Machine Learning in Mobile Crowd Sourcing: A Behavior-Based Recruitment Model

With the advent of mobile crowd sourcing (MCS) systems and its applications, the selection of the right crowd is gaining utmost importance. The increasing variability in the context of MCS tasks makes the selection of not only the capable but also the willing workers crucial for a high task completion rate. Most of the existing MCS selection frameworks rely primarily on reputation-based feedback mechanisms to assess the level of commitment of potential workers. Such frameworks select workers having high reputation scores but without any contextual awareness of the workers, at the time of selection, or the task. This may lead to an unfair selection of workers who will not perform the task. Hence, reputation on its own only gives an approximation of workers’ behaviors since it assumes that workers always behave consistently regardless of the situational context. However, following the concept of cross-situational consistency, where people tend to show similar behavior in similar situations and behave differently in disparate ones, this work proposes a novel recruitment system in MCS based on behavioral profiling. The proposed approach uses machine learning to predict the probability of the workers performing a given task, based on their learned behavioral models. Subsequently, a group-based selection mechanism, based on the genetic algorithm, uses these behavioral models in complementation with a reputation-based model to recruit a group of workers that maximizes the quality of recruitment of the tasks. Simulations based on a real-life dataset show that considering human behavior in varying situations improves the quality of recruitment achieved by the tasks and their completion confidence when compared with a benchmark that relies solely on reputation.

Decision from Experience Behavior Modeling (DEBM): an open-source Python package for developing, evaluating, and visualizing behavioral models.

The last decade was characterized by an emphasis on enhancing reproducibility and replicability in the social sciences. To contribute to these efforts within the decision-making research field, we introduce DEBM (Decision from Experience Behavior Modeling) – an open-source Python package. The main goal of DEBM is to serve as a central colloberative pool of models and methods in the decision from experience domain. Specifically, it provides a convenient “playground” for developing models or experimenting with existing ones. DEBM includes many features such as multiprocessing, parameter estimation, visualization, and more. In this paper we cover the basic functionality of DEBM by simulating behavior using an existing model and given parameters, and recovering these parameters using grid search.

Logical Fallacies and Misinterpretations that Hinder Progress in Translational Addiction Neuroscience

Substance use disorders (SUDs) are heterogenous and complex, making the development of translationally predictive rodent and non-human primate models to uncover their neurobehavioral underpinnings difficult. Neuroscience-focused outcomes have become highly prevalent, and with this, the notion that SUDs are disorders of the brain embraced as a dominant theoretical orientation to understand SUD etiology and treatment. These efforts, however, have led to few efficacious pharmacotherapies, and in some cases (as with cocaine or methamphetamine), no pharmacotherapies have translated from preclinical models for clinical use. In this review and theoretical commentary, we first describe the development of animal models of SUDs from a historical perspective. We then define and discuss three logical fallacies including 1) circular explanation, 2) affirming the consequent, and 3) reification that can apply to developed models. We then provide three case examples in which conceptual or logical issues exist in common methods (i.e., behavioral economic demand, escalation, and reinstatement). Alternative strategies to refocus behavioral models are suggested for the field in an attempt to better bridge the translational divide between animal models and the clinical condition of SUDs.

Validation of the South Korean Version of the Beliefs about Emotions Scale

Background Beliefs about the unacceptability of experiencing or expressing negative emotions can contribute to diverse psychological symptoms and associated with poor treatment outcomes and low treatment attempts. The Beliefs about Emotions Scale (BES) was developed to assess such beliefs based on the cognitive-behavioral models; however, no study has reported on the psychometric properties of the BES in Korea. The present study aimed to cross-culturally adapt and validate the BES for the Korean population (BES-K). Methods The BES-K was administered to 592 Korean adults (323 men and 269 women) aged 20–59 years. Exploratory and confirmatory factor analysis were used to assess the factor model of the scale. Pearson correlation coefficients were used to evaluate the relationships between the BES-K and other psychological measures. Results The result showed a two-factor model of the BES-K, with Factor 1 relating to Interpersonal and Factor 2 representing Intrapersonal aspects. The scale had significant yet moderately low correlations with measures of depression, anxiety, and difficulties in emotion regulation. Conclusion The BES-K is a useful instrument in evaluating the beliefs about emotions in the Korean population.

