<p>As the need for outdoor recreation grows, the profound impact of recreational activities upon wildlife is a major concern. For example, the presence of humans may increase risk-averse behavior by wildlife, restricting access to essential resources, and reducing foraging, thereby negatively impacting breeding. Ultimately, the impacts that recreationists have on wildlife include directly or indirectly altering population structure and community composition. Unfortunately, understanding the impacts of recreating humans upon wildlife is a complex challenge that is dependent upon wildlife species and human activity types. Our understanding of human-wildlife relationships can be improved by combining results from empirical studies with simulation models to extrapolate mechanisms to a broader range of circumstances and investigate their implications. Accordingly, we developed an ABM modeling framework, that enables both dynamic virtual human and wildlife agents to change their actions. These changes are based upon their state as a consequence of their interactions with their environment and other virtual agents. A unique aspect of the framework we developed is the explicit simulation of both wildlife and human agent behavior as emergent rather than imposed. We use this framework to model the disturbance of birds, in the Lawrence Creek Forest Unit (LCFU) of Fort Harrison State Park, IN, by human recreation. We parameterize the model with human recreation data collected through an intercept survey of recreationists at the park and bird data from published studies. We compare our modeling framework to a more traditional model type where human behavior is imposed while wildlife behavior is emergent. Our results indicate that the frequency of humans entering the park influences the rates of disturbance of birds more than model types. Examining simulation behavior within our new framework, the utility and off-trail options had the most influence across all scenarios. These comparisons illustrate that the use of a modeling framework that allows managers to explore factors altering wildlife disturbance rates. Despite the marginal influence of model type upon our results, our research elucidates the value of a model that allows emergent behavior for multiple agent types. The emergent human and wildlife responses of simulated interacting agents provides new insight when managing these relationships. <b></b></p>

In this master thesis, multiagent simulation was implemented with MATSim to study the change would take place on travel behaviors in Stockholm County when all residents travel in a sustainable way under a predefined emission limit. In this multiagent simulation, individual person was simulated as agent with attributes, daily travel plans and behaviors. The attributes contained home location, workplace locations, and some socioeconomic attributes, which were assigned according to the demographic data and travelling statistics data collected. Two trips, morning commuting from home to workplace and evening commuting from workplace to home, were simulated while the daily travel plans included travelling by car, public transit, bike and working at home. Each day, the person was set to select a travel plan based on socioeconomic attributes, his current greenhouse gas emission and a monthly emission limit. The selected plan was then executed and his emission was updated. In the model, a working population of 771614 people in Stockholm County was used and one month period with 21 working days was simulated. Totally four monthly emission limits were tested: 30kg, 37kg, 50kg, and infinity representing the current scenario. The research shows that multiagent simulation is effective in simulating individual travel behaviors. The results suggest that under current scenario car is the most frequently selected travel mode accounting for 32%, followed by public transit 31%. There are about 12% of people working at home and 25% travelling by bike. Nearly 1 percent fails to select a plan because of the plan selection setting. When emission limit is set, the percentage of people changing travel behaviors is 21.2%, 25.8% and 29.9% under the emission limit 50kg, 37kg and 30kg respectively. Most of them would abort from car and public transit to bike, public transit or even failing to keep their emission under the limit. The percentage of people changing plan to bike is 9.4%, 11.8%, 13.4% under the three limits 50kg, 37kg and 30kg respectively while the percentage of people changing plan to public transit or failure is 10.2%, 12.5% and 15.2%. The result also shows that when 37kg limit is set, the people having problems with keeping their emission under the limit are mainly distributed at three regions: Stockholm City, some cities in southwest and northeast of Stockholm County, where there would also be more demand for public transit service. The people changing plans to bike are mainly  distributed  in  Stockholm  City  area,  where  sustainable  travel  behavior  should  be promoted

Home to the world’s longest coastline, Canada has experienced devastating economic and social from coastal flooding events. While there have been a variety of mitigation methods employed over the years to increase a community’s resistance to coastal hazards, it is unrealistic to think that there exists a solution to guarantee a community’s safety under all possible flood hazards. Instead, the community’s efforts to raise their resistance to flood hazards should be augmented with careful planning and management to increase a community’s resilience to flood hazards, allowing them to recover quickly after a natural disaster. The first step in elevating a community’s resilience is to better understand the expected hazards that it may experience. This thesis presents two unique case studies to better understand the flooding hazards present on the Canadian coastline. A large-scale numerical model that accounts for the presence of ice was developed to investigate storm surges in Canada’s western Arctic. It was found that the quality of the climatic forcing data used, ERA5, was poor in capturing peak wind speeds, but could be compensated for by using elevated wind drag coefficients. The use of non-traditional high-water marks such as driftwood lines were validated and were shown to significantly alter expected flood return periods compared to the return periods estimated from only the incomplete tide gauge measurements present on the Arctic coastline. The second case study extends the results of a tsunami hydrodynamic simulation on Canada’s Pacific coastline through a life safety assessment. The performance between an agent-based and GIS-based approach to modelling tsunami evacuation were directly compared and were shown to yield different magnitudes in fatality rate and facility demand, but similar trends. Both models agreed on a mitigation option that can significantly reduce the loss of life during a tsunami.

