87,076 research outputs found
Discrete-continuous analysis of optimal equipment replacement
In Operations Research, the equipment replacement process is usually modeled in discrete time. The optimal replacement strategies are found from discrete (or integer) programming problems, well known for their analytic and computational complexity. An alternative approach is represented by continuous-time vintage capital models that explicitly involve the equipment lifetime and are described by nonlinear integral equations. Then the optimal replacement is determined via the optimal control of such equations. These two alternative techniques describe essentially the same controlled dynamic process. We introduce and analyze a model that unites both approaches. The obtained results allow us to explore such important effects in optimal asset replacement as the transition and long-term dynamics, clustering and splitting of replaced assets, and the impact of improving technology and discounting. In particular, we demonstrate that the cluster splitting is possible in our replacement model with given demand in the case of an increasinTheoretical findings are illustrated with numeric examples.vintage capital models, optimization, equipment lifetime, discrete-continuous models.
Animal Spirits, Lumpy Investment, and the Business Cycle
Empirical literature on investment and output dynamics is characterized by two robust stylized facts at the macro level. First, investment is considerably more volatile than output. Second, fluctuations of output and investment are highly synchronized. Furthermore, at the micro level, firm investment appears to be very lumpy. In this paper, we ask whether the two macroeconomic stylized facts above can be explained in terms of bounded rationality (i.e. "animal spirits") in firm investment behavior and the ensuing lumpiness in investment patterns. To address this question, we present an evolutionary, agent-based, model of industry dynamics and firm investment behavior. The economy is composed of consumers and firms, who belong to two industries. Firms in the first industry perform R&D and produce heterogeneous machine tools. Firms in the second industry invest in new machines and produce a consumption good. Lumpiness of firm investment is not grounded on non-convex adjustment costs, but on "animal spirits": manufacturing firms invest only if they expect a large growth in the demand for their product. Simulations show that the model is able to generate - as emergent properties - Keynesian endogenous business cycles and to reproduce the foregoing empirical macro output-investment regularities at the business cycle frequencies.Evolutionary Models, ACE Models, Animal Spirits, Lumpy Investment, Output Fluctuations, Endogenous Business Cycles
A Shared Task on Bandit Learning for Machine Translation
We introduce and describe the results of a novel shared task on bandit
learning for machine translation. The task was organized jointly by Amazon and
Heidelberg University for the first time at the Second Conference on Machine
Translation (WMT 2017). The goal of the task is to encourage research on
learning machine translation from weak user feedback instead of human
references or post-edits. On each of a sequence of rounds, a machine
translation system is required to propose a translation for an input, and
receives a real-valued estimate of the quality of the proposed translation for
learning. This paper describes the shared task's learning and evaluation setup,
using services hosted on Amazon Web Services (AWS), the data and evaluation
metrics, and the results of various machine translation architectures and
learning protocols.Comment: Conference on Machine Translation (WMT) 201
Valuing flexibility in the migration to flexible-grid networks
Increasing network demand is expected to put pressure on the available capacity in core networks. Flexible optical networking can now be installed to increase network capacity in light of future traffic demands. However, this technology is still in its infancy and might lack the full functionality that may appear within a few years. When replacing core network equipment, it is therefore important to make the right investment decision between upgrading toward flexible-grid or fixed-grid equipment. This paper researches various installation options using a techno-economic analysis, extended with real option insights, showing the impact of uncertainty and flexibility on the investment decision. By valuing the different options, a correct investment decision can be made
The Community Simulator: A Python package for microbial ecology
Natural microbial communities contain hundreds to thousands of interacting
species. For this reason, computational simulations are playing an increasingly
important role in microbial ecology. In this manuscript, we present a new
open-source, freely available Python package called Community Simulator for
simulating microbial population dynamics in a reproducible, transparent and
scalable way. The Community Simulator includes five major elements: tools for
preparing the initial states and environmental conditions for a set of samples,
automatic generation of dynamical equations based on a dictionary of modeling
assumptions, random parameter sampling with tunable levels of metabolic and
taxonomic structure, parallel integration of the dynamical equations, and
support for metacommunity dynamics with migration between samples. To
significantly speed up simulations using Community Simulator, our Python
package implements a new Expectation-Maximization (EM) algorithm for finding
equilibrium states of community dynamics that exploits a recently discovered
duality between ecological dynamics and convex optimization. We present data
showing that this EM algorithm improves performance by between one and two
orders compared to direct numerical integration of the corresponding ordinary
differential equations. We conclude by listing several recent applications of
the Community Simulator to problems in microbial ecology, and discussing
possible extensions of the package for directly analyzing microbiome
compositional data.Comment: 14 pages, 6 figure
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