How do we design algorithms to support decision makers to use this 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.
This project aims to enable farmers to participate profitably in greenhouse gas mitigation and maximise the benefits to the land from carbon markets. Can you imagine carbon markets not only supporting Australia in addressing climate change but also boosting livelihoods and leading to great environmental outcomes? Australia’s carbon markets could 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.
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.
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.
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.
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 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.
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.
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.
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.