As a fundraising method, Initial Coin Offering (ICO) has raised billions of dollars for thousands of startups in the past two years. Existing ICO mechanisms place more emphasis on the short-term benefits of maximal fundraising while ignoring the problem of unbalanced token allocation, which negatively impacts subsequent fundraising and has bad effects on introducing new investors and resources. We propose a new ICO mechanism which uses the concept of Gini index for the very first time as a mechanism design constraint to control allocation inequality. Our mechanism maintains an elegant and straightforward structure. It allows the agents to modify their bids as a price discovery process, while limiting the bids of whales. We analyze the agents' equilibrium behaviors under our mechanism. Under natural technical assumptions, we show that most agents have simple dominant strategies and the equilibrium revenue approaches the optimal revenue asymptotically in the number of agents. We verify our mechanism using real ICO dataset we collected, and confirm that our mechanism performs well in terms of both allocation fairness and revenue.
[1]
David Cerezo Sánchez.
An Optimal ICO Mechanism
,
2017
.
[2]
Paul Dütting,et al.
Optimal auctions through deep learning
,
2017,
ICML.
[3]
Pingzhong Tang,et al.
Automated Mechanism Design via Neural Networks
,
2018,
AAMAS.
[4]
Makoto Yokoo,et al.
The effect of false-name bids in combinatorial auctions: new fraud in internet auctions
,
2004,
Games Econ. Behav..
[5]
Paul P. Momtaz.
Initial Coin Offerings
,
2020,
PloS one.
[6]
Abhinav Sinha,et al.
Mechanism design for fair allocation
,
2015,
2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton).
[7]
Vincent Conitzer,et al.
Complexity of Mechanism Design
,
2002,
UAI.
[8]
International Foundation for Autonomous Agents and MultiAgent Systems ( IFAAMAS )
,
2007
.
[9]
J. Goodman.
Note on Existence and Uniqueness of Equilibrium Points for Concave N-Person Games
,
1965
.