1,337 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
NEMISA Digital Skills Conference (Colloquium) 2023
The purpose of the colloquium and events centred around the central role that data plays
today as a desirable commodity that must become an important part of massifying digital
skilling efforts. Governments amass even more critical data that, if leveraged, could
change the way public services are delivered, and even change the social and economic
fortunes of any country. Therefore, smart governments and organisations increasingly
require data skills to gain insights and foresight, to secure themselves, and for improved
decision making and efficiency. However, data skills are scarce, and even more
challenging is the inconsistency of the associated training programs with most curated for
the Science, Technology, Engineering, and Mathematics (STEM) disciplines.
Nonetheless, the interdisciplinary yet agnostic nature of data means that there is
opportunity to expand data skills into the non-STEM disciplines as well.College of Engineering, Science and Technolog
TaxAI: A Dynamic Economic Simulator and Benchmark for Multi-Agent Reinforcement Learning
Taxation and government spending are crucial tools for governments to promote
economic growth and maintain social equity. However, the difficulty in
accurately predicting the dynamic strategies of diverse self-interested
households presents a challenge for governments to implement effective tax
policies. Given its proficiency in modeling other agents in partially
observable environments and adaptively learning to find optimal policies,
Multi-Agent Reinforcement Learning (MARL) is highly suitable for solving
dynamic games between the government and numerous households. Although MARL
shows more potential than traditional methods such as the genetic algorithm and
dynamic programming, there is a lack of large-scale multi-agent reinforcement
learning economic simulators. Therefore, we propose a MARL environment, named
\textbf{TaxAI}, for dynamic games involving households, government, firms,
and financial intermediaries based on the Bewley-Aiyagari economic model. Our
study benchmarks 2 traditional economic methods with 7 MARL methods on TaxAI,
demonstrating the effectiveness and superiority of MARL algorithms. Moreover,
TaxAI's scalability in simulating dynamic interactions between the government
and 10,000 households, coupled with real-data calibration, grants it a
substantial improvement in scale and reality over existing simulators.
Therefore, TaxAI is the most realistic economic simulator, which aims to
generate feasible recommendations for governments and individuals.Comment: 26 pages, 8 figures, 12 table
Recommended from our members
Policy options for food system transformation in Africa and the role of science, technology and innovation
As recognized by the Science, Technology and Innovation Strategy for Africa – 2024 (STISA-2024), science, technology and innovation (STI) offer many opportunities for addressing the main constraints to embracing transformation in Africa, while important lessons can be learned from successful interventions, including policy and institutional innovations, from those African countries that have already made significant progress towards food system transformation. This chapter identifies opportunities for African countries and the region to take proactive steps to harness the potential of the food and agriculture sector so as to ensure future food and nutrition security by applying STI solutions and by drawing on transformational policy and institutional innovations across the continent. Potential game-changing solutions and innovations for food system transformation serving people and ecology apply to (a) raising production efficiency and restoring and sustainably managing degraded resources; (b) finding innovation in the storage, processing and packaging of foods; (c) improving human nutrition and health; (d) addressing equity and vulnerability at the community and ecosystem levels; and (e) establishing preparedness and accountability systems. To be effective in these areas will require institutional coordination; clear, food safety and health-conscious regulatory environments; greater and timely access to information; and transparent monitoring and accountability systems
Ethnographies of Collaborative Economies across Europe: Understanding Sharing and Caring
"Sharing economy" and "collaborative economy" refer to a proliferation of initiatives, business models, digital platforms and forms of work that characterise contemporary life: from community-led initiatives and activist campaigns, to the impact of global sharing platforms in contexts such as network hospitality, transportation, etc. Sharing the common lens of ethnographic methods, this book presents in-depth examinations of collaborative economy phenomena. The book combines qualitative research and ethnographic methodology with a range of different collaborative economy case studies and topics across Europe. It uniquely offers a truly interdisciplinary approach. It emerges from a unique, long-term, multinational, cross-European collaboration between researchers from various disciplines (e.g., sociology, anthropology, geography, business studies, law, computing, information systems), career stages, and epistemological backgrounds, brought together by a shared research interest in the collaborative economy. This book is a further contribution to the in-depth qualitative understanding of the complexities of the collaborative economy phenomenon. These rich accounts contribute to the painting of a complex landscape that spans several countries and regions, and diverse political, cultural, and organisational backdrops. This book also offers important reflections on the role of ethnographic researchers, and on their stance and outlook, that are of paramount interest across the disciplines involved in collaborative economy research
The University of Montana: A History Through the Lens of Physical Culture, PE, Health, Athletics, and Recreation 1897-2019: The Evolution of a Department
https://scholarworks.umt.edu/burns/1000/thumbnail.jp
New perspectives on A.I. in sentencing. Human decision-making between risk assessment tools and protection of humans rights.
The aim of this thesis is to investigate a field that until a few years ago was foreign to and distant from the penal system. The purpose of this undertaking is to account for the role that technology could plays in the Italian Criminal Law system. More specifically, this thesis attempts to scrutinize a very intricate phase of adjudication. After deciding on the type of an individual's liability, a judge must decide on the severity of the penalty. This type of decision implies a prognostic assessment that looks to the future. It is precisely in this field and in prognostic assessments that, as has already been anticipated in the United, instruments and processes are inserted in the pre-trial but also in the decision-making phase. In this contribution, we attempt to describe the current state of this field, trying, as a matter of method, to select the most relevant or most used tools. Using comparative and qualitative methods, the uses of some of these instruments in the supranational legal system are analyzed.
Focusing attention on the Italian system, an attempt was made to investigate the nature of the element of an individual's ‘social dangerousness’ (pericolosità sociale) and capacity to commit offences, types of assessments that are fundamental in our system because they are part of various types of decisions, including the choice of the best sanctioning treatment. It was decided to turn our attention to this latter field because it is believed that the judge does not always have the time, the means and the ability to assess all the elements of a subject and identify the best 'individualizing' treatment in order to fully realize the function of Article 27, paragraph 3 of the Constitution
Machine learning in portfolio management
Financial markets are difficult learning environments. The data generation process is time-varying,
returns exhibit heavy tails and signal-to-noise ratio tends to be low. These contribute to the challenge
of applying sophisticated, high capacity learning models in financial markets. Driven by recent
advances of deep learning in other fields, we focus on applying deep learning in a portfolio
management context. This thesis contains three distinct but related contributions to literature. First,
we consider the problem of neural network training in a time-varying context. This results in a neural
network that can adapt to a data generation process that changes over time. Second, we consider
the problem of learning in noisy environments. We propose to regularise the neural network using a
supervised autoencoder and show that this improves the generalisation performance of the neural
network. Third, we consider the problem of quantifying forecast uncertainty in time-series with
volatility clustering. We propose a unified framework for the quantification of forecast uncertainty that results in uncertainty estimates that closely match actual realised forecast errors in cryptocurrencies
and U.S. stocks
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