Web3 Meets Behavioral Economics: An Example of Profitable Crypto Lottery Mechanism Design

TL;DR

This paper integrates behavioral economics, specifically cumulative prospect theory, into the design of incentive mechanisms for crypto lottery games to better align with human decision-making biases, enhancing engagement and profitability.

Contribution

It introduces a novel incentive mechanism design incorporating behavioral economics into crypto lotteries, addressing limitations of traditional models.

Findings

Designed four lottery mechanisms and compared their utility and profitability.

Mechanisms that account for human biases improve participant engagement.

The approach is applicable to various crypto services under uncertainty.

Abstract

We are often faced with the non-trivial task of designing incentive mechanisms in the era of Web3. As history has shown, many Web3 services failed mostly due to the lack of a rigorous incentive mechanism design based on token economics. However, traditional mechanism design, where there is an assumption that the users of services strategically make decisions so that their expected profits are maximized, often does not capture their real behavior well as it ignores humans' psychological bias in making decisions under uncertainty. In this paper, we propose an incentive mechanism design for crypto-enabled services using behavioral economics. Specifically, we take an example of a crypto lottery game in this work and incorporate a seminal work of cumulative prospect theory into its lottery game mechanism (or rule) design. We designed four mechanisms and compared them in terms of utility, a metric of how appealing a mechanism is to participants, and a game operator's expected profit. Our approach is generic and will be applicable to a wide range of crypto-based services where a decision has to be made under uncertainty.