> For the complete documentation index, see [llms.txt](https://cascadia.gitbook.io/gitbook/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://cascadia.gitbook.io/gitbook/artificial-intelligence/incentives.md). # Incentives An incentive is something that motivates someone to take action. It can be a tangible reward, such as money or a gift, or it can be an intangible reward, such as recognition or a sense of accomplishment. There are four main types of incentives: * **Purpose-driven incentives:** These incentives are based on the belief that people are motivated by a sense of purpose. They might include opportunities to work on a project that is meaningful to the individual or the community. * **Social incentives:** These incentives are based on the belief that people are motivated by the desire to connect with others. They might include opportunities to work with a team, to meet new people, or to build relationships. * **Status incentives:** These incentives are based on the belief that people are motivated by the desire for recognition or prestige. They might include awards, public recognition, or the opportunity to be a leader. * **Material incentives:** These incentives are based on the belief that people are motivated by the desire for financial or material rewards. They might include money, gifts, or other tangible benefits. The right incentives can be a powerful tool for engaging web3 communities: * **Match the incentive to the target audience.** Different people are motivated by different things. We want to choose incentives that are appealing to the target market that we're trying to reach. * **Make the incentives clear and easy to understand.** People need to know what they're working towards and how they can earn the incentive. * **Communicate the incentives effectively.** Make sure that people know about the incentives and how they can earn them. * **Track results of the incentive programs.** This will help you to see what's working and what's not. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://cascadia.gitbook.io/gitbook/artificial-intelligence/incentives.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.