scott, todd, jb, angie's nsf triplet on genome

[hyunjimoon]

Disclaimer: this linearizing feedback loops is outcome of my 🤯 for few months so please understand for some cute assumptions in mapping one action with one or at most testing forms/functions. chain reaction of hierarchy of need-solution-fulfillment triplet (from below) would be cleanest representation.

Belief: scott, todd, jb's module on strategy, PMF, testing interface can be represented with Angie's GATC base

Goal: get three authors' feedback

Mockup:

Action	Subaction	Definition	Gene Therapeutics Industry	Application-Specific Processor Industry
G (Goal)	Aligning belief and goal	How the startup's team aligns its internal beliefs and objectives.	Coordination of team efforts to innovate in gene therapy, aligning with an overarching strategic vision.	Harmonizing internal visions to lead the advancement of AI processing technology in mobile devices.
	Form of Testing	- Mockup: At the goal setting and strategic alignment phase, mockups are useful for exploring various strategic directions without the constraints of actual implementation. They help in visualizing different strategic paths and getting internal consensus. - Prototype: Could also play a role here as a way to concretize strategic goals into tangible objectives, especially for tech startups where early product concepts need to be somewhat tangible to align team goals.	Engaging in stakeholder validation to ensure alignment with strategic goals in the realm of gene therapy.	Conducting strategic validation tests to affirm commitment to the development of optical I/O computing technologies.
	Function of Testing	employee's desirability
	Uncertainty Addressed	Identifying and reducing risks related to the startup's direction and innovation focus using feedback from testing phases.	Clarifying strategic directions and innovation priorities to mitigate internal uncertainties.	Ensuring clarity of strategic vision to maintain alignment with market leadership objectives.
AT (Asset with Supplier)	Aligning belief and goal	Alignment between the startup and its suppliers to close the gap between available assets and those needed for product implementation.	Establishing collaborative relationships with suppliers to secure essential assets for gene therapy solutions.	Strategic partnerships with suppliers to ensure asset provision for state-of-the-art AI processor production.
	Form of Testing	- POC (Proof of Concept): Best suited for this stage, especially when negotiating and aligning with suppliers. A POC can demonstrate the technical feasibility of integrating suppliers’ assets or materials into the startup's product, ensuring that both parties are aligned in capabilities and expectations.	Evaluating supplier relationships and agreements to ascertain their capability in supporting innovative product development.	Assessing supplier agreements to confirm their capacity to meet technological asset requirements.
	Function of Testing	financial viability
	Uncertainty Addressed	Reducing risk by ensuring supplier capabilities and asset availability align with the startup's production needs.	Minimizing supply chain uncertainties related to materials and technologies essential for gene therapies.	Reducing supply risk concerning the quality and availability of components for AI processors.
AC (Asset with Distributor)	Aligning belief and goal	Alignment between the startup and its distributors to ensure assets are utilized effectively to meet product function needs.	Forming strategic alliances with distributors to optimize market penetration for gene therapies.	Aligning with distributors to achieve efficient dissemination of AI processors to manufacturers.
	Form of Testing	- Prototype: Useful in this phase for testing logistical and distribution strategies with actual product versions, albeit early ones. This helps in aligning distribution strategies with the product's physical characteristics and requirements.	Testing and refining distribution strategies to maximize market reach and availability of gene therapies.	Verifying distribution network efficiency to ensure market coverage and supply chain effectiveness for AI processors.
	Function of Testing	operational feasibility
	Uncertainty Addressed	Addressing logistical and market access challenges to ensure product delivery aligns with customer demand.	Tackling market access and logistical uncertainties to streamline the distribution of gene therapies.	Addressing delivery risks and demand fulfillment challenges for global AI processor markets.
T (Technology)	Aligning belief and goal	Alignment between the startup's technological capabilities and their practical application.	Ensuring the startup’s technological expertise is applied effectively to gene therapy treatments.	Aligning the startup’s technical know-how with the practical demands for AI processors in mobile devices.
	Form of Testing	- POC (Proof of Concept): Essential for demonstrating the technological viability of the product. It’s about proving that the technology works as intended and can be developed into a viable product. - Prototype: Following a successful POC, moving to a prototype phase is natural here, incorporating real materials and beginning to address production constraints and technical specifications more closely.	Validating technological feasibility to confirm the applicability of the startup's knowledge to treatment solutions.	Testing technological capabilities to ensure they translate into high-performing AI processors.
	Function of Testing	technical feasibility
	Uncertainty Addressed	Ensuring the technology developed is applicable and meets the practical demands of product and market.	Bridging the gap between technological knowledge and its application in the development of gene therapy products.	Closing the gap between technical knowledge and market requirements for AI processors.
C (Customer)	Aligning belief and goal	Alignment between the startup's product offerings and the customer's needs and expectations.	Aligning the startup’s solutions with patient and healthcare provider needs in gene therapy.	Ensuring the AI processors' features align with the needs of mobile device manufacturers.
	Form of Testing	- MVP (Minimum Viable Product): The MVP is critical at this stage for testing the product in real market conditions with actual customers. It helps in validating the economic viability of the product, gathering feedback on its desirability, and understanding the product-market fit.	Validating customer desirability to ensure gene therapies meet the needs and gain acceptance by healthcare providers.	Engaging in iterative design and evaluation using MVPs to confirm that the processors not only meet technical specifications but also the real-world needs and performance expectations of device manufacturers.
	Function of Testing	customer desirability
	Uncertainty Addressed	Using feedback from MVP testing phases to address and reduce market, technological, and operational uncertainties, ensuring the product aligns with customer needs and market demands.	Reducing doubts about whether the gene therapies will be accepted by patients and adopted by healthcare providers by gathering and analyzing feedback on the product’s effectiveness and usability.	Minimizing market and product desirability uncertainties by demonstrating that the AI processors meet or exceed customer requirements and technological expectations for mobile devices, thereby confirming market fit and demand.

