🗺️ mapping mega mess around Ops4ENT

[hyunjimoon]

as i aim to represent #224 and #234 as bob horn's mural format, this thread aims to document his teachings

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Who your project will help

Entrepreneurs, which I define broadly as every human who has:

1. Freedom of choice
2. Constraint of resources
3. Noisy learning about the value of their ideas
4. Uncertainty in execution and the world around them

This includes traditional business founders, but also extends to individuals making decisions under uncertainty in various aspects of life.

How your project will help

1. Educate people about Bayesian reasoning and systems thinking
2. Help individuals embrace uncertainty
3. Encourage more entrepreneurial thinking in everyday life
4. Provide AI-augmented tools for better decision-making in entrepreneurial contexts
5. Democratize access to advanced decision-making frameworks using probabilistic program with this architecture to integrate probabilistic, relational, perceptual, social reasoning

Are you concerned with prediction or explanation or causality? Please explain it non-technically if possible.

My research is concerned with all three:

Prediction: Developing models to help entrepreneurs anticipate outcomes of their decisions in uncertain environments. I'm collaborating with JB on startup pivots and entrepreneur's prediction on the environment is key for pivot decisions.

Explanation/Causality: Explaining how entrepreneurial choices can be bundled, into choices around value creation, value delivery, value capture so that upstream choices can causally guide downstream choices on technology, customer, organization, competitor which can, in return, inform upstream choices

In short, I'm trying to help entrepreneurs make better decisions by:

• Predicting near future of environments for scenario planning to act
• Understanding interaction between entrepreneurial choice across time and space as well as cost of commitment

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Keystone problems

It would be helpful to me for you to describe one to three of what I like to think of as “Keystone problems.”

Who is the entrepreneur?

What is the industry?

Describe the product or service or action contemplated in the entrepreneurial activity?

In the form of question(s), indicate what prediction(s) would be helpful to the entrepreneur by when.

Perhaps you would like to do this for:

• a business entrepreneur

• a nonprofit idea or action entrepreneur

• an individual.

Arrows

Yes, arrows are vital in creating diagrams, and for the million diagram project. As the attached visual illustrates, I can be of help to anyone who wants to get started on the million diagram project. Feel free to spread the word. •DRAFTART-ARROWS-MEANINGS-PLAYFUL-NEW copy.pdf •HORN-2008-ArrowChart Color FINAL v5.pdf

[hyunjimoon]

