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UID:www.tcs.tifr.res.in/event/1532
DTSTAMP:20250507T050439Z
SUMMARY:Fair distribution of MEV in blockchains through Shapley Value
DESCRIPTION:Speaker: Sujit Gujar (International Institute of Information Te
 chnology\, Hyderabad (IIITH))\n\nAbstract: \nIn blockchains\, by simply re
 arranging transactions or leveraging opportunities across multiple exchang
 es\, a miner can extract more value than just collecting block rewards and
  transaction fees. This additional value is known as Maximal Extractable 
 Value (MEV) (formerly referred to as Miner Extractable Value). However\,
  extracting MEV requires additional resources and has given rise—especia
 lly in blockchain markets like Ethereum—to entities called builders. A 
 builder collects publicly known transactions as well as transactions purch
 ased from wallet service providers and constructs blocks designed to maxim
 ize MEV extraction. Such extraction amounts to millions of dollars annuall
 y.\nThis gives rise to two key questions:\nDo builders receive fair compe
 nsation for their work?\nDo transaction creators receive any reward for th
 eir transactions generating MEV?\nIn this talk\, we show that a cooperati
 ve game theory approach is better suited to model this situation. For the
  first question\, we demonstrate that the Shapley value can enhance fair
 ness in the system\, and that a cooperative approach can lead to higher ME
 V extraction than the traditional competitive methods. While computing the
  Shapley value is generally computationally challenging\, we derive a clo
 sed-form solution that can be computed in polynomial time.\nFor the seco
 nd question\, we model a separate cooperative game based on the revenue ge
 nerated\, involving wallet service providers\, and transaction creators. I
 f builders' valuations are additive\, one can easily deduce that the Shap
 ley values of the transactions. However\, we conjecture that if valuatio
 ns are single-minded\, then computing the Shapley value becomes a SUBEXP
  problem. To address this\, we present a simple sampling algorithm wit
 h PAC (Probably Approximately Correct) guarantees for approximating Shapl
 ey values.\n \nShort Bio:\nCurrently\, Dr. Sujit Gujar is working as an A
 ssociate Professor at the International Institute of Information Technolog
 y\, Hyderabad (IIITH).  He holds the CA Technologies Faculty Chair positi
 on at IIITH. His expertise is in Game Theory\, Blockchains and distributed
  AI. He has co-authored 100+ international peer-reviewed publications and 
 has filed 15+ patents in US. Prior to IIITH\, he was a post-doctoral resea
 rcher with Prof Boi Faltings\, LIA\, EPFL\, Lausanne (Jan'14-Oct'15)\, and
  a Sr Research Associate with Prof Y Narahari (Nov'15-Apr'16).  He also h
 as an expereience as a Research Scientist at Xerox Research Centre India (
 Jan'11-Nov'13).   He completed his Ph.D. in the Department of Computer Sc
 ience and Automation @ the Indian Institute of Science\, Bangalore. He wor
 ked with Prof Y Narahari\, in the Game Theory Lab. He is a recipient of th
 e Alumni Medal of IISc for the Best Thesis in CSA for the academic year 20
 11-12' for his Ph.D. Dissertation.\n
URL:https://www.tcs.tifr.res.in/web/events/1532
DTSTART;TZID=Asia/Kolkata:20250513T160000
DTEND;TZID=Asia/Kolkata:20250513T170000
LOCATION:A-201 (STCS Seminar Room)
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