This document poses a conversational structure as an alternative solution to social coordination. We expect the mechanism proposed here to be more efficient, effective, and humane than money or every other political system humanity tried out yet.
The network helps to coordinate who does what so that people get what they want at a lower cost, or at a better quality.
Coordination markets achieve a high quality of service by standardizing the expression of wants and aversions, essentially outsourcing market analysis and offer selection to professional market makers with just the final purchasing decision being signed by the user.
The conversation is built so that economic participants negotiate social coordination directly, in terms of what you do and don’t do. From that, a new paradigm of coordination markets emerges.
Traditional monetary economies base all economic activity on pairwise transactions, where a good or service flows from the seller to the buyer, with a symmetrical amount of money flowing from the buyer to the seller
In contrast, Ask Network accounts for future economic activity as conditional actions (“I do this if I observe that”), and past economic activity is based on observations of the outer world. From that, onchain reasoning quantifies expected (future) and actual (past) changes to reality caused by the accounted economic activity. Further, these changes can be evaluated against users’ Asks (wants or aversions) to quantify whether the economic activity has helped.
Negotiations in coordination markets are based on the idea of a match, essentially complementary statements of “I do this if you do that” with all participants accepting the plan.
The network provides an auction mechanism to negotiate matches, accounting of open interest from accepted matches, verification of physical delivery, and according settlement in the virtual.
Matchmaking and counterparty-discovery is facilitated by market makers who search for and propose possibly valuable matches, which are then automatically filtered, prioritized, and annotated based on how the users’ Asks (wants or aversions) are expectedly satisfied by it before affected users make the final call of committing to them or not.
Asks and service offerings are defined in terms of causal-semantic models of reality which enable onchain reasoning about effects of different possible action paths, and whether these effects are desired or not.
To ensure all desired economic activity can be accounted for regardless how everyday life changes over the next centuries, the network is invariant to how users view the world and what values they pursue in that world. That also makes it interoperable with existing coordination mechanisms like companies, money, laws, political institutions, and text messages.
It utilizes a data model that can express every possible representation of the world, with the state transition function being reality itself.
The rest of this document walks through the design of the network with the most fundamental parts first, towards more complex structures.
Data is our window into reality. All data in the network is modeled as messages. Messages can occur in the virtual as network traffic between computers, and in the actual as causal effects between physical systems. As cultural capital, messages are captured and preserved by default.
Software can only capture virtual messages as digital data and does not have direct access to causal effects in reality. To capture real world data, hardware sensors must be utilized. We assume that all real world sensors are communicating their measurements as virtual messages via networking protocols. Thus, every node records network protocol sessions only, with interpretations of reality being applied after consensus.
That message history is used to derive reproducible representations of the actual and virtual external world. Such reproducible representations are specified as domain models that take observations as input, and derive conclusions about the past, present, and future external world, subject to explicit assumptions about causal relationships.