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<p style="margin-top:0; margin-bottom:0"><span style="font-size:11pt; font-family:Calibri,sans-serif,serif,"EmojiFont""><a href="mailto:Turing-Southampton@ecs.soton.ac.uk" target="_blank" rel="noopener noreferrer" id="LPlnk780281" class="OWAAutoLink" previewremoved="true"></a></span>Dear
all, <br>
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<p style="margin-top:0; margin-bottom:0">We (from Law and Management) are looking for a Computer Science co-investigator for the following project. We are not entirely sure what sort of expertise may be needed but know that current 'Merger Simulation Models'
would need reviewing. We are also hoping for the possibility of exploring other data-driven avenues for predicting predatory corporate misbehaviour (market abuse by dominant players). Internal deadline for Pilot Project submission: 14 June</p>
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<p style="margin-top:0; margin-bottom:0">This is the abstract: <br>
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<p><span style="font-family:"Calibri","sans-serif"; color:black"><b>Online Monopoly Power: Market Abuse Predictions in Merger Inquiries in the Networked Sector</b><br>
</span></p>
<p><span style="font-family:"Calibri","sans-serif"; color:black"> </span></p>
<p><span style="font-family:"Calibri","sans-serif"; color:black">The growth and innovation of the data sector depends on an open access market that facilitates a diverse industry with robust competition, including from innovative start-ups. Yet, for online
services the market place has rapidly converged towards monopoly players (Big Five)... These firms control the infrastructure of and for network services to such an extent that they can detect, with the help of data analytics, possible competitors early on
and surpress or absorb them. The European Commission has facilitated that problematic convergance by allowing mergers in already highly concentrated fields, amongst others based on predictions of unlikely 'harms' that, in some cases, appear to be misjudged
within a short amount of time e.g. Google/DoubleClick or Facebook/Whatsapp. Reversing these decisions is significantly harder than not making them in the first place. This project seeks to bring data anlytics/computer science to merger inquiries and competition
law, with the view to making merger decisions and market abuse predictions more robust. This pilot project is scalable as it explores the possibility of using data analytics in the implementation of law where that implementation relies on predictions of uncertain
future behaviour of actors within a certain field. </span></p>
<p><span style="font-family:"Calibri","sans-serif"; color:black"> </span></p>
<p><span style="font-family:"Calibri","sans-serif"; color:black">and this is where I think we would fit in with the Turing objectives:
<a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.turing.ac.uk%2Fresearch%2Fchallenges%2Fchallenge-shine-light-our-economy&data=01%7C01%7C%7C727a7f41c1974b98b3c708d6e97a21e9%7C4a5378f929f44d3ebe89669d03ada9d8%7C0&sdata=rpUQywUMGnPYLl4MKE%2B%2BfQ8GF8m7t7sFJrvN1CtwP9M%3D&reserved=0" originalsrc="https://www.turing.ac.uk/research/challenges/challenge-shine-light-our-economy" shash="tXGGulTQGcBMRe4Xw0aub6B5ObgQNoxSZLlk6eMF9hSXCNIaSShmGxB75cAsgtMU1/Jmbs9ULmIrRT90c/YPwC+7/tYcZGv3o+HJgw+OYQxWVs+H9lrGi/Ne7K1ZlN9JY4OX7VGgU6AULqGI0NeKVfgx+lFlvZBGcqE46gQdAQI=" target="_blank" id="LPlnk871070" previewremoved="true">
https://www.turing.ac.uk/research/challenges/challenge-shine-light-our-economy</a>
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<div><font size="2" face="Arial">Uta Kohl</font></div>
<div><font size="2" face="Arial">Professor of Commercial Law</font></div>
<div><font size="2" face="Arial">Southampton Law School </font><br>
<font size="2" face="Arial">University of Southampton </font></div>
<div><font size="2" face="Arial">Highfield, Building 4</font><br>
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<div><font size="2" face="Arial">Southampton SO17 1BJ </font><font size="2" face="Arial"><br>
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<div><font size="2" face="Arial">Tel: + 44 23 8059 2444</font></div>
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