[Scott's four choice and strategy]

Choice of technology, organization, customer, competition are determined by four types of entrepreneurial strategy which exists on the axis of INVESTMENT and ORIENTATION. 2 by 2 combinations of INVESTMENT and ORIENTATION's instances are named ("execute" and "compete") as disruption, ("control" and "compete") as architectural, ("execute" and "collaborate") as value chain, ("control" and "collaborate") as intellectual property strategy. Intellectual property strategy focuses on gaining control of innovations through patents and trademarks, and collaborating to reduce costs and has examples like Harry Potter, gettyimages, xerox, DOLBY, INTELLECTUAL VENTURES, Genetech. Value chain strategy aims to be the preferred partner in a slice of an industry's value chain through strong execution and collaboration and has examples like Foxconn, PayPal, madaket, mattermark, DRIZLY, STRATACOM. Disruption strategy targets underserved segments and uses iteration and learning to expand and has examples like NETFLIX, Zipcar, salesforce, amazon, skype, oDesk. Architectural strategy creates an entirely new value chain by controlling a key resource or interface that coordinates multiple stakeholders to provide new consumer value and has examples like facebook, AngelList, ebay, Ford, Etsy, Dell.

[JB's four experiment tools]

Mockup: exploration without production constraints (for design phase, multiple representations and media)
Prototype: first of a series, including production constraints (real materials, cost limitations, etc)
POC: technical demonstrator (technological viability)
MVP: commercial demonstrator (economical viability)

e.g.📱mockup to visualize your idea, move to a 🤳prototype to get a feel for its physical presence, create a 📲POC to make sure the core feature (charging) works, and finally develop an ⚙️MVP to test the market with a basic but functional product. At each stage, you're learning more and getting closer to a product that people can actually buy.

