Inequality of opportunity in Australia. This project aims to develop econometric approaches for identifying opportunity gaps in Australia and other developed countries. Inequality of opportunity arises when the birth lottery or external factors in later life, rather than personal efforts, determine a person’s chances of economic success. A high level of inequality of opportunity holds people back from realising their potential and from contributing productively to society. The project will focus ....Inequality of opportunity in Australia. This project aims to develop econometric approaches for identifying opportunity gaps in Australia and other developed countries. Inequality of opportunity arises when the birth lottery or external factors in later life, rather than personal efforts, determine a person’s chances of economic success. A high level of inequality of opportunity holds people back from realising their potential and from contributing productively to society. The project will focus on the effect of inequality of opportunity on income, health and education with special emphasis placed on Indigenous and migrant populations. The findings should help formulate cost-efficient policy interventions aimed at levelling the economic playing field.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE130100819
Funder
Australian Research Council
Funding Amount
$281,600.00
Summary
Measuring the improbable: optimal Monte Carlo methods for rare event simulation of maxima of dependent random variables. Some events occurring with low frequency can have dramatic consequences: natural catastrophes, economic crises, system malfunctions. Estimating their probabilities is a very difficult problem. This project will develop new simulation methods capable of delivering the most precise and efficient estimators for the probabilities of such events.
Random network models with applications in biology. Complex biological systems consist of a large number of interacting agents or components, and so can be studied using mathematical random network models. We aim to gain deeper insights into the laws emerging as the random networks evolve in time. This can help us to deal with dangerous disease epidemics and better understand the human brain.