Matching Items (39)
Description
A key factor in the success of social animals is their organization of work. Mathematical models have been instrumental in unraveling how simple, individual-based rules can generate collective patterns via self-organization. However, existing models offer limited insights into how these patterns are shaped by behavioral differences within groups, in part because they focus on analyzing specific rules rather than general mechanisms that can explain behavior at the individual-level. My work argues for a more principled approach that focuses on the question of how individuals make decisions in costly environments.
In Chapters 2 and 3, I demonstrate how this approach provides novel insights into factors that shape the flexibility and robustness of task organization in harvester ant colonies (Pogonomyrmex barbatus). My results show that the degree to which colonies can respond to work in fluctuating environments depends on how individuals weigh the costs of activity and update their behavior in response to social information. In Chapter 4, I introduce a mathematical framework to study the emergence of collective organization in heterogenous groups. My approach, which is based on the theory of multi-agent systems, focuses on myopic agents whose behavior emerges out of an independent valuation of alternative choices in a given work environment. The product of this dynamic is an equilibrium organization in which agents perform different tasks (or abstain from work) with an analytically defined set of threshold probabilities. The framework is minimally developed, but can be extended to include other factors known to affect task decisions including individual experience and social facilitation. This research contributes a novel approach to developing (and analyzing) models of task organization that can be applied in a broader range of contexts where animals cooperate.
In Chapters 2 and 3, I demonstrate how this approach provides novel insights into factors that shape the flexibility and robustness of task organization in harvester ant colonies (Pogonomyrmex barbatus). My results show that the degree to which colonies can respond to work in fluctuating environments depends on how individuals weigh the costs of activity and update their behavior in response to social information. In Chapter 4, I introduce a mathematical framework to study the emergence of collective organization in heterogenous groups. My approach, which is based on the theory of multi-agent systems, focuses on myopic agents whose behavior emerges out of an independent valuation of alternative choices in a given work environment. The product of this dynamic is an equilibrium organization in which agents perform different tasks (or abstain from work) with an analytically defined set of threshold probabilities. The framework is minimally developed, but can be extended to include other factors known to affect task decisions including individual experience and social facilitation. This research contributes a novel approach to developing (and analyzing) models of task organization that can be applied in a broader range of contexts where animals cooperate.
ContributorsUdiani, Oyita (Author) / Kang, Yun (Thesis advisor) / Fewell, Jennifer H (Thesis advisor) / Janssen, Marcus A (Committee member) / Castillo-Chavez, Carlos (Committee member) / Arizona State University (Publisher)
Created2016
Description
The Axelrod Model is an agent-based adaptive model. The Axelrod Model shows the eects of a mechanism of convergent social inuence. Do local conver- gences generate global polarization ? Will it be possible for all dierences between individuals in a population comprised of neighbors to disappear ? There are many mechanisms to approach this issue ; the Axelrod Model is one of them.
ContributorsYu, Yili (Author) / Lanchier, Nicolas (Thesis director) / Kang, Yun (Committee member) / Brooks, Dan (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Department of Finance (Contributor)
Created2013-05
Description
The decline of honeybee colonies around the world has been linked to the presence of the Varroa destructor, a mite acting as a virus vector for the Acute Bee Paralysis Virus. We developed a model of the infestation of the Apis melliifera honeybee colony by the Acute Bee Paralysis Virus, which is transmitted by the parasitic Varroa destructor. This is a four dimensional system of nonlinear ODE's for healthy and virus infected bees, total number of mites in the colony and number of mites that carry the virus. The Acute Bee Paralysis Virus can be transmitted between infected and uninfected bees, infected mite to adult bee, infected bee to phoretic mite, and reproductive mites to bee brood. This model is studied with analytical techniques deriving the conditions under which the bee colony can fight off an Acute Bee Paralysis Virus epidemic.
ContributorsDavis, Talia Lasandra (Author) / Kang, Yun (Thesis director) / Lanchier, Nicolas (Committee member) / Moore, Marianne (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12
Description
Studying the effects of viruses and toxins on honey bees is important in order to understand the danger these important pollinators are exposed to. Hives exist in various environments, and different colonies are exposed to varying environmental conditions and dangers. To properly study the changes and effects of seasonality and pesticides on the population dynamics of honey bees, the presence of each of these threats must be considered. This study aims to analyze how infected colonies grapple more deeply with changing, seasonal environments, and how toxins in pesticides affect population dynamics. Thus, it addresses the following questions: How do viruses within a colony affect honey bee population dynamics when the environment is seasonal? How can the effects of pesticides be modeled to better understand the spread of toxins? This project is a continuation of my own undergraduate work in a previous class, MAT 350: Techniques and Applications of Applied Mathematics, with Dr. Yun Kang, and also utilizes previous research conducted by graduate students. Original research focused on the population dynamics of honey bee disease interactions (without considering seasonality), and a mathematical modeling approach to analyze the effects of pesticides on honey bees. In order to pursue answers to the main research questions, the model for honey bee virus interaction was adapted to account for seasonality. The adaptation of this model allowed the new model to account for the effects of seasonality on infected colony population dynamics. After adapting the model, simulations with arbitrary data were run using RStudio in order to gain insight into the specific ways in which seasonality affected the interaction between a honey bee colony and viruses. The second portion of this project examines a system of ordinary differential equations that represent the effect of pesticides on honey bee population dynamics, and explores the process of this model’s formulation. Both systems of equations used as the basis for each model’s research question are from previous research reports. This project aims to further that research, and explore the applications of applied mathematics to biological issues.