applying this to the first fit in #252
with eval(founder fit) cld

Question	Business Entrepreneur (EV Company)	Nonprofit/Action Entrepreneur (Clean Energy Equity)	Individual Entrepreneur (Adtech Business)
Who is the entrepreneur?	A seasoned tech executive with experience in battery technology and AI, who previously founded a successful energy storage startup	A team of social entrepreneurs with backgrounds in community organizing, clean energy policy, and workforce development	Recent computer science graduate with experience in digital marketing and data analytics
What is the industry?	Next-generation Electric Vehicle (EV) Manufacturing, focusing on autonomous and sustainable transportation	Clean energy equity and workforce development	Adtech (Advertising Technology)
Product/Service/Action	Developing and launching a high-performance, fully autonomous electric SUV with advanced battery technology, aiming for a 500-mile range and innovative self-driving capabilities	Developing and implementing community-based programs to increase access to energy efficiency and renewable energy in underserved communities, while creating job opportunities in the clean energy sector for diverse candidates	Creating a data-driven advertising platform that optimizes ad spend and user acquisition for mobile apps
Prediction Question 1	By Q4 2025, what will be the expected cost reduction in our battery pack production if we invest $500 million in a cutting-edge, AI-driven manufacturing facility versus partnering with an established Asian battery manufacturer?	By 2025, what will be the expected reduction in energy costs for underserved communities if we expand our no-cost Home Energy Assessment program to 10 new cities in Massachusetts?	By Q4 2024, what will be the expected user retention rate if we increase our ad spend by 5% per period while maintaining our current viral growth kicker of 0.75?
Action for Question 1	- Analyze current production costs - Gather historical cost reduction data - Obtain quotes from AI manufacturing suppliers - Assess Asian battery manufacturer cost structures - Benchmark industry production costs and trends	- Collect current energy costs in target communities - Analyze historical energy savings data - Profile demographics and energy consumption in expansion cities - Benchmark success metrics from similar programs in other states	- Analyze current user acquisition and retention rates - Study historical ad spend vs retention relationship - Evaluate viral growth metrics impact on retention - Benchmark competitor retention rates - Analyze user behavior in response to ad strategies
Prediction Question 2	How will different supply chain configurations (e.g., global sourcing vs. regional manufacturing hubs) affect our production ramp-up speed and vehicle delivery times across North America, Europe, and Asia over the next 36 months?	How will different outreach strategies (e.g., partnering with local organizations, multilingual campaigns, door-to-door outreach) affect community engagement and participation rates in our energy efficiency programs across diverse neighborhoods over the next 24 months?	How will different combinations of free-to-paid user conversion rates and revenue-per-paying-user affect our profitability over the next 20 periods?
Action for Question 2	- Assess current supply chain performance - Conduct geopolitical risk assessments - Analyze shipping and logistics data - Evaluate regional manufacturing site capacities - Forecast market demand by region	- Analyze past participation rates by neighborhood - Conduct community feedback surveys - Evaluate engagement statistics for different outreach methods	- Analyze current and historical conversion and revenue metrics - Segment users to identify likely converters - Conduct pricing elasticity analysis - Analyze cost structure for profitability thresholds
Prediction Question 3	What is the probability of achieving a production rate of 500,000 vehicles per year by 2028 if we secure an additional $2 billion in funding and successfully implement our planned vertical integration strategy for key components?	What is the probability of placing 1,000 diverse candidates in clean energy jobs by 2028 if we secure an additional $5 million in funding and successfully expand our workforce development programs to 5 new states?	What is the probability of achieving a total business value exceeding $10 million within the next 20 periods, given our current user acquisition and retention strategies?
Action for Question 3	- Analyze historical production ramp-up data - Estimate vertical integration costs and timelines - Conduct market analysis of key components - Model manufacturing capacity expansion - Benchmark industry production efficiency	- Analyze current placement rates - Study job market data in clean energy sectors - Profile demographics of potential candidates - Project cost per candidate for training and placement	- Assess current business valuation and growth - Analyze industry valuation metrics - Project cash flows based on current strategies - Conduct scenario analysis for different growth rates - Analyze competitive landscape and market share
Conversational Inference	Use LLMs to analyze complex data on global supply chains, autonomous driving technologies, and battery innovations. Model various scenarios for vertical integration vs. strategic partnerships, considering factors like geopolitical risks, technological advancements, and market demand shifts. Engage in nuanced dialogues with potential investors, translating technical challenges and opportunities into clear value propositions and risk assessments.	Use LLMs to analyze data on energy consumption patterns, demographic information, and workforce trends in the clean energy sector. Model various scenarios for program expansion, considering factors like local policies, community partnerships, and evolving clean energy technologies. Engage in nuanced dialogues with potential funders and community partners, translating complex energy and workforce data into clear social impact metrics and community benefits.	Employ conversational inference to interpret user acquisition data, retention rates, and ad performance metrics. The system can translate these into probabilistic models for business growth, helping to optimize ad spend, improve user retention strategies, and predict long-term business value. It can engage in nuanced dialogues with investors, translating complex adtech metrics into clear value propositions.

[hyunjimoon]

for social entrep. column, i started #260

[Pranit-c]

For social enterprises I believe it is well though out. However, in terms of prediction, I would also like to loop in something along the lines of policy, as a lot of social changes are motivated by the need for policy, especially in the US. I think this would more so affect prediction question 2, and would recommend this slight change: affect community engagement and participation rates and policy decisions.

[Pranit-c]

Prediction Question 1: By 2025, what will be the expected reduction in energy costs for underserved communities if we expand our no-cost Home Energy Assessment program to 10 new cities in Massachusetts?

Current energy costs for households in these communities.
Historical data on energy savings from the existing Home Energy Assessment program.
Demographic and energy consumption profiles in potential expansion cities, focusing on low-income households.
Success metrics from similar programs in neighboring states, such as energy reduction percentages, cost savings per household, and community participation rates.

