942 resultados para Markov decision processes
Resumo:
Some companies are heavily reliant on the capabilities of their manufacturing technology for product competitiveness. Likewise, the capabilities of a manufacturing technology are dependent on the sourcing policy that the host company practices. This paper describes research that has explored a wide variety of US companies to understand manufacturing technology sourcing policies and how they have been formed. This research finds that there is a preference amongst the US organizations studied not to become involved with equipment manufacture, though some examples of full integration do occur. These policies are not determined by formalized decision processes, rather they are formed implicitly during technology choice. In this research, factors that influence a technology source have been identified. These drivers are then used to establish a methodology that will help practising managers to form a technology sourcing decision. This methodology takes into account the business demands placed on a technology, along with the characteristics of the host company's supplier base.
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Previous research has produced contradictory findings about the impact of challenge stressors on individual and team creativity. Based on the challenge-hindrance stressors framework (LePine, Podsakoff, & LePine, 2005) and on regulatory focus theory (Higgins, 1997), we argue that the effect of challenge stressors on creativity is moderated by regulatory focus. We hypothesize that while promotion focus strengthens a positive relationship between challenge stressors and creativity, prevention focus reinforces a negative relationship. Experimental data showed that high demands led to better results in a creative insight task for individuals with a strong trait promotion focus, and that high demands combined with an induced promotion focus led to better results across both creative generation and insight tasks. These results were replicated in a field R&D sample. Furthermore, we found that team promotion focus moderated the effect of challenge stressors on team creativity. The results offer both theoretical insights and suggest practical implications. © 2013 Elsevier Inc.
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Although recent research highlights the role of team member goalorientation in team functioning, research has neglected the effects of diversity in goalorientation. In a laboratory study with groups working on a problem-solving task, we show that diversity in learning and performanceorientation are related to decreased group performance. Moreover, we find that the effect of diversity in learning orientation is mediated by group information elaboration and the effect of diversity in performanceorientation by group efficiency. In addition, we demonstrate that teamreflexivity can counteract the negative effects of diversity in goalorientation. These results suggest that models of goal orientation in groups should incorporate the effects of diversity in goal orientation.
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Smart grid technologies have given rise to a liberalised and decentralised electricity market, enabling energy providers and retailers to have a better understanding of the demand side and its response to pricing signals. This paper puts forward a reinforcement-learning-powered tool aiding an electricity retailer to define the tariff prices it offers, in a bid to optimise its retail strategy. In a competitive market, an energy retailer aims to simultaneously increase the number of contracted customers and its profit margin. We have abstracted the problem of deciding on a tariff price as faced by a retailer, as a semi-Markov decision problem (SMDP). A hierarchical reinforcement learning approach, MaxQ value function decomposition, is applied to solve the SMDP through interactions with the market. To evaluate our trading strategy, we developed a retailer agent (termed AstonTAC) that uses the proposed SMDP framework to act in an open multi-agent simulation environment, the Power Trading Agent Competition (Power TAC). An evaluation and analysis of the 2013 Power TAC finals show that AstonTAC successfully selects sell prices that attract as many customers as necessary to maximise the profit margin. Moreover, during the competition, AstonTAC was the only retailer agent performing well across all retail market settings.
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Similar to classic Signal Detection Theory (SDT), recent optimal Binary Signal Detection Theory (BSDT) and based on it Neural Network Assembly Memory Model (NNAMM) can successfully reproduce Receiver Operating Characteristic (ROC) curves although BSDT/NNAMM parameters (intensity of cue and neuron threshold) and classic SDT parameters (perception distance and response bias) are essentially different. In present work BSDT/NNAMM optimal likelihood and posterior probabilities are analytically analyzed and used to generate ROCs and modified (posterior) mROCs, optimal overall likelihood and posterior. It is shown that for the description of basic discrimination experiments in psychophysics within the BSDT a ‘neural space’ can be introduced where sensory stimuli as neural codes are represented and decision processes are defined, the BSDT’s isobias curves can simultaneously be interpreted as universal psychometric functions satisfying the Neyman-Pearson objective, the just noticeable difference (jnd) can be defined and interpreted as an atom of experience, and near-neutral values of biases are observers’ natural choice. The uniformity or no-priming hypotheses, concerning the ‘in-mind’ distribution of false-alarm probabilities during ROC or overall probability estimations, is introduced. The BSDT’s and classic SDT’s sensitivity, bias, their ROC and decision spaces are compared.
