mock competition on beachhead as startup educator vs startup

[hyunjimoon]

charlie and i are inspecting different beachhead for the simulation tool we're building.

goal: dynamic "canvas" on which

1. Theories can be tested to form cumulative startup science
2. Educators can map tested theories into curriculum to teach startups how to choose capabilities, update priors, and shape rewards
3. Startups can use it to make daily decisions

conflicting beliefs:

1. validity of our tool's output can only be tested by startup, which should precede before addressing educator market
2. we should get educator's help to go to startup market as the need for "settled science" is strongest in startup educator (e.g. scott > bill)

---

I prefer in order of startup educator > investor > startup, which is in order of my desired future identity. first, from evaluation perspective usefulness can be most accurately measured if one uses the tool. second use-inspired research will force me to address incoherency problem from Watts17_socsci_sol_ori.pdf as archivist and educator.

startup educator

Question	Answer
Product specification	Canvas on which: - Theories can be tested to form cumulative startup science - Educators can map tested theories into curriculum to teach startups how to choose capabilities, update priors, and shape rewards - Startups can use it to make daily decisions
What drives people to use/buy your product/service?	- Need tools to guide startups in making strategic decisions and adapting to market needs - Want to help startups choose the right capabilities, update their beliefs based on experimental results, and shape rewards to reach their goals faster
Who has the need or is affected by the problem?	- Educators who teach entrepreneurship and guide startups
What would happen if the problem was not solved?	- Educators would lack effective tools to teach startups how to make data-informed decisions and adapt to market needs - Startups would struggle to align their strategies and capabilities, leading to wasted resources and missed opportunities
Where is the problem taking place?	- Entrepreneurship education programs and startup accelerators
When does the problem need to be fixed?	- Educators need tools to guide startups from the ideation stage through scaling and sustainable growth
Why is it important for the problems to be fixed?	- Solving this problem can improve the effectiveness of entrepreneurship education and increase the success rate of startups
What are functional needs?	1. Interactive tools and simulations to teach startup decision-making 2. Analytical tools that combine Bayesian entrepreneurship with probabilistic computing 3. Databases and recommendation systems for data-informed guidance
What are emotional needs?	1. Confidence in guiding startups and providing actionable advice 2. Reassurance in making informed decisions and supporting startups effectively 3. Empowerment to make a positive difference in the startup ecosystem hard to keep track of new theory and confused with conflicting theories, ranking of each literature
Root Cause Analysis Example	1. Why are decisions in startups experiencing high cognitive overload? - Because the decision-making process is complex and requires processing vast amounts of information. 2. Why is the decision-making process complex and information-heavy? - Because startups operate in dynamic environments with rapidly changing market conditions, technology trends, and competitive landscapes. 3. Why are startups operating in such dynamic environments? - Because innovation and disruption drive the startup ecosystem, requiring businesses to adapt quickly to survive and thrive. 4. Why does this environment increase cognitive overload? - Because it forces startup teams to continually analyze and interpret a large volume of data, anticipate market shifts, and make rapid strategic adjustments.

startup

Question	Answer
Product specification	Canvas on which: - Theories can be tested to form cumulative startup science - Educators can map tested theories into curriculum to teach startups how to choose capabilities, update priors, and shape rewards - Startups can use it to make daily decisions
What drives people to use/buy your product/service?	- Need tools to guide startups in making strategic decisions and adapting to market needs - Want to help startups choose the right capabilities, update their beliefs based on experimental results, and shape rewards to reach their goals faster
Who has the need or is affected by the problem?	- Startups looking to align their strategies and capabilities
What would happen if the problem was not solved?	- Startups would struggle to align their strategies and capabilities, leading to wasted resources and missed opportunities
Where is the problem taking place?	- Startups operating in dynamic environments and looking to scale
When does the problem need to be fixed?	- Startups need to make decisions that help them scale effectively to improve the chances of success
Why is it important for the problems to be fixed?	- Solving this problem can help startups become more successful and sustainable
What are functional needs?	1. Analytical tools that combine Bayesian entrepreneurship with probabilistic computing 2. Databases and recommendation systems for data-informed guidance
What are emotional needs?	1. Confidence in making decisions to scale effectively 2. Assuredness that their actions will bring about a positive impact
Root Cause Analysis Example	1. Why are decisions in startups experiencing high cognitive overload? - Because the decision-making process is complex and requires processing vast amounts of information. 2. Why is the decision-making process complex and information-heavy? - Because startups operate in dynamic environments with rapidly changing market conditions, technology trends, and competitive landscapes. 3. Why are startups operating in such dynamic environments? - Because innovation and disruption drive the startup ecosystem, requiring businesses to adapt quickly to survive and thrive. 4. Why does this environment increase cognitive overload? - Because it forces startup teams to continually analyze and interpret a large volume of data, anticipate market shifts, and make rapid strategic adjustments.

startup investor

Question	Answer
Product specification	Canvas on which: - Theories can be tested to form cumulative startup science - Educators can map tested theories into curriculum to teach startups how to choose capabilities, update priors, and shape rewards - Startups can use it to make daily decisions
What drives people to use/buy your product/service?	- Need tools to guide startups in making strategic decisions and adapting to market needs - Want to help startups choose the right capabilities, update their beliefs based on experimental results, and shape rewards to reach their goals faster
Who has the need or is affected by the problem?	- Investors who fund and mentor startups
What would happen if the problem was not solved?	- Investors would continue to face high failure rates in their startup portfolios - Startups would struggle to align their strategies and capabilities, leading to wasted resources and missed opportunities
Where is the problem taking place?	- Investment firms that fund and support early-stage ventures
When does the problem need to be fixed?	- Investors need to support startups in making data-informed decisions throughout their lifecycle to maximize the chances of success
Why is it important for the problems to be fixed?	- It can help investors make better funding decisions and provide more targeted support to their portfolio companies
What are functional needs?	1. Analytical tools that combine Bayesian entrepreneurship with probabilistic computing 2. Databases and recommendation systems for data-informed guidance
What are emotional needs?	1. Reassurance in making informed decisions and supporting startups effectively 2. Confidence in providing actionable advice and maximizing ROI
Root Cause Analysis Example	1. Why are decisions in startups experiencing high cognitive overload? - Because the decision-making process is complex and requires processing vast amounts of information. 2. Why is the decision-making process complex and information-heavy? - Because startups operate in dynamic environments with rapidly changing market conditions, technology trends, and competitive landscapes. 3. Why are startups operating in such dynamic environments? - Because innovation and disruption drive the startup ecosystem, requiring businesses to adapt quickly to survive and thrive. 4. Why does this environment increase cognitive overload? - Because it forces startup teams to continually analyze and interpret a large volume of data, anticipate market shifts, and make rapid strategic adjustments.

---

some quick thoughts on discussion with charlie (TBC)

1. choose phenomenon relevant to
2. i don't think any market is stable
3. wikipedia is useful because it's "modularized, updated quickly, crowdsourced" encyclopedia. usefulness is from " " helping encyclopedia overcome temporal invalidity. i think usefulness of snackable-knowledge is hard to measure, so wish to avoid its production.