No description available.

acase@tulane.edu / Chlamydia trachomatis (Ct) is the most common bacterial sexually transmitted infection (STI) in the United States and is major cause of infertility, pelvic inflammatory disease, and ectopic pregnancy among women. Despite decades of screening women for Ct, rates continue to increase among them in high prevalent areas such as New Orleans. A pilot study in New Orleans found approximately 11% of 14-24 year old of African Americans (AAs) were infected with Ct. Our goal is to mathematically model the impact of different interventions for AA men resident in New Orleans on the general rate of Ct among women resident at the same region. We create and analyze mathematical models such as multi-risk and continuous-risk compartmental models and agent-based network model to first help understand the spread of Ct and second evaluate and estimate behavioral and biomedical interventions including condom-use, screening, partner notification, social friend notification, and rescreening. Our compartmental models predict the Ct prevalence is a function of the number of partners for a person, and quantify how this distribution changes as a function of condom-use. We also observe that although increased Ct screening and rescreening, and treating partners of infected people will reduce the prevalence, these mitigations alone are not sufficient to control the epidemic. A combination of both sexual partner and social friend notification is needed to mitigate Ct. / 1 / Asma Aziz Boroojeni

The focus of this thesis is to better explore and understand the effects of agent interactions, information feedback, and adaptive learning in a repeated game of bidding in farmland auction markets. This thesis will develop a multi-agent model of farm-land auction markets based on data from the Saskatchewan Dark Brown Soil Zone of the Canadian Prairies. Several auction types will be modeled and data will be gathered on land transactions between farm agents to ascertain which auction type (if any) is best suited for farmland markets. Specifically, the model gathers information for 3 types of sealed-bid auctions, and 1 English auction and compares them on the basis of efficiency, price information revelation, stability, and with respect to repeated bidding and agent learning. The effects of auction choice on macro-level indicators, such as farm exits, retirement, financial stability, average productivity, farm size, and participation were unknown at the outset of this thesis because of the complex dynamic nature of the environment. I find that the chosen learning mechanism employed here affects both price and variance of prices in all auctions. I also find that the second-price-sealed-bid auction generates the most perceived surplus, most equitable share of surplus, and also decreases uncertainty in the common-value element of prices. A priori it was believed that auction choice would have an impact on pricing efficiency, price levels, and shares of surplus generated from auctions as predicted by theoretical works. Surprisingly, auction choice does not influence market structure or evolution.

The focus of this thesis is to better explore and understand the effects of agent interactions, information feedback, and adaptive learning in a repeated game of bidding in farmland auction markets. This thesis will develop a multi-agent model of farm-land auction markets based on data from the Saskatchewan Dark Brown Soil Zone of the Canadian Prairies. Several auction types will be modeled and data will be gathered on land transactions between farm agents to ascertain which auction type (if any) is best suited for farmland markets. Specifically, the model gathers information for 3 types of sealed-bid auctions, and 1 English auction and compares them on the basis of efficiency, price information revelation, stability, and with respect to repeated bidding and agent learning. The effects of auction choice on macro-level indicators, such as farm exits, retirement, financial stability, average productivity, farm size, and participation were unknown at the outset of this thesis because of the complex dynamic nature of the environment. I find that the chosen learning mechanism employed here affects both price and variance of prices in all auctions. I also find that the second-price-sealed-bid auction generates the most perceived surplus, most equitable share of surplus, and also decreases uncertainty in the common-value element of prices. A priori it was believed that auction choice would have an impact on pricing efficiency, price levels, and shares of surplus generated from auctions as predicted by theoretical works. Surprisingly, auction choice does not influence market structure or evolution.

In a roadway network, infrastructure conditions determine efficient network operation and traveler safety, and thus roadway engineers need a sophisticated plan to monitor and maintain network performance. Developing a comprehensive maintenance and rehabilitation (M&R) strategy for an infrastructure system, specifically a roadway network, is a complicated process because of the system uncertainties and multiple parties involved. Traditional approaches are mostly top-down, and restrict the decision-making process. In contrast, agent-based models, a bottom-up approach, could well simulate and analyze the autonomy of each party and their interactions in the infrastructure network. In this thesis, an agent-based model prototype was developed to simulate the operations of a small roadway network with a high degree of simplification. The objective of this study is to assess the applicability of agent-based modeling for infrastructure management problems through the following four aspects: (1) to simulate the user route selection process in the network; (2) to analyze the impact of users’ choices on the congestion levels and structural conditions of roadway sections; (3) to help the engineer to determine M&R strategies under a certain budget; and (4) to investigate the impact due to different fare rates of the toll road section on the infrastructure conditions in the network. This prototype detected traffic flow, and gave appropriate M&R advice to each roadway segment. To improve this model, more investigation should be conducted to increase the level of sophistication for the interaction rules between agents, the route selection, and the budget allocation algorithm. Upon completion, this model can be applied to existing road networks to assist roadway engineers in managing the network with an efficient M&R plan and toll rate. / text

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)

