Prediction Markets have been used to forecast outcomes of research interest using market mechanisms (See https://www.nature.com/news/the-power-of-prediction-markets-1.20820). A decentralized prediction market, Auger, has been created on blockchain for betting purposes (See, https://www.augur.net/). An alternative approach to prediction market has been proposed in Dalal et al (https://www.sciencedirect.com/science/article/abs/pii/S0040162511000734). This project develops a new hybrid model for centralized and decentralized collaborative prediction market that can be used to elicit opinions of university researchers on socially important issues. Specifically, the project uses Django and Ethereum based platform to develop a smart contract and an ERC-20 compliant token for researchers to participate in the new market. The smart contract is being developed in Solidity and Javascript. The corresponding frontend and backend uses Django and python on AWS cloud. The project will require developing and experimenting with new innovative Automated Market Makers used in DeFi.

This project is eligible for a stipend, with matching funds from the faculty advisor and the Data Science Institute. This is not a guarantee of payment, and the total amount is subject to available funding.

Faculty Advisor

  • Professor: Siddhartha Dalal
  • Center/Lab: Applied Analytics/Statistics
  • Location: Lewisohn Hall, 502E
  • Professor Dalal has over 100 publications in Machine Learning/AI and Blockchain areas. This project is to examine mechanism for aggregating collaboratively experts' opinion using market mechanisms.

Project Timeline

  • Earliest starting date: 2/15/2023
  • End date: 8/15/2023
  • Number of hours per week of research expected during Spring-Summer 2023: ~10

Candidate requirements

  • Skill sets:

    • Strong interest in market based aggregation application on blockchain and centralized markets
    • Familiar with common algorithms and data structures
    • There are openings for 2 different roles- Pls let us know your interest. 1 . Exposure to full stack development (Python Django, Javascript, AWS Lambda)
    1. Or, exposure to solidity smart contracts, Truffle/Infura

  • Student eligibility: freshman, sophomore, junior, senior, master’s

  • International students on F1 or J1 visa: eligible

  • Academic Credit Possible: Yes

  • Additional comments: Nice-to-have - Exposure to privacy and security as well as the testing of these - Understanding of Blockchain, smart contracts and cryptocurrency methodology - Interest in market mechanisms like Automated Market Makers