tool dynamics in bayes/dynamic modeling ecosystem

[hyunjimoon]

based on https://claude.ai/chat/01058c11-1b87-41c1-aa3a-cbe7c7b77930

Tracing the effect of Microsoft's SPREADSHEETLLM paper on AI in Dynamic Model/Bayes ecosystem

Introduction:

As someone with the tool builder identity for dynamic modeling with probabilistic programs, I'm subscribing to business professors' perspective on how recent AI development (especially products from big tech companies) can reshape business. Different reaction between me (policy use case side) and Mathieu (software use case side) upon getting Ethan's comment bundled with the paper was interesting.

To be honest, I'm overwhelmed by new development of AI tools, but that particular paper felt special due to the author's affiliation.

on Microsoft's recent paper offers a view on how business people might perceive glimpse into how Large Language Models (LLMs) might integrate with traditional data tools. This intersection is particularly relevant to our ongoing discussions about the future of System Dynamics and probabilistic programming languages like Stan or Gen.

SPREADSHEETLLM: A Step Towards AI-Enhanced Dynamic Modeling?
Microsoft's SPREADSHEETLLM framework aims to enhance LLMs' ability to understand and manipulate spreadsheet data. While spreadsheets are just one tool in the dynamic modeler's arsenal, this development raises important questions about how AI might transform our broader modeling ecosystem.

Key features of SPREADSHEETLLM include:

1. Novel encoding methods for structured data
2. Improved compression techniques for large datasets
3. Enhanced performance on tasks like table detection and question answering

These advancements, while focused on spreadsheets, hint at potential applications in more complex dynamic modeling environments.

Implications for the Dynamic Modeling Ecosystem

Reflecting on recent discussions from our System Dynamics community, including insights from the SD_CustomerVoice.pdf document and collected feedback in #156, we can map SPREADSHEETLLM's potential impact on our field:

1. Model Building: AI could assist in initial data preparation and understanding, potentially accelerating the early stages of model development.
2. Model Analysis: LLMs might offer new ways to interrogate models, perhaps providing natural language interfaces for sensitivity analysis or policy testing.
3. Result Interpretation: AI could help translate model outputs into narratives, making results more accessible to stakeholders.
4. Model Validation: LLMs might assist in comparing model behavior against historical data or expert knowledge.

However, as my colleague Mathieu astutely pointed out, while these tools are interesting, they don't yet address core challenges in dynamic modeling, such as capturing complex feedback structures or representing deep uncertainty.

Analyzing the Impact on the Modeling Value Chain
Using a system dynamics perspective, we can break down how SPREADSHEETLLM-like technologies might influence different parts of our modeling ecosystem:

Below diagram template from accepted talk for 2024 StanCon Integrating Bayesian Inference and System Dynamics with Case Studies in Epidemiology.pdf, can be used for this analysis.

[Table 1: Node-based Analysis]

Node Type	Impact on Dynamic Modeling Ecosystem
Algorithm Builders	Potential for new methods in model structure identification and parameter estimation
Tool Builders	Opportunities for AI-enhanced interfaces in modeling software
Model Builders	Possible assistance in data preprocessing and initial model formulation
Policy Designers	Enhanced capabilities for scenario exploration and result communication
Stakeholders	Improved accessibility of model insights through natural language interactions

[Table 2: Edge-based Analysis]

Edge Type	From	To	Impact on Dynamic Modeling Ecosystem
Forward Flow	Algorithm Builders	Tool Builders	New AI-driven algorithms for model analysis and optimization
Forward Flow	Tool Builders	Model Builders	Integration of LLM capabilities into modeling software
Forward Flow	Model Builders	Policy Designers	AI-assisted scenario generation and policy option formulation
Forward Flow	Policy Designers	Stakeholders	Enhanced communication of model-based insights
Backward Flow (Policy Use Case Feedback)	Stakeholders & Policy Designers	Model Builders	AI-facilitated incorporation of stakeholder feedback into models
Backward Flow (Software Use Case Feedback)	Model Builders	Tool & Algorithm Builders	Identification of modeling tasks most amenable to AI assistance

Connecting to Probabilistic Programming Languages (PPL)
As we work towards developing CHI and other advanced PPLs for dynamic modeling, SPREADSHEETLLM offers valuable insights:

1. It demonstrates techniques for integrating AI with structured data, which could be adapted for more complex modeling environments.
2. It highlights the potential for natural language interfaces in technical tools, a feature that could make PPLs more accessible.
3. It underscores the ongoing need for tools that can handle the complexity and uncertainty inherent in dynamic systems.

Looking Ahead

While current AI advancements like SPREADSHEETLLM don't yet revolutionize dynamic modeling, they offer a glimpse of potential future directions. For those of us developing next-generation modeling tools like CHI, the challenge is to harness AI's strengths while addressing the unique needs of dynamic modeling:

1. Capturing and representing complex feedback structures
2. Integrating diverse data sources and expert knowledge
3. Communicating model behavior and insights effectively
4. Supporting rigorous uncertainty analysis and robust decision-making

Conclusion

As we continue to develop CHI and explore the future of dynamic modeling, innovations like SPREADSHEETLLM serve as important waypoints. They help us understand both the potential and limitations of current AI technologies in our field. The task ahead is to build upon these advancements, creating tools that not only leverage AI capabilities but also address the fundamental challenges in modeling complex, dynamic systems.

todo

• analyze feedback loop (which is similar to how startups should respond to big company's entrant -> pivot?)

• importance of synchronized demand pull and supply push for contrarians
  gutenberg's printing press drove the Scientific Revolution; however, there was a prior metal printing technology 90 years ahead that did not lead to any social impact