34 resultados para Minimization
em QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast
Resumo:
This paper compares the Random Regret Minimization and the Random Utility Maximization models for determining recreational choice. The Random Regret approach is based on the idea that, when choosing, individuals aim to minimize their regret – regret being defined as what one experiences when a non-chosen alternative in a choice set performs better than a chosen one in relation to one or more attributes. The Random Regret paradigm, recently developed in transport economics, presents a tractable, regret-based alternative to the dominant choice paradigm based on Random Utility. Using data from a travel cost study exploring factors that influence kayakers’ site-choice decisions in the Republic of Ireland, we estimate both the traditional Random Utility multinomial logit model (RU-MNL) and the Random Regret multinomial logit model (RR-MNL) to gain more insights into site choice decisions. We further explore whether choices are driven by a utility maximization or a regret minimization paradigm by running a binary logit model to examine the likelihood of the two decision choice paradigms using site visits and respondents characteristics as explanatory variables. In addition to being one of the first studies to apply the RR-MNL to an environmental good, this paper also represents the first application of the RR-MNL to compute the Logsum to test and strengthen conclusions on welfare impacts of potential alternative policy scenarios.
Resumo:
This paper introduces the discrete choice model-paradigm of Random Regret Minimization (RRM) to the field of environmental and resource economics. The RRM-approach has been very recently developed in the context of travel demand modelling and presents a tractable, regret-based alternative to the dominant choice-modelling paradigm based on Random Utility Maximization-theory (RUM-theory). We highlight how RRM-based models provide closed form, logit-type formulations for choice probabilities that allow for capturing semi-compensatory behaviour and choice set-composition effects while being equally parsimonious as their utilitarian counterparts. Using data from a Stated Choice-experiment aimed at identifying valuations of characteristics of nature parks, we compare RRM-based models and RUM-based models in terms of parameter estimates, goodness of fit, elasticities and consequential policy implications.
Resumo:
A new model to explain animal spacing, based on a trade-off between foraging efficiency and predation risk, is derived from biological principles. The model is able to explain not only the general tendency for animal groups to form, but some of the attributes of real groups. These include the independence of mean animal spacing from group population, the observed variation of animal spacing with resource availability and also with the probability of predation, and the decline in group stability with group size. The appearance of "neutral zones" within which animals are not motivated to adjust their relative positions is also explained. The model assumes that animals try to minimize a cost potential combining the loss of intake rate due to foraging interference and the risk from exposure to predators. The cost potential describes a hypothetical field giving rise to apparent attractive and repulsive forces between animals. Biologically based functions are given for the decline in interference cost and increase in the cost of predation risk with increasing animal separation. Predation risk is calculated from the probabilities of predator attack and predator detection as they vary with distance. Using example functions for these probabilities and foraging interference, we calculate the minimum cost potential for regular lattice arrangements of animals before generalizing to finite-sized groups and random arrangements of animals, showing optimal geometries in each case and describing how potentials vary with animal spacing. (C) 1999 Academic Press.</p>
Resumo:
Increasing energy consumption has exerted great pressure on natural resources; this has led to a move towards sustainable energy resources to improve security of supply and to reduce greenhouse gas emissions. However, the rush to the cure may have been made in haste. Biofuels in particular, have a bad press both in terms of competition with good agricultural land for food, and also in terms of the associated energy balance with the whole life cycle analysis of the biofuel system. The emphasis is now very much on sustainable biofuel production; biofuels from wastes and lignocellulosic material are now seen as good sustainable biofuels that affect significantly better greenhouse gas balances as compared with first generation biofuels. Ireland has a significant resource of organic waste that could be a potential source of energy through anaerobic digestion. Ireland has 8% of the cattle population of the EU with less than 1% of the human population; as a result 91% of agricultural land in Ireland is under grass. Residues such as slurries and slaughter waste together with energy crops such as grass have an excellent potential to produce biogas that may be upgraded to biomethane. This biomethane may be used as a natural gas substitute; bio-compressed natural gas may then be an avenue for a biofuel strategy. It is estimated that a maximum potential of 33% of natural gas may be substituted by 2020 with a practical obtainable level of 7.5% estimated. Together with biodiesel from residues the practical obtainable level of this strategy may effect greater than a 5% substitution by energy of transport. The residues considered in this strategy to produce biofuel (excluding grass) have the potential to save 93,000 ha of agricultural land (23% of Irish arable land) when compared to a rapeseed biodiesel strategy. © 2009 Elsevier Ltd. All rights reserved.
Resumo:
This paper proposes a discrete mixture model which assigns individuals, up to a probability, to either a class of random utility (RU) maximizers or a class of random regret (RR) minimizers, on the basis of their sequence of observed choices. Our proposed model advances the state of the art of RU-RR mixture models by (i) adding and simultaneously estimating a membership model which predicts the probability of belonging to a RU or RR class; (ii) adding a layer of random taste heterogeneity within each behavioural class; and (iii) deriving a welfare measure associated with the RU-RR mixture model and consistent with referendum-voting, which is the adequate mechanism of provision for such local public goods. The context of our empirical application is a stated choice experiment concerning traffic calming schemes. We find that the random parameter RU-RR mixture model not only outperforms its fixed coefficient counterpart in terms of fit-as expected-but also in terms of plausibility of membership determinants of behavioural class. In line with psychological theories of regret, we find that, compared to respondents who are familiar with the choice context (i.e. the traffic calming scheme), unfamiliar respondents are more likely to be regret minimizers than utility maximizers. © 2014 Elsevier Ltd.
