 Token economics represents the merging of economics and information technology. It shifts economics into a more technical realm. In the past, we could really just tweak around the edges, but now we can really design economies like we never could before. Once we shift business and economic organization into this more formal and technical realm, we can begin to bring very powerful mathematics and analytical tools to bear on what we are doing. One aspect of this is using the models from game theory to design these incentive systems. Game theory is the study of the strategic interaction between adaptive agents and the dynamics of cooperation and competition that emerge out of this. A much more recent extension of this is mechanism design. Mechanism design is a field in economics and game theory that takes an engineering approach to designing economic incentives toward desired objectives in strategic settings. Because it starts at the end of the game, then goes backward, it is also called reverse game theory. It has broad applications in the management of markets, auctions, voting procedures, and is of particular relevance to token economics. As an economic theory that seeks to determine the situations in which a particular strategy or mechanism will work efficiently, compared to situations in which the same strategy will not work as effectively, mechanism design theory allows economists to analyze and compare the way in which markets or institutions lead to certain outcomes because of their inherent incentive structures. With mechanism design, we are trying to design the system towards a certain desired equilibrium state. With this approach, we first think about what outcome we would like to see from the system. We can then build a set of rules that will hopefully lead to those optimal outcomes. Legal systems are a kind of mechanism, as they are a method for shaping human behavior. A particular set of laws is usually trying to shape a particular type of outcome through the imposition of a set of penalties, fines, rewards, or incentives such as tax breaks, etc. Of course, these existing systems are centralized in their design, but with token networks, we are looking for a mechanism design that does not depend upon a centralized authority specifying and enforcing the rules, but instead some kind of peer-to-peer value exchange mechanism that is self-regulating through direct information feedback loops. As previously mentioned, coordination within distributed systems, like token economies, is not achieved via centralized coordination, but instead by the interaction between members and the incentive structures created by the exchange of tokens. The primary dynamic for us to consider then is that of the feedback loops that are created out of people interacting peer-to-peer. Creating optimal outcomes for the whole system means effectively linking the payoffs of the individual to those of the whole system, and thus reducing negative externalities. Every action that an agent takes has an effect, and we can ask, what are the repercussions of those actions, and who bears the costs and benefits? When an actor gains from an action, but the costs are borne by others, this is a negative externality. Pollution is the classic example of a negative externality. So too excessive inequality may be seen as a negative externality. Negative externalities incentivize actors to overperform a given action, as they are not bearing the cost and leads to unsustainable results over time, as that cost is being borne by someone else, the whole system or environment. Building systems of cooperation in such a context means enabling ongoing interaction with identifiable others, with some knowledge of previous behavior, lists of reputations that are durable and searchable and accessible, feedback mechanisms, transparency, etc. The development of current web platforms is a good illustration of where we are going, as they often incorporate many of these design components. Sites like TripAdvisor and Yelp exist as standalone feedback platforms, while Amazon and eBay legitimize their product by allowing users to place feedback on their purchases. Feedback systems are used to rate and rank content on social media like Reddit and Facebook. All of the above have become an essential part of how we identify quality products and services that meet our needs. But while the Internet gives a voice to all, misinformation has become an accepted reality. Competitors may falsify reviews to discredit a product, while the review platforms themselves may modify or delete feedback that doesn't fit their agenda. The combination of blockchain tech and advanced analytics could take the possibility of bias and corruption out of current feedback systems with an end-to-end process designed to pick out quality feedback and then safeguard it. Reveign is one blockchain platform that works to secure feedback systems. All incoming reviews will have to pass an initial screening test with IBM's Watson AI platform analyzing emotional and unconstructive language. Users are rewarded with RVN tokens for submitting a review, while companies can use the token to purchase quality feedback direct from its customer base. And at the end of the journey, consumers have access to transparent, high-quality feedback to aid their decision-making. Uber is an example of mechanism design. Uber just adds the financial contract of paying someone to take you somewhere to a reputation feedback system. Uber adds reputation for both drivers and for riders, and adding reputation into the system actually significantly influences the way that people behave within that system. The goal is to shape the behavior of the participants, and adding that additional reputation can have a significant impact. But of course, with blockchain systems, this can all be tokenized, and because tokens can represent any form of value exchange, natural capital, social capital, cultural capital, industrial capital, etc., we can build in many different forms of feedback loops and different forms of mechanism design.