Advanced Game Theory for Blockchain Resource Mining

Resource mining hashes are moving and consist of multiple stakeholders, cross-playing roles, and an abundance of data that the average person would have no chance to sort through independently. The withdrawals on this platform are quick with extraordinary security. If you are looking for a reliable trading platform, you may visit Quantum Ai Trading – Official & UPDATED Site 2022

The best way to address this is through game theory — a science that can be applied to many fields, including finance and tech. In the below-mentioned portion, we will give you some advanced strategies on how blockchain resource mining games can be run optimally using game theory. Resource mining games are essentially mini-games with contract cards (the product) where players may win different in-game tokens by playing the game correctly based on which product the player receives.

Players participate in these resource-mining games by sending resources (e.g., Funds) to the blockchain contract in exchange for tokens (or a mixture of both). As you can see, there are many ways to play these games — some cooperative, some competitive. The purpose here will be to discuss the best strategies for playing these games and optimize your chances of winning.

Resource mining games allow users to earn tokens on multiple fronts. The more complex networked systems that these games are based on reward more players in the end. We will start with a simple example (dice game), as it provides an easy way to learn about game theory and its application in this field.

One-Attacker Mining Optimization:

In this section, we will determine the optimal strategy for an attacker to obtain the maximum gain by varying the mining and attack duration. In our example, we assume that 1% of the network participates in the gaming process.

It effectively means that the attacker can potentially “game” the system with a small number of miners if each miner performs their mining operation for a short duration. So, this particular attack may be considered an “optional attack” on a single miner through an intelligent contract with a small amount of computational power.

Using game theory to analyze two or more actors’ decisions, we can predict whether these actors, who are engaged in a given situation or interaction, are likely to behave in specific ways and take certain actions. Here, we will consider the attacker (miner) and the honest miner competing to gain the maximum possible profit. Finally, we will analyze the interaction between these two miners during mining (attacking) and determine who is more likely to win.

Game theory:

Game theory is a formal approach to modeling conflict and cooperation between intelligent, rational decision-makers. In this post, we used game theory to analyze interactions involving multiple parties in situations with a strategic component. These interactions include competition over limited resources, competition over the division of spoils from a jointly produced good, and strategic voting.

The non-cooperative games are also called zero-sum games because the total value one player takes equals the total sum available for all players.

Two-Player Mining Game:

In the two-player mining game, we will discuss the mining duration under the attack of the rational attacker and the mining duration without attack. We assume a miner has two action choices, i.e., no-attack (NA) and attack (A). If a miner NA, she keeps resources at the end of the game without any token. If she chooses A, her resources are converted into tokens, but if an attacker is present during their mining operations, they get nothing at the end of the game.

Condition 1: The rational number for miners to choose NA is more significant than 0. 5.

Condition 2: The rational number for miners to choose A is more significant than 0.5.

The attacker can earn the maximum gain by choosing A for the whole mining process (t) and choosing NA for a random period (t). This type of attack is optional, and the underlying assumption is that the attacker has limited computational power.

Hence, if the attacker chooses A for a large part of it, they will quickly become exposed to other honest miners working in their default or short timescale. When other honest miners mine on a large scale, the attacker’s mining operation will be quickly exposed by people. Hence, the attacker should choose A for a short period to avoid exposure.

Suppose that an attacker performs an optional attack on a miner who chooses NA (0 < p < 1) and follows her default mining setting (mean-mining). We assume that the honest miner is aware of the possibility of an optional attack and chooses to change their mining duration if they encounter another miner working with a short duration. In this scenario, we assume that the number of short-term miners increases linearly concerning time while all long-term miners follow their default setting.

The above equation implies that if an attacker creates a mining operation with a long duration, they will have to be exposed to all honest miners who are mining on an average timescale. And thus, the attacker will become quickly exposed when other miners begin working on a large scale. If this happens, not only the attacker’s resources are taken by other honest miners, but they are also entirely unable to mine. It is called the “prisoner’s dilemma,” and it is what happens in real life when you attack another person.