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The phenomenon of at-destination search activity and decision processes utilized by visitors to a location is predominantly an academic unknown. As destinations and organizations increasingly compete for their share of the travel dollar, it is evident that more research need to be done regarding how consumers obtain information once they arrive at a destination. This study examined visitor referral recommendations provided by hotel and non-hotel ''locals" in a moderately-sized community for lodging, food service, and recreational and entertainment venues.
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Integer programming, simulation, and rules of thumb have been integrated to develop a simulation-based heuristic for short-term assignment of fleet in the car rental industry. It generates a plan for car movements, and a set of booking limits to produce high revenue for a given planning horizon. Three different scenarios were used to validate the heuristic. The heuristic's mean revenue was significant higher than the historical ones, in all three scenarios. Time to run the heuristic for each experiment was within the time limits of three hours set for the decision making process even though it is not fully automated. These findings demonstrated that the heuristic provides better plans (plans that yield higher profit) for the dynamic allocation of fleet than the historical decision processes. Another contribution of this effort is the integration of IP and rules of thumb to search for better performance under stochastic conditions.
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PEDRO, Edilson da Silva. Estratégias para a organização da pesquisa em cana-de-açúcar: uma análise de governança em sistemas de inovação. 2008. 226f. Tese (Doutorado em Política Científica e Tecnológica) - Universidade Estadual de Campinas, Campinas, 2008.
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This paper introduces systems of exchange values as tools for the organization of multi-agent systems. Systems of exchange values are defined on the basis of the theory of social exchanges, developed by Piaget and Homans. A model of social organization is proposed, where social relations are construed as social exchanges and exchange values are put into use in the support of the continuity of the performance of social exchanges. The dynamics of social organizations is formulated in terms of the regulation of exchanges of values, so that social equilibrium is connected to the continuity of the interactions. The concept of supervisor of social equilibrium is introduced as a centralized mechanism for solving the problem of the equilibrium of the organization The equilibrium supervisor solves such problem making use of a qualitative Markov Decision Process that uses numerical intervals for the representation of exchange values.
Resumo:
PEDRO, Edilson da Silva. Estratégias para a organização da pesquisa em cana-de-açúcar: uma análise de governança em sistemas de inovação. 2008. 226f. Tese (Doutorado em Política Científica e Tecnológica) - Universidade Estadual de Campinas, Campinas, 2008.
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In this thesis, we evaluate consumer purchase behaviour from the perspective of heuristic decision making. Heuristic decision processes are quick and easy mental shortcuts, adopted by individuals to reduce the amount of time spent in decision making. In particular, we examine those heuristics which are caused by framing – prospect theory and mental accounting, and examine these within price related decision scenarios. The impact of price framing on consumer behaviour has been studied under the broad umbrella of reference price, which suggests that decision makers use reference points as standards of comparison when making a purchase decision. We investigate four reference points - a retailer's past prices, a competitor's current prices, a competitor's past prices, and consumers' expectation of immediate future price changes, to further our understanding of the impact of price framing on mental accounting, and in turn, contribute to the growing body of reference price literature in Marketing research. We carry out experiments in which levels of price frame and monetary outcomes are manipulated in repeated measures analysis of variance (ANOVA). Our results show that where these reference points are clearly specified in decision problems, price framing significantly affects consumers' perceptions of monetary gains derived through discounts, and leads to reversals in consumer preferences. We also found that monetary losses were not sensitive to price frame manipulations.
