prepare white paper for diamond model 🧬🗺️🧭

[hyunjimoon]

summarize Data4DM/BayesSD#159 for introduction part of the paper. this paper will describe the last row and explain observations using simulation models

[hyunjimoon]

business case studies + hypothesis that can requires inference, that has similar inference based sequential decision making structure.

This is code that has decision and inference

1. IT (AI-NoAI X B2B-B2C):
   An IT startup is considering whether to develop an AI-powered product or a non-AI product, and whether to target the B2B or B2C market. The company runs experiments by developing prototypes and testing them in different market segments. They analyze data on user adoption, engagement, and revenue to determine if they should scale the current product-market combination, pivot to a different product (AI to non-AI or vice versa), pivot to a different market (B2B to B2C or vice versa), or fail if the results are consistently poor.

2. MedTech Operation (Outsource-Inhouse, Global-Local):
   A MedTech startup is developing a medical imaging device and must decide on their manufacturing strategy. They can choose between outsourcing or in-house production, and between global or local manufacturing. Each option has different costs, production speeds, and quality outcomes. The company runs prototypes through each combination, measuring product performance (e.g., image fidelity and processing speed) against FDA requirements. They must balance the trade-offs between cost, time, and quality to achieve FDA approval before running out of funds.

3. Gene Therapeutics (Old vs New Base Editor X Dravet Syndrome vs SETBP1):
   A biotech startup is developing gene therapies using base editing technology. They must choose between an older, proven base editor and a newer, potentially more effective but less tested version. They're targeting two genetic conditions: Dravet syndrome and SETBP1 disorder. The company conducts clinical trials, analyzing efficacy data (e.g., percentage of cells successfully edited) and safety profiles. They must decide whether to continue with the current editor-disease combination, pivot to a different editor or disease target, or scale up based on promising results, all while considering manufacturing costs and market size for each condition.

(4. Quantum Computing for Drug Discovery)

three hypothesis:
H1: Signal Strength Hypothesis As the signal strength (clarity and reliability of information) increases in one dimension of innovation (e.g., product, technology, or market), the ratio of pivots in that dimension relative to other dimensions will increase. This is due to innovators having more confidence in making changes based on clearer signals.
H2: Uncertainty Reduction Hypothesis As the overall uncertainty (σ) in the innovation process decreases, the ratio of pivots will initially increase across all dimensions as more reliable information becomes available. However, this ratio will eventually stabilize or decrease as the innovation landscape becomes more predictable.
H3: Resource Accessibility Hypothesis The ratio of pivots in different dimensions (e.g., product vs. market) will be influenced by an organization's access to relevant resources. Organizations with greater access to resources in a particular dimension will show a higher pivot ratio in that dimension compared to organizations with limited resources.

Aspect	IT	MedTech	Gene Therapeutics
Upstream (Product) Choice	AI vs Non-AI	Outsource vs In-house	Old vs New Base Editor
Downstream (Market) Choice	B2B vs B2C	Global vs Local	Dravet Syndrome vs SETBP1
Combinations	AI-B2B, AI-B2C, Non-AI-B2B, Non-AI-B2C	Outsource-Global, Outsource-Local, In-house-Global, In-house-Local	Old-Dravet, Old-SETBP1, New-Dravet, New-SETBP1
Predict	Estimate user adoption, engagement, and revenue for each combination	Project product performance, cost, and time-to-market for each manufacturing strategy	Forecast efficacy, safety, and market potential for each editor-disease pair
Decide	Choose to scale, pivot product, pivot market, or fail based on data analysis	Select manufacturing strategy balancing FDA approval likelihood and fund availability	Opt to continue current path, switch editor/disease, or scale based on trial results
Infer	Update beliefs about product-market fit and adjust strategy for next iteration	Refine understanding of manufacturing trade-offs and adjust approach	Revise estimates of editor efficacy and disease treatability for future decisions
H1: Signal Strength Hypothesis	As AI model performance improves, product pivots (AI architecture changes) increase relative to market pivots	As manufacturing quality metrics improve, production strategy pivots increase relative to market pivots	As base editing efficiency increases, editor type pivots increase relative to disease target pivots
H2: Uncertainty Reduction Hypothesis	As AI development tools mature, pivot ratio increases initially but stabilizes as AI becomes more predictable	As manufacturing processes standardize, pivot ratio increases initially but stabilizes as outcomes become more predictable	As gene editing outcomes become more predictable, pivot ratio increases initially but stabilizes as the field matures
H3: Resource Accessibility Hypothesis	Companies with better AI talent and computing resources show higher product-to-market pivot ratios	Organizations with advanced manufacturing capabilities show higher production-to-market pivot ratios	Companies with better gene editing tools and expertise show higher editor-to-disease pivot ratios

[hyunjimoon]

both charlie and vikash have passion for "theory of everything"

this can be applied for innovative ecosystem

charlie asked me to

q2 charlie and vikash:

• do you think petitioning for Individually tailored interdisciplinary degree path on operations and innovations management can help me get the mentorship and training i'd need?
  https://oge.mit.edu/gpp/advanced-degrees/interdisciplinary-degree-paths/interdisciplinary-doctoral-degree/