feedback from matt, abdullah

#⚜️ For startup educators, teaching operations theory doesn't vouch for the use of that theory. Current approach is to teach knowhow (how to use) on top of the theory. However, choosing startups with high theory absorption capacity might be more effective. Just like a startup has a beachhead market, a startup educator should have a beachhead market (segment of startup). You might say, user-based innovation i.e. startup building theory can enhance develop theory for their own use. but they usually don't have enough bandwidth to establish settled science. | 🧊margine note (polyhedron) | [program theory]() [SAJ](marginnote3app://note/50401201-B4D3-41D7-9C84-C8BD347A63EB) [temporal validity as meta science](marginnote3app://note/651BF93B-8B8F-4260-BBA8-777452C2CD22) | | --------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | 🕸️vensim (graph) | ![[Pasted image 20240514084530.png]] ![[nsp_academia_v5.mdl]] | | 👫github (matrix) | https://github.com/Data4DM/BayesSD/discussions/227 | | 📚language model | added bayesdb https://claude.ai/chat/39db9a7b-4c4d-4a9b-b8b0-0fbbc8dafce5 update to 500 words: https://claude.ai/chat/54e1f51e-bec7-4698-887a-a6ba82a30342 | ----- 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. The system dynamics (SD) model provides a valuable framework for understanding the knowledge production process in scientific research. By capturing the key stages of identifying research needs, developing theoretical solutions, empirically testing need-solution pairs, and delivering validated knowledge to users, the model offers a comprehensive view of how scientific understanding accumulates over time. My personal journey as a researcher illustrates this process well. Starting with the need for accessible analytics tools for small businesses, I developed a Bayesian tool as a solution. As I delved deeper into statistics and probabilistic programming, I encountered new needs, such as scalable analytics and idea formation in Bayesian entrepreneurship. This led me to develop more advanced solutions like BayesDB and CHIExpert, while growing my synthesis capability through simulation-based calibration techniques and my need capability by understanding the phenomena business people wanted to explain. I further enhanced my empirical capability through experimental design and econometrics, and am now focusing on building my dissemination capability by collaborating with professors to share this research broadly. To make the ideas from this model more actionable, I propose two key strategies: 1. Segmenting the academic market based on customer needs to more effectively route knowledge to the right audiences. This involves developing tailored knowledge products, delivery channels, and communication strategies for key segments like researchers, educators, students, entrepreneurs, policymakers, and practitioners. Building out specialized dissemination infrastructure and incentives, and monitoring adoption in each segment, can help optimize this user-centric approach to accelerate the translation of knowledge into practice. 2. Using AI tools like BayesDB to partially automate knowledge synthesis and integration. BayesDB can ingest datasets from multiple sources, discover cross-cutting patterns, and surface high-value future research directions. Using hierarchical models encoding domain expertise as priors, BayesDB could reason robustly about research gaps and opportunities. With the right interface, it could become an AI copilot for science roadmapping, guiding research efforts toward the most promising avenues by learning from past findings. My competitive advantage lies in my solution capability with probabilistic programming languages and SD modeling, my need capability in Bayesian entrepreneurship and startup science, my dissemination capability through key connections, and my synthetic capability with tools like BayesDB and CHIExpert. By taking a systems thinking approach and viewing research as an interconnected system with feedback loops and accumulations, my model challenges us to think beyond individual studies and consider how they integrate into a larger body of knowledge. Adopting this perspective can help identify leverage points and bottlenecks in the research process, enabling targeted interventions to enhance the efficiency and effectiveness of scientific knowledge production. Overall, my thought process demonstrates the potential for customer segmentation in knowledge dissemination and AI-accelerated research synthesis to integrate and deploying scientific knowledge. The opportunities for human-machine collaboration in science are indeed immense and inspiring. | Desire | Bias | Prior | Act | | --------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------- | | Bridge Bayesian entrepreneurship and using value chain strategy | comparatively high capacity in - need (Bayesian entrepreneurship, startup science) - solution (probabilistic programming language, SD modeling), - synthetic capability (BayesDB, CHIExpert) - dissemination capability (connections with Matt and Abdullah) | SD model describes capability needed to productize research; current testing and routing functions are bad, and this act can help improve them | Testing desirability of BayesDB collaborative science with business school professors | action item - connect with SAJ ![[Pasted image 20240514092547.png|500]] - identify capacity for need (phenomena), solution (theory), fulfillment (empirics from measure/simulate), disseminate - seek potential with stock management model () - re-label SD model and see which part can be question to matt #### top level (individual) 1. is phenomena, method, data, synthesis capa, dissemination capa how to choose relevant phenomena (temporal validity)? 2. what can be automated? #### depth1 (individual + market) 3. 4. what can be automated? #### depth2 (individual + market + institution) 5. 6. what can be automated? goal: persuade matt and abdullah who enhance research's need capacity (existing problems in meta science) have informative priors and shortcut segment can my research beachhead market. if you'd like to learn how humans are using some substantive objects, why not get insight from use-dominating fields? (whatever earnes more money works) ### action: ### belief: ### goal: ### bet: - probabilistic programming technology can help solve "no testing function" problem - in this beachhead market, I'd like to test the following hypothesis: - Technology: I have competitive advantage on production feasibility (as a combination of probabilistic programming and entrepreneurial decision making modeling which requires identification of need and solution pairs and ) - Asset: I have enough asset (collaborators, ) to mashall invest to effectuate the required change - Goal: In 2024, it is better to fight for social science to be more cumulative - Customer: there exist customer group who are aware of the social science's non-cumulativeness (don't need persuasion on this level), open to applying new tools to support my research, belief: - customer group who can be the biggest fan of my research constraint: 500 words on what model does, convince you of.(delivery capacity); why we should learn 1. meta science vs substantive (what is "need") 2. be more clear about who to persuade? The purpose of this document is to persuade matt and abdullah that findings and knowhow from entrepreneurial domain would be the first step in solving what social scientists perceive to be the problem: social science non-cumulativeness 1. ratio of meta science to substantieness is extremely low in social science 2. startup world has faster clockspeed, measurable and not agreeable evaluation measures (revenue or growth) so that people are not overwhlemed by the heterogeneity before they even start to start strategy science 3. quality over quantity, consistency of use cases ceo's job is fundraising, make sure customers are your biggest fans, don't just have vision (know what steps it takes), be a fundraise ready; get all your ducks in a row more delivery capacity of the research product we have ### solution for the on a individual level,