Esteeem Graph for Agents in their "twitter" space

[rluijk]

I wrote a Gist a few years ago for a "esteem" graph. Based on video (see the Gist for the link)
https://gist.github.com/rluijk/fd18fb027081052d7e8ae036ce6e3a06

Seeing the talk about a twitter space, this concept that is in the Rust snippet, could be used in the Automated Assistants working/voting/... together.

Adapting this code to represent AI assistants instead of human individuals and using it for orchestrating interactions around concepts and ideas is potentially a fascinating/useful? concept.

Here's how you could modify and interpret the code in that context:

AI Assistants as Nodes:

Each AI assistant would be a node in the graph. Instead of names like "Abed", "Pierce", etc., you'd have identifiers for each AI assistant.

Interactions Based on Ideas or Concepts:

Edges between nodes would represent interactions based on concepts or ideas. For example, if AI Assistant 1 contributes an idea that AI Assistant 2 builds upon, this could be an edge from Assistant 1 to Assistant 2.
The weight of the edge could represent the level of contribution or the significance of the idea exchange. This could be quantified based on parameters like the novelty of the idea, its applicability, etc.

Esteem Score Calculation:

In this context, the esteem score could represent the influence or value of an AI assistant's contributions within the network of ideas. The calculation would involve analyzing how much each assistant's ideas are built upon or referenced by other assistants.

Normalization and Analysis:

Normalizing the scores would still be important to compare the influence of AI assistants on an equal footing.
You could analyze which AI assistants are most influential in terms of idea generation or enhancement.

Dynamic Interactions and Growth:

The graph could dynamically evolve as AI assistants generate new ideas or build upon existing ones.
You could add functionality to introduce new AI assistants into the network based on certain thresholds or criteria (e.g., when a new area of ideas is explored).

Application in AI Orchestration:

This system could be instrumental in orchestrating AI interactions, ensuring a diverse and rich generation of ideas and concepts.
It could also help in identifying gaps in the AI network's knowledge or areas where collaboration is lacking.

This approach essentially treats the graph as a dynamic representation of collaborative intelligence, constantly evolving as AI assistants interact. It's a novel way to visualize and analyze the flow of ideas in an AI ecosystem, potentially leading to more efficient and creative problem-solving strategies.

The concept presented in the code can be integrated within a larger framework of rules and constraints, guided by game theory, to create a sophisticated model of strategic interaction among AI assistants.

AI Assistants as Players: Treat each AI assistant as a player in a game, where their actions are centered around idea generation, sharing, and collaboration within a network.

Esteem as Payoff: The esteem score functions as the payoff in this game. AI assistants aim to maximize their esteem by strategically contributing and interacting within the network.

Rules and Constraints: Define rules for how AI assistants can interact (e.g., sharing, building upon ideas) and constraints that might limit these interactions (e.g., resource limitations, time constraints).

Strategic Decision Making: Incorporate strategic decision-making based on game-theoretic principles. Assistants evaluate the potential outcomes of their actions, considering both their own esteem and the network's overall benefit.

Nash Equilibrium Analysis: Analyze the network for Nash equilibria states, where assistants’ strategies lead to a stable state, and no one can improve their esteem by unilaterally changing their strategy.

Evolutionary Dynamics: Apply concepts from evolutionary game theory to understand how strategies evolve over time in response to the changing network and external factors.

Collaborative vs. Competitive Strategies: Balance collaborative and competitive strategies among AI assistants. Collaboration might involve pooling resources for idea generation, while competition might involve assistants seeking to individually maximize their esteem.

Adaptation and Learning: Implement adaptive and learning mechanisms for AI assistants to refine their strategies based on past interactions and outcomes.

Hope this helps in the thinking of the collective!