[[🌙simulated collaboration based on observed belief and goal of role model charlie, scott, vikash]] | Professor | Key Expertise | Connection to Tesla Case Study | Optimism of the Professor I'm Betting On | models i've used<br>(some i found not useful and/or usable) | Contribution to Your Research | dilemmas (tradeoff) | evaluate(angie's research) | | ----------------------- | ----------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- | | 🟩**Vikash Mansinghka** | Probabilistic programming (GEN platform, SPPL, rational semantic frameworks) | Could enhance modeling of Tesla's "cowboy engineer" culture through probabilistic programming principles | Highly optimistic about probabilistic programming as a human-like, rational alternative to deep learning for complex decision environments | probabilistic program - localization in my domain - my pose and head<br><br>(many are not usuable yet) | Technical foundation for your Domain-Specific Language development; expertise in rational alternatives to deep learning | [[📜planning with theory of mind for few shot adaptation in sequential social dilemmas]] | technical feasibility<br><br>[[nishad_gothoskar]]'s<br>[[nishad office hour_otter_ai.txt]] | | 🔴🟩**Moshe Ben-Akiva** | Discrete choice modeling, random utility theory, latent class analysis | Can help formalize Tesla's evolving market conditions and stakeholder utility functions | Confident that precise quantitative modeling of decision-making behavior provides essential foundations for strategic planning | made a survey featuring case study re-structuring to elicit stated and revealed preference<br><br>(biogeme needs software update) | Rigorous methods to enhance your stakeholder utility structures and decision-making primitives | | technical feasibility | | 🔴🔷**Charlie Fine** | Value chain models, industry clockspeed, "nail it, scale it, sail it" framework | Direct application to Tesla's scaling challenges and operational evolution | Believes in sustainable entrepreneurial success through adaptive operations and understanding industry evolution patterns | value chain of my vision (10yr plan)<br> | Operational/value-chain perspective to validate your five primitives within industry contexts | [[dilemma]]<br><br>[[cul_collab_cap_seg_eval]] | operational feasibility<br>[[ocean_jangda]]'s <br>[[10104research to ocean_otter_ai.txt]] | | 💜**Scott Stern** | Bayesian economic frameworks, innovation ecosystem mapping, Entrepreneurial Compass | Valuable for modeling Tesla's innovation ecosystem and entrepreneurial decision-making approach | Optimistic about Bayesian inference as foundational for entrepreneurial decision-making; advocates for systematic testing in innovation | test two choose one <br><br>four strategy<br><br>(high/low bar only useful for knowing relative optimism, not absolute) | Entrepreneurial economics lens and theoretical positioning for your work; aligns with your Bayesian structure learning | choose 1 (resource-constraint) | desirability<br>[[svafa_gronfeldt]]'s<br><br>[[Entrepreneur Strategy Integration_otter_ai.txt]] | | 💜**Jinhua Zhao** | Behavioral science implementation, field experiments, transportation focus | Insights on behavioral aspects of Tesla's market approach and transportation industry transformation | Optimistic about behavioral science-based interventions for effective policy and operations in transportation and mobility | strategy guides operation and operation informs strategy<br><br>i admire T5 vision but i reserved concerns for applying inverse RL to career path <br> | Knowledge for translating your framework into practical applications, especially in mobility | | desirability | 2025-04-23 four step logic from [preparing vikash's class proposal cld](https://claude.ai/chat/c1adb345-6443-413d-8ae2-60248e149703) 1. state of the art of entrepreneurship decision making does not have a formal way to include operational variables and does not have a way to simultaneously consider multiple stakeholders in a resource-rational manner 2. my contribution to entrepreneurship decision making is: 1. to include certain entrepreneur operational variables explicitly into the entrepreneurship decision process 2. to explicitly include multiple stakeholders decision process 3. In the absence of thinking about operational variables, an entrepreneur is likely to do 🚨, but when including the considerations of operational variables, the entrepreneur is has a much more subtle, sophisticated appreciation of the decision that must be made, and therefore they'll do not 🚨. 