An Introduction into Conversational Game Theory.
Conversational Game Theory (CGT) is, amongst other things, a method for building a continually resolving consensus between ideological divides. It applies principles of game theory to conversations — especially disputes and disagreements — with only one possible computational and cognitive outcome: mutual resolution.
Since its inception, CGT has evolved into a computational system currently running a closed pilot, with strong applications in artificial intelligence. It’s both a computational and a cognitive system that not only facilitates consensus building but also composes a cohesive narrative from conversations.
CGT can elegantly achieve resolution in an online discussion without the need for any third-party mediator, attorney, or negotiator — and especially without voting.
It is decentralized consensus building via direct one-on-one conversations through dynamic pairing.
Wait, what?
CGT is defined as a collaborative process of consensus building, as opposed to a dualistic process, which is competitive (like voting) to arrive at an outcome.
A consensus, from a CGT perspective, is comprised of both conflict and resolution, and all of the stages in between. A CGT system of consensus building is, by definition, a system of conflict resolution that produces a result: a consensus article published inside a consensus library.
While this may seem daunting — perhaps even an impossible level of chaos to manage — it’s a lot simpler than you might imagine. And it only requires a computational interface to make it a reality.
Essentially, voting is a form of consensus building too — just a flawed one, both on social media and in governance. But instead of giving us one reliable consensus, voting gives us consensus ad nauseam.
Voting is emotional consensus building.
Conversational Game Theory is a way to build an open and transparent process of scaled consensus without voting, through the construction of a shared narrative article — a truly rational consensus.
Instead of voting, all sides in ideological struggles participate in building a shared narrative in an article format.
Building the shared narrative requires collaboration, and participants who are shown to collaborate when in disagreement are awarded micro-permissions to publish and curate the consensus article and the library that publishes it.
What’s more, CGT has only one computational and cognitive outcome: resolution. It can only publish a resolution. This means that as long as viewpoints continue in CGT, resolution is the only possible outcome.
Note that all forms of dualistic consensus building, which are competitive by definition, have win-or-lose outcomes for various sides in the consensus process.
CGT, in comparison, allows for all sides in a consensus process to “win” influence on the consensus. It’s simply not possible to lose; “losing” in CGT is not an outcome that exists within the system — unless someone prefers to leave the process before completion, at which point they would lose all of their contributions within the consensus process.
CGT as a Computational and Cognitive System
With its evolution into a computational system, CGT harnesses the power of artificial intelligence to facilitate and streamline the consensus-building process. The computational aspect allows for the management of complex dialogues and large-scale conversations, making it scalable and efficient.
The cognitive system component ensures that human reasoning, emotions, and psychological nuances are incorporated into the consensus-building process. By combining computational algorithms with cognitive insights, CGT builds compositions from conversations, transforming disparate viewpoints into a cohesive narrative.
This synergy between computation and cognition enables CGT to handle the chaos of disagreement by providing a structured yet flexible framework that adapts to the dynamics of human interaction.
How Does CGT Build Consensus and Compose Conversations?
CGT entails building a shared narrative through the writing and publication of text around the subjects involved in the disagreement.
- What is the subject of the consensus?
- What is the origin of the subject?
- What are the terms of the subject?
- Where is there conflict around the subject?
- What is the conflict within the subject?
- What are the proposed solutions within the conflict?
All of these questions are answered by narratives, collaboratively written and computationally managed.
All of the answers to these questions have a natural follow-up question: How was this conclusion or narrative arrived at?
Those narratives have competing ideas between ideological divides. It is this disagreement specifically that is assigned to a pair of individuals who are on opposite sides of a claim in the consensus.
Well, what happens when one side wants to lie? Or just try to bully their answers? What happens when any side approaches the process in bad faith toward their counterpart?
Well, nothing happens. There is no possible outcome for those choices in CGT. So while there will be those who attempt such tactics, eventually they will learn there is no possible payoff within the system for those very same processes.
