Interactive Strategy-Based Validation of Behavioral Models

When behavioral models are derived automatically based on observed stakeholder interactions, requirements engineers need to validate whether the stakeholders agree with the synthesized behavioral models. Allowing stakeholders to experience such models through simulation and animation allows them to comment on, amend to and correct these models. However, to ensure an efficient stakeholder validation, the simulation has to be guided instead of confronting the user with random situations over and over again. In this paper, we present a strategy-driven simulator capable of guiding the execution of behavioral models based on graph transformations. By analyzing either the overall structure of a partial state space (look ahead) or by performing an in-depth analysis of the states therein, the simulator is able to determine which transformations should be executed next to continue on the most promising path through the overall state space. The discussed implementation is illustrated with a case study

Validating simulated interaction for retrieval evaluation

A searcher’s interaction with a retrieval system consists of actions such as query formulation, search result list interaction and document interaction. The simulation of searcher interaction has recently gained momentum in the analysis and evaluation of interactive information retrieval (IIR). However, a key issue that has not yet been adequately addressed is the validity of such IIR simulations and whether they reliably predict the performance obtained by a searcher across the session. The aim of this paper is to determine the validity of the common interaction model (CIM) typically used for simulating multi-query sessions. We focus on search result interactions, i.e., inspecting snippets, examining documents and deciding when to stop examining the results of a single query, or when to stop the whole session. To this end, we run a series of simulations grounded by real world behavioral data to show how accurate and responsive the model is to various experimental conditions under which the data were produced. We then validate on a second real world data set derived under similar experimental conditions. We seek to predict cumulated gain across the session. We find that the interaction model with a query-level stopping strategy based on consecutive non-relevant snippets leads to the highest prediction accuracy, and lowest deviation from ground truth, around 9 to 15% depending on the experimental conditions. To our knowledge, the present study is the first validation effort of the CIM that shows that the model’s acceptance and use is justified within IIR evaluations. We also identify and discuss ways to further improve the CIM and its behavioral parameters for more accurate simulations

The effect of social media communication on consumer perceptions of brands

Researchers and brand managers have limited understanding of the effects social media communication has on how consumers perceive brands. We investigated 504 Facebook users in order to observe the impact of firm-created and user-generated social media communication on brand equity, brand attitude and purchase intention by using a standardized online survey throughout Poland. To test the conceptual model, we analyzed 60 brands across three different industries: non-alcoholic beverages, clothing and mobile network operators. When analyzing the data, we applied the structural equation modeling technique to both investigate the interplay of firm-created and user-generated social media communication and examine industry-specific differences. The results of the empirical studies showed that user-generated social media communication had a positive influence on both brand equity and brand attitude, whereas firm-created social media communication affected only brand attitude. Both brand equity and brand attitude were shown to have a positive influence on purchase intention. In addition, we assessed measurement invariance using a multi-group structural modeling equation. The findings revealed that the proposed measurement model was invariant across the researched industries. However, structural path differences were detected across the models

Measuring consumers' engagement with brand-related social-media content: development and validation of a scale that identifies levels of social-media engagement with brands

The main purpose of this study is to develop a scale to measure consumer's engagement with social media brand-related content. The reported scale (CESBC) comprises three dimensions: consumption, contribution, and creation. Qualitative techniques were used to generate an initial pool of items that capture different levels of consumer engagement with social media brand-related content. Quantitative data from a consumer survey (=2252) was then collected in two phases to calibrate and validate the ensuing scale. Results confirmed the structure and psychometric properties of the CESBC. As expected, each subscale is significantly correlated with nomological network constructs, i.e., brand equity and brand attitudes

The conditional process model of mindfulness and emotion regulation: An empirical test

BACKGROUND: The conditional process model (CPM) of mindfulness and emotion regulation posits that specific mediators and moderators link these constructs to mental health outcomes. The current study empirically examined the central tenets of the CPM, which posit that nonreactivity moderates the indirect effect of observation on symptoms of emotional disorders through cognitive emotion regulation strategies. METHODS: A clinical sample (n=1667) of individuals from Japan completed a battery of self-report instruments. Several path analyses were conducted to determine whether cognitive emotion regulation strategies mediate the relationship between observation and symptoms of individual emotional disorders, and to determine whether nonreactivity moderated these indirect effects. RESULTS: Results provided support the CPM. Specifically, nonreactivity moderated the indirect effect of observation on symptoms through reappraisal, but it did not moderate the indirect effect of observation on symptoms through suppression. LIMITATIONS: Causal interpretations are limited, and cultural considerations must be acknowledged given the Japanese sample CONCLUSIONS: These results underscore the potential importance of nonreactivity and emotion regulation as targets for interventions.R01 AT007257 - NCCIH NIH HHS; R34 MH099311 - NIMH NIH HH

Predicting Human Cooperation

The Prisoner's Dilemma has been a subject of extensive research due to its importance in understanding the ever-present tension between individual self-interest and social benefit. A strictly dominant strategy in a Prisoner's Dilemma (defection), when played by both players, is mutually harmful. Repetition of the Prisoner's Dilemma can give rise to cooperation as an equilibrium, but defection is as well, and this ambiguity is difficult to resolve. The numerous behavioral experiments investigating the Prisoner's Dilemma highlight that players often cooperate, but the level of cooperation varies significantly with the specifics of the experimental predicament. We present the first computational model of human behavior in repeated Prisoner's Dilemma games that unifies the diversity of experimental observations in a systematic and quantitatively reliable manner. Our model relies on data we integrated from many experiments, comprising 168,386 individual decisions. The computational model is composed of two pieces: the first predicts the first-period action using solely the structural game parameters, while the second predicts dynamic actions using both game parameters and history of play. Our model is extremely successful not merely at fitting the data, but in predicting behavior at multiple scales in experimental designs not used for calibration, using only information about the game structure. We demonstrate the power of our approach through a simulation analysis revealing how to best promote human cooperation.Comment: Added references. New inline citation style. Added small portions of text. Re-compiled Rmarkdown file with updated ggplot2 so small aesthetic changes to plot

The efficacy of a web-based gambling intervention program for high school students: A preliminary randomized study

Early onset in adolescent gambling involvement can be a precipitator of later gambling problems. The aim of the present study was to test the preliminary efficacy of a web-based gambling intervention program for students within a high school-based setting. Students attending a high school in Italy (N=168) participated in the present study (58% male–age, M=15.01; SD=0.60). Twelve classes were randomly assigned to one of two conditions: intervention ( N=6; 95 students) and control group (N=6; 73 students). Both groups received personalized feedback and then the intervention group received online training (interactive activities) for three weeks. At a two-month follow-up, students in the intervention group reported a reduction in gambling problems relative to those in the control group. However, there were no differences in gambling frequency, gambling expenditure, and attitudes toward the profitability of gambling between the two groups. In addition, frequent gamblers (i.e., those that gambled at least once a week at baseline) showed reductions in gambling problems and gambling frequency post- intervention. Frequent gamblers that only received personalized feedback showed significantly less realistic attitudes toward the profitability of gambling post-intervention. The present study is the first controlled study to test the preliminary efficacy of a web-based gambling intervention program for students within a high school-based setting. The results indicate that a brief web-based intervention delivered in the school setting may be a potentially promising strategy for a low-threshold, low-cost, preventive tool for at-risk gambling high school students