In North America, the grizzly bear (Ursus arcos) is one of many species increasingly threatened by the consequences of human-wildlife conflict, with human-bear encounters on the rise due to increased human activity near or in bear habitat. As a result, a growing number of bears are subjected to management measures such as translocations in which animals are moved to areas with lower risk of human conflict, although these measures are not always successful. Previous research has attempted to understand factors associated with translocation success, but new methods are needed to address the continuous and complex nature of issues related to how animals move and learn about their surroundings as well as how they adapt to novel environments. The objective of my MSc thesis is to develop and employ a novel agent-based computer simulation model to analyze how grizzly bears learn and respond following a translocation event. This modelling effort attempts to capture how bears make decisions based on multiple factors, and represent how grizzly bears interact with their environment and make movement decisions based on learned behaviours. First, an agent-based movement model was developed for female grizzly bears using GPS-location data for bears within a region in west-central Alberta, Canada. The model, which incorporates multi-scale decision-making and machine learning, generated movement patterns similar to those observed in radio-collared females in the study area. Home range sizes and movement metrics produced by the model were consistent with those observed in female grizzly bears in the area. The model was then used to simulate translocation events in which bears with varying “exploration” propensities were translocated to habitats with familiar or novel landscape characteristics. In general, bears translocated to habitats with similar landscape features to their original habitat were more likely to use high-quality habitat than bears moved to areas with very different landscape features. However, while increased exploration led to greater use of high-quality habitat in the long run, exploratory behaviour was found to be mostly detrimental during the first years following a translocation, the period considered critical for translocation success. Model results were found to be scale-dependent with results varying both in time and space, highlighting the need for a multi-scale approach to animal movement studies. The findings presented here also emphasize the need to account for behavioural traits of wildlife and habitat characteristics of the capture and release sites when selecting suitable translocation locations. This work highlights the potential for agent-based modelling as a tool to study animal movement as a continuous and complex process and evaluate conservation policies. / Graduate / 2021-08-24

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Traffic conflicts associated to signalized intersections are one of the major contributing factors to crash occurrences. Driver behavior plays an important role in the safety concerns related to signalized intersections. In this research effort, dynamics of driver behavior in relation to the traffic conflicts occurring at the onset of yellow is investigated. The area ahead of intersections in which drivers encounter a dilemma to pass through or stop when the yellow light commences is called Dilemma Zone (DZ). Several DZ-protection algorithms and advance signal settings have been developed to accommodate the DZ-related safety concerns. The focus of this study is on drivers' decision dynamics, human learning, and choice behavior in DZ, and DZ-related safety measures. First, influential factors to drivers' decision in DZ were determined using a driver behavior survey. This information was applied to design an adaptive experiment in a driving simulator study. Scenarios in the experimental design are aimed at capturing drivers learning process while experiencing safe and unsafe signal settings. The result of the experiment revealed that drivers do learn from some of their experience. However, this learning process led into a higher level of risk aversion behavior. Therefore, DZ-protection algorithms, independent of their approach, should not have any concerns regarding drivers learning effect on their protection procedure. Next, the possibility of predicting drivers' decision in different time frames using different datasets was examined. The results showed a promising prediction model if the data collection period is assumed 3 seconds after yellow. The prediction model serves advance signal protection algorithms to make more intelligent decisions. In the next step, a novel Surrogate Safety Number (SSN) was introduced based on the concept of time to collision. This measure is applicable to evaluate different DZ-protection algorithms regardless of their embedded methodology, and it has the potential to be used in developing new DZ-protection algorithms. Last, an agent-based human learning model was developed integrating machine learning and human learning techniques. An abstracted model of human memory and cognitive structure was used to model agent's behavior and learning. The model was applied to DZ decision making process, and agents were trained using the driver simulator data. The human learning model resulted in lower and faster-merging errors in mimicking drivers' behavior comparing to a pure machine learning technique. / Ph. D.

