Juxtaposition of system dynamics and agent-based simulation for a case study in immunosenescence

Advances in healthcare and in the quality of life significantly increase human life expectancy. With the aging of populations, new un-faced challenges are brought to science. The human body is naturally selected to be well-functioning until the age of reproduction to keep the species alive. However, as the lifespan extends, unseen problems due to the body deterioration emerge. There are several age-related diseases with no appropriate treatment; therefore, the complex aging phenomena needs further understanding. It is known that immunosenescence is highly correlated to the negative effects of aging. In this work we advocate the use of simulation as a tool to assist the understanding of immune aging phenomena. In particular, we are comparing system dynamics modelling and simulation (SDMS) and agent-based modelling and simulation (ABMS) for the case of age-related depletion of naive T cells in the organism. We address the following research questions: Which simulation approach is more suitable for this problem? Can these approaches be employed interchangeably? Is there any benefit of using one approach compared to the other? Results show that both simulation outcomes closely fit the observed data and existing mathematical model; and the likely contribution of each of the naive T cell repertoire maintenance method can therefore be estimated. The differences observed in the outcomes of both approaches are due to the probabilistic character of ABMS contrasted to SDMS. However, they do not interfere in the overall expected dynamics of the populations. In this case, therefore, they can be employed interchangeably, with SDMS being simpler to implement and taking less computational resources

Using Case-Based Reasoning for Simulation Modeling in Healthcare

The healthcare system is always defined as a complex system. At its core, it is a system composed of people and processes and requires performance of different tasks and duties. This complexity means that the healthcare system has many stakeholders with different interests, resulting in the emergence of many problems such as increasing healthcare costs, limited resources and low utilization, limited facilities and workforce, and poor quality of services. The use of simulation techniques to aid in solving healthcare problems is not new, but it has increased in recent years. This application faces many challenges, including a lack of real data, complicated healthcare decision making processes, low stakeholder involvement, and the working environment in the healthcare field. The objective of this research is to study the utilization of case-based reasoning in simulation modeling in the healthcare sector. This utilization would increase the involvement of stakeholders in the analysis process of the simulation modeling. This involvement would help in reducing the time needed to build the simulation model and facilitate the implementation of results and recommendations. The use of case-based reasoning will minimize the required efforts by automating the process of finding solutions. This automation uses the knowledge in the previously solved problems to develop new solutions. Thus, people could utilize the simulation modeling with little knowledge about simulation and the working environment in the healthcare field. In this study, a number of simulation cases from the healthcare field have been collected to develop the case-base. After that, an indexing system was created to store these cases in the case-base. This system defined a set of attributes for each simulation case. After that, two retrieval approaches were used as retrieval engines. These approaches are K nearest neighbors and induction tree. The validation procedure started by selecting a case study from the healthcare literature and implementing the proposed method in this study. Finally, healthcare experts were consulted to validate the results of this study

The use of computer science practices and methods for developing social simulations to stimulate changes in travellers’ mode choice

In this thesis, Computer Science practices and methods including Software Engineering and Artificial Intelligence techniques are explored to incorporate Human Factors and Psychology knowledge in a structured way into agent-based models to model modal shift in a social system. Observations of peoples’ behaviours in social systems regarding choice-making suggest that they tend to have preferences among the available alternatives in many situations. Experts in the domain of Psychology have been interested in the relationships that exist between the psychological processes (factors) and peoples’ behaviours. Human Factors’ experts are concerned with, among other things, the study of factors and development of tools that improve users’ experiences. The findings from the literature suggest that the two groups have been working from the perspective of their domains without much collaboration. Also, no known framework or methodology offers the required collaborative modelling support and techniques to model people’s emotion as they traverse the system. The aim of this thesis is, therefore, to provide modelling techniques that better support the use of Human Factors and Psychology knowledge in understanding factors that influence travellers’ decision-making in travel mode choice so as to stimulate changes in their behaviours. The support also provides collaboration among relevant stakeholders to work on modal shift project in the transport system. The method adopted in carrying out the research reported in this thesis is informed by the descriptive, developmental, and exploratory nature of the objectives of the research. Our novel methodology which includes a framework is named MOdal SHift (MOSH) methodology. Its development process involves the use of design principles that include encapsulation, data abstraction, inheritance, and polymorphism in defining and integrating the Human Factors and Psychology practices into the methodology. The structures and behaviours of the system components are described and documented using the Unified Modelling Language (UML) as a standard specification language to promote uniform communication among a group of experts. The decision variable decomposition module and techniques for deriving travellers’ emotions that correspond to their context involved the use of the Fuzzy sets system. The methodology contains guides that include the process map diagram showing the major stages in the methodology as well as the step-by-step development guidelines. To verify and to validate the methodology, two case studies in the transport domain are selected. The first case study aims at demonstrating the use of the framework included in the methodology for policy formulation. The second case study has the goal of demonstrating the use of the methodology for understanding individuals’ abilities to satisfy travel requirements. Data Science methods including both supervised and unsupervised learning algorithms are applied at relevant stages of the case studies. The reflection from the cases investigated with the MOSH methodology reveals its novelty in modelling interdependencies among the transport system’s constraints and in modelling travellers’ emotional state as they traverse the transport system’s environment. In addition, the adoption of the standard specification language in the design of the methodology provides the means for easy communication and transfer of knowledge among stakeholders. The use of Software Engineering tools and methods in conjunction with the agent-based modelling paradigm in the MOSH methodology design and development phases promotes the separation of concerns for the interrelated and non-linear levels of organisation within a sociotechnical system. It also promotes extensibility of various aspect of the methodology as a result of the independence among the components and makes reusability of relevant aspects possible when there are needs to use the same functionality in a new project. The agent-based modelling paradigm provides opportunities for investigating the interactions among the agents and the environment as well as providing insights into the various complex interrelated behaviours

