Thin Market Design Model

The following diagram depicts the current process framework for a Cosolvent-based system. If it looks complicated, that’s because the task of creating a viable business linkage between arms length buyers, sellers and service providers in a thin market is a difficult task. If it were simple the market wouldn’t be thin.

In Cosolvent’s conceptual model, all real-world transactions require the creation of trust. The problem in thin markets is that the parties probably don’t know one another or don’t deal very often. That makes trust elusive. Deals won’t happen unless a) there is an unusually strong incentive for parties to reach out and engage, or b) some mechanism makes it much easier to find and understand the potential counterparty. Any mechanism like that must achieve three stages of trust building:

Trust to Explore – Most people won’t “waste time” on something even as simple as an internet search unless they believe there is a chance it might succeed. In thin markets, the odds are generally low, so reaching out seldom occurs. If we hope to overcome this resistance in any significant way, we have to make potential market participants believe that the odds of success have shifted in their favor and we have to make the task of discovery much easier to pursue.
Trust to Engage – If we can entice potential market participants to explore thin market opportunities, the next step is to convince them that it is safe for them to engage with other participants. We are hoping to tap into AI to help create a “safe zone” with rules that protect the parties.
Trust to Accept – Finally, if the parties have worked their way towards an agreement in principle, there is the final gut-check step of making a commitment that could cost real time, materials, effort and money.

To achieve these trust stages, Cosolvent plans to build a supporting toolkit that operates in four supportive stages:

The Discover Phase – Cosolvent is betting that there are ways to use emerging AI technologies to make this task easier. In particular, Cosolvent wants to make it much easier for a participant to safely share detailed information about itself that could make a counter party less anxious. It then wants to use AI tech to make it easier to ask deeper and more context-specific search questions and get better, richer answers. We are hoping that, even if a participant is not an imminent buyer or seller, they will see the Cosolvent-based system as a useful tool for brainstorming and planning their thin market role.
The Communicate Phase – When participants think they have found an interesting prospect, we have to convince them that it is safe and productive for them to make “first contact”. This requires a safe messaging system that will avoid things like spamming and marketing message blasts. We are hoping that we can tap into AI capabilities to filter and suggest constructive and fruitful first contacts. The messaging mechanism needs to be secure, but it probably doesn’t need to be sophisticated. Once the contact is made, subsequent communications will probably move outside the Cosolvent system to more familiar channels such as email, phone, WhatsApp, etc.
The Negotiate Phase – This phase is still conceptual and therefore a bit hazy. The good news is that transaction negotiations have a well-established structure, validated by countless occurrences throughout history. There are offers, counter-offers, acceptances and rejections, and all of these follow the dictum that “nothing is agreed until everything is agreed”. The bad news is that there can be conflicts, misunderstandings, errors and omissions. We are hoping that AI can help to minimize the bad aspects, while reinforcing and facilitating the good structure.
The Specification and Documentation Phase – IF the parties can reach a point where they have agreement on all of the relevant issues, they will be ready to craft, sign and execute contracts. At that point Cosolvent can’t or shouldn’t go any farther. The best thing it can do is to summarize the process to date in a formal specification document (or set of documents) that summarizes the “meeting of the mind” to be passed to banks, lawyers, suppliers and the like. The actual execution of the deal must be managed in the real-world. As currently conceived, a Cosolvent system should not assume liability for real-world execution of the deal.

Surrounding the main axis of the deal making, there are some additional ingredients that contribute to trust building that a mature Cosolvent-based system should accomodate and facilitate. The three that seem most immediately obvious are the following:

Industry Context – Real world deals typically operate in some real-world context that is separate from the specific participants, but can influence what is desired, allowed or possible. Examples are government laws and regulations, industry norms, technical constraints, trade agreements, geographical realities, time frames, etc. These considerations tend to affect many potential deals and may be fairly stable and long lived considerations. Cosolvent views these “industry context” considerations as something that should be identified and documented independently for each thin market that Cosolvent intends to help. The contents may vary from thin market to thin market, but the AI should account for them in every query and response.
Market Making Memory – This is currently the least well defined aspect of the Cosolvent design. Hopefully that will change soon, but for now this is a placeholder for something that seems very important. Briefly, it seems axiomatic that no one can build trust unless they can build memories of prior and related transactions. If a deal between two parties went well in the recent past, that should make trust on the current deal much easier. The question is how to structure, organize and access this historical information in a way that will ease new transactions while protecting the privacy and integrity of previous ones.
Handling of Samples – One of the most common ways to build trust between strangers is to offer a sample of the potential deliverable. The nature of the samples will clearly vary greatly from one thin market to another. However, it seems plausible that there may be generalizable aspects of the sampling mechanism that can be supported in Cosolvent.

This roadmap is clearly ambitious and it still contains lots of unknowns, both challenges and opportunites. However, Deeperpoint plans to keep chipping away at this model, both on the conceptual Cosolvent design and in the practical Grainplaza simulator.

View the Cosolvent Design Plan

Imagining a Thin Market Ecosystem

Please note that this is a high level description intended for this website. As Cosolvent develops, the practical design and installation details will reside in the Github repository with the source code.

Github

Visit our Github Repository to follow the Cosolvent Project as it develops.