Modelling human support agent for managers during stress

Patterns of stress at work become a popular topic and have been reported everywhere.Work related performance during stress is a pattern of reactions that occurs when managers are presented with work demands that are not matched with their knowledge, skills, or abilities, and which challenge their ability to cope.Although there are many prior findings pertaining to explain the development of manager performance during stress, less attention has been given to explain the same concept through computational models. In such, a descriptive nature in psychological theories about managers’ performance during stress can be transformed into a causal-mechanistic stage that explains the relationship between a series of observed phenomena.This paper proposed a human support agent model for analyzing managers’ performance during stress. Set of properties and variables are identified through past literatures to construct the model.Differential equations have been used in formalizing the model.Set of equations reflecting relations involved in the proposed model are presented. The developed model has been simulated by applying it to different scenarios.Mathematical analysis has been used for the evaluation of the model.Results showed that the support model is able to show the effects of different levels of stress on managers’ performance.The proposed model is essential and can be encapsulated within an intelligent agent or robots that can be used to support managers during stress

Modelling dynamics of victims' stress during natural disaster

Natural disaster is one of the inescapable phenomenon through which numerous number of individuals are being affected via developing psychological problems. Stress is one of the essential psychological effects of natural disaster; it is a reality of nature where forces from the outside world affect individuals exposed to such phenomenon. In computational psychology domains, computational models were used as tools for understanding human cognitive functions and behavioural patterns. Meanwhile, psychological and cognitive theories as well as empirical studies have provided convergent evidence to identify important factors and psychological attributes that affect the stress level of victims during natural disaster. Therefore, this study implements a formal model (computational model) to understand the current state of victims' stress during natural disaster. From related theories, 22 of basic factors have been established and grouped into 7 main categories that include predisposed factors, resources, individual attributes, appraisal, resilience, coping, and stress. Those factors provide the fundamental knowledge of the behaviours of victims after disaster occurrence. A formal model was developed by using a set of differential equations. Later, this model was simulated by applying related scenarios based on three different cases, namely; 1) a good victim with low level of stress, 2) victim with high level of stress, and 3) victim with moderate level of stress) through the use of Matlab as a programming language. This computational model was then verified using two techniques; 1) logical verification (Temporal Trace Language) and 2) mathematical verification (stability analysis). The experimental results have approximately predicted why victims develop stress differently when facing natural disasters

A formal model for analyzing manager’s performance during stress

Managers who are exposed to stress have the risk of taking insufficient decisions, which will affect their performance levels. The affect could be either positive or negative, depending on the individual’s perception on stress. Many inadequate conventional studies have been conducted for analyzing the complicated relationship of stress and performance. Hence this study introduces a formal model supports managers’ performance during stress. This model can be encapsulated within an intelligent agent or robots that can be used to support managers. The methodology was used to explore human cognitive processes during stress consisted of four phases: identification of local and non-local properties, conceptualization of the model of these properties, formalization, and evaluation. Deferential equations have been used in formalizing the properties. The developed model has been simulated by applying it to different scenarios. Mathematical analysis has been used for the evaluation of the model. Results showed that the formal model was able to show the effects of different levels of stress on managers’ performance

Verification analysis of an agent based model in behaviour change process

This paper describes the verification analysis for agent formal model of behaviour change process. The verification analysis was based on two widely used approaches in agent formal evaluation namely mathematical and automated analysis. The mathematical analysis made use of stability equilibria point while the automated, made use of Temporal Trace Language (TTL). The results obtained verify the formal model validity

A theoretical and practical approach to a persuasive agent model for change behaviour in oral care and hygiene

There is an increased use of the persuasive agent in behaviour change interventions due to the agent‘s features of sociable, reactive, autonomy, and proactive. However, many interventions have been unsuccessful, particularly in the domain of oral care. The psychological reactance has been identified as one of the major reasons for these unsuccessful behaviour change interventions. This study proposes a formal persuasive agent model that leads to psychological reactance reduction in order to achieve an improved behaviour change intervention in oral care and hygiene. Agent-based simulation methodology is adopted for the development of the proposed model. Evaluation of the model was conducted in two phases that include verification and validation. The verification process involves simulation trace and stability analysis. On the other hand, the validation was carried out using user-centred approach by developing an agent-based application based on belief-desire-intention architecture. This study contributes an agent model which is made up of interrelated cognitive and behavioural factors. Furthermore, the simulation traces provide some insights on the interactions among the identified factors in order to comprehend their roles in behaviour change intervention. The simulation result showed that as time increases, the psychological reactance decreases towards zero. Similarly, the model validation result showed that the percentage of respondents‘ who experienced psychological reactance towards behaviour change in oral care and hygiene was reduced from 100 percent to 3 percent. The contribution made in this thesis would enable agent application and behaviour change intervention designers to make scientific reasoning and predictions. Likewise, it provides a guideline for software designers on the development of agent-based applications that may not have psychological reactance

An enhanced computational integrated decision model for prime decision-making in driving

Recent development of technology has led to the invention of driver assistance systems that support driving and prevent accidents. These systems employ Recognition-Primed Decision (RPD) model that use driver prior experience to make prime decision during emergencies. However, the existing RPD model does not include necessary training factors. Although, there is existing integrated RPD-SA model known as Integrated Decision-making Model (IDM) that includes training factors from Situation Awareness (SA) model, the training factors were not detailed (IDM has only six training factors). Hence, the model could not provide reasoning capability. Therefore, this study enhanced the IDM by proposing Computational-Rabi’s Driver Training (C-RDT) model that improves the RPD component with 18 additional training factors obtained from cognitive theories. The designed model is realized by identifying factors for prime decision-making in driving domain, designing the conceptual model of the RDT and formalizing it using differential equation. The model is verified through simulation, mathematical and automated analyses and then validated by human experiment. Verification result shows positive equilibrium conditions of the model (stability) and confirms the structural and theoretical correctness of the model. Furthermore, the validation result shows that the inclusion of the 18 training factors in the RPD training component of the IDM can improve driver’s prime decision-making. This study demonstrated the ability of the enhanced C-RDT model to backtrack and provide reasoning on the undertaking decisions. Hence, the model can also serve as a guideline for software developers in developing driving assistance systems