Prediction Question 2: How will different outreach strategies (e.g., partnering with local organizations, multilingual campaigns, door-to-door outreach) affect community engagement and participation rates in our energy efficiency programs across diverse neighborhoods over the next 24 months?
Participation rates from past initiatives in different neighborhoods.
Community feedback surveys to assess satisfaction and barriers to participation.
Engagement statistics based on outreach methods (e.g., local partnerships, door-to-door outreach, social media campaigns).

Prediction Question 3: What is the probability of placing 1,000 diverse candidates in clean energy jobs by 2028 if we secure an additional $5 million in funding and successfully expand our workforce development programs to 5 new states?
Current placement rates from our workforce development program and similar programs by other clean energy organizations.
Job market data in clean energy sectors, including the demand for roles in solar energy, energy efficiency, and green building across the target states.
Demographics of potential job candidates, focusing on marginalized groups (e.g., racial minorities, women, veterans) to assess recruitment feasibility.
Cost per candidate for training and job placement, using current financial data to project how an additional $5 million in funding could scale these efforts.

[hyunjimoon]

transferring @Pranit-c 's knowhow of Turning Prediction Questions into Actionable Data in social venture and generalizing to other two columns using cld, i added three columns (actions given question) above

[hyunjimoon]

Component	Deal-Investor Fit Mega Diagram
Objective	Create a comprehensive visual representation of deal-investor dynamics to improve decision-making and resource allocation for socially beneficial projects
Core Elements	1. Deal structures 2. Investor profiles 3. Market trends 4. Social impact metrics
Diagram Types	1. Process flows for deal negotiations 2. Decision trees for investor choices 3. Heat maps for market opportunities 4. Network diagrams for investor relationships 5. Impact matrices for social outcomes
Integration Points	1. Hyperlinks between related concepts across diagrams 2. Interactive layers showing different levels of detail 3. Time-based animations to show evolving trends
AI Application	1. Automated diagram generation from deal data 2. Pattern recognition in investor behavior 3. Predictive modeling of deal outcomes 4. Natural language processing for deal term analysis
Social Mess Cleanup	1. Visualizing resource allocation inefficiencies 2. Highlighting misalignments between investor goals and social needs 3. Identifying overlooked opportunities for impactful investments
Learning Outcomes	1. Faster comprehension of complex deal structures 2. Improved pattern recognition in successful deals 3. Enhanced understanding of the relationship between deal terms and social impact
Challenges	1. Data privacy and security 2. Keeping diagrams up-to-date with rapidly changing markets 3. Ensuring accessibility for users with varying levels of financial expertise
Next Steps	1. Define a taxonomy of deal-investor diagrams 2. Develop prototypes for key diagram types 3. Create an AI-powered diagram generation and analysis system 4. Pilot the system with a group of impact investors and social entrepreneurs

[hyunjimoon]

.

[hyunjimoon]

Aspect	Segmentation	Collaboration
Conceptual Definition	- Process of expanding beyond initial beachhead market into adjacent/different market segments during scaling - Requires additional and more fragmented efforts across marketing, sales, product development, finance, and operations - Often necessitates specialized subprocesses - Adds complexity and cost to operations functions	- Partnership with suppliers, channels, technology providers, and distributors - Critical for firms that cannot "do it all" themselves - Involves sharing value chain benefits/profits - Particularly challenging for small startups due to limited leverage with larger partners
Key Operational Implications	- May require rework of automated processes - Needs specialized subprocesses for different segments - Increases operational complexity - Challenges operations to expand activity breadth - Requires adaptation of existing processes	- Requires coordination across organizational boundaries - May involve sharing operational resources/capabilities - Needs alignment of operational processes - Must manage power dynamics in partnerships
Case Examples	- Angularity: Pivoted between equipment manufacturing and end-product markets, leveraging deep manufacturing capabilities for different segments - ASB: Added new degree programs and executive education, requiring new operational subprocesses - BGI: Developed multiple revenue streams (research, sequencing, education, healthcare, agriculture, cloud services) - Micrometal: Expanded from basic machining to serve diverse industries (machine tools, printing, aerospace, semiconductors) - SkinnyGirl: Targeted women's segment in spirits market, previously ignored by major companies	- ASB: Leveraged partnerships with MIT and Bank Negara Malaysia for resources and capabilities - MediTech: Struggled with collaboration due to power imbalance with large suppliers, leading to operational delays and eventual failure - SkinnyGirl: Initial collaboration partner couldn't scale operations to meet demand, forcing sale to larger company
Success Factors	- Strong operational capabilities that can adapt to different segments - Well-defined processes that can be modified for new segments - Ability to manage increased operational complexity - Clear understanding of operational requirements for each segment	- Clear value proposition for all partners - Balanced power relationships - Aligned operational capabilities - Strong communication and coordination mechanisms
Challenges	- Increased operational complexity - Need for specialized processes - Higher costs - Resource strain - Process adaptation requirements	- Power imbalances - Coordination difficulties - Dependency risks - Operational alignment challenges

