Introduction: Designing Economies for Humans, Not Perfect Models

In Web3, many of the tokenized economies are built under an assumption that is as widespread as it is dangerous: that users will act rationally and stably if incentives are well aligned. But the recent history of DeFi, DAOs, and play-to-earn games shows that agents don't just think, they also learn, copy, flee, adapt, and evolve.

In this article, we explore a different approach: applying evolutionary game theory (EGT) to the design of Web3 ecosystems. A lens that allows us to see not only what equilibrium might be reached, but also how we get there, and whether it can be sustained over time.

Bounded Rationality and Adaptation: Why Classical Theory Isn't Enough

Traditional game theory assumes agents with perfect information and optimal decisions. Under this framework, tokens, voting protocols, and liquidity structures are designed with the expectation that everyone will behave as "ideal players." However, this rarely happens in practice.

Evolutionary theory, on the other hand, assumes that users imitate others, change strategies if they see better results, and learn over time. Initially minority strategies—such as leaving a DAO or migrating capital into DeFi—can become dominant if they offer a larger payoff, even if that destroys the system in the long run.

Token Abandonment and Collective Trust

A token doesn't fail because of its code, but because its users lose trust. Models like Terra/Luna collapsed in 2022 when users began withdrawing capital for fear of losing value. What seemed like a stable equilibrium (everyone stays because of the high APY) turned into a massive outflow driven by collective learning and protection strategies. The EGT predicts: the "first-to-exit" strategy can spread like a virus, bringing down the system.

Governance in DAOs: From Democratic Ideal to Evolutionary Plutocracy

DAOs imagine decentralized communities voting with tokens. But practice shows otherwise: low participation, vote concentration, and mass token buying attacks. Why? Because for many small voters, participating isn't worth it. If the cost of voting exceeds the marginal benefit, the "abstain" strategy becomes dominant.

In contrast, large holders have clear incentives to accumulate more power. Over time, these strategies evolve into a plutocracy that perpetuates their control, unless the design accommodates mechanisms like quadratic voting, non-transferable reputation, or rewards for active participation.

Mercenary Capital and Liquidity Cycles in DeFi

In the DeFi summer of 2020, hundreds of protocols grew rapidly, offering high yields. But that liquidity disappeared just as quickly when APYs dropped. Why? Because liquidity providers (LPs) learned that entering, farming, and exiting was more profitable than staying. The result: cycles of entry and exit, with no real stability.

The EGT offers tools to model these dynamics: if a strategy like continuous migration offers a higher return, it will eventually dominate. That's why solutions like Protocol-Owned Liquidity or retention rewards are now being sought, which change the game to favor long-term cooperation.

Blockchain Games and the "Farm and Leave" Economy

Play-to-earn games like Axie Infinity attracted millions of users who played for real-world revenue. But when the reward tokens lost value, those players became mass sellers. The problem? The systems were designed to reward retention, but not to punish churn.

According to the EGT, this was predictable: players maximize their profit at any given time. If everyone learns that it's best to sell quickly, they will. That's why today we're experimenting with systems that gamify permanence or limit inflationary issuance.

Conclusion: A new engineering for sustainable economies

Designing Web3 ecosystems isn't just about writing smart contracts. It's about predicting how a community will evolve. Evolutionary game theory makes it possible to anticipate deviations, test the real stability of models, and avoid fragile equilibria that collapse at the first change.

Web3 needs to think not just about static incentives, but about populations that learn, imitate, and change. Only then will we build tokenized economies that not only work in theory, but also stand the test of time.