Human-robot interaction among ARC success

Innovative research improving our interactions with robots in financial markets is one of three new research projects awarded to the Faculty of Business and Economics by the Australian Research Council (ARC), placing it equal first among comparable faculties in Australia.

Overall, the Faculty received more than $960,000 in the ARC’s latest Discovery Projects round.

Associate Dean of Research, Professor Graham Sewell, says the results reflect the Faculty’s expertise and dedication to world-class research.

“I’m proud to say we had a 25 per cent success rate in this latest round, well above the national average of 19 per cent for similar projects,” Professor Sewell added.

I’d like to extend my congratulations to our successful applicants on their outstanding work, as well as the wider team across the Faculty who helped make this happen. Professor Graham Swell

A new framework to improve human-robot interaction in financial markets

Professor Peter Bossaerts from the Brain, Mind and Markets Lab will investigate how humans interact with automatic algorithmic traders in financial markets.  The project will also explore how the use of robots affects price behavior, and efficiency of allocation.

Ready to launch? Young Australians work and family transitions

Professor David Ribar from the Melbourne Institute, Applied Economic and Social Research will lead a project that is also likely to yield important policy implications. It will explore the reasons why young Australians are delaying major life events. The project will investigate the timing and circumstances behind household transitions, such as marriage and moving out of the family home.

Prior sensitivity analysis for Bayesian Markov Chain Monte Carlo Output

Associate Professor Liana Jacobi from the Department of Economics received a grant to further work into a new approach that allows researchers to undertake a comprehensive sensitivity analysis of Bayesian inference obtained through Markov chain Monte Carlo (MCMC) methods. MCMC is a powerful method for gaining information about numerically untracktable high dimensional distributions such as those typically arising in Bayesian inference when analyzing empirical problems in many areas in economics, such as health and labour economics, macroeconomics and financial economics. The research will develop techniques and software to assess the robustness of results to key prior assumptions and inputs in to Bayesian based MCMC analysis in a routine manner.

Announced by the Minister for Education and Training Simon Birmingham, the grants aim to grow knowledge and innovation for the benefit of the Australian community.

Banner image: katjen/