Rational bidding using reinforcement learning: an application in automated resource allocation

The application of autonomous agents by the provisioning and usage of computational resources is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic resource provisioning and usage of computational resources, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems. The contributions of the paper are threefold. First, we present a framework for supporting consumers and providers in technical and economic preference elicitation and the generation of bids. Secondly, we introduce a consumer-side reinforcement learning bidding strategy which enables rational behavior by the generation and selection of bids. Thirdly, we evaluate and compare this bidding strategy against a truth-telling bidding strategy for two kinds of market mechanisms – one centralized and one decentralized

Q-Strategy: A Bidding Strategy for Market-Based Allocation of Grid Services

The application of autonomous agents by the provisioning and usage of computational services is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic service provisioning and usage of Grid services, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems. The contributions of the paper are threefold. First, we present a bidding agent framework for implementing artificial bidding agents, supporting consumers and providers in technical and economic preference elicitation as well as automated bid generation by the requesting and provisioning of Grid services. Secondly, we introduce a novel consumer-side bidding strategy, which enables a goal-oriented and strategic behavior by the generation and submission of consumer service requests and selection of provider offers. Thirdly, we evaluate and compare the Q-strategy, implemented within the presented framework, against the Truth-Telling bidding strategy in three mechanisms – a centralized CDA, a decentralized on-line machine scheduling and a FIFO-scheduling mechanisms

A Comprehensive Survey of Data Mining-based Fraud Detection Research

This survey paper categorises, compares, and summarises from almost all published technical and review articles in automated fraud detection within the last 10 years. It defines the professional fraudster, formalises the main types and subtypes of known fraud, and presents the nature of data evidence collected within affected industries. Within the business context of mining the data to achieve higher cost savings, this research presents methods and techniques together with their problems. Compared to all related reviews on fraud detection, this survey covers much more technical articles and is the only one, to the best of our knowledge, which proposes alternative data and solutions from related domains.Comment: 14 page

The Evolution of Neural Network-Based Chart Patterns: A Preliminary Study

A neural network-based chart pattern represents adaptive parametric features, including non-linear transformations, and a template that can be applied in the feature space. The search of neural network-based chart patterns has been unexplored despite its potential expressiveness. In this paper, we formulate a general chart pattern search problem to enable cross-representational quantitative comparison of various search schemes. We suggest a HyperNEAT framework applying state-of-the-art deep neural network techniques to find attractive neural network-based chart patterns; These techniques enable a fast evaluation and search of robust patterns, as well as bringing a performance gain. The proposed framework successfully found attractive patterns on the Korean stock market. We compared newly found patterns with those found by different search schemes, showing the proposed approach has potential.Comment: 8 pages, In proceedings of Genetic and Evolutionary Computation Conference (GECCO 2017), Berlin, German

Managing extreme cryptocurrency volatility in algorithmic trading: EGARCH via genetic algorithms and neural networks.

Política de acceso abierto tomada de: https://www.aimspress.com/index/news/solo-detail/openaccesspolicyThe blockchain ecosystem has seen a huge growth since 2009, with the introduction of Bitcoin, driven by conceptual and algorithmic innovations, along with the emergence of numerous new cryptocurrencies. While significant attention has been devoted to established cryptocurrencies like Bitcoin and Ethereum, the continuous introduction of new tokens requires a nuanced examination. In this article, we contribute a comparative analysis encompassing deep learning and quantum methods within neural networks and genetic algorithms, incorporating the innovative integration of EGARCH (Exponential Generalized Autoregressive Conditional Heteroscedasticity) into these methodologies. In this study, we evaluated how well Neural Networks and Genetic Algorithms predict “buy” or “sell” decisions for different cryptocurrencies, using F1 score, Precision, and Recall as key metrics. Our findings underscored the Adaptive Genetic Algorithm with Fuzzy Logic as the most accurate and precise within genetic algorithms. Furthermore, neural network methods, particularly the Quantum Neural Network, demonstrated noteworthy accuracy. Importantly, the X2Y2 cryptocurrency consistently attained the highest accuracy levels in both methodologies, emphasizing its predictive strength. Beyond aiding in the selection of optimal trading methodologies, we introduced the potential of EGARCH integration to enhance predictive capabilities, offering valuable insights for reducing risks associated with investing in nascent cryptocurrencies amidst limited historical market data. This research provides insights for investors, regulators, and developers in the cryptocurrency market. Investors can utilize accurate predictions to optimize investment decisions, regulators may consider implementing guidelines to ensure fairness, and developers play a pivotal role in refining neural network models for enhanced analysis.This research was funded by the Universitat de Barcelona, under the grant UB-AE-AS017634

A reconstructive critique of IPE and GPE from a critical scientific realist perspective: An alternative Keynesian-Kaleckian approach

This paper offers, first, a critique of the relative lack of economic theory in ‘British’ Global Political Economy and then use of neoclassical rational choice theory in American mainstream IPE from the perspective of critical scientific realism. Keynesian economic theories provide perhaps the most obvious alternative. Keynes’ General Theory has been followed by many, forming also the basis of Minsky’s long ignored but now, after the 2008-9 crisis, all of a sudden famous explorations on the mechanisms of financial markets. While a major leap forward, we argue that these theories are historically and conceptually limited. Keynes’ critique of neoclassical economic theory and his alternative theories of particularly the effective demand and of money and credit can be strengthened by following also a neo-Kaleckian approach which avoids some of the inconsistencies of neo-Keynesianism. We indicate where further conceptual work is required and provide several illustrations from the neo-Kaleckian and neo-Keynesian theory to suggest a partial agenda of further scientific work including the explanation of unnecessary and undesired global fluctuations, tendencies and crises and possible collective responses to them. We also suggest the possibility of going beyond Keynes and Kalecki in terms of a general field theory of global political economy that can accommodate the deep normative and institutional underpinnings of the historically evolving planetary political economy

Reinforcement Learning: A Survey

This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word ``reinforcement.'' The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning.Comment: See http://www.jair.org/ for any accompanying file

AI-driven approaches for optimizing the energy efficiency of integrated energy system

To decarbonize the global energy system and replace the unidirectional architecture of existing grid networks, integrated and electrified energy systems are becoming more demanding. Energy integration is critical for renewable energy sources like wind, solar, and hydropower. However, there are still specific challenges to overcome, such as their high reliance on the weather and the complexity of their integrated operation. As a result, this research goes through the study of a new approach to energy service that has arisen in the shape of data-driven AI technologies, which hold tremendous promise for system improvement while maximizing energy efficiency and reducing carbon emissions. This research aims to evaluate the use of data-driven AI techniques in electrical integrated energy systems, focusing on energy integration, operation, and planning of multiple energy supplies and demand. Based on the formation point, the main research question is: "To what extent do AI algorithms contribute to attaining greater efficiency of integrated grid systems?". It also included a discussion on four key research areas of AI application: Energy and load prediction, fault prediction, AI-based technologies IoT used for smart monitoring grid system optimization such as energy storage, demand response, grid flexibility, and Business value creation. The study adopted a two-way approach that includes empirical research on energy industry expert interviews and a Likert scale survey among energy sector representatives from Finland, Norway, and Nepal. On the other hand, the theoretical part was from current energy industry optimization models and a review of publications linked to a given research issue. The research's key findings were AI's significant potential in electrically integrated energy systems, which concluded AI's implication as a better understanding of energy consumption patterns, highly effective and precise energy load and fault prediction, automated energy management, enhanced energy storage system, more excellent business value, a smart control center, smooth monitoring, tracking, and communication of energy networks. In addition, further research directions are prospects towards its technical characteristics on energy conversion