Economic Crisis Policy Analytics Based on Artificial Intelligence
| Autor: | Manolis Maragoudakis, Euripidis N. Loukis, Niki Kyriakou |
|---|---|
| Přispěvatelé: | Department of Information & Communication Systems Engineering [Grece], University of the Aegean, Ida Lindgren, Marijn Janssen, Habin Lee, Andrea Polini, Manuel Pedro Rodríguez Bolívar, Hans Jochen Scholl, Efthimios Tambouris, TC 8, WG 8.5 |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Business information
050101 languages & linguistics Government Artificial intelligence business.industry [SHS.INFO]Humanities and Social Sciences/Library and information sciences 05 social sciences Big data Policy informatics Public policy 02 engineering and technology Private sector Policy analytics Crisis Analytics Feature selection 0202 electrical engineering electronic engineering information engineering Revenue 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences [INFO]Computer Science [cs] Business Dynamism |
| Zdroj: | Lecture Notes in Computer Science 18th International Conference on Electronic Government (EGOV) 18th International Conference on Electronic Government (EGOV), Sep 2019, San Benedetto del Tronto, Italy. pp.262-275, ⟨10.1007/978-3-030-27325-5_20⟩ Lecture Notes in Computer Science ISBN: 9783030273248 EGOV |
| DOI: | 10.1007/978-3-030-27325-5_20⟩ |
| Popis: | Part 4: AI, Data Analytics and Automated Decision Making; International audience; An important trend in the area of digital government is its expansion beyond the support of internal processes and operations, as well as transactions and consultations with citizens and firms, which were the main objectives of its first generations, towards the support of higher-level functions of government agencies, with main emphasis on public policy making. This gives rise to the gradual development of policy analytics. Another important trend in the area of digital government is the increasing exploitation of artificial intelligence techniques by government agencies, mainly for the automation, support and enhancement of operational tasks and lower-level decision making, but only to a very limited extent for the support of higher-level functions, and especially policy making. Our paper contributes towards the advancement and the combination of these two important trends: it proposes a policy analytics methodology for the exploitation of existing public and private sector data, using a big data oriented artificial intelligence technique, feature selection, in order to support policy making concerning one of the most serious problems that governments face, the economic crises. In particular, we present a methodology for exploiting existing data of taxation authorities, statistical agencies, and also of private sector business information and consulting firms, in order to identify characteristics of a firm (e.g. with respect to strategic directions, resources, capabilities, practices, etc.) as well as its external environment (e.g. with respect to competition, dynamism, etc.) that affect (positively or negatively) its resilience to the crisis with respect to sales revenue; for this purpose an advanced artificial intelligence feature selection algorithm, the Boruta ‘all-relevant’ variables identification one, is used. Furthermore, an application of the proposed economic crisis policy analytics methodology is presented, which provides a first validation of the usefulness of our methodology. |
| Databáze: | OpenAIRE |
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