10,054 research outputs found

    Graduate Catalog of Studies, 2023-2024

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    A framework for conceptualising hybrid system dynamics and agent-based simulation models

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    The growing complexity of systems and problems that stakeholders from the private and public sectors have sought advice on has led systems modellers to increasingly use multimethodology and to combine multiple OR/MS methods. This includes hybrid simulation that combines two or more of the following methods: system dynamics (SD), discrete-event simulation, and agent-based models (ABM). Although a significant number of studies describe the application of hybrid simulation across different domains, research on the theoretical and practical aspects of combining simulation modelling methods, particularly the combining of SD and ABM, is still limited. Existing frameworks for combining simulation methods are high-level and lack methodological clarity and practical guidance on modelling decisions and elements specific to hybrid simulation that modellers need to consider. This paper proposes a practical framework for developing a conceptual hybrid simulation model that is built on reviews and reflections of theoretical and application literature on combining methods. The framework is then used to inform and guide the process of conceptual model building for a case study in controlling the spread of COVID-19 in care homes. In addition, reflection on the use of the framework for the case study led to refining the framework itself. This case study is also used to demonstrate how the framework informs the structural design of a hybrid simulation model and relevant modelling decisions during the conceptualisation phase

    Exact steady states of minimal models of nonequilibrium statistical mechanics

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    Systems out of equilibrium with their environment are ubiquitous in nature. Of particular relevance to biological applications are models in which each microscopic component spontaneously generates its own motion. Known collectively as active matter, such models are natural effective descriptions of many biological systems, from subcellular motors to flocks of birds. One would like to understand such phenomena using the tools of statistical mechanics, yet the inherent nonequilibrium setting means that the most powerful classical results of that field cannot be applied. This circumstance has fuelled interest in exactly solvable models of active matter. The aim in studying such models is twofold. Firstly, as exactly solvable model are often minimal, it makes them good candidates as generic coarse-grained descriptions of real-world processes. Secondly, even if the model in question does not correspond directly to some situation realizable in experiment, its exact solution may suggest some general principles, which could also apply to more complex phenomena. A typical tool to investigate the properties of a large system is to study the behaviour of a probe particle placed in such an environment. In this context, cases of interest are both an active particle in a passive environment or an active particle in an active environment. One model that has attracted much attention in this regard is the asymmetric simple exclusion process (ASEP), which is a prototypical minimal model of driven diffusive transport. In this thesis, I consider two variations of the ASEP on a ring geometry. The first is a system of symmetrically diffusing particles with one totally asymmetric (driven) defect particle. The second is a system of partially asymmetric particles, with one defect that may overtake the other particles. I analyze the steady states of these systems using two exact methods: the matrix product ansatz, and, for the second model the Bethe ansatz. This allows me to derive the exact density profiles and mean currents for these models, and, for the second model, the diffusion constant. Moreover, I use the Yang-Baxter formalism to study the general class of two-species partially asymmetric processes with overtaking. This allows me to determine conditions under which such models can be solved using the Bethe ansatz

    Non-Market Food Practices Do Things Markets Cannot: Why Vermonters Produce and Distribute Food That\u27s Not For Sale

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    Researchers tend to portray food self-provisioning in high-income societies as a coping mechanism for the poor or a hobby for the well-off. They describe food charity as a regrettable band-aid. Vegetable gardens and neighborly sharing are considered remnants of precapitalist tradition. These are non-market food practices: producing food that is not for sale and distributing food in ways other than selling it. Recent scholarship challenges those standard understandings by showing (i) that non-market food practices remain prevalent in high-income countries, (ii) that people in diverse social groups engage in these practices, and (iii) that they articulate diverse reasons for doing so. In this dissertation, I investigate the persistent pervasiveness of non-market food practices in Vermont. To go beyond explanations that rely on individual motivation, I examine the roles these practices play in society. First, I investigate the prevalence of non-market food practices. Several surveys with large, representative samples reveal that more than half of Vermont households grow, hunt, fish, or gather some of their own food. Respondents estimate that they acquire 14% of the food they consume through non-market means, on average. For reference, commercial local food makes up about the same portion of total consumption. Then, drawing on the words of 94 non-market food practitioners I interviewed, I demonstrate that these practices serve functions that markets cannot. Interviewees attested that non-market distribution is special because it feeds the hungry, strengthens relationships, builds resilience, puts edible-but-unsellable food to use, and aligns with a desired future in which food is not for sale. Hunters, fishers, foragers, scavengers, and homesteaders said that these activities contribute to their long-run food security as a skills-based safety net. Self-provisioning allows them to eat from the landscape despite disruptions to their ability to access market food such as job loss, supply chain problems, or a global pandemic. Additional evidence from vegetable growers suggests that non-market settings liberate production from financial discipline, making space for work that is meaningful, playful, educational, and therapeutic. Non-market food practices mend holes in the social fabric torn by the commodification of everyday life. Finally, I synthesize scholarly critiques of markets as institutions for organizing the production and distribution of food. Markets send food toward money rather than hunger. Producing for market compels farmers to prioritize financial viability over other values such as stewardship. Historically, people rarely if ever sell each other food until external authorities coerce them to do so through taxation, indebtedness, cutting off access to the means of subsistence, or extinguishing non-market institutions. Today, more humans than ever suffer from chronic undernourishment even as the scale of commercial agriculture pushes environmental pressures past critical thresholds of planetary sustainability. This research substantiates that alternatives to markets exist and have the potential to address their shortcomings

