This thesis investigates the early stages of the software development process for agent-based models of pedestrian behaviour. Planning for pedestrians is becoming more important as planners and engineers become more aware of the sustainability and environmental aspects of transport and infrastructure. It is also necessary for the planning and management of pedestrian areas and events. Pedestrian behaviour is more difficult to model than other transport modes as it is not as constrained and operates at a finer scale. Many approaches have been developed for modelling pedestrian behaviour. The simplest involve a single mathematical equation taking into account area and attractiveness of an area to calculate the maximum capacity. More complicated mathematical models involving differential equations have also been used. Agent-based modelling is a recent development in modelling and simulation. These simulations contain agents who interact with each other and the environment in which they are situated. Their similarity to human societies has led to their use for many social applications. Many modellers are unsure of what agents are and how to develop models using them. In some cases, agents may be useful. In other cases, the model outputs and realism may not offset the learning curve, development time, and increased complexity of an agent-based model. (For complete abstract open document)

Initiatives to electrify urban transport promote the purchase and usage of Electric Vehicles (EVs) and have great potential to mitigate the pressing challenges of climate change, energy scarcity and local air quality. Transportation electrification is a huge innovation and could directly and indirectly impact and/or be impacted by several urban sub-systems. This project develops an agent-based integrated framework for investigating how the EV market expands in the context of urban evolution at the micro scale, and assessing the potential impacts of the market expansion on the environment, power grid system and transport facilities, considering the interactions and dynamics found there. The framework may be useful for stakeholders, such as governments, as an aid to decision making. The integrated framework, SelfSim-EV, is updated from a Land Use and Transport (L-T) model, SelfSim, by incorporating several EV-related modules, including an EV market model, an activity-based travel demand model, a transport facility development model and a social network model. In order to somewhat present the behavioural rules of some key agents in SelfSim-EV, two questionnaire surveys on individual EV travel and purchase behaviours were delivered to members of the general public in Beijing, and semi-structured interviews with EV stakeholders were also carried out. The collected data was analysed using discrete choice models and Geographic Information System (GIS). SelfSim-EV was fully tested within two test cases in China, Baoding (a medium-sized city) and Beijing (the capital of China): first, parameter Sensitivity Analyses (SAs) were carried out to test SelfSim-EV within the test case of Baoding from both global and local perspectives, investigating the relationships between the 127 model parameters and 78 outputs of interest; Then SelfSim-EV was further tested within the case study of Beijing, involving in model initialisation, calibration, validation and prediction. Specifically, the SA results were used to calibrate SelfSim-EV in Beijing from 2011 to 2014 by matching various observed and simulated data types at both city- and district-levels, and the calibrated SelfSim-EV model was further validated against historical data in 2015. Then the future of EVs in Beijing was explored within a Reference Scenario (RefSc) from 2016 to 2020. Due to the model uncertainty in future events, several "what-if" scenarios were set up with the SelfSim-EV Beijing model to explore how three typical types of driving factors, namely policy, technology and infrastructure, may influence the EV market expansion at both aggregate and disaggregate levels. The results indicate that policies tend to be more influential than technologies and infrastructures in terms of EV penetration rates. RefSc eventually shows some improvement in total emissions, however, boosting sales of EVs (particularly PHEVs) in the wrong way could have negative impacts. Charging demand accounting for around 4% of total residential electricity demand in 2020 may put slight pressure on the power grid system in RefSc, and it does not increase linearly as the EV sales rise. Slow charging posts appear to be necessary, whereas fast charging facilities seem to contribute slightly to the EV market expansion and thus may be not necessary at the current stage.

For over 40 years archaeologists have been using predictive modelling to locate archaeological sites. While great strides have been made in the theory and methods of site predictive modelling there are still unresolved issues like a lack of theory, poor data, biased datasets and poor accuracy and precision in the models. This thesis attempts to address the problems of poor model performance and lack of theory driven models through the development of a new method for predictive modelling, agent based modelling. Applying GIS and agent based modelling tools to a project area in southeaster New Mexico this new methodology explored possible behaviours that resulted in site formation such as access to water resources, travel routes and resource exploitation. The results in regards to improved accuracy over traditional methods were inconclusive as a data error was found in the previously created predictive models for the area that were to be used as a comparison. But, the project was more successful in providing explanatory reasons for site placement based on the models created. This work has the potential to open up predictive modelling to wider archaeology audiences, such as those based at universities. Additional findings also impacted other areas of archaeological investigation outside of predictive modelling, such as least cost path analyses and resource gathering analyses.

