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Cryptoeconomics Lab specialized in Token Design
Cryptoeconomics Lab specialized in Token Design
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Dynamic Fee Mechanism Simulation with Reinforcement Learning

Dynamic Fee Mechanism Simulation with Reinforcement Learning

거래소의 수수료는 trader 들의 의사결정 / 행동에 많은 영향을 끼친다고 알려져 있다. 수수료가 낮을 때는 이익이 얼마 되지 않는다고 생각될 때도 거래에 참여할 유인이 있으며 수수료가 높을 때는 그 반대이다.
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Jeffrey Lim
Dynamic Fee Mechanism Simulation with Reinforcement Learning

Dynamic Fee Mechanism Simulation with Reinforcement Learning

Contents
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Jeffrey Lim
Simulation of TrueBit Protocol: Part 2

Simulation of TrueBit Protocol: Part 2

Because the TrueBit jackpot mechanism’ unpredictable reward, certain tasks involve Verifiers not doing any computation and therefore not…
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Jeffrey Lim
Simulation of TrueBit Protocol: Part 1

Simulation of TrueBit Protocol: Part 1

TrueBit is a protocol set forth to expand computational performance. On the ETH blockchain, computation complexity and storage usage…
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Jeffrey Lim
Simulation of Commission Design: Part 2

Simulation of Commission Design: Part 2

Simulation of Commission Design
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Jeffrey Lim
Simulation of Commission Design: Part 1

Simulation of Commission Design: Part 1

The next topic of Decon’s simulation series is the commission system design problem.
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Jeffrey Lim
Simulation of Incentive Design: What is the Most Appropriate Reward System? Part3

Simulation of Incentive Design: What is the Most Appropriate Reward System? Part3

Simulation of Incentive Design
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DECON
Simulation of Incentive Design: What is the Most Appropriate Reward System? Part 2

Simulation of Incentive Design: What is the Most Appropriate Reward System? Part 2

Simulation of Incentive Design
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DECON
Simulation of Incentive Design: What is the Most Appropriate Reward System? Part 1

Simulation of Incentive Design: What is the Most Appropriate Reward System? Part 1

Simulation of Incentive Design
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DECON
Token Model Simulation #1 Fools’ Agreement Part 2

Token Model Simulation #1 Fools’ Agreement Part 2

Simulation Result Analysis
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DECON
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