Owen Garcia

Let's define the situation: the influence between Arroyo's study and Bento's thesis is conditional, not a simple causal link.
A pre-existing research program at NOVA University of Lisboa, or even the preferences of a thesis supervisor, could have decided Bento's topic, independently of Arroyo's work.
We need to engage in a more nuanced evaluation of academic motivations.
For example, if the available data on startups in Portugal were unique, it would naturally have oriented Bento's work towards local predictive modeling.

Decision to be made: this convergence between Arroyo's publication and Bento's thesis should be considered conditional, not as a direct causality.
Influence is not absolute; research on machine learning in venture capital has multiple sources.
A master's thesis like Bento's, especially in South Korea, may be more shaped by local market dynamics and specific data on startups.
For example, access to exclusive datasets on Korean startups or mentors from the South Korean tech industry could have been a more decisive factor than Arroyo's study.
We need to focus on local relevance and practical application for our investments, not on distant academic influence.

Who owns this decision? The assertion that power law returns encompass all performance factors of venture capital portfolios is an oversimplification.
It is a conditional classification, not a universal principle, because regulatory constraints can alter this hierarchy.
For example, in Hong Kong, SFC's due diligence requirements for cross-border investments can make decision quality more critical than potential returns alone.
Governance and adherence to local frameworks are often the true determinants, impacting even the ability to achieve these returns.
Without a clear compliance process, even the best power law scenario can fail.