Subjects: Psychology >> Social Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： The era of artificial intelligence has already arrived. With the rapid development of intelligent technology, more and more companies are adopting this technology into their business processes to enhance their core competitiveness. Subsequently, human-agent collaborative work is becoming common, and human-agent collaborative decision-making (HACDM) is evolving as a new form of organizational decision-making. However, evidence shows that HACDM still faces challenges, such as low trust and controllability toward agents, low transparency of agents, and low collaboration between humans and agents. Therefore, how these challenges can be overcome to improve the decision quality, decision efficiency, and user experience of HACDM is crucial to the field of organizational decision-making.This project suggests that human-agent compatibility, especially human-agent inner compatibility (HAIC) which consists of cognitive, affective, and value compatibility, might be the fundamental factor affecting the performance of HACDM. Following the perspective of HAIC theory and using the multi-disciplinary methods from psychology, cognitive science, and organizational behavior, we intend to 1) reveal the existing problems within HACDM; 2) explore the impact of HAIC on the process and performance of HACDM; 3) propose methods to improve the performance of HACDM. Thus, this project consists of three studies. Study 1 aims to investigate real-world intelligent organizations to uncover the current usage of agents, the willingness of human employees and managers to collaborate with agents, and the possible problems within HACDM. Based on the findings of study 1, study 2 adopts HAIC theory as its framework and explores the influence of cognitive, affective, and value compatibilities on the process and performance of HACDM. Finally, study 3 tests the effectiveness of the several methods suggested by HAIC theory for improving HACDM, such as increasing the transparency of agents’ decisions and providing decision feedback to human employees.This project’s findings will contribute both theoretically and practically. Theoretically, this project examines the components of HAIC (i.e., cognitive, affective, and value compatibilities) and investigates their influence on HACDM. Thus, it will contribute to the further development of human-agent compatibility theory and human-agent collaboration theory. Practically, the project proposes several methods that can effectively improve the performance of HACDM. Therefore, it will improve the performance of intelligent organizations and promote the intelligentization progress of HACDM.
Subjects: Psychology >> Statistics in Psychology submitted time 2020-05-14
Abstract： Psychological researches focus on describing, explaining and predicting behavior, and having a good understanding of the association between variables is an essential part of this process. Regression analysis, a method to evaluate the relationship between variables, is widely used in psychological studies. However, due to its highly focus on the interpretation of sample data, the traditional ordinary least squares regression has several drawbacks, such as over-fitting problem and limitation on dealing with multicollinearity, which may undermine the generalizability of the model. These drawbacks have an inevitable influence on the promotion and prediction of the model conclusion. With the rapid development of methodology, Least absolute shrinkage and selection operator (Lasso) regression has been emerged to better compensate for the limitations of traditional methods. By introducing a penalty term in the model and shrinking the regression coefficients to zero, Lasso regression can achieve a higher accuracy of model prediction and model generalizability with the cost of a certain estimation bias. Besides, Lasso regression can also effectively deal with the multicollinearity problem. Therefore, it has been widely used in medicine, economics, neuroscience and other fields. In psychology, due to the limitations of computer computing power, researchers used to mainly rely on hypothesis testing to understand the association among variables to verify theories. Now, with the rapid development of machine learning, a shift from focusing on interpretation of the regression coefficients to improving the prediction of the model has emerged and become more and more important. Therefore, based on fundamental theories and real data analysis, the aim of this paper is to introduce the principles, implementation steps and advantages of the Lasso regression. With the help of statistic science, it is promising that more and more applied researchers will be called upon to focus on the emerging statistical tools to promote the development of psychology.
Subjects: Psychology >> Statistics in Psychology submitted time 2018-12-27
Abstract： Structural equation modeling (SEM) has been widely used in psychological researches to investigate the casual relationship among latent variables. Model estimation can be conducted under both the frequentist framework (e.g., maximum-likelihood approach) and the Bayesian framework. In recent years, with the prevalence of Bayesian statistics and its advantages in dealing with small samples, missing data and complex models in SEM, Bayesian structural equation modeling (BSEM) has developed rapidly. However, in China its application in the field of psychology is still insufficient. Therefore, this paper mainly focuses on presenting this new research method to applied researchers. We explain the theoretical and methodological basis of BSEM, as well as its advantages and disadvantages compared with the traditional frequentist approach. We also introduce several commonly used BSEM models and their applications. "
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