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Esteve Almirall

Publications and source records attributed to Esteve Almirall.

2 recordsLinked to original sources

A few misfits can Change the World

Rising inequality is a critical concern for societies worldwide, to the extent that emerging high-growth economies such as China have identified common prosperity as a central goal. However, the mechanisms by which digital disruptions contribute to inequality and the efficacy of existing remedies such as taxation, must be better understood. This is particularly true for the implications of the complex process of technological adoption that requires extensive social validation beyond weak ties and, how to trigger it in the hyperconnected world of the 21st century. This study aims to shed light on the implications of market evolutionary mechanism from the lenses of technological adoption as a social process. Our findings underscore the pivotal importance of connectivity in this process while also revealing the limited effectiveness of taxation as a counterbalance for inequality. Our research reveals that widespread cultural change is not a prerequisite for technological disruption. The injection of a small cohort of entrepreneurs - a few misfits - can expedite technology adoption even in conservative, moderately connected societies and, change the world.

cs.SI

The use of Synthetic Data to solve the scalability and data availability problems in Smart City Digital Twins

The A.I. disruption and the need to compete on innovation are impacting cities that have an increasing necessity to become innovation hotspots. However, without proven solutions, experimentation, often unsuccessful, is needed. But experimentation in cities has many undesirable effects not only for its citizens but also reputational if unsuccessful. Digital Twins, so popular in other areas, seem like a promising way to expand experimentation proposals but in simulated environments, translating only the half-baked ones, the ones with higher probability of success, to real environments and therefore minimizing risks. However, Digital Twins are data intensive and need highly localized data, making them difficult to scale, particularly to small cities, and with the high cost associated to data collection. We present an alternative based on synthetic data that given some conditions, quite common in Smart Cities, can solve these two problems together with a proof-of-concept based on NO2 pollution.

cs.AI