4,699 research outputs found
Agent-Based Computational Economics
Agent-based computational economics (ACE) is the computational study of economies modeled as evolving systems of autonomous interacting agents. Starting from initial conditions, specified by the modeler, the computational economy evolves over time as its constituent agents repeatedly interact with each other and learn from these interactions. ACE is therefore a bottom-up culture-dish approach to the study of economic systems. This study discusses the key characteristics and goals of the ACE methodology. Eight currently active research areas are highlighted for concrete illustration. Potential advantages and disadvantages of the ACE methodology are considered, along with open questions and possible directions for future research.Agent-based computational economics; Autonomous agents; Interaction networks; Learning; Evolution; Mechanism design; Computational economics; Object-oriented programming.
When More is Better-Design Principles for Prediction Markets in Defense Acquisition Cost Forecasting
Naval Postgraduate School Acquisition Research Progra
Decentralized Energy Marketplace via NFTs and AI-based Agents
The paper introduces an advanced Decentralized Energy Marketplace (DEM)
integrating blockchain technology and artificial intelligence to manage energy
exchanges among smart homes with energy storage systems. The proposed framework
uses Non-Fungible Tokens (NFTs) to represent unique energy profiles in a
transparent and secure trading environment. Leveraging Federated Deep
Reinforcement Learning (FDRL), the system promotes collaborative and adaptive
energy management strategies, maintaining user privacy. A notable innovation is
the use of smart contracts, ensuring high efficiency and integrity in energy
transactions. Extensive evaluations demonstrate the system's scalability and
the effectiveness of the FDRL method in optimizing energy distribution. This
research significantly contributes to developing sophisticated decentralized
smart grid infrastructures. Our approach broadens potential blockchain and AI
applications in sustainable energy systems and addresses incentive alignment
and transparency challenges in traditional energy trading mechanisms. The
implementation of this paper is publicly accessible at
\url{https://github.com/RasoulNik/DEM}.Comment: 6 page
Algorithms in future capital markets: A survey on AI, ML and associated algorithms in capital markets
This paper reviews Artificial Intelligence (AI), Machine Learning (ML) and associated algorithms in future Capital Markets. New AI algorithms are constantly emerging, with each 'strain' mimicking a new form of human learning, reasoning, knowledge, and decisionmaking. The current main disrupting forms of learning include Deep Learning, Adversarial Learning, Transfer and Meta Learning. Albeit these modes of learning have been in the AI/ML field more than a decade, they now are more applicable due to the availability of data, computing power and infrastructure. These forms of learning have produced new models (e.g., Long Short-Term Memory, Generative Adversarial Networks) and leverage important applications (e.g., Natural Language Processing, Adversarial Examples, Deep Fakes, etc.). These new models and applications will drive changes in future Capital Markets, so it is important to understand their computational strengths and weaknesses. Since ML algorithms effectively self-program and evolve dynamically, financial institutions and regulators are becoming increasingly concerned with ensuring there remains a modicum of human control, focusing on Algorithmic Interpretability/Explainability, Robustness and Legality. For example, the concern is that, in the future, an ecology of trading algorithms across different institutions may 'conspire' and become unintentionally fraudulent (cf. LIBOR) or subject to subversion through compromised datasets (e.g. Microsoft Tay). New and unique forms of systemic risks can emerge, potentially coming from excessive algorithmic complexity. The contribution of this paper is to review AI, ML and associated algorithms, their computational strengths and weaknesses, and discuss their future impact on the Capital Markets
Automated peer-to-peer negotiation for energy contract settlements in residential cooperatives
This paper presents an automated peer-to-peer negotiation
strategy for settling energy contracts among prosumers in a Residential
Energy Cooperative considering heterogeneity prosumer preferences. The
heterogeneity arises from prosumers' evaluation of energy contracts
through multiple societal and environmental criteria and the prosumers'
private preferences over those criteria. The prosumers engage in
bilateral negotiations with peers to mutually agree on periodical energy
contracts/loans consisting of the energy volume to be exchanged at that
period and the return time of the exchanged energy. The negotiating
prosumers navigate through a common negotiation domain consisting of
potential energy contracts and evaluate those contracts from their
valuations on the entailed criteria against a utility function that is
robust against generation and demand uncertainty. From the repeated
interactions, a prosumer gradually learns about the compatibility of its
peers in reaching energy contracts that are closer to Nash solutions.
Empirical evaluation on real demand, generation and storage profiles –
in multiple system scales – illustrates that the proposed negotiation
based strategy can increase the system efficiency (measured by
utilitarian social welfare) and fairness (measured by Nash social
welfare) over a baseline strategy and an individual flexibility control
strategy representing the status quo strategy. We thus elicit system
benefits from peer-to-peer flexibility exchange already without any
central coordination and market operator, providing a simple yet
flexible and effective paradigm that complements existing markets
Proceedings of the Sintelnet WG5 Workshop on Crowd Intelligence : Foundations, Methods and Practices
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