Increased data gathering capacity, together with the spreading of data analytics techniques, has allowed an unprecedented concentration of information related to the individuals' preferences in the hands of a few gatekeepers. In such context, the traditional economic literature has been attempting to frame all the data-driven economy features. Such features, although being able to bring about a more efficient matching of people and relevant purchase opportunities, also result into distortions and disequilibria, up to market failures. Data-economy market disequilibria can be decrypted by leveraging on some of the known network properties, thus obtaining general results suitable for building a new theoretical framework for economic phenomena. Starting from the hypothesis that a digital company can always benefit from an underlying network of consumers or items related to its market, their representation can indeed provide significant competitive advantages, also enhancing the  platforms' capacity to implement discriminatory practices by means of an increased ability to estimate individuals' preferences. In the present paper, we propose a measure called  Information Patrimony, considering the amount of information available within the system and we look into how platforms may exploit data stemming from connected profiles within a network, with a view to obtaining competitive advantages. Such information flow may eventually allow to envisage the emergence of a new hybrid price discrimination pattern, through which platforms may influence and steer individuals' purchase choices, as well as to apply different prices to different customers .
Increased data gathering capacity, together with the spread of data analytics techniques, has prompted an unprecedented concentration of information related to the individuals' preferences in the hands of a few gatekeepers. In the present paper, we show how platforms' performances still appear astonishing in relation to some unexplored data and networks properties, capable to enhance the platforms' capacity to implement steering practices by means of an increased ability to estimate individuals' preferences. To this end, we rely on network science whose analytical tools allow data representations capable of highlighting relationships between subjects and/or items, extracting a great amount of information. We therefore propose a measure called Network Information Patrimony, considering the amount of information available within the system and we look into how platforms could exploit data stemming from connected profiles within a network, with a view to obtaining competitive advantages. Our measure takes into account the quality of the connections among nodes as the one of a hypothetical user in relation to its neighbourhood, detecting how users with a good neighbourhood -- hence of a superior connections set -- obtain better information. We tested our measures on Amazons' instances, obtaining evidence which confirm the relevance of information extracted from nodes' neighbourhood in order to steer targeted users .
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spreading
spread
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allowed
prompted
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such context, the traditional economic literature has been attempting to frame all the data-driven economy features. Such features, although being able to bring about a more efficient matching of people and relevant purchase opportunities, also result into distortions and disequilibria, up to market failures. Data-economy market disequilibria can be decrypted by leveraging on some of the known network properties, thus obtaining general results suitable for building a new theoretical framework for economic phenomena. Starting from the hypothesis that a digital company can always benefit from an underlying network of consumers or items related to its market, their representation can indeed provide significant competitive advantages, also enhancing
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present paper, we show how platforms' performances still appear astonishing in relation to some unexplored data and networks properties, capable to enhance the
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discriminatory
steering
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In the present paper, we
To this end, we rely on network science whose analytical tools allow data representations capable of highlighting relationships between subjects and/or items, extracting a great amount of information. We therefore
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Network
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may
could
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Such information flow may eventually allow to envisage the emergence of a new hybrid price discrimination pattern, through which platforms may influence and steer individuals' purchase choices, as well as to apply different prices to different customers
Our measure takes into account the quality of the connections among nodes as the one of a hypothetical user in relation to its neighbourhood, detecting how users with a good neighbourhood -- hence of a superior connections set -- obtain better information. We tested our measures on Amazons' instances, obtaining evidence which confirm the relevance of information extracted from nodes' neighbourhood in order to steer targeted users