    Graduate Catalog of Studies, 2023-2024

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    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Non-perturbative renormalization group analysis of nonlinear spiking networks

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    The critical brain hypothesis posits that neural circuits may operate close to critical points of a phase transition, which has been argued to have functional benefits for neural computation. Theoretical and computational studies arguing for or against criticality in neural dynamics largely rely on establishing power laws or scaling functions of statistical quantities, while a proper understanding of critical phenomena requires a renormalization group (RG) analysis. However, neural activity is typically non-Gaussian, nonlinear, and non-local, rendering models that capture all of these features difficult to study using standard statistical physics techniques. Here, we overcome these issues by adapting the non-perturbative renormalization group (NPRG) to work on (symmetric) network models of stochastic spiking neurons. By deriving a pair of Ward-Takahashi identities and making a ``local potential approximation,'' we are able to calculate non-universal quantities such as the effective firing rate nonlinearity of the network, allowing improved quantitative estimates of network statistics. We also derive the dimensionless flow equation that admits universal critical points in the renormalization group flow of the model, and identify two important types of critical points: in networks with an absorbing state there is Directed Percolation (DP) fixed point corresponding to a non-equilibrium phase transition between sustained activity and extinction of activity, and in spontaneously active networks there is a \emph{complex valued} critical point, corresponding to a spinodal transition observed, e.g., in the Lee-Yang ϕ3\phi^3 model of Ising magnets with explicitly broken symmetry. Our Ward-Takahashi identities imply trivial dynamical exponents z∗=2z_\ast = 2 in both cases, rendering it unclear whether these critical points fall into the known DP or Ising universality classes

    Data-assisted modeling of complex chemical and biological systems

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    Complex systems are abundant in chemistry and biology; they can be multiscale, possibly high-dimensional or stochastic, with nonlinear dynamics and interacting components. It is often nontrivial (and sometimes impossible), to determine and study the macroscopic quantities of interest and the equations they obey. One can only (judiciously or randomly) probe the system, gather observations and study trends. In this thesis, Machine Learning is used as a complement to traditional modeling and numerical methods to enable data-assisted (or data-driven) dynamical systems. As case studies, three complex systems are sourced from diverse fields: The first one is a high-dimensional computational neuroscience model of the Suprachiasmatic Nucleus of the human brain, where bifurcation analysis is performed by simply probing the system. Then, manifold learning is employed to discover a latent space of neuronal heterogeneity. Second, Machine Learning surrogate models are used to optimize dynamically operated catalytic reactors. An algorithmic pipeline is presented through which it is possible to program catalysts with active learning. Third, Machine Learning is employed to extract laws of Partial Differential Equations describing bacterial Chemotaxis. It is demonstrated how Machine Learning manages to capture the rules of bacterial motility in the macroscopic level, starting from diverse data sources (including real-world experimental data). More importantly, a framework is constructed though which already existing, partial knowledge of the system can be exploited. These applications showcase how Machine Learning can be used synergistically with traditional simulations in different scenarios: (i) Equations are available but the overall system is so high-dimensional that efficiency and explainability suffer, (ii) Equations are available but lead to highly nonlinear black-box responses, (iii) Only data are available (of varying source and quality) and equations need to be discovered. For such data-assisted dynamical systems, we can perform fundamental tasks, such as integration, steady-state location, continuation and optimization. This work aims to unify traditional scientific computing and Machine Learning, in an efficient, data-economical, generalizable way, where both the physical system and the algorithm matter

    Deterministic Approximation of a Stochastic Imitation Dynamics with Memory

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    We provide results of a deterministic approximation for non-Markovian stochastic processes modeling finite populations of individuals who recurrently play symmetric finite games and imitate each other according to payoffs. We show that a system of delay differential equations can be obtained as the deterministic approximation of such a non-Markovian process. We also show that if the initial states of stochastic process and the corresponding deterministic model are close enough, then the trajectory of stochastic process stays close to that of the deterministic model up to any given finite time horizon with a probability exponentially approaching one as the population size increases. We use this result to obtain that the lower bound of the population size on the absorption time of the non-Markovian process is exponentially increasing. Additionally, we obtain the replicator equations with distributed and discrete delay terms as examples and analyze how the memory of individuals can affect the evolution of cooperation in a two-player symmetric Snow-drift game. We investigate the stability of the evolutionary stable state of the game when agents have the memory of past population states, and implications of these results are given for the stochastic model.Comment: 23 pages, 2 figures one of which includes 4 subfigure

    Critical growth of cerebral tissue in organoids: theory and experiments

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    We develop a Fokker-Planck theory of tissue growth with three types of cells (symmetrically dividing, asymmetrically dividing and non-dividing) as main agents to study the growth dynamics of human cerebral organoids. Fitting the theory to lineage tracing data obtained in next generation sequencing experiments, we show that the growth of cerebral organoids is a critical process. We derive analytical expressions describing the time evolution of clonal lineage sizes and show how power-law distributions arise in the limit of long times due to the vanishing of a characteristic growth scale
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