jeanbaptiste
commented
4 months ago 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
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:
- 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.
- 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.
[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 & LaureiroMartınez, 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.