ContributorsReveles, Anika (Author) / Kang, Yun (Thesis director) / Nishimura, Joel (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Natural Sciences (Contributor) / School of Earth and Space Exploration (Contributor)
Created2023-05
Description
Variation in living systems and how it cascades across organizational levels is central to biology. To understand the constraints and amplifications of variation in collective systems, I mathematically study how group-level differences emerge from individual variation in eusocial-insect colonies, which are inherently diverse and easily observable individually and collectively. Considering collective processes in three species where increasing degrees of heterogeneity are relevant, I address how individual variation scales to colony-level variation and to what degree it is adaptive. In Chapter 2, I introduce a Markov-chain decision model for stochastic individual quorum-based recruitment decisions of rock-ant workers during house hunting, and how they determine collective speed--accuracy balance. Differences in the average threshold-dependent response characteristics of workers between colonies cause collective differences in decision-making. Moreover, noisy behavior may prevent drastic collective cascading into poor nests. In Chapter 3, I develop an ordinary differential equation (ODE) model to study how cognitive diversity among honey-bee foragers influences collective attention allocation between novel and familiar resources. Results provide a mechanistic basis for changes in foraging activity and preference with group composition. Moreover, sensitivity analysis reveals that the main individual driver for foraging allocation shifts from recruitment (communication) to persistence (independent effort) as colony composition changes. This might favor specific degrees of heterogeneity that best amplify communication in wild colonies. Lastly, in Chapter 4, I consider diversity in size, age, and task for nest defense in stingless bees. To better understand how these dimensions of diversity interact to balance defensive demands with other colony needs, I study their effect on colony size and task allocation through a demographic Filippov ODE model. Along each dimension, variation is beneficial in a certain range, outside of which colony adaptation and survival are compromised. This work elucidates how variation in collective properties emerges from nonlinear interactions between varying components in eusocial insects, but it can be generalized to other biological systems with similar fundamental characteristics but less empirical tractability. Moreover, it has the potential of inspiring algorithms that capitalize on heterogeneity in engineered systems where simple components with limited information and no central control must solve complex tasks.
ContributorsNavas Zuloaga, Maria Gabriela (Author) / Kang, Yun (Thesis advisor) / Smith, Brian H (Thesis advisor) / Pavlic, Theodore P (Committee member) / Arizona State University (Publisher)
Created2022
Description
A pneumonia-like illness emerged late in 2019 (coined COVID-19), caused by SARSCoV-2, causing a devastating global pandemic on a scale never before seen sincethe 1918/1919 influenza pandemic. This dissertation contributes in providing deeper
qualitative insights into the transmission dynamics and control of the disease in the
United States. A basic mathematical model, which incorporates the key pertinent
epidemiological features of SARS-CoV-2 and fitted using observed COVID-19 data,
was designed and used to assess the population-level impacts of vaccination and face
mask usage in mitigating the burden of the pandemic in the United States. Conditions
for the existence and asymptotic stability of the various equilibria of the model were
derived. The model was shown to undergo a vaccine-induced backward bifurcation
when the associated reproduction number is less than one. Conditions for achieving
vaccine-derived herd immunity were derived for three of the four FDA-approved vaccines (namely Pfizer, Moderna and Johnson & Johnson vaccine), and the vaccination
coverage level needed to achieve it decreases with increasing coverage of moderately and highly-effective face masks. It was also shown that using face masks as a singular
intervention strategy could lead to the elimination of the pandemic if moderate or
highly-effective masks are prioritized and pandemic elimination prospects are greatly
enhanced if the vaccination program is combined with a face mask use strategy that
emphasizes the use of moderate to highly-effective masks with at least moderate coverage. The model was extended in Chapter 3 to allow for the assessment of the
impacts of waning and boosting of vaccine-derived and natural immunity against
the BA.1 Omicron variant of SARS-CoV-2. It was shown that vaccine-derived herd
immunity can be achieved in the United States via a vaccination-boosting strategy
which entails fully vaccinating at least 72% of the susceptible populace. Boosting
of vaccine-derived immunity was shown to be more beneficial than boosting of natural immunity. Overall, this study showed that the prospects of the elimination of
the pandemic in the United States were highly promising using the two intervention
measures.
