[by] Laszlo Tisza, Paul M. Quay. / Repr. from Annals of physics. v. 25, no. 1. Oct. 1963.

Thesis (Ph. D.)--University of Oregon, 2006. / Typescript. Includes vita and abstract. Includes bibliographical references (leaves 96-104). Also available for download via the World Wide Web; free to University of Oregon users.

As fishery production and habitat quality continues to experience declines, there is a growing need to improve the scientific methodologies used to assess and sustain economically and ecologically important fisheries. This requires a sound understanding of the life histories and population dynamics of each species, and development of a robust framework for population modeling. Realization of the multi-species nature of fisheries has prompted a shift towards ecosystem-based approaches to modeling. To evaluate alternative methods for modeling predator-prey interactions within a physically variable coastal-estuarine ecosystem, a food web of national economic and ecological significance comprised of Atlantic menhaden (Brevoortia tyrannus), striped bass (Morone saxatilis), and bluefish (Pomatomus saltatrix) is considered in this study. Understanding the dynamics of biological communities such as this is challenging and requires the formulation of realistic mathematical models. This should be a stepwise process in which the underlying assumptions, parameter sensitivities, and fundamental behaviors of interacting species dynamics described by relatively "simple" to more "complex" models are delineated and quantified. In this study two alternative multispecies modeling frameworks were utilized to evaluate the dynamic interactions between predator and prey populations, and to understand the influence of fisheries and environmental temperature change on predator-prey and food chain communities. First, relatively "simple," age-independent, predator-prey and food chain models representing generalized, ecological-scale interactions between different trophic groups were developed and analyzed. Sensitivity analyses revealed the relative importance of model parameters and the effect of varying levels of fishing mortality on model dynamics. Overall, the predator-prey and food chain models were shown to be a valuable tool for understanding general patterns in the dynamic behavior of interacting populations. Next, an environment-dependent, age-structured, Atlantic coast spatial dynamic multispecies model was investigated. This more complex model links individual-scale bioenergetic processes controlling growth to ecological-scale rates of natural and predation mortality. Simulations were performed using recent stock assessment estimates of fishery condition and stock sizes to evaluate the nature and magnitude of linkages among menhaden and their key predators, specifically bluefish. This ecosystem model was demonstrated to provide valuable insights into the dynamics of menhaden and bluefish given the underlying dynamics and forcing in the Atlantic States fishery coastal ocean ecosystem. Additionally, the influence of environmental temperature on both modeling frameworks was investigated. This iterative process of model development and analysis advances the current understanding of the species and ecosystem of interest, and ultimately provides an improved basis for multispecies fisheries assessments.

In a transferable utility context, Choo and Siow (2006) introduced a competitive model of the marriage market with gumbel distributed stochastic part, and derived its equilibrium output, a marriage match- ing function. The marriage matching function defines the gains generated by a marriage between agents of prescribed types in terms of the observed frequency of such marriages within the population, relative to the number of unmarried individuals of the same types. Left open in their work is the issue of existence and uniqueness of equilibrium. We resolve this question in the affirmative, assuming the norm of the gains matrix (viewed as an operator) to be less than two. Our method adapts a strategy called the continuity method,more commonly used to solve elliptic partial differen- tial equations, to the new setting of isolating positive roots of polynomial systems. Finally, the data estimated in [4] falls within the scope of our results.

In a transferable utility context, Choo and Siow (2006) introduced a competitive model of the marriage market with gumbel distributed stochastic part, and derived its equilibrium output, a marriage match- ing function. The marriage matching function defines the gains generated by a marriage between agents of prescribed types in terms of the observed frequency of such marriages within the population, relative to the number of unmarried individuals of the same types. Left open in their work is the issue of existence and uniqueness of equilibrium. We resolve this question in the affirmative, assuming the norm of the gains matrix (viewed as an operator) to be less than two. Our method adapts a strategy called the continuity method,more commonly used to solve elliptic partial differen- tial equations, to the new setting of isolating positive roots of polynomial systems. Finally, the data estimated in [4] falls within the scope of our results.

Essay one develops and tests a revenue-maximizing tax structure model. This model represents one of the first attempts to evaluate and compare the responsiveness of various tax instruments to tax evasion within a tax revenue maximization framework. We use data from both the OECD and East African countries and estimation is via a seemingly unrelated regression model. The GDP share of agricultural income is used as an instrument to correct for the simultaneity between tax revenue shares and tax evasion. Our findings indicate that tax evasion increases the tax authority’s reliance on consumption taxes vis-à-vis taxes on income, suggesting that diverse tax instruments respond differently to tax evasion, and as such the choice of a revenue-maximizing tax structure is influenced by the amount of revenue lost through tax evasion. Essay two analyzes the incidence of tax evasion in both the formal and informal sectors of the economy using a computable general equilibrium model. This essay incorporates the element of uncertainty in an individual’s decision to evade so as to account for the uncertainty of returns to the tax evader. We also allow for varying degrees of competi¬tion or entry across sectors in the economy to examine how much of the tax advantage is retained by the initial evaders and how much is shifted via factor and commodity price changes. Our simulation results show that the evading households’ post-evasion welfare is only 0.68-3.40 percent higher than the post-tax welfare if it had fully complied with taxes. The simulation results further reveal that the evading household keeps 77.1-83.2 percent of this initial increase in welfare, while 16.8-22.9 percent of this initial gain is competed away as a result of increased competition and entry into the informal sector. The compliant households’ welfare increases by 58.8-101.7 percent with increased competition in the informal sector. Therefore, if we construe the changes in consumer welfare as an overall indicator of the gains and/or losses from tax evasion, then the evading household only benefits marginally and this advantage diminishes with increased entry or competition in the informal sector.