Because CGT does not rely on a voting process, any type of “brute force” manipulation of a good-faith process is stopped at the level of a direct one-on-one conversation, monitored and facilitated by the computational system.
In CGT, “mistakes” or misunderstandings are actual entry points into influence status within the process.
In the CGT process, acts of rationality and honesty — through the acknowledgment of mistakes or misunderstandings — are not only measurable but also award permissions to begin writing the consensus.
How Does All of This Become Measurable and Publishable?
Logic. Literally. And by literally, I also mean literary. The logic of CGT has a hyper-rational razor-edge as found computationally, but it also has a hyper-intuitive element, allowing the process itself to tell its own story through naturally occurring narrative arcs that exist cognitively between conflict and resolution.
The computational system employs algorithms that track and analyze the dialogue, identifying points of agreement and contention. It uses natural language processing and machine learning to assist in composing a unified narrative from the conversation, ensuring that all perspectives are fairly represented.
Let’s look at voting and dualistic (competitive) consensus building. What type of logic operates it?
Binary. On or off. Up or down.
And certainly win or lose.
While it’s clear the logical operation of voting is binary, what about the cognitive?
What about the actual state a consensus builder is in when they approach the ideological divide from a competitive place?
This is also binary, although we experience it directly as something more commonly known as “black or white” thinking — a cognitive state with its own binary logic embedded into its conceptualization and symbology.
Black or white thinking is dualistic thinking.
Things are either wrong or right, always us versus them, or you versus me. You’re either with me on this or against me.
Conversational Game Theory logic is not bivalent.
It is not binary, on or off, left or right.
Technically, it’s a form of ternary logic, and quite likely an unbalanced ternary paraconsistent logic.
This means that it can account for states that are on or off as well as states that are both on and off at once — which is the whole system itself.
That’s all CGT is: the whole system of duality functioning as one unified system of opposites.
It may seem arcane or too complex at first, but the important feature here is that regardless of its computational elements or properties, the CGT view is natural to us. It’s something we already intuitively understand, and it also happens to have an elegant computational form, allowing CGT to enter into very practical domains.
While the 0 and 1 of binary logic may govern dualistic and voting-ruled consensus building — as well as computer science — 0, 1, and 2 govern CGT.
And this is true both logically and cognitively. In some ways, you could say that just as binary logic relates to the programming and operation of computer software and hardware, ternary logic — CGT, 0, 1, 2 — is equally programmatic, but applies to consensus psychology and the complexity of human behavior and decision-making.
CGT and AI: A Powerful Combination
The integration of AI into CGT brings a new dimension to consensus building. AI algorithms can process vast amounts of conversational data, identify patterns, and assist in resolving conflicts by suggesting compromises or highlighting areas of agreement.
This computational power enables CGT to scale efficiently, handling multiple conversations simultaneously and facilitating consensus building in complex scenarios that would be overwhelming for humans alone.
Moreover, AI can help mitigate biases and ensure that all voices are heard, contributing to a more inclusive and balanced consensus.
Ternary Logic: Embracing the Unknown
Ternary logic allows for an “unknown” to exist logically within the subject — a third value, or variation, or absorption between two opposing viewpoints.
What’s more, from a cognitive perspective, ternary logic gives the mind a complete rational environment for consensus building, reviewing all ideas exchanged within the consensus through their naturally occurring states that are, empirically, either unknown in some sense, true in some sense, or false in some sense. In what sense exactly? Well, that’s for the consensus to work through.
Ternary logic allows a whole-system accounting of concepts exchanged within narratives as they are experienced directly by those building the consensus.
It provides an environment for consensus building that is thorough, but more importantly, it collapses “dualistic” or competitive cognitive approaches, which usually form a type of groupthink.
Cognitively speaking, black or white thinking seeks to avoid the “middle” or “third value” of the subject in conversation.
A Historical Example: The WMD Debate
Remember Weapons of Mass Destruction? The conversation around WMDs as a justification for going to war in Iraq in 2003?
