goals of neuroethology is to study an organism's behavior in the context of evolution and ecology. Here, I focus on studying ecologically relevant behaviors in giant danio in order to better understand collective behavior in fish. The experiments in this dissertation provide contributions to fish ecology, collective behavior, and biologically-inspired robotics.

Responsabilidade social das empresas: caso de estudo

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica

The relationship of early social, mental, and behavioral experiences with adult obesity and Alcohol Use Disorder

Stressful life events early in life, including symptoms of mental disorders or childhood maltreatment, may increase risk for worse mental and physical health outcomes in adulthood. The purpose of this dissertation was to examine the effects of childhood Attention Deficit Hyperactivity Disorder (ADHD) symptoms and maltreatment experience on two adult outcomes: obesity and alcohol use disorder (AUD). Mediational effects of adolescent characteristics were explored. This dissertation used Waves I, III, and IV of the National Longitudinal Study of Adolescent to Adult Health. In Paper 1 (Chapter 3), we investigated the association between multiple types of child maltreatment and adult objective (body mass index; BMI) and subjective (self-rated) obesity, as well as mediating effects by adolescent characteristics including depressive symptoms and BMI. Results showed that after adjusting for sex, race/ethnicity, and maternal education, physical maltreatment was moderately associated with adulthood obesity as measured by BMI and self-reported obesity, while sexual maltreatment was more strongly associated with the objective measure but not the subjective measure. The indirect effects of mediation of adolescent BMI and depressive symptoms were statistically significant. In Paper 2 (Chapter 4), the objective was to examine mediation by adolescent depressive symptoms, alcohol consumption, peer alcohol consumption, and delinquency in the relationship between ADHD symptoms and adult AUD. The indirect effects of mediation of adolescent delinquency, alcohol consumption, and peer alcohol consumption were statistically significant in single and multiple mediator models. In Paper 3 (Chapter 5), the objective was to assess the joint effects of maltreatment/neglect on adult AUD. After adjusting for sex, race/ethnicity, child maltreatment, and parental AUD, ADHD symptoms were significantly associated with increased odds of AUD. There was no strong evidence of multiplicative interaction by maltreatment. This association was stronger for males than females, although the interaction term was not statistically significant. This dissertation adds to the literature by examining relationships between several major public health problems: ADHD symptoms, childhood maltreatment, AUD, depressive symptoms, and obesity. This project has implications for understanding how early life stress increases risk for later physical and mental health problems, and identifying potential intervention targets for adolescents.

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