Benchmarking Biologically-Inspired Automatic Machine Learning for Economic Tasks

Teddy Lazebnik (), Tzach Fleischer and Amit Yaniv-Rosenfeld
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Teddy Lazebnik: Department of Cancer Biology, Cancer Institute, University College London, London WC1E 6BT, UK
Tzach Fleischer: Department of Computer Science, Holon Institute of Technology, Holon 5810201, Israel
Amit Yaniv-Rosenfeld: Shalvata Mental Health Care Center, Hod Hasharon 45100, Israel

Sustainability, 2023, vol. 15, issue 14, 1-9

Abstract: 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.

Keywords: automatic machine learning; economy–biology interactions; data-driven economic tasks; evolutionary computation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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