⛓️Charlie-Angie as intermediation chains for Scott-Vikash

[hyunjimoon]

👓 Vision

I argue how Charlie and Angie can serve as an intermediation chain that bridges the gap between Scott's entrepreneurship theory and innovation ecosystem knowledge (customer) and Vikash's entrepreneurship practice and probabilistic programming expertise (technology). This chain facilitates bidirectional knowledge transfer and integration of Bayesian entrepreneurship principles with scalable probabilistic programming platforms for world modeling and decision making in entrepreneurial contexts.

🛠️ Toolbox

Using the process of constructing probabilistic world models for Bayesian entrepreneurship, we synthesize insights from Charlie's operational strategy, Vikash's probabilistic programming, and Scott's innovation ecosystem research. This toolbox includes:

• Probabilistic world modeling techniques
• Operational strategy frameworks
• Innovation ecosystem analysis methods
• Bayesian entrepreneurship principles
• Scalable probabilistic programming platforms

🗄️ Table

Scenario	Entrepreneurship & Ecosystem Knowledge Transfer (Scott to Vikash)	Practice & Programming Knowledge Transfer (Vikash to Scott)	Integration of Principles
No Intermediary	Limited due to large gap in technical and practical understanding. Scott may present overly theoretical concepts.	Limited due to large gap in ecosystem and theoretical context. Vikash may present overly technical implementations.	Minimal integration due to communication barriers.
⛓️With Charlie	Improved. Charlie translates Scott's theories into more operational terms and ecosystem applications. Knowledge transfer occurs in two steps: Scott to Charlie, then Charlie to Vikash.	Improved. Charlie provides entrepreneurship and ecosystem context to Vikash's technical and operational knowledge. Transfer occurs: Vikash to Charlie, then Charlie to Scott.	Moderate integration. Charlie helps connect theoretical principles with practical implementations and ecosystem considerations.
🕸️With Charlie and Angie	Highly efficient. Scott's theories are gradually translated through Charlie's operational expertise and Angie's technical understanding, incorporating ecosystem insights.	Highly efficient. Vikash's technical and practical knowledge is contextualized through Angie's understanding of entrepreneurial applications and Charlie's theoretical framework, with ecosystem considerations.	Significant integration. The chain allows for nuanced translation of concepts, facilitating deep integration of Bayesian principles with probabilistic programming platforms in the context of innovation ecosystems.

🗣️ Language

We compare three situations of bilateral information asymmetry:

1. No Intermediary (🔴💜):
   Imagine Scott explaining advanced innovation theories (test2choose1, low-high bar experiment) to a operational-minded programmer, while Vikash describes complex algorithms to a business theorist. Both are likely to either oversimplify or overwhelm, leading to misunderstandings and missed collaboration opportunities.

2. With Charlie (🔴⛓️💜):
   Picture Charlie as a bilingual friend understanding both fields at a moderate level. When Scott explains a concept, Charlie gauges if it's basic (v_m < 1.5) or advanced (v_m ≥ 1.5), translating accordingly for Vikash, and vice versa. This "translation" reduces communication guesswork, improving knowledge transfer.

3. With Charlie and Angie (🔴⛓️🕸️💜):
   Envision a relay race of understanding. Scott passes ideas to Charlie, who understands the business and ecosystem side deeply. Charlie then passes to Angie, who bridges both worlds. Angie explains to Vikash in terms he understands, and vice versa. This chain creates finer knowledge spectrum divisions, allowing more nuanced translation and integration of complex ideas from both fields.

🔢 Math

Definitions:
v_m: Level of knowledge about entrepreneurship theory and innovation ecosystem (customer)
v_t: Level of knowledge about entrepreneurship practice and probabilistic programming (technology)
pi: Price (or level of complexity) at which intermediary i presents information

Assumptions:

• Scott's knowledge: v_m ~ U[1, 2]
• Vikash's knowledge: v_t ~ U[1, 2]

1. No Intermediary:
   Both parties face max_p E[(v - p)1{v ≥ p}], leading to inefficient screening.

2. With Charlie:
   Charlie partitions knowledge:

• v_m, v_t ∈ [1, 1.5) or [1.5, 2]
  Reduces asymmetry: E[v|v ∈ [a,b]] = (a+b)/2

3. With Charlie and Angie:
   Further partitioning:

• Charlie: v_m ∈ [1, 1.5) or [1.5, 2], v_t ∈ [1, 1.5)
• Angie: v_m ∈ [1, 1.67) or [1.67, 2], v_t ∈ [1.33, 2]

Each intermediary i chooses pi to max E[(v - pi)1{v ≥ pi}|v ∈ [ai, bi]]

As partition granularity increases, |bi - ai| decreases, reducing incentives for inefficient screening and improving knowledge transfer.

The chain progressively narrows information gaps, facilitating more efficient bidirectional knowledge transfer and integration of Bayesian entrepreneurship with probabilistic programming platforms in the context of innovation ecosystems.

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Reference:

1. using the process of constructing probabilistic world model for bayes.ent cld, I synthesized product from charlie (🧬⚙️selling value chain tools as evolutionary entrepreneurship #100), vikash (🛠️selling probabilistic program to implement Entrepreneurship #224), scott (🧭🗺️selling entrepreneurial choice/map as Bayes.Entrep #234)'s teaching on programming for entrepreneur / entrepreneurship program.