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Natural language processing has achieved great success in a wide range of ap- plications, producing both commercial language services and open-source language tools. However, most methods take a static or batch approach, assuming that the model has all information it needs and makes a one-time prediction. In this disser- tation, we study dynamic problems where the input comes in a sequence instead of all at once, and the output must be produced while the input is arriving. In these problems, predictions are often made based only on partial information. We see this dynamic setting in many real-time, interactive applications. These problems usually involve a trade-off between the amount of input received (cost) and the quality of the output prediction (accuracy). Therefore, the evaluation considers both objectives (e.g., plotting a Pareto curve). Our goal is to develop a formal understanding of sequential prediction and decision-making problems in natural language processing and to propose efficient solutions. Toward this end, we present meta-algorithms that take an existent batch model and produce a dynamic model to handle sequential inputs and outputs. Webuild our framework upon theories of Markov Decision Process (MDP), which allows learning to trade off competing objectives in a principled way. The main machine learning techniques we use are from imitation learning and reinforcement learning, and we advance current techniques to tackle problems arising in our settings. We evaluate our algorithm on a variety of applications, including dependency parsing, machine translation, and question answering. We show that our approach achieves a better cost-accuracy trade-off than the batch approach and heuristic-based decision- making approaches. We first propose a general framework for cost-sensitive prediction, where dif- ferent parts of the input come at different costs. We formulate a decision-making process that selects pieces of the input sequentially, and the selection is adaptive to each instance. Our approach is evaluated on both standard classification tasks and a structured prediction task (dependency parsing). We show that it achieves similar prediction quality to methods that use all input, while inducing a much smaller cost. Next, we extend the framework to problems where the input is revealed incremen- tally in a fixed order. We study two applications: simultaneous machine translation and quiz bowl (incremental text classification). We discuss challenges in this set- ting and show that adding domain knowledge eases the decision-making problem. A central theme throughout the chapters is an MDP formulation of a challenging problem with sequential input/output and trade-off decisions, accompanied by a learning algorithm that solves the MDP.
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Urban agriculture, a dynamic multifunctional phenomenon, affects the spatial diversification of urban land use, its valorization and its governance. Literature acknowledges its contribution to the development of sustainable cities. The dimension and extent of this contribution depends significantly on the particular form and function of urban agriculture. However, the complexity of interests and dimensions is insufficiently covered by theory. This paper proposes a typology for urban agriculture, supporting both theory building and practical decision processes. We reviewed and mapped the diversity of the types of agriculture found along three beneficial dimensions (self-supply, socio-cultural, commercial) for product distribution scale and actors. We distinguish between ideal types, subtypes and mixed types. Our intention is to include a dynamic perspective in the typology of urban agricultural land use because transition processes between types are observable due to the existence of complex motivations and influences. In a pilot study of 52 urban agriculture initiatives in Germany, we tested the validity of the typology and discussed it with stakeholders, proving novelty and relevance for profiling discussions.
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Entrepreneurship education has emerged as one popular research domain in academic fields given its aim at enhancing and developing certain entrepreneurial qualities of undergraduates that change their state of behavior, even their entrepreneurial inclination and finally may result in the formation of new businesses as well as new job opportunities. This study attempts to investigate the Colombian student´s entrepreneurial qualities and the influence of entrepreneurial education during their studies.
Resumo:
Este trabajo se inscribe en uno de los grandes campos de los estudios organizacionales: la estrategia. La perspectiva clásica en este campo promovió la idea de que proyectarse hacia el futuro implica diseñar un plan (una serie de acciones deliberadas). Avances posteriores mostraron que la estrategia podía ser comprendida de otras formas. Sin embargo, la evolución del campo privilegió en alguna medida la mirada clásica estableciendo, por ejemplo, múltiples modelos para ‘formular’ una estrategia, pero dejando en segundo lugar la manera en la que esta puede ‘emerger’. El propósito de esta investigación es, entonces, aportar al actual nivel de comprensión respecto a las estrategias emergentes en las organizaciones. Para hacerlo, se consideró un concepto opuesto —aunque complementario— al de ‘planeación’ y, de hecho, muy cercano en su naturaleza a ese tipo de estrategias: la improvisación. Dado que este se ha nutrido de valiosos aportes del mundo de la música, se acudió al saber propio de este dominio, recurriendo al uso de ‘la metáfora’ como recurso teórico para entenderlo y alcanzar el objetivo propuesto. Los resultados muestran que 1) las estrategias deliberadas y las emergentes coexisten y se complementan, 2) la improvisación está siempre presente en el contexto organizacional, 3) existe una mayor intensidad de la improvisación en el ‘como’ de la estrategia que en el ‘qué’ y, en oposición a la idea convencional al respecto, 4) se requiere cierta preparación para poder improvisar de manera adecuada.