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k-Means Clusterization and Machine Learning Prediction of European Most Cited Scientific Publications
Leogrande, Angelo; Costantiello, Alberto; Laureti, Lucio - 2022
In this article we investigate the determinants of the European “Most Cited Publications”. We use data from the European Innovation Scoreboard-EIS of the European Commission for the period 2010-2019. Data are analyzed with Panel Data with Fixed Effects, Panel Data with Random Effects, WLS,...
Persistent link: https://www.econbiz.de/10014078245
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How Soon is Now? Predicting the Expected Arrival Date of AGI- Artificial General Intelligence
Macey-Dare, Rupert - 2023
This paper uses some economic modelling techniques to predict the expected arrival date of AGI- Artificial General Intelligence. The average predicted date from this analysis is 2041, with a likely range of 2032 to 2048, and an estimated earliest possible arrival date of 2028, i.e. just 5 years...
Persistent link: https://www.econbiz.de/10014346946
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Predicting Startup Survival Using the Normalized Burn Rate
Berman, Ron - 2017
(proxies for precise knowledge) lead entrepreneurs to spend closer to the optimum. These results suggest that spending per …
Persistent link: https://www.econbiz.de/10012949653
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Predicting the Leadership Mindset of Wildland Firefighters
Gapud, Stephanie; Hair, Joseph F. - 2021
Leadership behaviors of wildland firefighting teams engaged in firefighting activities are “shared” despite the existence of a highly bureaucratic and hierarchical organizational structure. This paradoxical type of leadership appears to be based on individual perceptions of the effectiveness...
Persistent link: https://www.econbiz.de/10013226275
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Technological Interdependencies Predict Innovation Dynamics
Pichler, Anton; Lafond, Francois; Farmer, J. Doyne - 2020
We propose a simple model where the innovation rate of a technological domain depends on the innovation rate of the technological domains it relies on. Using data on US patents from 1836 to 2017, we make out-of-sample predictions and fond that the predictability of innovation rates can be...
Persistent link: https://www.econbiz.de/10014101485
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Can unbiased predictive AI amplify bias?
Khan, Tanvir Ahmed - 2023
Predictive AI is increasingly used to guide decisions on agents. I show that even a bias-neutral predictive AI can potentially amplify exogenous (human) bias in settings where the predictive AI represents a cost-adjusted precision gain to unbiased predictions, and the final judgments are made by...
Persistent link: https://www.econbiz.de/10014452605
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The bright but right view? : a new type of evidence on entrepreneurial optimism
Bengtsson, Ola; Ekeblom, Daniel - 2014
Existing empirical evidence suggests that entrepreneurs are optimists, a finding researchers often interpret as … bias or not. We show that entrepreneurs have more favorable beliefs about nationwide conditions and then that these … entrepreneurs’ beliefs are relatively good predictors of the future. We conclude from these two findings that entrepreneurs are less …
Persistent link: https://www.econbiz.de/10010247380
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Usage of neural-based predictive modeling and IIoT in wind energy applications
Buturache, Adrian-Nicolae; Stancu, Stelian - In: Amfiteatru economic : an economic and business research … 23 (2021) 57, pp. 412-428
The adoption of wind energy has grown significantly in recent years. New, cost-effective technologies have been developed, led by customer awareness of green technologies and a legal framework proposed at the European Union level. The stochastic nature of wind speed is transferred to wind...
Persistent link: https://www.econbiz.de/10012939091
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Human Resources in Europe. Estimation, Clusterization, Machine Learning and Prediction
Leogrande, Angelo; Costantiello, Alberto - 2021
-school entrepreneurial education and training”, “Employment MHT manufacturing KIS services”, “Employment share Manufacturing (SD)”, “Lifelong …
Persistent link: https://www.econbiz.de/10013211273
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Predictability of Financial Markets in ASEAN Countries using Machine Learning Techniques
Jayasuriya, Dulani - 2019
This paper develops several efficient machine learning models and compare their performance in forecasting the value and direction of stock prices and indices from the ASEAN countries. Although all models adequately forecast the stock indices ranging from 40% to 95% accuracy and outperform...
Persistent link: https://www.econbiz.de/10012894650