2. three scenario comparison follows Asymmetric Information and Intermediation Chains.pdf which Josh Lerner's recommended on resolving bilateral information asymmetry.

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Action Items for Charlie and Angie

Given the demonstrated benefits of two intermediaries, here are the refined roles and action items:

Charlie's role:

• Understands entrepreneurship theory and innovation ecosystems: v_m ∈ [1, 1.5) for basic concepts, v_m ∈ [1.5, 2] for advanced concepts.
• Has some understanding of entrepreneurship practice and basic probabilistic programming: v_t ∈ [1, 1.5).

Angie's role:

• Understands entrepreneurship applications and innovation ecosystems: v_m ∈ [1, 1.67) for basic to intermediate concepts, v_m ∈ [1.67, 2] for advanced applications.
• Understands entrepreneurship practice and probabilistic programming: v_t ∈ [1.33, 2] for intermediate to advanced concepts.

In this chain:

1. Scott communicates his Bayesian entrepreneurship theories and innovation ecosystem insights to Charlie.
2. Charlie translates these into operational strategies, incorporating ecosystem considerations, and passes them to Angie.
3. Angie further refines these concepts, bridging the gap between operational strategy, innovation ecosystems, and probabilistic programming implementation.
4. Angie also takes Vikash's advanced probabilistic programming concepts and entrepreneurship practice, framing them in terms of innovation ecosystem applications.
5. Charlie then takes these technologically-informed strategies and relates them back to Scott's theoretical framework, enriching the innovation ecosystem perspective.

This bidirectional flow of information allows for more efficient knowledge transfer by:

• Breaking down Scott's theoretical insights into more operational, implementable concepts within the context of innovation ecosystems.
• Translating Vikash's technical knowledge and practical experience into entrepreneurship-relevant applications, considering ecosystem dynamics.
• Allowing Charlie to ensure operational validity and ecosystem relevance of ideas flowing in both directions.
• Enabling Angie to act as a "translator" between the theoretical, practical, and technological domains, integrating ecosystem considerations.

By facilitating this bidirectional flow of information, the intermediation chain reduces the bilateral information asymmetry. It increases the likelihood of successful collaboration, potentially leading to a more effective integration of Bayesian entrepreneurship principles with scalable probabilistic programming platforms for world modeling and decision making in entrepreneurial contexts, all while considering the broader innovation ecosystem.

This approach aligns with the paper's principle of breaking down large information asymmetries into smaller, more manageable steps, while also incorporating the crucial aspect of innovation ecosystems in entrepreneurial decision-making.

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Some concerns existed whether "translating theory into practical terms" may offend theorists but I disagree.

Theorists should feel less offended (if they are) by their output perceived as less practical.

Unit theorists' recognition of the noisiness+inefficiency of directly targeting "end customer" (practical implications in attached ⛓️value chain.png) will motivate them to gather evaluation from closer "customer" i.e. programmatic theorists.

This will fasten the flow of unit theory's revision or integration (hence my proposition to acculturate rejection sampling structure yesterday).

As clockspeed+NSS are examples of programmatic theory that came to my mind while reading Matt's paper which defines unit/programmatic theory and knowledge production chain which consists of abductive findings, empirical validation, unit theory, programmatic theory, business school curricula, practice implications

[hyunjimoon]

applying the intermediation chain concept, i'm constructing charlie, vikash, scott's world model. the more the chain participants communicate on their belief and incentive on meaningful language (algorithm/program/process), the easier it is to enhance quality of product collaboratively.

currently my every prompt starts as below:

1. let's construct charlie and vikash's world model on Probabilistic Computing for Bayesian Entrepreneurship (PC4BE).
2. based on scott24👓_bayesian_entrepreneurship.pdf, construct world model on the market bayesian entrepreneurship
3. based on charlie24🛠️_nss.txt which is charlie's technology, understand charlie's world model on Probabilistic Computing for Bayesian Entrepreneurship (PC4BE). charlie's work operationalize entrepreneurial strategy so this is computing part.
4. based on vikash24🛠️_scaleAI_understands_world_pp🗣️.txt and vikash24👓_word2world_prob_lang_thought🗣️.txt, understand vikash's world model on Probabilistic Computing for Bayesian Entrepreneurship (PC4BE).
5. based on charlie's world model constructed from 3, understand what charlie wish to know from [charlie's question to vikash]. summarize this into one sentence.
6. based on vikash's model constructed from 4, imagine what vikash would answer to charlie.
7. based on the asymmetric information and intermediation chains.pdf, understand the mechanism behind intermediation chains 
8. angie's goal of [angie's information injection as intermediation chain] is to serve intermediation chain by sharing information with charlie and vikash on PC4BE. based on knowhow of intermediation chains in 7, please update [angie's information injection as intermediation chain] so that it serves its goal most effectively.

scott23🛠️_econ_idea_innov_ent.pdf
scott24👓_Bayesian_Entrepreneurship.pdf

charlie24🛠️_nss🗣️.txt

vikash24🛠️_ scaleAI_understands_world_pp🗣️.txt
vikash👓_rational_meaning_prob_lang_thought🗣️.txt

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based on above, ❤️‍🩹cvs world model are in: #262 (comment)