That’s a perfect historical example where a national conversation avoided the third value — unknown — and could only process a discussion around the existence of WMDs as either true or false. Fear of a “dirty bomb” ensured the dualistic swing, avoiding the third-value consideration.
What different outcome could the United States have built a consensus around if the actual truth value — unknown — was applied to their existence instead of true?
It’s easy, some 20 years later, to understand the tragedy of the decision to invade Iraq and Afghanistan. While many may have good reason to lay blame on various institutions, the full tragedy may simply be that we had a breakdown of understanding. War was nothing more than a result based on a misunderstanding around something as simple as unknown — zero confused as something real, true, something that had actual existence. We simply had no way to process a rational conversation about an unknown.
Managing Chaos Through Computation
This ternary application, as a design principle for a computational interface, can well manage the chaos of disagreement because it provides a whole-system view. This view shows us that conflict itself — disagreement — is necessary to build resolution; in fact, it’s the only way to build resolution.
Ternary logic — or CGT — allows us to view the conflict from a whole-system perspective, which exalts itself, both computationally and cognitively, in win-win scenarios.
How does this “win-win” play out in consensus between ideological divides?
In the form of permission to edit, write, rewrite, or co-write the consensus agreement itself.
CGT as a Human Psychological “Blockchain”
CGT can form a human psychological “blockchain,” comprised of our better natures and wiser decisions.
It achieves this by overlaying a meta “game,” where the most collaborative and rational decision-makers are awarded the keys to the publication of the consensus.
The computational system ensures transparency and immutability of decisions, much like a blockchain ledger, but applied to human interactions and consensus narratives.
The Great Game
The Great Game is a collaborative one; the algorithm of CGT ensures only the most rational, honest, and collaborative among us — performing to our best abilities — are the guardians of the knowledge and understanding of the consensus itself.
While The Great Game is collaborative, its tensions are cognitively very real because the stakes of the game are the control of the consensus broadcast voice.
On a social level, The Great Game is for the control of editing and writing permissions in the consensus-building library of Aiki Wiki and the front-page view of a consensus article.
On a personal level, The Great Game is a cognitive matrix for the conflict of ideas, requiring the direct — and even brutally honest — confrontation of contradictions that are unresolved, unaddressed, and unaccounted for.
Those who control the permissions of the Digital Library control the voice of the rational consensus, and therefore control the content of the consensus article.
These are combined simultaneously by the algorithm and the users “gaming” the tagging and selection permissions.
Combined, these emerge within the whole system of consensus building as a way to view the conflict from a new and even rewarding perspective, where disagreements become less ideological and more an “administrative” deliberation around which selection of 0, 1, or 2 best fits the consensus points. This creates a pathway of exchange that fosters a natural process of self-reflection and thorough critical thinking.
Composing a Cohesive Narrative from Conversations
One of the most powerful aspects of CGT is its ability to transform a complex web of conversations into a coherent composition. By utilizing computational methods, CGT structures dialogues into narratives that reflect the collective reasoning and insights of all participants.
This composition process involves:
- Data Aggregation: Collecting all conversational inputs and organizing them systematically.
- Analysis: Using AI to identify key themes, arguments, and counterarguments.
- Synthesis: Combining these elements into a unified narrative that represents the consensus.
- Publication: Presenting the consensus narrative in an accessible format for all stakeholders.
This approach not only resolves conflicts but also creates valuable knowledge assets that can inform future decisions and policies.
The Future of CGT
As CGT continues to develop, its applications in AI and computational systems will expand. The closed pilot currently running is just the beginning. The potential for CGT to revolutionize how we approach consensus building, conflict resolution, and collaborative decision-making is immense.
By integrating computational power with cognitive understanding, CGT offers a path toward more rational, inclusive, and effective consensus processes.
Conclusion
Conversational Game Theory is more than just a method; it’s a paradigm shift in how we think about consensus. By embracing both the computational and cognitive aspects of human interaction, CGT paves the way for resolutions that are not only computationally sound but also cognitively satisfying.
This is Conversational Game Theory, and a demonstration is available upon request! 🙂
