Compositional agent-based models for electricity distribution networks

This thesis presents a novel approach to building large-scale agent-based models of networked physical systems using a compositional approach to provide extensibility and flexibility in building the models and simulations. A software framework (MODAM - MODular Agent-based Model) was implemented for this purpose, and validated through simulations. These simulations allow assessment of the impact of technological change on the electricity distribution network looking at the trajectories of electricity consumption at key locations over many years.

From words to models : a bibliometric approach to designing agent-based models in entrepreneurship

Agent-based modeling and simulation (ABMS) may fit well with entrepreneurship research and practice because the core concepts and basic premises of entrepreneurship coincide with the characteristics of ABMS. However, it is difficult to find cases where ABMS is applied to entrepreneurship research. To apply ABMS to entrepreneurship and organization studies, designing a conceptual model is important; thus to effectively design a conceptual model, various mixed method approaches are being attempted. As a new mixed method approach to ABMS, this study proposes a bibliometric approach to designing agent based models, which establishes and analyzes a domain corpus. This study presents an example on the venture creation process using the bibliometric approach. This example shows us that the results of the multi-agent simulations on the venturing process based on the bibliometric approach are close to each nation’s surveyed data on the venturing activities. In conclusion, by the bibliometric approach proposed in this study, all the agents and the agents’ behaviors related to a phenomenon can be extracted effectively, and a conceptual model for ABMS can be designed with the agents and their behaviors. This study contributes to the entrepreneurship and organization studies by promoting the application of ABMS.

An agent-based approach to immune modelling

This study focuses on trying to understand why the range of experience with respect to HIV infection is so diverse, especially as regards to the latency period. The challenge is to determine what assumptions can be made about the nature of the experience of antigenic invasion and diversity that can be modelled, tested and argued plausibly. To investigate this, an agent-based approach is used to extract high-level behaviour which cannot be described analytically from the set of interaction rules at the cellular level. A prototype model encompasses local variation in baseline properties contributing to the individual disease experience and is included in a network which mimics the chain of lymphatic nodes. Dealing with massively multi-agent systems requires major computational efforts. However, parallelisation methods are a natural consequence and advantage of the multi-agent approach. These are implemented using the MPI library.

Considerations for a social and geographical framework for agent-based epidemics

Understanding the dynamics of disease spread is of crucial importance, in contexts such as estimating load on medical services to risk assessment and intervention policies against large-scale epidemic outbreaks. However, most of the information is available after the spread itself, and preemptive assessment is far from trivial. Here, we investigate the use of agent-based simulations to model such outbreaks in a stylised urban environment. For most diseases, infection of a new individual may occur from casual contact in crowds as well as from repeated interactions with social partners such as work colleagues or family members. Our model therefore accounts for these two phenomena.Presented in this paper is the initial framework for such a model, detailing implementation of geographical features and generation of social structures. Preliminary results are a promising step towards large-scale simulations and evaluation of potential intervention policies.

Agent-based modelling of worker interactions and related impacts on workplace dynamics

This study uses agent based modelling to simulate the worker interactions within a workplace and to investigate how the interactions can have impact on the workplace dynamics. Two new models (Bounded Confidence with Bias model and Relative Agreement with Bias model) are built based on the theoretical foundation of two existing models. A new factor, namely bias, is added into the new models which raises several issues to be studied.

Maximising the manufacturer performance value through lean initiatives using cost based model

Numerous tools and techniques have been developed to eliminate or reduce waste and carry out lean concepts in the manufacturing environment. However, appropriate lean tools need to be selected and implemented in order to fulfil the manufacturer needs within their budgetary constraints. As a result, it is important to identify manufacturer needs and implement only those tools, which contribute maximum benefit to their needs. In this research a mathematical model is proposed for maximising the perceived value of manufacturer needs and developed a step-by-step methodology to select best performance metrics along with appropriate lean strategies within the budgetary constraints. With the help of a case study, the proposed model and method have been demonstrated.

Relative entropy rate based model selection for linear hybrid system filters of uncertain nonlinear systems

Hybrid system representations have been exploited in a number of challenging modelling situations, including situations where the original nonlinear dynamics are too complex (or too imprecisely known) to be directly filtered. Unfortunately, the question of how to best design suitable hybrid system models has not yet been fully addressed, particularly in the situations involving model uncertainty. This paper proposes a novel joint state-measurement relative entropy rate based approach for design of hybrid system filters in the presence of (parameterised) model uncertainty. We also present a design approach suitable for suboptimal hybrid system filters. The benefits of our proposed approaches are illustrated through design examples and simulation studies.

A hybrid model for studying spatial aspects of infectious diseases

We consider a hybrid model, created by coupling a continuum and an agent-based model of infectious disease. The framework of the hybrid model provides a mechanism to study the spread of infection at both the individual and population levels. This approach captures the stochastic spatial heterogeneity at the individual level, which is directly related to deterministic population level properties. This facilitates the study of spatial aspects of the epidemic process. A spatial analysis, involving counting the number of infectious agents in equally sized bins, reveals when the spatial domain is nonhomogeneous.

Morphology-based model for predicting osteocyte-like cell line (MLO-Y4) growth process

This work has led to the development of empirical mathematical models to quantitatively predicate the changes of morphology in osteocyte-like cell lines (MLO-Y4) in culture. MLO-Y4 cells were cultured at low density and the changes in morphology recorded over 11 hours. Cell area and three dimensional shape features including aspect ratio, circularity and solidity were then determined using widely accepted image analysis software (ImageJTM). Based on the data obtained from the imaging analysis, mathematical models were developed using the non-linear regression method. The developed mathematical models accurately predict the morphology of MLO-Y4 cells for different culture times and can, therefore, be used as a reference model for analyzing MLO-Y4 cell morphology changes within various biological/mechanical studies, as necessary.