Pricing and capacity allocation are two important decisions that a service provider needs to make to maximize service quality and profit. This thesis attempts to address the pricing and capacity planning problems in operations management from the following three aspects. We first study a capacity planning and short-term demand management problem faced by firms with industrial customers that are insensitive to price incentives when placing orders. Industrial customers usually have downstream commitments that make it too costly to instantaneously adjust their schedule in response to price changes. Rather, they can only react to prices set at some earlier time. We propose a hierarchical planning model where price decisions and capacity allocation decisions must be made at different points of times. Customers first sign a service contract specifying how capacity at different times will be priced. Then, when placing an order, they choose the service time that best meets their needs. We study how to price the capacity so that the customers behave in a way that is consistent with a targeted demand profile at the order period. We further study how to optimally allocate capacity. Our numerical computations show that the model improves the operational revenue substantially. Second, we explore how a profit maximizing firm is to locate a single facility on a general network, to set its capacity and to decide the price to charge for service. Stochastic demand is generated from nodes of the network. Customers demand is sensitive to both the price and the time they expect to spend on traveling and waiting. Considering the combined effect of location and price on the firm's profit while taking into account the demand elasticity, our model provides managerial insights about how the interactions of these decision variables impact the firm's profit. Third, we extend this single facility problem to a multiple facility problem. Customers have multiple choices for service. The firm maximizes its profit subject to customers' choice criteria. We propose a system optimization model where customers cooperate with the firm to choose the facility for service and a user equilibrium model where customers choose the facilities that provide the best utility to them. We investigate the properties of the optimal solutions. Heuristic algorithms are developed for the user equilibrium model. Our results show that capacity planning and location decisions are closely related to each other. When customers are highly sensitive to waiting time, separating capacity planning and location decisions could result in a highly suboptimal solution.

Pricing and capacity allocation are two important decisions that a service provider needs to make to maximize service quality and profit. This thesis attempts to address the pricing and capacity planning problems in operations management from the following three aspects. We first study a capacity planning and short-term demand management problem faced by firms with industrial customers that are insensitive to price incentives when placing orders. Industrial customers usually have downstream commitments that make it too costly to instantaneously adjust their schedule in response to price changes. Rather, they can only react to prices set at some earlier time. We propose a hierarchical planning model where price decisions and capacity allocation decisions must be made at different points of times. Customers first sign a service contract specifying how capacity at different times will be priced. Then, when placing an order, they choose the service time that best meets their needs. We study how to price the capacity so that the customers behave in a way that is consistent with a targeted demand profile at the order period. We further study how to optimally allocate capacity. Our numerical computations show that the model improves the operational revenue substantially. Second, we explore how a profit maximizing firm is to locate a single facility on a general network, to set its capacity and to decide the price to charge for service. Stochastic demand is generated from nodes of the network. Customers demand is sensitive to both the price and the time they expect to spend on traveling and waiting. Considering the combined effect of location and price on the firm's profit while taking into account the demand elasticity, our model provides managerial insights about how the interactions of these decision variables impact the firm's profit. Third, we extend this single facility problem to a multiple facility problem. Customers have multiple choices for service. The firm maximizes its profit subject to customers' choice criteria. We propose a system optimization model where customers cooperate with the firm to choose the facility for service and a user equilibrium model where customers choose the facilities that provide the best utility to them. We investigate the properties of the optimal solutions. Heuristic algorithms are developed for the user equilibrium model. Our results show that capacity planning and location decisions are closely related to each other. When customers are highly sensitive to waiting time, separating capacity planning and location decisions could result in a highly suboptimal solution.

An outbreak of Foot and Mouth disease in Canada would result in the closing of borders to trade in meat and livestock between Canada and the US. The loss of export market access would result in losses to Canadian producers and negatively affect Canada’s reputation as a trading partner. Under a Regionalization Policy, trade could be allowed from disease-free regions of Canada during an outbreak. This would allow a limited amount of trade to continue and mitigate the losses to producers in uninfected areas. This thesis examined scenarios that involve various degrees of regionalization to determine the effects on producers, consumers and taxpayers. A partial- equilibrium model is used to determine the impact on economic welfare under each scenario and comparisons are made to help evaluate the relative outcomes of policies towards regionalization.

This article considers three standard asset pricing models with adaptive agents and stochastic non-stationary dividends. We assume that the parameters are estimated by exponential smoothing, such that prices and returns remain random variables. This paper provides sufficient conditions for the ergodicity of the return process and checks whether the perceived law assumed by the bounded rational agents can be considered to be sound with the returns observed. (author's abstract) / Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"