The governmenbt of Australia is subsidizing the study of responsible, ethical, and inclusive autonomous decision-making technologies. The Australian government is providing AU$31.8 million to the Australian Research Council to study responsible, ethical, and inclusive autonomous decision-making technologies. The Center of Excellence for Automated Decision-Making and Society, which will be based at the Royal Melbourne Institute of Technology (RMIT), will house researchers who will work with experts from seven other Australian universities, as well as 22 academic and industry partner organizations in Australia, Europe, Asia, and the U.S. The global research project aims to ensure machine learning and decision-making technologies can be used safely and ethically. Said RMIT researcher Julian Thomas, "Working with international partners and industry, the research will help Australians gain the full benefits of these new technologies, from better mobility, to improving our responses to humanitarian emergencies."
Every business needs experts responsible for analyzing pertinent data and helping inform employee decision-making. But many leaders aren't taking full advantage of the analytical tools at their disposal and rely heavily on gut-instinct in situations where data provides a more complete picture. In situations without data or precedent, instinctive decision-making is likely the most viable option. But this strategy is unnecessarily risky in cases where the data shows the outcome of similar situations that have occurred in the past. Educating employees on the historical odds of decisions prevents them from making unnecessarily risky decisions and gives leadership a chance to carefully consult the data and weigh the consequences and costs of failure.
We are researchers and leaders who advise product and business decisions and identify ways to use data and models to develop new capabilities. We are an interdisciplinary team leveraging experts in analytics, data science, and user and audience research to improve the world's leading livestreaming platform. Our team seeks an outstanding data scientist with demonstrated experience delivering insights from complex data. We prize creative problem solvers with the ability to draw on an expansive methodological toolkit to understand the unique Twitch environment. Our problems include attributing values to actions in complex, multi-sided markets and helping diverse creators discover their drivers of success.
Recognizing the importance of biodiversity to human well-being, most nations have committed to the Convention on Biological Diversity's Aichi Biodiversity Targets and the Sustainable Development Goals. However, the pressures on biodiversity are increasing, and its status is declining globally (1), raising concerns that national plans and targets are not ambitious enough (2) and showing that new solutions are needed (3). Recognition of synergies among different targets and goals (4) has brought forest to the forefront of national land-use decision-making, which must balance multiple objectives that all demand land (5). Efforts to support decision-making on forests have focused on individual (typically vertebrate) species and on carbon and other ecosystem services. Highly resolved views of functional trait variation in tropical forests reported by Asner et al. on page 385 of this issue (6) may provide a further basis for making such decisions.
In this paper, we present a cognitive model of frequency-based decision-making applied to the task of landmine detection. The model is implemented in the ACT-R cognitive architecture and is strongly constrained by the cognitive primitives of the architecture. We then generalize the model to another task in the domain of macroeconomic decision-making using the same architecture, pursuing theoretical parsimony. We describe each model's representation requirements, assess their fits to the data, and analyze their performance scaling as a function of task and architectural parameters. Efforts to generalize the landmine detection model to macroeconomic decision making showed that reasonable fits to the macro-economic performance data could be achieved by models based either on procedural knowledge or declarative knowledge. This finding underscores the importance of distinguishing between processing strategies employed to execute tasks. Such detail appears needed to understand the neural foundations of frequency-based decision-making.