This paper examines the possible contribution of evolutionary economics to macro-economic modelling of flood impacts to provide guidance for future economic risk modelling. Most macro-economic models start from a neoclassical economic perspective and focus on equilibrium outcomes, either in a static or dynamic way, and describe economic processes at a high level of aggregation. As a consequence, they typically fail to account for the complexity of social interactions and other behavioural responses of consumers and producers to disasters, which may affect the macroeconomic impacts of floods. Employing evolutionary principles and methods, such as agent-based modelling, may help to address some of the shortcomings of current macro-economic models. We explore and discuss the implications of applying consumer and producer heterogeneity, bounded rationality, network effects, social and technological learning, co-evolution and adaptive policy-making concepts into existing economic frameworks for the assessment of macro-economic impacts of floods. (authors' abstract)

An agent based-model of mobility of pastoralists was developed and applied to the semi-arid rangeland region extending from southern Ethiopia to northern Kenya. This model was used to investigate temporal adaptation of pastoralists to the spatial heterogeneity of their environment. This dissertation describes the development, structure, and corroboration process of the simulation model, Pastoral Livestock Movement Model (PLMMO). PLMMO is a spatially explicit, individual-based pastoralists-animal foraging and movement model. It simultaneously simulates the foraging and movement behavior of individual pastoralists and their livestock in a rangeland ecosystem. Pastoralists?? herd mobility patterns and other measures of movement were compared to data from field studies. Predictions of the model correspond to observed mobility patterns across seasons. The distances moved were found to be significantly correlated (r2 = 0.927 to 0.977, p<0.0001) to drought and non-drought climatic regimes. The PLMMO model therefore proved to be a useful tool for simulating general movement patterns of pastoralists relative to movement range sizes in the pastoral rangelands of southern Ethiopia and northern Kenya. We then used the PLMMO model to explore the impact of emerging changes in rangeland use in the study area. The ways in which pastoralists?? mobility patterns adapt to emerging challenges in the study area were explored by simulating the following four scenarios: 1) climate change with concomitant reduction in forage yield, 2) climate change with concomitant improvement and higher variability in forage yield, 3) increased livestock population densities and 4) improved access to water. The climate induced change scenario with increased and more variable forage production resulted in the shortest distances moved by pastoralists in comparison to all other scenarios. The total search distances under this scenario were only 20% of normal season distances. The improved water access scenario also returned a significant (p=0.017) drop in distances moved. There was, however, no significant impact on either increase in livestock numbers or reduction in available forage on mobility. We judged the agent-based model PLMMO developed here as a robust system for emulating pastoral mobility in the rangelands of eastern Africa and for exploring the consequences of climate change and adaptive management scenarios.

Freight transport is the output of an economic market, which converts commodity flows into vehicle flows. Interactions in this market influence vehicle flows and since freight market characteristics (product differentiation and economies of scale/scope) violate perfect competition conditions, the output of this market cannot be predicted directly, unless these interactions are represented in the forecasting models. Traditional freight modelling frameworks do not consider these interactions and consequently they may provide inaccurate freight flow forecasts. In this dissertation, a freight modelling framework is proposed using simulation of freight agent interactions in the economic market to forecast freight flows. The framework is named FREMIS (FREight Market Interactions Simulation). The FREMIS framework consists of two demand models to represent shipper decisions (bundling of shipments and carrier selection) in the market and functions based on profit maximizing behaviour to simulate carrier proposals for contracts. Besides that, learning models are proposed to simulate agent learning processes based on their interactions. The framework was developed aiming to create a realistic representation of freight markets using feasible data collection methods. To illustrate the feasibility of the data collection, a customized web survey was implemented with shippers and carriers in a freight market. Two probabilistic models were developed using the data. The first model, a shipment bundling model was proposed combining a probabilistic model and a vehicle routing algorithm. The results of the probabilistic model are presented in this dissertation, where the locations of shipments (origin and destination) influence the probability of bundling them. Second, three carrier selection models were developed aiming to analyse the nonresponse bias and non-attendance problem in the survey. All of these models assumed heteroskedasticity (different scale or variance) in shipper behaviour. In all models, the hypothesis of agents’ heteroskedasticity cannot be rejected. Besides that, nonresponse bias and non-attendance problem were identified in the survey. In conclusion, the models obtained from the survey were consistent with their behavioural assumptions and therefore they can be adopted during FREMIS implementation.

