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Benchmarking Biologically-Inspired Automatic Machine Learning for Economic Tasks

Author

Listed:
  • 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
    Sacklar Faculty of Medicine, Tel-Aviv University, Tel-Aviv 6997801, Israel
    Department of Management, Bar-Ilan University, Ramat-Gan 529002, Israel)

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.

Suggested Citation

  • Teddy Lazebnik & Tzach Fleischer & Amit Yaniv-Rosenfeld, 2023. "Benchmarking Biologically-Inspired Automatic Machine Learning for Economic Tasks," Sustainability, MDPI, vol. 15(14), pages 1-9, July.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:11232-:d:1197230
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    1. Teddy Lazebnik & Dan Gorlitsky, 2023. "Can We Mathematically Spot the Possible Manipulation of Results in Research Manuscripts Using Benford’s Law?," Data, MDPI, vol. 8(11), pages 1-11, October.

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