The Circular Economy is a growing research field and policy agenda. Yet, integrating the social dimensions of sustainability into the Circular Economy remains a challenge. The significance of reactions to an implemented Circular Economy is poorly understood.  Contrary to the narrative that consumer demand shapes supply, affordance theory stresses the significance of considering the exogenous physical context that shapes user decisions. Building on affordance theory and insights from the social sciences, this study develops an agent-based model, TransportTransform, to analyse the interactions of the individual-, meso-, and system-levels. The agent-based model connects individual mobility choices with network decision-making mechanisms. Looking at user decision-making on transportation modes, the model yields insights into the interaction of mode occupancy and social norms to assess system patterns of user behaviour. The model design was informed by eleven interviews with researchers in the field and is initialized with data from an empirical survey conducted in Germany. The TransportTransform agent-based model confirms the importance of affordances as an important factor in modal choice. Model results show the relevance of including habitual behaviour when modelling transport mode choice, with the car being the most popular mode, followed by biking and public transport. Incorporating mode occupancy significantly reduces car usage, offering potential policy avenues for redirecting mode capacities towards desired modes. The impact of social norms on mode choice was less pronounced, highlighting the need to further explore norm internalisation and the indirect effects of social norms in future model iterations. The study emphasizes the need for further model expansions to better understand the impact of Circular Economy policies on user decision-making. Overall, the study highlights the importance of considering the social dimensions of sustainability in the Circular Economy and provides a valuable framework and implemented agent-based model for analysing user behaviour in this context.

The uninterrupted spread of malaria, besides its seasonal uncertainty, is due to the lack of suitable planning and intervention mechanisms and tools. Several studies have been carried out to understand the factors that affect the development and transmission of malaria, but these efforts have been largely limited to piecemeal specific methods, hence they do not offer comprehensive solutions to predict disease outbreaks. This thesis introduces a ’holistic’ approach to understand the relationship between climate parameters and the occurrence of malaria using both mathematical and computational methods. In this respect, we develop new climate-based models using mathematical, agent-based and data-driven modelling techniques. A malaria model is developed using mathematical modelling to investigate the impact of temperature-dependent delays. Although this method is widely applicable, but it is limited to the study of homogeneous populations. An agent-based technique is employed to address this limitation, where the spatial and temporal variability of agents involved in the transmission of malaria are taken into account. Moreover, whilst the mathematical and agent-based approaches allow for temperature and precipitation in the modelling process, they do not capture other dynamics that might potentially affect malaria. Hence, to accommodate the climatic predictors of malaria, an intelligent predictive model is developed using machine-learning algorithms, which supports predictions of endemics in certain geographical areas by monitoring the risk factors, e.g., temperature and humidity. The thesis not only synthesises mathematical and computational methods to better understand the disease dynamics and its transmission, but also provides healthcare providers and policy makers with better planning and intervention tools.

Delegative democracy is a new concept of democratic governance. It is meant as a new system of trust among people. It is derived from principles of direct democracy and presents only minimal form of representation. The voter is not forced to cast his vote directly, but it can be transferred to a delegate. Delegative democracy is based on openness, awareness and trust. It is still only a concept and for real application it has to face rising critics. Main issues are high costs, threat of populism and corruption and overall complexity. Agent-based modelling is chosen to test the validity of delelagative democracy principles. It is suitable for studying social phenomena such as democratic governance. Agent-based models apply a "new kind" of artificial intelligence -- a dynamic system composed of individual and autonomous units -- agents -- which interact within the environment. Universal methodology called Agentology is used to propose a new agent-based model. This methodology is composed of subsequent steps. These steps cover initial analysis, conceptual and technological proposal and development of the model itself. Assessment of delegative democracy principles is achieved with data acquired from the agent-based model. Delegative democracy is less effective for small and cooperative system than direct democracy. If more voters are delegating, this effectiveness decreases even more. This conclusion is based on initial parameters of the model. Delegating voters count is indeed a relevant parameter whereas total voters count is not. The model has its basic predicative value and is open to further elaboration.

Present-day Agent Based Modelling is used to simulate complex systems in which agents are explicitly heterogeneous. Researchers within the field of ABM have a set of tools at their disposal, yet little is known about the usability and learnability of these systems. Information Architecture establishes a set of guidelines for constructing digital spaces that facilitate the fulfilment of the user’s goal; these guidelines are expressed as Principles of Information Architecture and categories of user behaviour. The purpose of this paper is to determine the needs of ABM researchers and explore how scientific software can be improved to better support them in their work. A System Usability Scale questionnaire quantifies the current level of usability on ABM frameworks while semi-structured interviews with six expert modellers provide data on user needs and user behaviour. The participants are allowed to review more than one ABM framework by means of questionnaires and a cognitive walkthrough that exposes GUI elements and other framework features linked to procedural steps of modelling. Information Architecture principles are exposed in each interface along with user behaviour categories. Albeit limited in its scope of participants, the survey with in-depth interviews provides valuable information on the needs of domain experts. Data is analysed both quantitatively and qualitatively; the paper follows, therefore, a mixed-method approach. It is proven that, at the present moment, most ABM frameworks fail to meet established standards for usability and learnability. User needs are exposed through an analysis of the data reported by experts. Finally, considerations are presented upon the impact of implementing Information Architecture guidelines within ABM frameworks.