8 resultados para Efficient market theory

em Duke University


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This paper presents an economic model of the effects of identity and social norms on consumption patterns. By incorporating qualitative studies in psychology and sociology, I propose a utility function that features two components – economic (functional) and identity elements. This setup is extended to analyze a market comprising a continuum of consumers, whose identity distribution along a spectrum of binary identities is described by a Beta distribution. I also introduce the notion of salience in the context of identity and consumption decisions. The key result of the model suggests that fundamental economic parameters, such as price elasticity and market demand, can be altered by identity elements. In addition, it predicts that firms in perfectly competitive markets may associate their products with certain types of identities, in order to reduce product substitutability and attain price-setting power.

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Localized molecular orbitals (LMOs) are much more compact representations of electronic degrees of freedom than canonical molecular orbitals (CMOs). The most compact representation is provided by nonorthogonal localized molecular orbitals (NOLMOs), which are linearly independent but are not orthogonal. Both LMOs and NOLMOs are thus useful for linear-scaling calculations of electronic structures for large systems. Recently, NOLMOs have been successfully applied to linear-scaling calculations with density functional theory (DFT) and to reformulating time-dependent density functional theory (TDDFT) for calculations of excited states and spectroscopy. However, a challenge remains as NOLMO construction from CMOs is still inefficient for large systems. In this work, we develop an efficient method to accelerate the NOLMO construction by using predefined centroids of the NOLMO and thereby removing the nonlinear equality constraints in the original method ( J. Chem. Phys. 2004 , 120 , 9458 and J. Chem. Phys. 2000 , 112 , 4 ). Thus, NOLMO construction becomes an unconstrained optimization. Its efficiency is demonstrated for the selected saturated and conjugated molecules. Our method for fast NOLMO construction should lead to efficient DFT and NOLMO-TDDFT applications to large systems.

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Theory suggests that economic instruments, such as pollution taxes or tradable permits, can provide more efficient technology adoption incentives than conventional regulatory standards. We explore this issue for an important industry undergoing dramatic decreases in allowed pollution - the U.S. petroleum industry's phasedown of lead in gasoline. Using a duration model applied to a panel of refineries from 1971-1995, we find that the pattern of technology adoption is consistent with an economic response to market incentives, plant characteristics, and alternative policies. Importantly, evidence suggests that the tradable permit system used during the phasedown provided incentives for more efficient technology adoption decisions.

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Market failures associated with environmental pollution interact with market failures associated with the innovation and diffusion of new technologies. These combined market failures provide a strong rationale for a portfolio of public policies that foster emissions reduction as well as the development and adoption of environmentally beneficial technology. Both theory and empirical evidence suggest that the rate and direction of technological advance is influenced by market and regulatory incentives, and can be cost-effectively harnessed through the use of economic-incentive based policy. In the presence of weak or nonexistent environmental policies, investments in the development and diffusion of new environmentally beneficial technologies are very likely to be less than would be socially desirable. Positive knowledge and adoption spillovers and information problems can further weaken innovation incentives. While environmental technology policy is fraught with difficulties, a long-term view suggests a strategy of experimenting with policy approaches and systematically evaluating their success. © 2005 Elsevier B.V. All rights reserved.

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Scheduling a set of jobs over a collection of machines to optimize a certain quality-of-service measure is one of the most important research topics in both computer science theory and practice. In this thesis, we design algorithms that optimize {\em flow-time} (or delay) of jobs for scheduling problems that arise in a wide range of applications. We consider the classical model of unrelated machine scheduling and resolve several long standing open problems; we introduce new models that capture the novel algorithmic challenges in scheduling jobs in data centers or large clusters; we study the effect of selfish behavior in distributed and decentralized environments; we design algorithms that strive to balance the energy consumption and performance.

The technically interesting aspect of our work is the surprising connections we establish between approximation and online algorithms, economics, game theory, and queuing theory. It is the interplay of ideas from these different areas that lies at the heart of most of the algorithms presented in this thesis.

The main contributions of the thesis can be placed in one of the following categories.

1. Classical Unrelated Machine Scheduling: We give the first polygorithmic approximation algorithms for minimizing the average flow-time and minimizing the maximum flow-time in the offline setting. In the online and non-clairvoyant setting, we design the first non-clairvoyant algorithm for minimizing the weighted flow-time in the resource augmentation model. Our work introduces iterated rounding technique for the offline flow-time optimization, and gives the first framework to analyze non-clairvoyant algorithms for unrelated machines.

2. Polytope Scheduling Problem: To capture the multidimensional nature of the scheduling problems that arise in practice, we introduce Polytope Scheduling Problem (\psp). The \psp problem generalizes almost all classical scheduling models, and also captures hitherto unstudied scheduling problems such as routing multi-commodity flows, routing multicast (video-on-demand) trees, and multi-dimensional resource allocation. We design several competitive algorithms for the \psp problem and its variants for the objectives of minimizing the flow-time and completion time. Our work establishes many interesting connections between scheduling and market equilibrium concepts, fairness and non-clairvoyant scheduling, and queuing theoretic notion of stability and resource augmentation analysis.

