mit-enterprise matt, abdullah, angie

[hyunjimoon]

March: #187 (comment)
April: #190 (comment)
May:

summary of chat so far

For instance, based on my "search" from "method" stack, I short-listed below to represent testing, routing, and sharing functions from sequential decision making literature.

1. transfer learning (p.40-52)
2. Bayesian theory of mind

I am at the AOM editors meeting. We actually are going to talk today about AI and how it should (and should not) inform science. We also talked about the short and long term KPIs we need to think about for the health of our science. While I am at SIOP I will work on the stock and flow knowledge production model in such a way that advances the nesting and building of knowledge products as per the model we worked on

"AI assessment of data quality" from BayesDB may be the most relevant to enterprise vision.

Social science leaders like you and Abdullah's support would help me persuade the lab PI of this collaboration. Knowledge production system (KPS) I envision is:

• generating: probabilistic computing lab, theory of automated testing tool
• synthesizing, educating: Matt, Abdullah, Angie
• training, applying: automated tools and end users

Apr.23 agenda (no meaning in order)

with gpt's help (all errors are gpt's 😂), I simulated meeting agenda, focusing on:

• function: aligning beliefs and goals, exploring different actions, and assigning tasks according to individual strengths and competitive advantage
• format: meeting's outcome can be integrated into why early social science scholars fail doc

not perfect but hope it to act as useful prior.

Agenda for Collaboration Meeting on "enterprise.txt"

4.2.2 Individual-Level Solutions: Eliciting Expertise and Preferences (15 minutes)

• Objective: Collect insights from Matt and Abdullah about their areas of expertise and their preferred strategies for advancing the project.
• Activities:
  • Request Matt to share his insights related to knowledge production systems that could be integrated with the project goals.
  • Invite Abdullah to discuss educational needs and methods for effectively engaging various stakeholders.
  • Present possible technological supports and methodologies to be utilized, seeking feedback.

4.2.3 Institution-Level Solutions: Discussion of Action Candidates (10 minutes)

• Objective: Explore potential institutional-level actions that align with the vision of "enterprise.txt".
• Activities:
  • Discuss the potential integration of BayesDB and Bayesian belief updating methods as institutional resources.
  • Consider the development of educational programs or workshops that could support the project's objectives.
  • Brainstorm potential collaborative platforms for real-time interaction between academia and industry.

5 Solution Production Plan: Exploring Division of Labor (20 minutes)

• Objective: Explore how tasks might be distributed based on each participant’s strengths and competitive advantages.
• Activities:
  • Suggest that Matt could help refine and integrate theoretical frameworks according to his research interests.
  • Propose that Abdullah could outline educational modules and strategies for stakeholder engagement.
  • Discuss your potential role in coordinating technological integration and managing the overall project.
  • Agree on milestones and a timeline for reviewing progress and outcomes.

5.1 Individual: Closing and Next Steps (10 minutes)

• Objective: Clarify the immediate actions, responsibilities, and set up the next checkpoint.
• Activities:
  • Summarize the discussions and any agreements made during the meeting.
  • Propose scheduling the next meeting to assess progress and refine strategies.
  • Open the floor for any final questions to ensure alignment and commitment to the outlined tasks.
    This agenda is designed to maximize the use of your collective time and expertise, ensuring that each participant's strengths are effectively utilized to push forward the goals of "enterprise.txt."

reference

• pairing enterprise need and BayesDB and Bayesian belief updating paper solution

• enterprise.txt

• matt's slide on The Enterprise.pdf

[hyunjimoon]

Reviewing last meeting to form team prior

Abdullah's comments:

1. Clarify the context and purpose of the meeting, as the discussion seems to have shifted from the original focus on knowledge production frameworks.
2. Be careful not to overfit the model to meta-scientific issues, as the model should be general enough to apply to substantive problems as well.
3. Consider whose mind you are trying to change and about what, as this will help guide your communication and development of the model.

Matt's comments:

1. The model is a good start, but it needs to be more user-friendly and easier to understand for those in management.
2. Focus on clarifying and simplifying the key aspects of the model, such as identifying needs, developing solutions, testing need-solution pairs empirically, and delivering to users.
3. Think about how the model relates to programmatic theory and how unit theories are integrated and routed to build coherent scientific understanding.
4. Experiment with using tools like GPT to assist with literature review, but be cautious about relying on them too heavily, as they may not capture the nuances and depth required for research.
5. Write a short paragraph (around 500 words) explaining what the model does, why it's important, and what you want to convince them of, as this will help clarify your thinking and communication.