Matching Items (4)
Filtering by
- Genre: Doctoral Dissertation
Description
The Hohokam of central Arizona left behind evidence of a culture markedly different from and more complex than the small communities of O'odham farmers first encountered by Europeans in the sixteenth and seventeenth centuries A.D. Archaeologists have worked for well over a century to document Hohokam culture history, but much about Pre-Columbian life in the Sonoran Desert remains poorly understood. In particular, the organization of the Hohokam economy in the Phoenix Basin has been an elusive and complicated subject, despite having been the focus of much previous research. This dissertation provides an assessment of several working hypotheses regarding the organization and evolution of the pottery distribution sector of the Hohokam economy. This was accomplished using an agent-based modeling methodology known as pattern-oriented modeling. The objective of the research was to first identify a variety of economic models that may explain patterns of artifact distribution in the archaeological record. Those models were abstract representations of the real-world system theoretically drawn from different sources, including microeconomics, mathematics (network/graph theory), and economic anthropology. Next, the effort was turned toward implementing those hypotheses as agent-based models, and finally assessing whether or not any of the models were consistent with Hohokam ceramic datasets. The project's pattern-oriented modeling methodology led to the discard of several hypotheses, narrowing the range of plausible models of the organization of the Hohokam economy. The results suggest that for much of the Hohokam sequence a market-based system, perhaps structured around workshop procurement and shopkeeper merchandise, provided the means of distributing pottery from specialist producers to widely distributed consumers. Perhaps unsurprisingly, the results of this project are broadly consistent with earlier researchers' interpretations that the structure of the Hohokam economy evolved through time, growing more complex throughout the Preclassic, and undergoing a major reorganization resulting in a less complicated system at the transition to the Classic Period.
ContributorsWatts, Joshua (Author) / Abbott, David R. (Thesis advisor) / Barton, C Michael (Committee member) / Van Der Leeuw, Sander (Committee member) / Janssen, Marcus (Committee member) / Arizona State University (Publisher)
Created2013
Description
Supply chains are increasingly complex as companies branch out into newer products and markets. In many cases, multiple products with moderate differences in performance and price compete for the same unit of demand. Simultaneous occurrences of multiple scenarios (competitive, disruptive, regulatory, economic, etc.), coupled with business decisions (pricing, product introduction, etc.) can drastically change demand structures within a short period of time. Furthermore, product obsolescence and cannibalization are real concerns due to short product life cycles. Analytical tools that can handle this complexity are important to quantify the impact of business scenarios/decisions on supply chain performance. Traditional analysis methods struggle in this environment of large, complex datasets with hundreds of features becoming the norm in supply chains. We present an empirical analysis framework termed Scenario Trees that provides a novel representation for impulse and delayed scenario events and a direction for modeling multivariate constrained responses. Amongst potential learners, supervised learners and feature extraction strategies based on tree-based ensembles are employed to extract the most impactful scenarios and predict their outcome on metrics at different product hierarchies. These models are able to provide accurate predictions in modeling environments characterized by incomplete datasets due to product substitution, missing values, outliers, redundant features, mixed variables and nonlinear interaction effects. Graphical model summaries are generated to aid model understanding. Models in complex environments benefit from feature selection methods that extract non-redundant feature subsets from the data. Additional model simplification can be achieved by extracting specific levels/values that contribute to variable importance. We propose and evaluate new analytical methods to address this problem of feature value selection and study their comparative performance using simulated datasets. We show that supply chain surveillance can be structured as a feature value selection problem. For situations such as new product introduction, a bottom-up approach to scenario analysis is designed using an agent-based simulation and data mining framework. This simulation engine envelopes utility theory, discrete choice models and diffusion theory and acts as a test bed for enacting different business scenarios. We demonstrate the use of machine learning algorithms to analyze scenarios and generate graphical summaries to aid decision making.
