Large lahars threaten communities living near volcanoes all over the world. Evacuations are a critical strategy for reducing vulnerability and mitigating a disaster. Hazard perceptions, transportation infrastructure, and transportation mode choice are all important factors in determining the effectiveness of an evacuation. This research explores the effects of population, whether individuals drive or walk, response time, and exit closures on an evacuation in a community threatened by a large lahar originating on Mount Rainier, Washington. An agent-based model employing a co-evolutionary learning algorithm is used to simulate a vehicular evacuation. Clearance times increase when the population is larger and when exits are blocked. Clearance times are reduced when a larger proportion of agents opt out of driving, and as the model learns. Results indicate evacuation times vary greatly due to spatial differences in the transportation network, the initial population distribution, and individual behaviors during the evacuation.

As technology advances, many practical applications require human-controlled robots. For such applications, it is useful to determine the optimal number of robots an operator should control to maximize human efficiency given different situations. One way to achieve this is through computer simulations of team performance. In order to factor in various parameters that may affect team performance, an agent-based model will be used. Agent-based modeling is a computational method that enables a researcher to create, analyze, and experiment with models composed of agents that interact within an environment [12]. We construct an agent-based model of humans interacting with robots, and explore how team performance relates to different agent parameters and team organizational structures [21]. Prior work describes interaction between a single operator and multiple robots, while this work includes multi-operator performance and coordination. Model parameters include neglect time, interaction time, operator slack time, level of robot autonomy, etc. Understanding the parameters that influence team performance will be a step towards finding ways to maximize performance in real life human-robot systems.

archives@tulane.edu / The patellar tendon transmits loads from the quadriceps to the tibia promoting locomotion. The main etiological factor behind patellar tendinopathies is thought to be excessive loading and unloading during athletic activity (Pearson & Hussain, 2014). The extracellular matrix (ECM) composition and fibroblast-like tenocytes dictate tendon’s uniaxial mechanical properties (Kannus, 2000). Following injury, a flood of inflammatory cells and spike in certain gene expressions work together to remove damaged tissue, trigger fibroblast proliferation, and deposit a provisional collagen matrix (Thomopoulos et al., 2015). Despite these processes, healed tendons demonstrate significant functional deficits (Mienaltowski et al., 2016). Moreover decrease in cell migration and fiber alignment with age further hampers healing outcomes(Dunkman et al., 2013). Efforts to restore tendon function are impeded by a lack of understanding of the early healing process, which may be age- and sex-dependent (Fryhofer et al., 2016; Mienaltowski et al., 2016). The tendon healing process can be further understood using an agent-based model (ABM). ABMs simulate individual agents and the interactions between them and their environment. This approach has the advantage of building complexity from the ground up, mimicking the underlying tendon physiology (Conte & Paolucci, 2014). Therefore, the objectives of this study were to 1) formulate a literature based ABM of murine patellar tendon healing with varying initial conditions to recapitulate changes observed with aging, and 2) Conduct simulations to determine whether ABM recapitulated salient features of healing, and to make predictions about healing outcomes. / 1 / Jordan Robinson

Hurricane Sandy hit New York City on October 29, 2012 and greatly disrupted transportation systems, power systems, work, and schools. This research used survey data from 397 respondents in the NYC Metropolitan Area to develop an agent-based model for capturing commuter behavior and adaptation after the disruption. Six different recovery scenarios were tested to find which systems are more critical to recover first to promote a faster return to productivity. Important factors in the restoration timelines depends on the normal commuting pattern of people in that area. In the NYC Metropolitan Area, transit is one of the common modes of transportation; therefore, it was found that the subway/rail system recovery is the top factor in returning to productivity. When the subway/rail system recovers earlier (with the associated power), more people are able to travel to work and be productive. The second important factor is school and daycare closure (with the associated power and water systems). Parents cannot travel unless they can find a caregiver for their children, even if the transportation system is functional. Therefore, policy makers should consider daycare and school condition as one of the important factors in recovery planning. The next most effective scenario is power restoration. Telework is a good substitute for the physical movement of people to work. By teleworking, people are productive while they skip using the disrupted transportation system. To telework, people need power and communication systems. Therefore, accelerating power restoration and encouraging companies to let their employees' telework can promote a faster return to productivity. Finally, the restoration of major crossings like bridges and tunnels is effective in the recovery process. / Master of Science / Natural and man-made disasters, cause massive destruction of property annually and disrupt the normal economic productivity of an area. Although the occurrence of these disasters cannot be controlled, society can minimize the effects with post-disaster recovery strategies. Hurricane Sandy hit New York City on October 29, 2012 and greatly disrupted transportation systems, power systems, work, and schools. In this research, commuter behavior and adaptation after the hurricane were captured by using a survey data that asked questions from people living in NYC metropolitan area about their commuting behavior before and after Hurricane Sandy. An agent-based model was developed and six different recovery strategies were tested in order to find effective factors in returning people to normal productive life faster. In the NYC Metropolitan Area, transit is one of the common modes of transportation; therefore, it was found that the subway/rail system recovery is the top factor in returning to productivity. The next important factor is school and daycare closure. Parents are responsible for their children, therefore; they may not travel to work when school and daycares are closed. The third important factor is power restoration. To telework, people need power and communication systems. By teleworking, people are productive while they skip using the disrupted transportation system. The final important factor is the restoration of major crossings like bridges and tunnels.

