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Neural Networks – originally inspired from Neuroscience – provide powerful models for statistical data analysis. Their most prominent feature is their ability to “learn” dependencies based on a finite number of observations.
- Computational Techniques for Modelling Learning in Economics
Computational Techniques for Modelling Learning in Economics...
- Computational Techniques for Modelling Learning in Economics
1. Introduction. Neural Networks - originally inspired from Neuroscience - provide powerful models for statistical data analysis. Their most prominent feature is their ability to "learn" dependencies based on a finite number of observations.
Then we gave an overview of existing economic applications of neural networks, where we distinguished between three types: Classification of economic agents, time series prediction and the modelling of bounded rational agents.
31 Αυγ 1998 · Neural Networks – originally inspired from Neuroscience – provide powerful models for statistical data analysis. Their most prominent feature is their ability to “learn” dependencies based ...
4 Αυγ 2017 · Neural networks are kinda black-boxes — we can train them, get results, enhance them but the actual decision path is mostly hidden from us. The NTM is an attempt to fix it — is it a FF with memory cells extracted.
4 Ιουλ 2022 · This work proposes a novel approach using neural networks to construct a portfolio of exchange traded funds (ETFs) based on the financial statement data of their components, and finds that this approach can be more beneficial when managing recently listed ETFs, such as thematic ETFs.
Neural Networks, Genetic Algorithms and Economic Models: An Introduction -- 1. Artificial neural networks and genetic algorithms -- 2. Economic models and decision-making -- Pt. 2. Experiments with Artificial Agents -- 3. Neural networks and economics -- 4. cross-target method -- Pt. 3. Models of Artificial Markets -- 5. One-agent models -- 6.
