Survey report: intersections of mining and agriculture, Boddington Radius

There is considerable evidence that the recent strength of Australia’s export oriented mining sector has contributed to economic growth both nationally and in the main mining states and regions although at uneven rates of growth. However investigation and analysis of the internal distribution of costs and benefits from mining within host regions transitioning from agricultural economies has been limited.This document reports results from a survey conducted by the lead author in the Peel Region during March-June 2012 as a part of the Regions in Transition (RiT) project under the umbrella of the CSIRO Minerals Down Under Flagship. The survey examines changing patterns of workforce participation, changing patterns of rural land use, income and expenditure flows and cross-sectoral influences between mining and agriculture. The targeted survey sample comprises adults over 18 years of age either living or working within a radius of approximately 50 km from Boddington town in the most sparsely populated shire of the region, where two separate mineral extraction and processing operations have been undergoing significant expansion. The data reveals that during the RiT project period (2009-2012) these developments triggered a considerable change in the existing socio-economic fabric sustaining proximate towns, communities and individuals. The particularities of the case mean that this report is most relevant to those with a close interest in the future wellbeing of the Boddington 50 km Radius during and beyond the life of current mining operations.The survey also makes a contribution to the wider literature concerning the socio-economic implications of mining. It investigates and confirms the possibility raised by Hajkowicz et al (2011) that the quantifiable benefits of mineral wealth they identify across 71 LGAs may “mask highly localised inequalities and disadvantage”. By providing a nuanced account of the uneven impacts of mining experienced in one region, the survey serves to illuminate the temporally specific economic trends in mining LGAs that Measham and Reeson (2011) identify from ABS statistical data. The findings presented here are undergoing further analysis as a component of an interdisciplinary study at Curtin Graduate School of Business utilizing economic multiplier analysis and qualitative social data to track and map economic impacts of mine operations income expenditure at regional and state level

Mining gold from implicit models to improve likelihood-free inference

Simulators often provide the best description of real-world phenomena. However, they also lead to challenging inverse problems because the density they implicitly define is often intractable. We present a new suite of simulation-based inference techniques that go beyond the traditional Approximate Bayesian Computation approach, which struggles in a high-dimensional setting, and extend methods that use surrogate models based on neural networks. We show that additional information, such as the joint likelihood ratio and the joint score, can often be extracted from simulators and used to augment the training data for these surrogate models. Finally, we demonstrate that these new techniques are more sample efficient and provide higher-fidelity inference than traditional methods.Comment: Code available at https://github.com/johannbrehmer/simulator-mining-example . v2: Fixed typos. v3: Expanded discussion, added Lotka-Volterra example. v4: Improved clarit

Approximating Likelihood Ratios with Calibrated Discriminative Classifiers

In many fields of science, generalized likelihood ratio tests are established tools for statistical inference. At the same time, it has become increasingly common that a simulator (or generative model) is used to describe complex processes that tie parameters $\theta$ of an underlying theory and measurement apparatus to high-dimensional observations $\mathbf{x}\in \mathbb{R}^p$. However, simulator often do not provide a way to evaluate the likelihood function for a given observation $\mathbf{x}$, which motivates a new class of likelihood-free inference algorithms. In this paper, we show that likelihood ratios are invariant under a specific class of dimensionality reduction maps $\mathbb{R}^p \mapsto \mathbb{R}$. As a direct consequence, we show that discriminative classifiers can be used to approximate the generalized likelihood ratio statistic when only a generative model for the data is available. This leads to a new machine learning-based approach to likelihood-free inference that is complementary to Approximate Bayesian Computation, and which does not require a prior on the model parameters. Experimental results on artificial problems with known exact likelihoods illustrate the potential of the proposed method.Comment: 35 pages, 5 figure

Celia Cussen (Editora): Huellas de África en América, perspectivas para Chile.

sin resume

Ressenyes

Index de les obres ressenyades: Lourdes GAITÁN (dir.), Los niños como actores en los procesos migratorios. Implicaciones para los proyectos de cooperació