ContributorsShinde, Amit (Author) / Runger, George C. (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Villalobos, Rene (Committee member) / Janakiram, Mani (Committee member) / Arizona State University (Publisher)
Created2012
Description
Public risk communication (i.e. public emergency warning) is an integral component of public emergency management. Its effectiveness is largely based on the extent to which it elicits appropriate public response to minimize losses from an emergency. While extensive studies have been conducted to investigate individual responsive process to emergency risk information, the literature in emergency management has been largely silent on whether and how emergency impacts can be mitigated through the effective use of information transmission channels for public risk communication. This dissertation attempts to answer this question, in a specific research context of 2009 H1N1 influenza outbreak in Arizona. Methodologically, a prototype agent-based model is developed to examine the research question. Along with the specific disease spread dynamics, the model incorporates individual decision-making and response to emergency risk information. This simulation framework synthesizes knowledge from complexity theory, public emergency management, epidemiology, social network and social influence theory, and both quantitative and qualitative data found in previous studies. It allows testing how emergency risk information needs to be issued to the public to bring desirable social outcomes such as mitigated pandemic impacts. Simulation results generate several insightful propositions. First, in the research context, emergency managers can reduce the pandemic impacts by increasing the percent of state population who use national TV to receive pandemic information to 50%. Further increasing this percent after it reaches 50% brings only marginal effect in impact mitigation. Second, particular attention is needed when emergency managers attempt to increase the percent of state population who believe the importance of information from local TV or national TV, and the frequency in which national TV is used to send pandemic information. Those measures may reduce the pandemic impact in one dimension, while increase the impact in another. Third, no changes need to be made on the percent of state population who use local TV or radio to receive pandemic information, and the frequency in which either channel is used for public risk communication. This dissertation sheds light on the understanding of underlying dynamics of human decision-making during an emergency. It also contributes to the discussion of developing a better understanding of information exchange and communication dynamics during a public emergency and of improving the effectiveness of public emergency management practices in a dynamic environment.
ContributorsZhong, Wei (Author) / Lan, Zhiyong (Thesis advisor) / Kim, Yushim (Committee member) / Corley, Elizabeth (Committee member) / Lant, Timothy (Committee member) / Jehn, Megan (Committee member) / Arizona State University (Publisher)
Created2012
Description
Resistance to existing anti-cancer drugs poses a key challenge in the field of medical oncology, in that it results in the tumor not responding to treatment using the same medications to which it responded previously, leading to treatment failure. Adaptive therapy utilizes evolutionary principles of competitive suppression, leveraging competition between drug resistant and drug sensitive cells, to keep the population of drug resistant cells under control, thereby extending time to progression (TTP), relative to standard treatment using maximum tolerated dose (MTD). Development of adaptive therapy protocols is challenging, as it involves many parameters, and the number of parameters increase exponentially for each additional drug. Furthermore, the drugs could have a cytotoxic (killing cells directly), or a cytostatic (inhibiting cell division) mechanism of action, which could affect treatment outcome in important ways. I have implemented hybrid agent-based computational models to investigate adaptive therapy, using either a single drug (cytotoxic or cytostatic), or two drugs (cytotoxic or cytostatic), simulating three different adaptive therapy protocols for treatment using a single drug (dose modulation, intermittent, dose-skipping), and seven different treatment protocols for treatment using two drugs: three dose modulation (DM) protocols (DM Cocktail Tandem, DM Ping-Pong Alternate Every Cycle, DM Ping-Pong on Progression), and four fixed-dose (FD) protocols (FD Cocktail Intermittent, FD Ping-Pong Intermittent, FD Cocktail Dose-Skipping, FD Ping-Pong Dose-Skipping). The results indicate a Goldilocks level of drug exposure to be optimum, with both too little and too much drug having adverse effects. Adaptive therapy works best under conditions of strong cellular competition, such as high fitness costs, high replacement rates, or high turnover. Clonal competition is an important determinant of treatment outcome, and as such treatment using two drugs leads to more favorable outcome than treatment using a single drug. Switching drugs every treatment cycle (ping-pong) protocols work particularly well, as well as cocktail dose modulation, particularly when it is feasible to have a highly sensitive measurement of tumor burden. In general, overtreating seems to have adverse survival outcome, and triggering a treatment vacation, or stopping treatment sooner when the tumor is shrinking seems to work well.
ContributorsSaha, Kaushik (Author) / Maley, Carlo C (Thesis advisor) / Forrest, Stephanie (Committee member) / Anderson, Karen S (Committee member) / Cisneros, Luis H (Committee member) / Arizona State University (Publisher)
Created2023