Agent-based models are computer simulations in which entities (agents) interact with each other and their environment according to local update rules. Local interactions give rise to global dynamics. These models can be thought of as in silico laboratories that can be used to investigate the system being modeled. Optimization problems for agent-based models are problems concerning the optimal way of steering a particular model to a desired state. Given that agent-based models have no rigorous mathematical formulation, standard analysis is difficult, and traditional mathematical approaches are often intractable. This work presents techniques for the analysis of agent-based models and for solving optimization problems with such models. Techniques include model reduction, simulation optimization, conversion to systems of discrete difference equations, and a variety of heuristic methods. The proposed strategies are novel in their application; results show that for a large class of models, these strategies are more effective than existing methods. / Ph. D.

The purpose of this dissertation is to present our agent-based human tracking framework, and to evaluate the results of our work in light of the previous research in the same field. Our agent-based approach departs from a process-centric model where the agents are bound to specific processes, and introduces a novel model by which agents are bound to the objects or sub-objects being recognized or tracked. The hierarchical agent-based model allows the system to handle a variety of cases, such as single people or multiple people in front of single or stereo cameras. We employ the job-market model for agents' communication. In this dissertation, we will present several experiments in detail, which demonstrate the effectiveness of the agent-based tracking system. Per our research, the agents are designed to be autonomous, self-aware entities that are capable of communicating with other agents to perform tracking within agent coalitions. Each agent with high-level abstracted knowledge seeks evidence for its existence from the low-level features (e.g. motion vector fields, color blobs) and its peers (other agents representing body-parts with which it is compatible). The power of the agent-based approach is its flexibility by which the domain information may be encoded within each agent to produce an overall tracking solution. / Ph. D.

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.

This research investigates approaches to improve the performance of multi-agent based simulation (MABS) applications executed in distributed computing environments. MABS is a type of micro-level simulation used to study dynamic systems consisting of interacting entities, and in some cases, the number of the simulated entities can be very large. Most of the existing publicly available MABS tools are single-threaded desktop applications that are not suited for distributed execution. For this reason, general-purpose multi-agent platforms with multi-threading support are sometimes used for deploying MABS on distributed resources. However, these platforms do not scale well for large simulations due to huge communication overheads. In this research, different strategies to deploy large scale MABS in distributed environments are explored, e.g., tuning existing multi-agent platforms, porting single-threaded MABS tools to distributed environment, and implementing a service oriented architecture (SOA) deployment model. Although the factors affecting the performance of distributed applications are well known, the relative significance of the factors is dependent on the architecture of the application and the behaviour of the execution environment. We developed mathematical performance models to understand the influence of these factors and, to analyze the execution characteristics of MABS. These performance models are then used to formulate algorithms for resource management and application tuning decisions. The most important performance improvement solutions achieved in this thesis include: predictive estimation of optimal resource requirements, heuristics for generation of agent reallocation to reduce communication overhead and, an optimistic synchronization algorithm to minimize time management overhead. Additional application tuning techniques such as agent directory caching and message aggregations for fine-grained simulations are also proposed. These solutions were experimentally validated in different types of distributed computing environments. Another contribution of this research is that all improvement measures proposed in this work are implemented on the application level. It is often the case that the improvement measures should not affect the configuration of the computing and communication resources on which the application runs. Such application level optimizations are useful for application developers and users who have limited access to remote resources and lack authorization to carry out resource level optimizations.

2006 Dozier Award Winner / Agent-based models can provide paleoanthropologists with a view of behavioral dynamics and site formation processes as they unfold in digital caricatures of past societies and paleoenvironments. This paper argues that the agent-based methodology has the most to offer when used to conduct controlled, repeatable experiments within the context of behavioral laboratories. To illustrate the potential of this decidedly heuristic approach, I provide a case study of a simple agent-based model currently being used to investigate the evolution of Plio-Pleistocene hominin food sharing in East Africa. The results of this null model demonstrate that certain levels of ecological patchiness can facilitate the evolution of even simple food sharing strategies among equally simple hominin foragers. More generally, they demonstrate the potential that agent-based models possess for helping historical scientists act as their own informants as to what could have happened in the past.

In this thesis, we present a novel approach to solving optimization problems that are defined on agent-based models (ABM). The approach utilizes concepts in genetic programming (GP) and is demonstrated here using an optimization problem on the Sugarscape ABM, a prototype ABM that includes spatial heterogeneity, accumulation of agent resources, and agents with different attributes. The optimization problem seeks a strategy for taxation of agent resources which maximizes total taxes collected while minimizing impact on the agents over a finite time. We demonstrate how our GP approach yields better taxation policies when compared to simple flat taxes and provide reasons why GP-generated taxes perform well. We also look at ways to improve the performance of the GP optimization method. / McAnulty College and Graduate School of Liberal Arts; / Computational Mathematics / MS; / Thesis;