[Todd's belief formation and product-market fit]

framework in Todd.png. In sum, actors in the market collectively evaluate the entrepreneur’s belief, which the entrepreneur then discovers by testing the belief in the market—a process that ultimately reveals the accuracy of assumptions about market needs, the feasibility of product features, and fit of product features to market needs. We denote the market’s view of the belief as the market model, or, in economic terms, the demand function for the belief, with V(M) 5 Needs (M) 3 Features (M) (right-hand part of Figure 1). 2 The weighting of the needs and feature assumptions underlying V(M) typically differs from that of V(E) because the belief is self-generated by the entrepreneur, resulting in a misfit between V(E) and V(M). Hence, the true value of the opportunity belief (V) is proportional to the fit between V(E) and V(M), with fit being the correlation between V(E) and V(M), what we define as product–market fit. The entrepreneur may thus hypothesize a promising product, based on plausible assumptions about demand and a host of features deemed feasible to satisfy the demand, such that V(E) is high. But, such an idea may still fail to create value when, as is often the case at the outset, the assumptions about demand and features inherent to the belief model fail to fit the revealed market model (low correlation between V(E) and V(M)). The value of an opportunity belief (V) is thus V / correlation (V(E),V(M)).

Viewing the value of an opportunity belief as scaled by its fit to the market is aligned with cognitive sciences and lens models of human judgment, which denote the fit of a judgment as the extent to which the mental representation matches the environment (Brunswik, 1956; Csaszar & LaureiroMart�ınez, 2018; Kozyreva & Hertwig, 2021; Shepherd & Zacharakis, 2002). This view of belief fit is aligned with Todd and Gigerenzer’s (2012) idea of ecological rationality, defined as the fit between a cognitive tool and the environment (Hertwig et al., 2019). Relative to this work, our notion of fit is more problem or belief specific—how well hypothesized solutions solve specific market problems. We thus view product–market fit as the correlation between (a) the entrepreneur’s own projections about the value-creation capacity of assumed needs and associated product features, and (b) the revealed value-creation capacity of these needs and features in the market. We call the former the belief model (Figure 2, left) and the latter the market model of the belief (Figure 2, right).

[need, solution, fulfillment triplet]

Detail in need, solution, fulfillment triplet with summary below:

Imagine data as needed, computational algorithms as fulfillment, statistical model p(theta, y) as need-solution pair. Mapping this with SBC code (architecture) in the second row below teaches us: (a) function (N,S, NSP, F, E) and object (paired need-sol fulfilled need-sol evaluated fulfilled need-sol) can be separated (need-solution pairing function paired need-sol) concepts (b) need, fulfillment, evaluating function themselves can be parallelly developed.

Precedence graph is as follows: The NS() function is the starting point. There are no incoming arrows, which implies it is the first function in the sequence.
From NS(), there are two paths diverging:
One path leads to SNR() function.
The other path leads directly to the A() function.
The SNR() function then sequentially leads to the E() function.
The E() function, in turn, leads to the G() function.
It appears that NS() is a prerequisite for both SNR() and A(), meaning that NS() should be completed before moving to SNR() or A(). After SNR(), the functions must proceed in a linear order from E() to G(). There is no direct connection between A() and any other function, suggesting that A() can be considered a separate or parallel process to the SNR()-E()-G() sequence.

[jeanbaptiste]

Looking good! This table is a nice operationalization of Design Thinking's classic venn diagram (Desirable, Technology feasable, economically viable) in terms of KPI's and risk-assessment.

I wonder where clininal trials and CRO's (contract research organisations) would fit in this table ?

Ah, also, you might like this process visualisation language from the 80's space program https://en.wikipedia.org/wiki/DRAKON

This JS implementation of it could be interesting for you to make a mockup of an interactive version of your table as an FSM (Finite State Machine) https://drakon.tech/read/state_machines

Other flow-based visual programming languages might be interesting as well as well as other niceities coming from CS/HCI such as Coloured Petri nets http://cpntools.org // https://cpnide.org

[jeanbaptiste]

Answering your questions:

1. Does the mapping between action and the third column ("Definition") match your belief?

• Yes, definitely, i like the extra detail of separating assets in two classes ( supplier && distributor)

2. What is one thing you like about the table?

• That you tried to keep it simple. I would even try to simplify it even more for better memorization and operationalisation by users.

3. What is one thing you think can be improved from the table?

• More visual / icons / graphics that describe the flow / process of the different components.