Benchmarking Biologically-Inspired Automatic Machine Learning for Economic Tasks

Data-driven economic tasks have gained significant attention in economics, allowing researchers and policymakers to make better decisions and design efficient policies. Recently, with the advancement of machine learning (ML) and other artificial intelligence (AI) methods, researchers can now solve c...

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Vydáno v:Sustainability Ročník 15; číslo 14; s. 11232
Hlavní autoři: Lazebnik, Teddy, Fleischer, Tzach, Yaniv-Rosenfeld, Amit
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.07.2023
Témata:
Analysis
Artificial intelligence
Automation
Benchmarks
Datasets
Economic aspects
Economics
Energy consumption
Genetic algorithms
Investment analysis
Libraries
Machine learning
Optimization algorithms
Popularity
Statistical analysis
Sustainability
Time series
Germany
Israel
ISSN:2071-1050, 2071-1050
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Shrnutí:Data-driven economic tasks have gained significant attention in economics, allowing researchers and policymakers to make better decisions and design efficient policies. Recently, with the advancement of machine learning (ML) and other artificial intelligence (AI) methods, researchers can now solve complex economic tasks with previously unseen performance and ease. However, to use such methods, one is required to have a non-trivial level of expertise in ML or AI, which currently is not standard knowledge in economics. In order to bridge this gap, automatic machine learning (AutoML) models have been developed, allowing non-experts to efficiently use advanced ML models with their data. Nonetheless, not all AutoML models are created equal in general, particularly for the unique properties associated with economic data. In this paper, we present a benchmarking study of biologically inspired and other AutoML techniques for economic tasks. We evaluate four different AutoML models alongside two baseline methods using a set of 50 diverse economic tasks. Our results show that biologically inspired AutoML models (slightly) outperformed non-biological AutoML in economic tasks, while all AutoML models outperformed the traditional methods. Based on our results, we conclude that biologically inspired AutoML has the potential to improve our economic understanding while shifting a large portion of the analysis burden from the economist to a computer.
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ISSN:2071-1050
2071-1050
DOI:10.3390/su151411232