The ill-effects of stress due to fatigue significantly impact the welfare of individuals and consequently impact overall corporate productivity. This study introduces a simplified model of stress in the workplace using agent-based simulation. This study represents a novel contribution to the field of evolutionary computation. Agents are encoded initially using a String Representation and later expanded to multi-state Binary Decision Automata to choose between work on a base task, special project or rest. Training occurs by agents inaccurately mimicking behaviour of highly productive mentors. Stress is accumulated through working long hours thereby decreasing productivity performance of an agent. Lowest productivity agents are fired or retrained. The String representation for agents demonstrated near average performance attributed to the normally distributed tasks assigned to the string. The BDA representation was found to be highly adaptive, responding robustly to parameter changes. By reducing the number of simplifications for the model, a more accurate representation of the real world can be achieved.

Freight transport is the output of an economic market, which converts commodity flows into vehicle flows. Interactions in this market influence vehicle flows and since freight market characteristics (product differentiation and economies of scale/scope) violate perfect competition conditions, the output of this market cannot be predicted directly, unless these interactions are represented in the forecasting models. Traditional freight modelling frameworks do not consider these interactions and consequently they may provide inaccurate freight flow forecasts. In this dissertation, a freight modelling framework is proposed using simulation of freight agent interactions in the economic market to forecast freight flows. The framework is named FREMIS (FREight Market Interactions Simulation). The FREMIS framework consists of two demand models to represent shipper decisions (bundling of shipments and carrier selection) in the market and functions based on profit maximizing behaviour to simulate carrier proposals for contracts. Besides that, learning models are proposed to simulate agent learning processes based on their interactions. The framework was developed aiming to create a realistic representation of freight markets using feasible data collection methods. To illustrate the feasibility of the data collection, a customized web survey was implemented with shippers and carriers in a freight market. Two probabilistic models were developed using the data. The first model, a shipment bundling model was proposed combining a probabilistic model and a vehicle routing algorithm. The results of the probabilistic model are presented in this dissertation, where the locations of shipments (origin and destination) influence the probability of bundling them. Second, three carrier selection models were developed aiming to analyse the nonresponse bias and non-attendance problem in the survey. All of these models assumed heteroskedasticity (different scale or variance) in shipper behaviour. In all models, the hypothesis of agents’ heteroskedasticity cannot be rejected. Besides that, nonresponse bias and non-attendance problem were identified in the survey. In conclusion, the models obtained from the survey were consistent with their behavioural assumptions and therefore they can be adopted during FREMIS implementation.

Competition has been considered as an effective means to improve business and economic competitiveness. However, competition in supply chain management (SCM) can be viewed as a source of uncertainty. Most recommended collaboration strategies in SCM literature tend to avoid the emergence of competition inside the supply chain, but, in reality, these strategies do not lead all supply chains to success. In addition, from strategic management perspective, these collaboration strategies are not believed to encourage firms to improve their performance. Both competition and collaboration are critical issues in achieving business success, but the effect of both factors on the market has not been explored concurrently in the literature. The complexity of this issue should be investigated using a comprehensive perspective, and it is hard to undertake by using an empirical approach.

Human language is both a cognitive and a cultural phenomenon. Any evolutionary account of language, then, must address both biological and cultural evolution. In this thesis, I give a mainly cultural evolutionary answer to two main questions: firstly, how do working systems of learned communication arise in populations in the absence of external or internal guidance? Secondly, how do those communication systems take on the fundamental structural properties found in human languages, i.e. systematicity at both a meaningless and meaningful level? A large, multi-disciplinary literature exists for each question, full of apparently conflicting results and analyses. My aim in this thesis is to survey this work, so as to find any commonalities and bring this together in order to provide a minimal account of the cultural evolution of language. The first chapter of this thesis takes a number of well-established models of the emergence of signalling systems. These are taken from several different fields: evolutionary linguistics, evolutionary game theory, philosophy, artificial life, and cognitive science. By using a common framework to directly compare these models, I show that three underlying commonalities determine the ability of any population of agents to reliably develop optimal signalling. The three requirements are that i) agents can create and transfer referential information, ii) there is a systemic bias against ambiguity, and iii) some mechanism leading to information loss exists. Following this, I extend the model to determine the effects of including referential uncertainty. I show that, for the group of models to which this applies, this places certain extra restrictions on the three requirements stated above. In the next chapter, I use an information-theoretic framework to construct a novel analysis of signalling games in general, and rephrase the three requirements in more formal terms. I then show that we can use these 3 criteria as a diagnostic for determining whether any given signalling game will lead to optimal signalling, without the requirement for repeated simulations. In the final, much longer, chapter, I address the topic of duality of patterning. This involves a lengthy review of the literature on duality of patterning, combinatoriality, and compositionality. I then argue that both levels of systematicity can be seen as a functional adaptation which maintains communicative accuracy in the face of noisy processes at different levels of analysis. I support this with results from a new, minimally-specified model, which also clarifies and informs a number of long-fought debates within the field.