A computational model of affective moral decision making that predicts human criminal choices

In the present paper we show that a computational model of affective moral decision making can fit human behavior data obtained from an empirical study on criminal decision making. By applying parameter tuning techniques on data from an initial sample, optimal fits of the affective moral decision making model were found supporting the influences of honesty/humility, perceived risk and negative state affect on criminal choice. Using the parameter settings from the initial sample, we were able to predict criminal choices of participants in the holdout sample. The prediction errors of the full model were fairly low. Moreover, they compared favorably to the prediction errors produced by constrained variants of the model where either the moral, rational or affective influences or a combination of these had been removed

Modeling computational dynamics of job interview candidate's mental states using cognitive agent based approach

Support for job interview is a domain that can benefit from the research on human-aware AI systems. A developed cognitive model provides the awareness of interviewee behaviours as a mechanism for intelligent support processes. The interplaying constructs of self-efficacy, motivation and anxiety has been hypothesized to define the mental states of an interviewee. However, these constructs have not been integrated, formalized and evaluated for their dynamic intricacies in previous studies hence cannot be implemented as the reasoning component in human-aware system. This study has developed a cognitive agent model as a basic intelligent mechanism for interview coaching systems. The model integrates three constructs; self-efficacy, motivation and anxiety. Each of the constructs is formalized as an entity agent model and then integrated. Design Science Research Processes framework and Agent Based Modelling methodology were used to conduct this study. Factors interaction and overlapping relationship approach was adopted to integrate the proposed constructs. The model is formalized using Ordinary Differential Equation technique and later being simulated. Generated cases were verified with stability analysis and automatic logical verifications techniques. For model validation, 36 undergraduate students were studied in a mock interview experiment. The results generated from the model simulation were then compared against human experiment. The evaluation was based on a statistical technique namely Hotelling’s T2. The simulation results have confirmed a number of patterns identified in the domain literature. The behavioural patterns of the agent models conform to the expected behavioural dynamics of candidate in interview situation. Results from the validation showed that there is no significant difference (i.e. ρ values: anxiety = 0.391, self-efficacy = 0.128 and motivation = 0.466) between the simulation and human experiments. Theoretically, by integration of the three constructs, the model could better represent the mental state of candidates in interviews. In general, by formalizing the model, it can define the dynamic properties in details. The integrated cognitive model serves as a platform for designing a human-aware system that understands the behavioural intricacies of the user during job interview sessions

An ambient agent model for reading companion robot

Reading is essentially a problem-solving task. Based on what is read, like problem solving, it requires effort, planning, self-monitoring, strategy selection, and reflection. Also, as readers are trying to solve difficult problems, reading materials become more complex, thus demands more effort and challenges cognition. To address this issue, companion robots can be deployed to assist readers in solving difficult reading tasks by making reading process more enjoyable and meaningful. These robots require an ambient agent model, monitoring of a reader’s cognitive demand as it could consist of more complex tasks and dynamic interactions between human and environment. Current cognitive load models are not developed in a form to have reasoning qualities and not integrated into companion robots. Thus, this study has been conducted to develop an ambient agent model of cognitive load and reading performance to be integrated into a reading companion robot. The research activities were based on Design Science Research Process, Agent-Based Modelling, and Ambient Agent Framework. The proposed model was evaluated through a series of verification and validation approaches. The verification process includes equilibria evaluation and automated trace analysis approaches to ensure the model exhibits realistic behaviours and in accordance to related empirical data and literature. On the other hand, validation process that involved human experiment proved that a reading companion robot was able to reduce cognitive load during demanding reading tasks. Moreover, experiments results indicated that the integration of an ambient agent model into a reading companion robot enabled the robot to be perceived as a social, intelligent, useful, and motivational digital side-kick. The study contribution makes it feasible for new endeavours that aim at designing ambient applications based on human’s physical and cognitive process as an ambient agent model of cognitive load and reading performance was developed. Furthermore, it also helps in designing more realistic reading companion robots in the future

How outraged customers react : from the antecedents to the consequences of customer rage emotions in service failure and intervention strategies

PhD ThesisOutraged customers experience the unfair treatment commonly across the world in various industries. But only a small number of studies provide comprehensive measures of the customer rage construct and associated behaviours. Addressing this issue, the present research focuses on four objectives: (1) to develop and empirically test the scale of customer rage; (2) to identify the mediators of customer rage in the service context.; (3) to clarify the concepts of customer rage and rage behaviours, and to empirically test casual relationships between them; (4) to investigate the intervention strategies in different contexts of rage emotions and test the efficiency with diverse rage behaviours. The research adopts a mixed research method, consisting of preliminary qualitative interviews, and a quantitative survey. A measurement scale of customer rage is developed and empirically evaluated, following the established scale development procedure. The conceptual framework of customer rage and associated behaviours is tested with a structural equation model. Results reveal two different types of rage emotions, i.e. impulsive and forethought rage, and three mediators of the relationship between service failure and rage emotions, i.e. anger, betrayal and frustration. Seven rage behaviours and fifteen intervention tactics are tested in the model. Positive relationships are found between two types of rage emotions and different behaviours. Eleven out of fifteen intervention strategies are found to have buffering effects and thirteen out fifteen are found to have amplifying effects on the links between rage emotions and behaviours. This research contributes to the academics by establishing the scales of two customer rage emotions and figuring out the causal relationships with rage behaviours. It implies to the managers that the efficiency of the same intervening tactics may vary on different time and targets where the intervention take place