Information-theoretic Reasoning in Distributed and Autonomous Systems

The increasing prevalence of distributed and autonomous systems is transforming decision making in industries as diverse as agriculture, environmental monitoring, and healthcare. Despite significant efforts, challenges remain in robustly planning under uncertainty. In this thesis, we present a number of information-theoretic decision rules for improving the analysis and control of complex adaptive systems. We begin with the problem of quantifying the data storage (memory) and transfer (communication) within information processing systems. We develop an information-theoretic framework to study nonlinear interactions within cooperative and adversarial scenarios, solely from observations of each agent's dynamics. This framework is applied to simulations of robotic soccer games, where the measures reveal insights into team performance, including correlations of the information dynamics to the scoreline. We then study the communication between processes with latent nonlinear dynamics that are observed only through a filter. By using methods from differential topology, we show that the information-theoretic measures commonly used to infer communication in observed systems can also be used in certain partially observed systems. For robotic environmental monitoring, the quality of data depends on the placement of sensors. These locations can be improved by either better estimating the quality of future viewpoints or by a team of robots operating concurrently. By robustly handling the uncertainty of sensor model measurements, we are able to present the first end-to-end robotic system for autonomously tracking small dynamic animals, with a performance comparable to human trackers. We then solve the issue of coordinating multi-robot systems through distributed optimisation techniques. These allow us to develop non-myopic robot trajectories for these tasks and, importantly, show that these algorithms provide guarantees for convergence rates to the optimal payoff sequence

The Effect of Auditor’s Work Stress on Audit Quality of Listed Companies in Indonesia

Audit failure practices have been the headlines in the past decade. At the same time, auditing is associated with high stress and over-timed work. However, a concern regarding the importance of audit quality rises nowadays. This research aims to find the effect of auditor’s work stress to audit quality. Additionally, it is intended to find how the presence of certain condition, such as such as initial audit partner engagement, audit firm size, and client litigation risk, impact the effect of auditor work stress to audit quality. This research utilizes data of listed Indonesian companies during 2014 – 2016. The methodology used is multiple linear regression. This research finds that auditor’s work stress affect audit quality significantly and negatively. This finding enhances Interaction Theory, where generally in Indonesian audit profession; the increased job-demand is not balanced by good job-control and social support. However, initial audit partner engagement and big audit firm size can mitigate the effect of such stress. While client litigation risk does not affect the impact of auditor’s work stress to audit quality. This study suggests that public accounting firms pay attention to job demand, low job control, and low social support, which are elements of work stress, to improve audit quality

How will mass-vaccination change COVID-19 lockdown requirements in Australia?

To prevent future outbreaks of COVID-19, Australia is pursuing a mass-vaccination approach in which a targeted group of the population comprising healthcare workers, aged-care residents and other individuals at increased risk of exposure will receive a highly effective priority vaccine. The rest of the population will instead have access to a less effective vaccine. We apply a large-scale agent-based model of COVID-19 in Australia to investigate the possible implications of this hybrid approach to mass-vaccination. The model is calibrated to recent epidemiological and demographic data available in Australia, and accounts for several components of vaccine efficacy. Within a feasible range of vaccine efficacy values, our model supports the assertion that complete herd immunity due to vaccination is not likely in the Australian context. For realistic scenarios in which herd immunity is not achieved, we simulate the effects of mass-vaccination on epidemic growth rate, and investigate the requirements of lockdown measures applied to curb subsequent outbreaks. In our simulations, Australia's vaccination strategy can feasibly reduce required lockdown intensity and initial epidemic growth rate by 43\% and 52\%, respectively. The severity of epidemics, as measured by the peak number of daily new cases, decreases by up to two orders of magnitude under plausible mass-vaccination and lockdown strategies. The study presents a strong argument for a large-scale vaccination campaign, which would significantly reduce the intensity of non-pharmaceutical interventions in Australia and curb future outbreaks

Modelling transmission and control of the COVID-19 pandemic in Australia

There is a continuing debate on relative benefits of various mitigation and suppression strategies aimed to control the spread of COVID-19. Here we report the results of agent-based modelling using a fine-grained computational simulation of the ongoing COVID-19 pandemic in Australia. This model is calibrated to match key characteristics of COVID-19 transmission. An important calibration outcome is the age-dependent fraction of symptomatic cases, with this fraction for children found to be one-fifth of such fraction for adults. We apply the model to compare several intervention strategies, including restrictions on international air travel, case isolation, home quarantine, social distancing with varying levels of compliance, and school closures. School closures are not found to bring decisive benefits, unless coupled with high level of social distancing compliance. We report several trade-offs, and an important transition across the levels of social distancing compliance, in the range between 70% and 80% levels, with compliance at the 90% level found to control the disease within 13--14 weeks, when coupled with effective case isolation and international travel restrictions.Comment: 45 pages, 19 figure

On the information-theoretic formulation of network participation

The participation coefficient is a widely used metric of the diversity of a node's connections with respect to a modular partition of a network. An information-theoretic formulation of this concept of connection diversity, referred to here as participation entropy, has been introduced as the Shannon entropy of the distribution of module labels across a node's connected neighbors. While diversity metrics have been studied theoretically in other literatures, including to index species diversity in ecology, many of these results have not previously been applied to networks. Here we show that the participation coefficient is a first-order approximation to participation entropy and use the desirable additive properties of entropy to develop new metrics of connection diversity with respect to multiple labelings of nodes in a network, as joint and conditional participation entropies. The information-theoretic formalism developed here allows new and more subtle types of nodal connection patterns in complex networks to be studied

Physical activity levels and patterns of 19-month-old children

Purpose: It is a commonly held perception that most young children are naturally active and meet physical activity recommendations. However, there is no scientific evidence available on which to confirm or refute such perceptions. The purpose of this study was to describe the physical activity levels and patterns of Australian toddlers. Methods: Physical activity and demographic data of two hundred ninety-five 19-month-old children from the Melbourne InFANT Program were measured using accelerometers and parent surveys. Validated cut points of 192&ndash;1672 and &gt;1672 counts per minute were used to determine time spent in light- (LPA) and moderate-to-vigorous- (MVPA) intensity physical activity, respectively. To be included in the analysis, children were required to have four valid days of accelerometer data to provide an acceptable (&gt;0.70) reliability estimate of LPA and MVPA. Physical activity data for different periods of the day were examined. Results: On average, toddlers engaged in 184 min of LPA and 47 min of MVPA daily, and 90.5% met the current Australian physical activity recommendations for 0- to 5-yr-olds (180 min of LPA/MVPA per day). Physical activity levels during mid morning and mid afternoon were higher than those during other periods. Physical activity patterns for boys and girls were similar, although boys engaged in more physical activity during the morning hours than girls did. Conclusions: Most children meet the physical activity recommendations, although the majority of activity undertaken in the study was of light intensity. Boys were more active than girls were in the morning hours, but there were no differences between sexes over the entire day. Certain periods of the day may hold more promise for intervention implementation than others do. <br /

A framework for considering the utility of models when facing tough decisions in public health: a guideline for policy-makers

The COVID-19 pandemic has brought the combined disciplines of public health, infectious disease and policy modelling squarely into the spotlight. Never before have decisions regarding public health measures and their impacts been such a topic of international deliberation, from the level of individuals and communities through to global leaders. Nor have models-developed at rapid pace and often in the absence of complete information-ever been so central to the decision-making process. However, after nearly 3 years of experience with modelling, policy-makers need to be more confident about which models will be most helpful to support them when taking public health decisions, and modellers need to better understand the factors that will lead to successful model adoption and utilization. We present a three-stage framework for achieving these ends