The use of computer science practices and methods for developing social simulations to stimulate changes in travellers’ mode choice

In this thesis, Computer Science practices and methods including Software Engineering and Artificial Intelligence techniques are explored to incorporate Human Factors and Psychology knowledge in a structured way into agent-based models to model modal shift in a social system. Observations of peoples’ behaviours in social systems regarding choice-making suggest that they tend to have preferences among the available alternatives in many situations. Experts in the domain of Psychology have been interested in the relationships that exist between the psychological processes (factors) and peoples’ behaviours. Human Factors’ experts are concerned with, among other things, the study of factors and development of tools that improve users’ experiences. The findings from the literature suggest that the two groups have been working from the perspective of their domains without much collaboration. Also, no known framework or methodology offers the required collaborative modelling support and techniques to model people’s emotion as they traverse the system. The aim of this thesis is, therefore, to provide modelling techniques that better support the use of Human Factors and Psychology knowledge in understanding factors that influence travellers’ decision-making in travel mode choice so as to stimulate changes in their behaviours. The support also provides collaboration among relevant stakeholders to work on modal shift project in the transport system. The method adopted in carrying out the research reported in this thesis is informed by the descriptive, developmental, and exploratory nature of the objectives of the research. Our novel methodology which includes a framework is named MOdal SHift (MOSH) methodology. Its development process involves the use of design principles that include encapsulation, data abstraction, inheritance, and polymorphism in defining and integrating the Human Factors and Psychology practices into the methodology. The structures and behaviours of the system components are described and documented using the Unified Modelling Language (UML) as a standard specification language to promote uniform communication among a group of experts. The decision variable decomposition module and techniques for deriving travellers’ emotions that correspond to their context involved the use of the Fuzzy sets system. The methodology contains guides that include the process map diagram showing the major stages in the methodology as well as the step-by-step development guidelines. To verify and to validate the methodology, two case studies in the transport domain are selected. The first case study aims at demonstrating the use of the framework included in the methodology for policy formulation. The second case study has the goal of demonstrating the use of the methodology for understanding individuals’ abilities to satisfy travel requirements. Data Science methods including both supervised and unsupervised learning algorithms are applied at relevant stages of the case studies. The reflection from the cases investigated with the MOSH methodology reveals its novelty in modelling interdependencies among the transport system’s constraints and in modelling travellers’ emotional state as they traverse the transport system’s environment. In addition, the adoption of the standard specification language in the design of the methodology provides the means for easy communication and transfer of knowledge among stakeholders. The use of Software Engineering tools and methods in conjunction with the agent-based modelling paradigm in the MOSH methodology design and development phases promotes the separation of concerns for the interrelated and non-linear levels of organisation within a sociotechnical system. It also promotes extensibility of various aspect of the methodology as a result of the independence among the components and makes reusability of relevant aspects possible when there are needs to use the same functionality in a new project. The agent-based modelling paradigm provides opportunities for investigating the interactions among the agents and the environment as well as providing insights into the various complex interrelated behaviours