[Comment from Dr. Alfred Taudes]
It was a great pleasure for me to present the research done at the Research Institute for Cryptoeconomics at WU – Vienna University of Economics and Business. Cryptoeconomics is a new research field created by Satoshi Nakamato, the inventor of Bitcoin. It blends cryptography with game theory and is based on the work of famous Austrian researchers like Kurt Gödel, Oskar Morgenstern and August von Hayek. To illustrate the principles of Cryptoeconomics we present the way how in which liveness, non-revision and consistency is achieved in Bitcoin. Here, cryptography is used as proof of work and proof of ownership. The combination of mining and the incentive to get new bitcoin and transaction fees for the successful miner ensures that the equilibrium strategy of a miner is to honestly mine based on the longest chain. This also holds if more than 50% of the computing power of the network is controlled by a single entity and a double spend attack could be successful. In the second part of the talk, I apply this principle to design blockchain-based federated learning systems. In such systems, the data is stored at the local devices and the nodes of the network exchange parameters to improve their local models. To combat fraudulent behavior nodes must pay tokens as collateral to participate and receive a reward when the other nodes evaluate their parameters favorably. We combine hashing, a slot system and a reward mechanism based on the evaluations stored on-chain to make honest contribution to the distributed AI task the equilibrium strategy.

[Japanese translation]
In this talk, we are very happy to present the research we carried out at the Research Institute for Cryptoeconomics at the WU – Vienna University of Economics and Business. Cryptoeconomics is a new research field founded by Bitcoin inventor Satoshi Nakamoto. This field combines cryptography and game theory and is closely linked to the work of such famous Austrian researchers as mathematician and mathematical logician Kurt Gödel, game theoretic economist Oskar Morgenstern and liberal economist August von Hayek. In the first part of this talk, we will illustrate the principles of cryptoeconomics by using examples of how liveness, immutability and consistency are achieved in Bitcoin. Here, cryptography is used as "proof of work" and "proof of ownership". Proof of Work (PoW) is a distributed consensus mechanism used in blockchain technology, and the reliability of the blockchain is ensured by a method called mining. Proof of Ownership is a way to prove who holds Bitcoin assets. The combination of the method of consuming computational resources called mining and the incentive to obtain new Bitcoins and transaction fees makes it a Nash equilibrium strategy for successful miners to mine honestly based on the longest chain. This means that even if more than 51% of the computational resources of the entire network are controlled under a single entity, or even if a double-spend attack is likely to be successful, it is still a Nash equilibrium strategy to mine honestly. In the second part of the lecture, we will explain how to apply this principle to design a distributed federated learning system using blockchain. Federated learning means that independent nodes jointly train machine learning models without sharing data. However, traditional methods that rely on a central server have reliability and security issues. In such a system, data is stored on local devices, and the nodes of the network exchange parameters to improve the local learning model. To combat network cheating, each node must pay a token as collateral to participate in learning, and is rewarded if other nodes rate its parameters favorably. The combination of hashing, a slot system, and a reward mechanism based on ratings stored on-chain, combined with honest behavior of individual nodes on distributed AI tasks, results in a Nash equilibrium strategy in a federated learning system.