Sustainability of Cultural Heritage-Related Projects: Use of Socio-Economic Indicators in Latvia

Kairišs, Andris; Geipele, Ineta; Oļevska, Irina

doi:10.3390/su151310109

Cited by 1 publication

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nonetheless, it is particularly important in economics, as a wide range of economic tasks require the understanding and modeling of numerical patterns, a challenge in which ML models typically excel [24][25][26]. In particular, sustainability projects require careful and accurate analysis and planning, with a large number of parameters and processes to execute correctly [27,28]. To bridge this gap, automatic machine learning (AutoML) solutions have been developed, which allow non-experts to use advanced ML models on their data with ease [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Benchmarking Biologically-Inspired Automatic Machine Learning for Economic Tasks

2023

View full text Add to dashboard Cite

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.

show abstract