Efficient Giving: Revolutionizing Donation Systems
With grants from the Ethereum Foundation and Keio University’s Faculty of Economics, we address the challenges in fundraising through innovative solutions. Drawing on the resources of ONGAESHI and the Fukuhara Masahiro Seminar at the Keio University Faculty of Economics, we are dedicated to reshaping donation methods to empower impactful change.
Discover an efficient donation system design supported by economic studies
What Problems Do Fundraising Projects Face?
Without any incentives, fundraising projects need to depend on altruistic motivation, which may result in lower donation amount compared to the potential amount. Fundraising projects also face the problem of transparency; donors need to trust the intermediaries. The lack of transparency in fundraising can lead to lower contribution level, if the potential donors cannot entrust the intermediaries with their donations.
Limited Donor Incentives
Lack of Transparency
Web3 Technology as a Solution
Crypto and Web3 technology provides powerful solutions to address these challenges, offering enhanced transparency and innovative incentive structures to drive engagement:
82x Donation
$2B+
Experience through simulation: the effect of incentive rewards in increasing donation
Adjust Incentive Value
Simulation Results
Try to collect enough fund for your project by giving incentive rewards!
What is incentive?
Let’s try adjusting the “incentive” slider to see how the donation amount change. We define “incentive” here as an additional monetary rewards that donors receive by donating. In reality, it is not easy to create a monetary reward. This reward can be created using the power of Non-Fungible Tokens to create a new assets out of invisible assets, as explained in our paper. ONGAESHI serves as a case study to implement NFT-based insentivisation mechanism.
What does the value of incentive mean?
The value of “incentive slider” increments a value to the ratio of expected return from donation to the cost of donation. By default, the value of expected return per cost is set to be 0.5, which means the donors expect to enjoy only half the value of what they donated, which is a common situation where free-rider problem arise. By setting the “incentive” slider value to 0.5, this will increase to 1.
What is going on inside the simulation?
This simulation generates 10 virtual agents, each representing a potential donor on the platform. These agents utilize a reinforcement learning algorithm to determine the optimal donation amount from their allocated budget of $100. The behavior of these agents adapts according to the “incentive” value you set. They are assumed to be rational, selfish, & risk-neutral in this simple simulation, but we have more sophisticated model here, which incorporates altruism and risk preferences.
Press "Run Simulation" To See the Results!
Economic studies have confirmed these results
Explore More
How to Improve Transparency and Build Trust with a Decentralized Approach
Explore ONGAESHI, a Case-Study for a Web3-Based Funding Businesses
- (1) The Giving Block, 2024 Annual Report: Crypto Philanthropy Data, Trends & Predictions.
- (2) Verified Market Research, Global Fundraising Market Size by Entity, End User, Method, Geographic Scope and Forecast, August 2024.