One of the most classic examples of Game Theory is the Prisoner’s Dilemma, which illustrates the conflict between individual rationality and collective rationality.

In this article, we will explore what game theory acn teach us about AI adoption and how the dominant strategy may lead to a race to the bottom.

The Prisoner’s Dilemma

The Prisoner’s Dilemma involves two prisoners suspected of committing a crime together. They are interrogated in separate rooms and cannot communicate with each other. Each prisoner has two options: to remain silent or confess. The potential outcomes are as follows:

Dominant Strategy and Suboptimal Results

In this context, the dominant strategy for each prisoner is to confess. This is because:

• If Prisoner B remains silent, Prisoner A gets a better outcome by confessing - going free instead of 3 year in prison.
• If Prisoner B confesses, Prisoner A gets a better outcome by confessing - 3 years in prison instead of 5 years in prison.

However, when both prisoners follow their dominant strategy and confess, they end up with a worse collective outcome of 3 years each, compared to if they had both cooperated, receiving only 1 year each.

This demonstrates how rational individual decisions can lead to suboptimal results for the group.

Impact of Dominant Strategy in AI

The concept of the dominant strategy can be applied to the context of AI adoption in a competitive landscape.

For example, Company A loosens its moderation strategy to attract more users to its platform and it improves its user adoption. Consequently, Company B, a competitor, also loosens its moderation strategy to avoid losing market share. Both companies now have less strict moderation, leading to a suboptimal outcome with both platforms containing potentially biased, inappropriate, and harmful content, diminishing value for their end users.

In another example, Company A uses copyrighted content without respecting intellectual property to enhance its model and it improves engagement with its client base. Company B follows suit to avoid falling behind. The result of these strategies is an environment where there is no respect for intellectual property, which may lead to a loss of trust for both companies.

Mitigating Challenges

To address the challenges arising from the dominant strategy in AI adoption, industries must consider an iterative strategy where organizations collaborate with each other. For example, industry-wide collaboration can help establish standards and best practices for AI moderation and content use. Regulatory engagement is also crucial, and organizations must work closely with regulators to share policies that balance innovation with public good.

By recognizing the suboptimal outcomes of dominant strategies and taking proactive steps to address these challenges, organizations can ensure that AI adoption benefits not just themselves but the broader customer community and industry as well.

Conclusion

The lessons from game theory, particularly the Prisoner’s Dilemma, highlight the potential pitfalls of pursuing dominant strategies in AI adoption. While individual rationality may drive companies to make decisions that seem beneficial in the short term, collective rationality and collaborative efforts can help mitigate the negative impacts and ensure more sustainable and ethical AI development.

This article is inspired by a course in the Large Language Models Operations (LLMOps) Specialization from Duke University.