ContributorsSafdar, Salman (Author) / Gumel, Abba (Thesis advisor) / Kostelich, Eric (Committee member) / Kang, Yun (Committee member) / Fricks, John (Committee member) / Espanol, Malena (Committee member) / Arizona State University (Publisher)
Created2023
Description
Dominance behavior can regulate a division of labor in a group, such as that between reproductive and non-reproductive individuals. Manipulations of insect societies in a controlled environment can reveal how dominance behavior is regulated. Here, I examined how morphological caste, fecundity, group size, and age influence the expression of dominance behavior using the ponerine ant Harpegnathos saltator. All H. saltator females have the ability to reproduce. Only those with a queen morphology that enables dispersal, however, show putative sex pheromones. In contrast, those with a worker morphology normally express dominance behavior. To evaluate how worker-like dominance behavior and associated traits could be expressed in queens, I removed the wings from alate gynes, those with a queen morphology who had not yet mated or left the nest, making them dealate. Compared to gynes with attached wings, dealates frequently performed dominance behavior. In addition, only the dealates demonstrated worker-like ovarian activity in the presence of reproductive individuals, whereas gynes with wings produced sex pheromones exclusively. Therefore, the attachment of wings determines a gyne’s expression of worker-like dominance behavior and physiology. When the queen dies, workers establish a reproductive hierarchy among themselves by performing a combination of dominance behaviors. To understand how reproductive status depends on these interactions as well as a worker’s age, I measured the frequency of dominance behaviors in groups of different size composed of young and old workers. The number of workers who expressed dominance scaled with the size of the group, but younger ones were more likely to express dominance behavior and eventually become reproductive. Therefore, the predisposition of age integrates with a self-organized process to form this reproductive hierarchy. A social insect’s fecundity and fertility signal depends on social context because fecundity increases with colony size. To evaluate how a socially dependent signal regulates dominance behavior, I manipulated a reproductive worker’s social context. Reproductive workers with reduced fecundity and a less prominent fertility signal expressed more dominance behavior than those with a stronger fertility signal and higher fecundity. Therefore, dominance behavior reinforces rank to compensate for a weak signal, indicating how social context can feed back to influence the maintenance of dominance. Mechanisms that regulate H. saltator’s reproductive hierarchy can inform how the reproductive division of labor is regulated in other groups of animals.
ContributorsPyenson, Benjamin (Author) / Liebig, Jürgen (Thesis advisor) / Hölldobler, Bert (Committee member) / Fewell, Jennifer (Committee member) / Pratt, Stephen (Committee member) / Kang, Yun (Committee member) / Arizona State University (Publisher)
Created2022
Description
Network analysis is a key conceptual orientation and analytical tool in the social sciences that emphasizes the embeddedness of individual behavior within a larger web of social relations. The network approach is used to better understand the cause and consequence of social interactions which cannot be treated as independent. The relational nature of network data and models, however, amplify the methodological concerns associated with inaccurate or missing data. This dissertation addresses such concerns via three projects. As a motivating substantive example, Project 1 examines factors associated with the selection of interaction partners by students at a large urban high school implementing a reform which, like many organizational improvement initiatives, is associated with a theory of change that posits changes to the structuring of social interactions as a central causal pathway to improved outcomes. A distinctive aspect of the data used in Project 1 is that it was a complete egocentric network census – in addition to being asked about their own relationships, students were asked about the relationships between alters that they nominated in the self-report. This enables two unique examinations of methodological challenges in network survey data collection: Project 2 examines the factors related to how well survey respondents assess the strength of social connections between others, finding that "informant" competence corresponds positively with their social proximity to target dyad as well as their centrality in the network. Project 3 explores using such third-party reports to augment network imputation methods, and finds that incorporating third-party reports into model-based methods provides a significant boost in imputation accuracy. Together these findings provide important implications for collecting and extrapolating data in research contexts where a complete social network census is highly desirable but infeasible.