In the recent years, the concept of “port community” has been adopted by the maritime transport industry in order to achieve a higher degree of coordination and cooperation amongst organizations involved in the transfer of goods through the port area. The business processes of the port community supply chain form a complicated process which involves several process steps, multiple actors, and numerous information exchanges. One of the widely used applications of ICT in ports is the Port Community System (PCS) which is implemented in ports in order to reduce paperwork and to facilitate the information flow related to port operations and cargo clearance. However, existing PCSs are limited in functionalities that facilitate the management and coordination of material, financial, and information flows within the port community supply chain. This research programme addresses the use of agent technology to introduce business process management functionalities, which are vital for port communities, aiming to the enhancement of the performance of the port community supply chain. The investigation begins with an examination of the current state in view of the business perspective and the technical perspective. The business perspective focuses on understanding the nature of the port community, its main characteristics, and its problems. Accordingly, a number of requirements are identified as essential amendments to information systems in seaports. On the other hand, the technical perspective focuses on technologies that are convenient for solving problems in business process management within port communities. The research focuses on three technologies; the workflow technology, agent technology, and service orientation. An analysis of information systems across port communities enables an examination of the current PCSs with regard to their coordination and workflow management capabilities. The most important finding of this analysis is that the performance of the business processes, and in particular the performance of the port community supply chain, is not in the scope of the examined PCSs. Accordingly, the Agent-Based Middleware for Port Community Management (ABMPCM) is proposed as an approach for providing essential functionalities that would facilitate collaborative planning and business process management. As a core component of the ABMPCM, the Collaborative Planning Facility (CPF) is described in further details. A CPF prototype has been developed as an agent-based system for the domain of inland transport of containers to demonstrate its practical effectiveness. To evaluate the practical application of the CPF, a simulation environment is introduced in order to facilitate the evaluation process. The research started with the definition of a multi-agent simulation framework for port community supply chain. Then, a prototype has been implemented and employed for the evaluation of the CPF. The results of the simulation experiments demonstrate that our agent-based approach effectively enhances the performance of business process in the port community.

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.

Social-ecological systems (SES) may become “locked in” particular states or configurations due to various constraints on adaptability imposed by feedback mechanisms or by processes designed to incentivize certain behavior. While these locked-in states may be desirable and robust to disturbances over relatively short time periods, limits on system adaptations may diminish the longer-term resilience of these states, and potentially of the system itself. The agricultural SES in the Iowa-Cedar River Basin in eastern Iowa is one such system. While highly productive, culturally important, and essential to local economies, the system is facing significant economic and environmental challenges. This dissertation presents the results of a project designed to survey the adaptability of farmers in the ICRB, model their actions subject to constraints, and plot potential future states under scenarios of climate change, policy, and market conditions. We utilize a coupled agent-based model (ABM) to examine the specified resilience of the system to future climate, leveraging the ability of ABMs to integrate heterogeneous actors, dynamic couplings of natural and human systems, and processes across spatiotemporal scales. We find that farmer behavior is primarily constrained by economic factors, including federal crop insurance subsidies and the financial risk of implementing different crops or practices. Finally, we generate alternative system trajectories by modeling twenty-one scenarios, identifying actionable adaptations and pathways for transforming the system to alternative, more sustainable states.

Like many North American agricultural regions, Saskatchewan has experienced significant fundamental structural changes in farming. Structural change encompasses evolution in distribution of farm sizes, land tenure and financial characteristics, as well as variations in demographic and production characteristics. These issues are often a source of discontent among farm populations as it implies these populations are forced to adapt in a number of potentially unpleasant ways. These changes have profound and sometimes poorly understood effects on the rural economy for example, structural change affects rural population and therefore demand for rural infrastructure. <p>Traditional agricultural farm level analysis is often conducted using a representative farm or group, but this framework cannot capture the growing heterogeneity of modern farm operators or the current operating environment in agricultural regions. Farm profiles vary by demographic characteristics, such as age and education, and resource endowments. Agent-based simulation captures this heterogeneity through a farm by farm analysis, where after initialization, the regional economy evolves over time.<p>A synthetic population is created based on survey data and the land characteristics based on the actual land data in CAR 7B of Saskatchewan. A number of different price and yield time paths were created using a bootstrap procedure on historical data and the model evolved to potential agriculture structures that may occur in the model region, 30 years in the future.<p>Structural change occurs endogenously as farms interact in land markets, and make decisions on land use. Agents compete for available land in a purchase and lease market with land selling to the highest bidder. The dynamic nature of agent-based models allows individual farms to adjust land use in response to changing economic conditions and individual preferences. How individuals organize their resources will be critical to farm survival and growth.<p>The results indicate that many of the trends are the same under the different price and yield time paths, however the rate of change is significantly impacted by the price and yield time path that occurs. The model predicted the trend to fewer and larger farms will continue into the future. The forecasted distribution of smaller farms will decline and proportion of large farms will increase, while mid sized farms will remain relatively unchanged. The proportion of mixed farms, land use, and total livestock numbers depend significantly on the price and yield time path. The actual structure that will occur will be the result of the actual individual price and yield time path that occurs.