How do we design digital products and algorithms to support decision makers in using information to equitably allocate scarce resources? And how do we design economic and social systems (e.g., markets, data sharing platforms) that encourage people to efficiently and effectively reveal truthful information in a safe and equitable way?
Our market design research. The group integrates economic and behavioural sciences to develop mechanisms which provide the assurances and incentives for people to reveal salient information. This work is relevant not only to the growing number of digital marketplaces, but can be applied far more broadly to digital mechanisms which facilitate any form of exchange. For example, the group is currently working on the design of a novel carbon market which can recognise the multiple values associated with land sector carbon projects such as tree planting, applying insights from economics and user behaviour. Another project combines economic, social and behavioural sciences to establish the socio-technical architecture needed for efficient and equitable data sharing. This social and economic work complements Data61’s science vision through designing mechanisms which can reveal data in a fair and representative way.
Our human factors research. Sitting at the intersection of humans, their work environments, and supporting technologies, this line of research is an important piece of projects that look to transform the way that complex systems operate including aspects of human behaviour and decision making. Using mixed methods both quantitative (physiological and other aspects of human performance) and qualitative (subjective perceptions and judgement), human factors is being used to help interdisciplinary project teams scope technology development opportunities with the human operator / decision maker in mind. This research is currently being applied to the development of automated systems, digital agriculture products, AI and other forms of decision support.
Data sharing platforms for smart cities:
The increasing numbers of sensors within cities gather large amounts of data, ranging from environmental (e.g. weather stations) through to individual’s movements (e.g. from cameras and smartphones). There are many ways in which this data can be used, but not all of them benefit the people and communities from whom the data is collected (whether knowingly or otherwise). This project considers the social, economic and institutional aspects of data platforms in the smart city context. A ‘landscape scan’ of existing smart city initiatives highlighted that most are driven by technology and focussed on government applications, with less consideration of the social and economic concerns of citizens and communities. Providing privacy can be protected (e.g. with privacy preserving technologies), the value of data can generally be maximised by sharing it widely. However, the economies of scale and scope enjoyed by digital platforms create the potential for privileged access to data leading to reduced competition and inequitable outcomes across a range of markets. A forthcoming report makes a series of recommendations to improve the durability and equitability of city data platforms, through better engaging with civil society, developing economic models which see data platforms as infrastructure, and more inclusive governance arrangements.
Digital services for carbon farming markets:
This project aims to enable farmers to participate profitably in greenhouse gas mitigation and transition to more sustainable land use practices. This project ambitiously combines the development of viable carbon markets with a human centred design approach, both needed to support Australia in addressing climate change and to help farmers achieve best management practices, steward the land and supplement farm incomes. Individually, Australia’s 140,000 farmers are small but collectively results could be large. More information is available here.
Resilience in Socio-Economic Systems:
We use analytical tools to measure and value resilience in socio-economic systems. Resilience in socio-economic systems is the ability to remain in a particular pattern of social and economic arrangements by managing and responding to uncertainty in particular outcomes (i.e. employment, profitability, GDP) before reaching a threshold which causes entry into a new set of social and economic arrangements. Policy changes frequently attempt to influence the proximity of social and economic systems to thresholds; attempting to expedite the crossing of thresholds towards a more desirable set of arrangements. Using analytical tools, we measure the proximity of social and economic systems to thresholds of interest. Our approach also allows us to value social and economic resilience, and to identify best-value policy options for achieving viable change.
Rapid assessment of disaster damage:
Carbon market design:
Through CSIRO’s Digiscape Future Science Platform we are working to design, and ultimately build, a digital platform through which Australian landholders can offer carbon and related social and environmental co-benefits into the market. We are applying behavioural, institutional and network economics to design and test alternative market mechanisms. A successful market will need to encourage landholders to accurately and safely reveal information, and allow buyers to express preferences over different types of carbon and associated co-benefits, build portfolios and manage risks across different projects. To this end, we are applying human centred design practices to the development of products that provide an intuitive and trusted digital experience that will be adopted by the various market stakeholders.
Equitable supply chains:
We are assessing the nature of various agricultural commodities and supply chains in Australia to estimate the degree of market failure resulting from asymmetric information. We will use this information to consider how digital technology may be applied to build structured marketplaces which overcome these market failures, appropriate marketplace models, what institutions (e.g. governance arrangements) would be required to facilitate such marketplaces, and how this can improve the stability and profitability of the sector.
Automation, trust and workload:
Research on human interactions with automated systems and decision support systems indicates that the level of cognitive resources a human operator has available (cognitive load) and trust in the system affects the success of the interaction on a number of levels, and should therefore be taken into account as a major factor in the design process. In collaboration with our university partners (UTAS and UniSA CRP) we investigate the relation between automation, trust, and workload and work towards developing a model that can help inform the design and development of future human-machine systems. This research also includes workplace automation and thus is linked to the agile workforce theme.
Resilience in Socio-Economic Systems:
In this report, we present a framework that shows how economic resilience can be defined, measured, and valued from the perspective of a decision-maker. To demonstrate the potential of this framework in a broad range of economic contexts, we demonstrate the application of new tools that enable the quantitative analysis of economic resilience. These developments open up exciting opportunities to better understand how complex social-ecological systems respond to shocks and variability, and to better evaluate policy interventions that affect the resilience of economic regimes.
Potential alternative data sources to meet tourism data needs:
Allocating resources to support a sector, such as tourism, requires detailed, up to date data. Traditional survey techniques are effective but are becoming increasingly expensive. Data61 investigated big data alternatives to traditional surveys. The report found the experience of big data promising but different from surveys. Combining multiple big data sources such as transactions and location information gives significantly better results. Data61 recommended trialling big data services (combining data from than one source if possible) alongside surveys while to better understand the errors in big data sources.
Market based project allocation:
When building project teams or recruiting employees a preference based matchmaking system can produce stable matches. The Gale and Shapely algorithm produces stable matches where no pair would do better by going outside the allocation mechanism. However when you introduce additional constrains such as limited funding pool the traditional method cannot guarantee a stable match. A new algorithm was developed and tested that guarantees a weakly stable match.
- Analysis of social media data
- Behavioural economics
- Human factors
- Analytical analysis (statistics and modelling)