ContributorsBates, Jordan T (Author) / Maroulis, Spiro J (Thesis advisor) / Kang, Yun (Thesis advisor) / Frank, Kenneth A. (Committee member) / Arizona State University (Publisher)
Created2019
Description
This dissertation seeks to understand and study the process of attention harvesting and knowledge production on typical online Q&A communities. Goals of this study include quantifying the attention harvesting and online knowledge, damping the effect of competition for attention on knowledge production, and examining the diversity of user behaviors on question answering. Project 1 starts with a simplistic discrete time model on a scale-free network and provides the method to measure the attention harvested. Further, project 1 highlights the effect of distractions on harvesting productive attention and in the end concludes which factors are influential and sensitive to the attention harvesting. The main finding is the critical condition to optimize the attention harvesting on the network by reducing network connection. Project 2 extends the scope of the study to quantify the value and quality of knowledge, focusing on the question answering dynamics. This part of research models how attention was distributed under typical answering strategies on a virtual online Q&A community. The final result provides an approach to measure the efficiency of attention transferred into value production and observes the contribution of different scenarios under various computed metrics. Project 3 is an advanced study on the foundation of the virtual question answering community from project 2. With highlights of different user behavioral preferences, algorithm stochastically simulates individual decisions and behavior. Results from sensitivity analysis on different mixtures of user groups gives insight of nonlinear dynamics for the objectives of success. Simulation finding shows reputation rewarding mechanism on Stack Overflow shapes the crowd mixture of behavior to be successful. In addition, project proposed an attention allocation scenario of question answering to improve the success metrics when coupling with a particular selection strategy.
ContributorsYu, Fan, Ph.D (Author) / Janssen, Marcus A (Thesis advisor) / Kang, Yun (Committee member) / Castillo-Chavez, Carlos (Committee member) / Arizona State University (Publisher)
Created2019
Description
Immunotherapy has received great attention recently, as it has become a powerful tool in fighting certain types of cancer. Immunotherapeutic drugs strengthen the immune system's natural ability to identify and eradicate cancer cells. This work focuses on immune checkpoint inhibitor and oncolytic virus therapies. Immune checkpoint inhibitors act as blocking mechanisms against the binding partner proteins, enabling T-cell activation and stimulation of the immune response. Oncolytic virus therapy utilizes genetically engineered viruses that kill cancer cells upon lysing. To elucidate the interactions between a growing tumor and the employed drugs, mathematical modeling has proven instrumental. This dissertation introduces and analyzes three different ordinary differential equation models to investigate tumor immunotherapy dynamics.
The first model considers a monotherapy employing the immune checkpoint inhibitor anti-PD-1. The dynamics both with and without anti-PD-1 are studied, and mathematical analysis is performed in the case when no anti-PD-1 is administrated. Simulations are carried out to explore the effects of continuous treatment versus intermittent treatment. The outcome of the simulations does not demonstrate elimination of the tumor, suggesting the need for a combination type of treatment.
An extension of the aforementioned model is deployed to investigate the pairing of an immune checkpoint inhibitor anti-PD-L1 with an immunostimulant NHS-muIL12. Additionally, a generic drug-free model is developed to explore the dynamics of both exponential and logistic tumor growth functions. Experimental data are used for model fitting and parameter estimation in the monotherapy cases. The model is utilized to predict the outcome of combination therapy, and reveals a synergistic effect: Compared to the monotherapy case, only one-third of the dosage can successfully control the tumor in the combination case.
Finally, the treatment impact of oncolytic virus therapy in a previously developed and fit model is explored. To determine if one can trust the predictive abilities of the model, a practical identifiability analysis is performed. Particularly, the profile likelihood curves demonstrate practical unidentifiability, when all parameters are simultaneously fit. This observation poses concerns about the predictive abilities of the model. Further investigation showed that if half of the model parameters can be measured through biological experimentation, practical identifiability is achieved.
The first model considers a monotherapy employing the immune checkpoint inhibitor anti-PD-1. The dynamics both with and without anti-PD-1 are studied, and mathematical analysis is performed in the case when no anti-PD-1 is administrated. Simulations are carried out to explore the effects of continuous treatment versus intermittent treatment. The outcome of the simulations does not demonstrate elimination of the tumor, suggesting the need for a combination type of treatment.
An extension of the aforementioned model is deployed to investigate the pairing of an immune checkpoint inhibitor anti-PD-L1 with an immunostimulant NHS-muIL12. Additionally, a generic drug-free model is developed to explore the dynamics of both exponential and logistic tumor growth functions. Experimental data are used for model fitting and parameter estimation in the monotherapy cases. The model is utilized to predict the outcome of combination therapy, and reveals a synergistic effect: Compared to the monotherapy case, only one-third of the dosage can successfully control the tumor in the combination case.
Finally, the treatment impact of oncolytic virus therapy in a previously developed and fit model is explored. To determine if one can trust the predictive abilities of the model, a practical identifiability analysis is performed. Particularly, the profile likelihood curves demonstrate practical unidentifiability, when all parameters are simultaneously fit. This observation poses concerns about the predictive abilities of the model. Further investigation showed that if half of the model parameters can be measured through biological experimentation, practical identifiability is achieved.
ContributorsNikolopoulou, Elpiniki (Author) / Kuang, Yang (Thesis advisor) / Gardner, Carl (Committee member) / Gevertz, Jana (Committee member) / Kang, Yun (Committee member) / Kostellich, Eric (Committee member) / Arizona State University (Publisher)
Created2020