Resumo:
13.Vidovic M., Miljus M., Vlajic J., (2002), "Risk minimization in logistic processes with oil products", Proceedings of the 6th International Conference on Traffic Science, ICTS 2002, Portorož, Slovenia, pp. 568-577;
Resumo:
A new battery modelling method is presented based on the simulation error minimization criterion rather than the conventional prediction error criterion. A new integrated optimization method to optimize the model parameters is proposed. This new method is validated on a set of Li ion battery test data, and the results confirm the advantages of the proposed method in terms of the model generalization performance and long-term prediction accuracy.
Resumo:
Demand response (DR) algorithms manipulate the energy consumption schedules of controllable loads so as to satisfy grid objectives. Implementation of DR algorithms using a centralized agent can be problematic for scalability reasons, and there are issues related to the privacy of data and robustness to communication failures. Thus, it is desirable to use a scalable decentralized algorithm for the implementation of DR. In this paper, a hierarchical DR scheme is proposed for peak minimization based on Dantzig-Wolfe decomposition (DWD). In addition, a time weighted maximization option is included in the cost function, which improves the quality of service for devices seeking to receive their desired energy sooner rather than later. This paper also demonstrates how the DWD algorithm can be implemented more efficiently through the calculation of the upper and lower cost bounds after each DWD iteration.
Resumo:
In the present paper, a phase-field model is developed to simulate the formation and evolution of lamellar microstructure in γ-TiAl alloys. The mechanism of formation of TiAl lamellae proposed by Denquin and Naka is incorporated into the model. The model describes the formation and evolution of the face-centered cubic (fcc) stacking lamellar zone followed by the subsequent appearance and growth of the γ-phase, involving both the chemical composition change by atom transfer and the ordering of the fcc lattice. The thermodynamics of the model system and the interaction between the displacive and diffusional transformations are described by a non-equilibrium free energy formulated as a function of concentration and structural order parameter fields. The long-range elastic interactions, arising from the lattice misfit between the α, fcc (A1) and the various orientation variants of the γ-phase are taken into account by incorporating of the elastic strain energy into the total free energy. Simulation studies based on the model successfully predicted some essential features of the lamellar structure. It is found that the formation and evolution of the lamellar structure are predominantly controlled by the minimization of the elastic energy of the interfaces between the different fcc stacking groups, low-symmetry product phase γ and the high-symmetry α-phase, as well as between the various orientation variants of the product phase.
Resumo:
Currents across thin insulators are commonly taken as single electrons moving across classically forbidden regions; this independent particle picture is well-known to describe most tunneling phenomena. Examining quantum transport from a different perspective, i.e., by explicit treatment of electron-electron interactions, we evaluate different single particle approximations with specific application to tunneling in metal-molecule-metal junctions. We find maximizing the overlap of a Slater determinant composed of single-particle states to the many-body current-carrying state is more important than energy minimization for defining single-particle approximations in a system with open boundary conditions. Thus the most suitable single particle effective potential is not one commonly in use by electronic structure methods, such as the Hartree-Fock or Kohn-Sham approximations.
Resumo:
We analyse a picture of transport in which two large but finite charged electrodes discharge across a nanoscale junction. We identify a functional whose minimization, within the space of all bound many-body wavefunctions, defines an instantaneous steady state. We also discuss factors that favour the onset of steady-state conduction in such systems, make a connection with the notion of entropy, and suggest a novel source of steady-state noise. Finally, we prove that the true many-body total current in this closed system is given exactly by the one-electron total current, obtained from time-dependent density-functional theory.
Resumo:
To give the first demonstration of neighboring group-controlled drug delivery rates, a series of novel, polymerizable ester drug conjugates was synthesized and fully characterized. The monomers are suitable for copolymerization in biomaterials where control of drug release rate is critical to prophylaxis or obviation of infection. The incorporation of neighboring group moieties differing in nucleophilicity, geometry, and steric bulk in the conjugates allowed the rate of ester hydrolysis, and hence drug liberation, to be rationally and widely controlled. Solutions (2.5 x 10-5 mol dm-3) of ester conjugates of nalidixic acid incorporating pyridyl, amino, and phenyl neighboring groups hydrolyzed according to first-order kinetics, with rate constants between 3.00 ( 0.12 10-5 s -1 (fastest) and 4.50 ( 0.31 10- 6 s-1 (slowest). The hydrolysis was characterized using UV-visible spectroscopy. When copolymerized with poly(methyl methacrylate), free drug was shown to elute from the resulting materials, with the rate of release being controlled by the nature of the conjugate, as in solution. The controlled molecular architecture demonstrated by this system offers an attractive class of drug conjugate for the delivery of drugs from polymeric biomaterials such as bone cements in terms of both sustained, prolonged drug release and minimization of mechanical compromise as a result of release. We consider these results to be the rationale for the development of 'designer' drug release biomaterials, where the rate of required release can be controlled by predetermined molecular architecture.