3. Energy Efficient Scheduling: We give the first non-clairvoyant algorithm for minimizing the total flow-time + energy in the online and resource augmentation model for the most general setting of unrelated machines.

4. Selfish Scheduling: We study the effect of selfish behavior in scheduling and routing problems. We define a fairness index for scheduling policies called {\em bounded stretch}, and show that for the objective of minimizing the average (weighted) completion time, policies with small stretch lead to equilibrium outcomes with small price of anarchy. Our work gives the first linear/ convex programming duality based framework to bound the price of anarchy for general equilibrium concepts such as coarse correlated equilibrium.

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The purpose of this dissertation is to contribute to a better understanding of how global seafood trade interacts with the governance of small-scale fisheries (SSFs). As global seafood trade expands, SSFs have the potential to experience significant economic, social, and political benefits from participation in export markets. At the same time, market connections that place increasing pressures on resources pose risks to both the ecological and social integrity of SSFs. This dissertation seeks to explore the factors that mediate between the potential benefits and risks of global seafood markets for SSFs, with the goal of developing hypotheses regarding these relationships.

The empirical investigation consists of a series of case studies from the Yucatan Peninsula, Mexico. This is a particularly rich context in which to study global market connections with SSFs because the SSFs in this region engage in a variety of market-oriented harvests, most notably for octopus, groupers and snappers, lobster, and sea cucumber. Variation in market forms and the institutional diversity of local-level governance arrangements allows the dissertation to explore a number of examples.

The analysis is guided primarily by common-pool resource (CPR) theory because of the insights it provides regarding the conditions that facilitate collective action and the factors that promote long-lasting resource governance arrangements. Theory from institutional economics and political ecology contribute to the elaboration of a multi-faceted conceptualization of markets for CPR theory, with the aim of facilitating the identification of mechanisms through which markets and CPR governance actually interact. This dissertation conceptualizes markets as sets of institutions that structure the exchange of property rights over fisheries resources, affect the material incentives to harvest resources, and transmit ideas and values about fisheries resources and governance.

The case studies explore four different mechanisms through which markets potentially influence resource governance: 1) Markets can contribute to costly resource governance activities by offsetting costs through profits, 2) markets can undermine resource governance by generating incentives for noncompliance and lead to overharvesting resources, 3) markets can increase the costs of resource governance, for example by augmenting monitoring and enforcement burdens, and 4) markets can alter values and norms underpinning resource governance by transmitting ideas between local resource users and a variety of market actors.

Data collected using participant observation, survey, informal and structured interviews contributed to the elaboration of the following hypotheses relevant to interactions between global seafood trade and SSFs governance. 1) Roll-back neoliberalization of fisheries policies has undermined cooperatives’ ability to achieve financial success through engagement with markets and thus their potential role as key actors in resource governance (chapter two). 2) Different relations of production influence whether local governance institutions will erode or strengthen when faced with market pressures. In particular, relations of production in which fishers own their own means of production and share the collective costs of governance are more likely to strengthen resource governance while relations of production in which a single entrepreneur controls capital and access to the fishery are more likely to contribute to the erosion of resource governance institutions in the face of market pressures (chapter three). 3) By serving as a new discursive framework within which to conceive of and talk about fisheries resources, markets can influence norms and values that shape and constitute governance arrangements.

In sum, the dissertation demonstrates that global seafood trade manifests in a diversity of local forms and effects. Whether SSFs moderate risks and take advantage of benefits depends on a variety of factors, and resource users themselves have the potential to influence the outcomes of seafood market connections through local forms of collective action.

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This dissertation contributes to the economic literature on the market for professional fundraising with two theoretical and one empirical essays. The second chapter rationalizes the decision of charities to outsource fundraising. The third chapter shows theoretically how liquidity considerations affect the contractual choices and campaign results between charities and professional solicitors. The fourth chapter tests the empirical implications of the theory, using a unique dataset I collected from the North Carolina Secretary of State website.

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I demonstrate a powerful tension between acquiring information and incorporating it into asset prices, the two core elements of price discovery. As a salient case, I focus on the transformative rise of algorithmic trading (AT) typically associated with improved price efficiency. Using a measure of the relative information content of prices and a comprehensive panel of 37,325 stock-quarters of SEC market data, I establish instead that algorithmic trading strongly decreases the net amount of information in prices. The increase in price distortions associated with the AT “information gap” is roughly $42.6 billion/year for U.S. common stocks around earnings announcement events alone. Information losses are concentrated among stocks with high shares of algorithmic liquidity takers relative to algorithmic liquidity makers, suggesting that aggressive AT powerfully deters fundamental information acquisition despite its importance for translating available information into prices.