[Pranit-c]

Questions from my side:

1. How can startups develop operational flexibility to pivot between specialized market segments while avoiding common biases (like the status quo bias or availability heuristic) that could hinder effective collaboration and segmentation?
2. What role does discipline play in establishing clear collaboration protocols and aligning operational processes across partners, especially when power dynamics and resource sharing are involved?
3. What strategies can startups employ to apply the MOM Test when establishing collaborative partnerships, ensuring that alignment of goals and value propositions is based on genuine partner needs and not biased assumptions?

[hyunjimoon]

Discussion Prompt: Using Resource-Rational Model to Bridge Our Research

I've been baking 🧱bricks for my four research products: resource-rational model (Product1) - notations for below explained in this link.
I have three questions where I'd love your thoughts on our on-going chat on sequential vs parallel search and prediction questions for entrepreneurs in different environment.

1. Environmental Selection Through Uncertainty Lens

[For Bob, regarding EV vs AdTech comparison]

Question: Could we interpret environment's selection pressure as two distinct uncertainties?

• Uncertainty about idea value
• Uncertainty about execution effectiveness

My current thinking:
I believe we could use my model's Beta distributions (pc, pr) to represent these uncertainties:

Physical (EV):
- High idea value certainty (narrow Beta for pc)
- High execution uncertainty (wide Beta for pr)

Digital (AdTech):
- High execution certainty (narrow Beta for pr)
- High idea value uncertainty (wide Beta for pc)

Key Question: Bob, how well does this map to your observations of different venture environments?

2. Parallel vs Sequential Search Parameters

[For JB, regarding search strategy optimization]

Question: Could these three parameters from my model help explain when parallel search becomes optimal?

My hypothesis for mapping:

Product1 Parameter -> Search Strategy Implication

1. σ (processing speed) -> 💸 Test/pivot cost ratio
   - High σ = cheap tests = parallel search viable
   - Low σ = expensive tests = sequential preferred

2. Beta variance -> 🎲 Uncertainty level
   - Wide Beta = high uncertainty = parallel needed
   - Narrow Beta = low uncertainty = sequential ok

3. pc/pr correlation -> 🧩 Process independence
   - Low correlation = parallel beneficial
   - High correlation = sequential better

Key Question: JB, does this mapping help explain your findings about parallel search effectiveness?

3. Dynamic Resource Allocation During Transitions

[For both]

Question: How should companies adjust their pc/pr ratio when transitioning between physical/digital products?

My thinking:

Physical -> Digital Transition:
- Initially: high pc (physical constraints)
- Gradually: increase pr weight as digital metrics emerge
- Challenge: managing dual feedback speeds

Digital -> Physical Transition:
- Initially: maintain high pr for market validation
- Gradually: increase pc as supply chain builds
- Challenge: avoiding premature pc focus

Key Questions:

• Bob: How does this match your observations of companies making these transitions?
• JB: Could this inform your thinking about adaptive search strategies?

Suggested Next Steps

1. Run simulations comparing different parameter combinations
2. Test predictions against your empirical observations
3. Identify specific cases where our models give different predictions

Thoughts? I'm particularly interested in where you see potential misalignments or missing factors in my framework.