PROJECT-BASED LEARNING: A TEACHING APPROACH WHERE LEARNING COMES ALIVE

“Doing projects” is a long-standing tradition in American education. According to Buck Institute for Education (BIE) (www.pblworks.org) the roots of PBL lie in this tradition. But the emergence of a method of teaching and learning called Project Based Learning (PBL) is the result of two important developments over the last 25 years. First, there has been a revolution in learning theory. Research in neuroscience and psychology has extended cognitive and behavioral models of learning—which support traditional direct instruction—to show that knowledge, thinking, doing, and the contexts for learning are inextricably tied. We now know that learning is partly a social activity; it takes place within the context of culture, community, and past experiences.

The Foundational Lawlessness of the Law Itself: Racial Criminalization & the Punitive Roots of Punishment in America

Empirical researchers and criminal justice practitioners have generally set aside history in exchange for behavioral models and methodologies that focus primarily on crime itself as the most measurable and verifiable driver of American punitiveness. There are innumerable legal and political questions that have arisen out of these approaches. Everything from the social construction of illegality to the politicization of punishment to the stigmatization of physical identities and social statuses have long called into question the legal structures that underpin what counts as crime and how punishment is distributed. And yet, until quite recently, the question of what history has to offer has mostly been left to historians, historically minded social scientists, critical race and ethnic studies scholars, community and prison-based activists, investigative journalists, and rights advocates. What is at stake is precisely the foundational lawlessness of the law itself. At all times, a White outlaw culture that rewarded brute force and strength of arms against racialized others unsettles basic assumptions about how we are to understand criminalization and punitiveness over time: that is, who has counted as a criminal and to what end has the state used violence or punishment?

Digital school: Searching for Explanatory Models. Part 1

Introduction. The purpose of this study was to analyze L. S. Vygotsky’s cultural-historical psychology from the perspective of distinguishing the structure of the act of development within its framework and understanding its explanatory potential as a whole. This analysis enables to clarify and understand the essence of the digital and virtual shift. Moreover, it is required in order to overcome the limitations associated with the currently dominant behavioral models, which are used by researchers in their attempts to explain and describe the phenomenon of the influence of digital technologies on people, in particular, on schoolchildren and students. Materials and Methods. The paper considers the concept derived from L. S. Vygotsky’s cultural-historical psychology, which is proposed to be adopted as a basic one in order to build an explanatory model used by the author to describe and comprehend the phenomenon of transformation of the human development process in the new reality of the digital environment. Results. The article introduces the basic principles and provisions, the explanatory model is built on, concerning the role of symbolic-instrumental mediation in human development, the role of an adult as a mediator, the structure of the act of thinking and the act of development, the basic mechanism of mastering a person's behavior, which permeates the formation of higher mental functions. The author compares this explanatory model and the behavioral model used in most modern research investigations that examine the impact of digital technologies on schoolchildren and students. Conclusions. The findings show that the student is presented in the behavioral model as a passive reactive being that reacts to external stimuli. In this sense, a human being is not treated as a personality with highest abilities, but is considered as a function. The proposed model cannot be used to explain and comprehend modern development processes.

Commemorative Practices in Urban Discourse Space: Historical-Political Projection

The paper focuses on the potential of urban communicative space to transmit basic ideology paradigm and values of the current political regime in the course of interaction with city residents. Commemoration is regarded as one the key entities involved in transmitting ideologically charged information. The phenomenon of commemoration is understood as a collection of public practices aimed at creating government-approved values and behavioral models via regular reproduction schemes implemented on the basis of perception of past recognized in the society. The goal of the research was to examine verbalization of commemoration in urban space with regard to the historical-political context. The empirical basis of the study includes a corpus of commemorative texts collected by the authors. Commemorative texts were extracted from the open data portal developed and supported by the Government of Moscow. The total number of records registered in the corpus amounted to over 1700. The language data were processed via the AntConc software that allows obtaining information about word frequency and the contexts in which the relevant word occurs. In the next step conclusions about topical and conceptual dominants of commemorative texts were made. Further investigation allowed describing the structural scheme of commemorative texts, determining its zero variability in different temporal periods, identifying an obligatory structural element that displayed sensitiveness to political climate and specifying key discourse strategies correlated with the ideological paradigm of the current political regime.