4. secret sauce of 3 is, probabilistic program which is syntactically restricted and probabilistic while semantically rich. using this, program for founder that develops using three tools (culturate, collaborate, capitalize) interact with synthetic population's utility and resource of mobility innovation ecosystem using two tools (evaluate, segment) to aid entrepreneurs make meaningful choices by lowering its complexity. | Complexity Type | Challenge | Description | Mathematical Expression | | ----------------------- | --------------------------------------------- | ------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------- | | **Temporal Complexity** | Struggle with timing decisions | Difficulty determining when to pivot versus persevere due to lack of structured feedback mechanisms | O_t+1 = g(O_t, ω_t) creates uncertainty about future states | | **Temporal Complexity** | Miss critical bottlenecks | Failure to identify constraints that could be addressed with minimal resources but yield maximum impact | Suboptimal identification of constraint shifts in A_t·x_t ≤ b_t | | **Temporal Complexity** | Make suboptimal resource allocation decisions | Inability to identify which activities create the most value over time | U_t = f(x_t, O_t) evolution not optimally tracked | | **Spatial Complexity** | Face overwhelming cognitive complexity | Simultaneously managing 100+ decision variables without clear prioritization framework | Dimensionality of x = (x_1, x_2, ..., x_n) exceeds cognitive capacity | | **Spatial Complexity** | Experience decision paralysis | Multiple stakeholder needs without structured evaluation method | Non-linear interaction effects δ_ij·x_i·x_j create evaluation challenges | | **Spatial Complexity** | Follow industry norms or biases | Defaulting to conventions rather than systematic evaluation of operational contexts | Simplified heuristics used instead of properly modeling U(x_1, x_2, ..., x_n) | Entrepreneurial decision-making faces dual complexity challenges that render traditional optimization approaches computationally intractable. The state of the art in entrepreneurship research lacks formal methods to incorporate operational variables and simultaneously consider multiple stakeholders in a resource-rational manner. This paper introduces a novel framework addressing both temporal complexity (opportunity-dependent utility functions evolving stochastically over time) and spatial complexity (non-additive utility structures from interdependent stakeholder decisions). Without structured approaches to these complexities, entrepreneurs face overwhelming cognitive demands, miss critical bottlenecks, experience decision paralysis across stakeholder needs, and default to industry norms rather than context-appropriate strategies. We demonstrate that probabilistic programming—specifically, syntactically restricted but semantically rich models—can transform this NP-complete decision space into manageable heuristics. Our approach explicitly integrates operational variables (culturate, collaborate, capitalize) with multi-stakeholder considerations (evaluate, segment) to create a generative program that supports entrepreneurial decision-making under uncertainty. Using Tesla's electric vehicle development as a case study, we show how this probabilistic framework identifies bottleneck-breaking operations that lower temporal complexity while maintaining strategic-operational consistency to address spatial complexity. This research contributes to entrepreneurship theory by formalizing the relationship between resource-rational decision-making and operational variables, while providing practitioners with practical tools for navigating complex entrepreneurial landscapes, particularly in rapidly evolving domains like mobility innovation. [[🗄️🧠vikash]] [[🗄️n2s2n]] [[📝Programmatic Theory in Entrepreneurship with Integrated Reasoning and Rational Meaning Construction]] [[📜zhao24_interdisciplinary_urban]] 2025-04-21 [[🌙simulated collaboration based on observed belief and goal of role model charlie, scott, vikash]] | # | User | Sections | 🛠️ NSS Tool | Basis | Collaborate | Three Bullet Point Summary | Subsections | On Probabilistic Program | By Product | Relevant Paper | Example (EV Startup: "UrbanVolt") | | -------------------------------------------------------------------------------------- | ---------------------- | ------------------------------------------------------------------------------- | -------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------ | ---------------------------------------------- | ---------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **0. Probabilistic Program** | Entrepreneur + Society | Probabilistic Reasoning | | Probabilistic frameworks, hierarchical Bayes, exchangeability, generative + inference programs | [[🗄️🧠vikash]] | - Formalize nested uncertainties- Dynamic inference from subjective beliefs- Probabilistic reasoning for irreversible decisions | - Inference | Hierarchical Bayesian, Generative Programs | | Rationalizing Entrepreneurial Learning | UrbanVolt uses a Bayesian program to dynamically infer market entry timing based on uncertain urban EV policy changes. They regularly update market-entry probabilities using policy monitoring and customer sentiment analysis. | | **1. Founder's Perspective Culturate-Collaborate-Capitalize-Segment by Charlie-Scott** | Entrepreneur | Founder's perspective culturate-collaborate-capitalize-segment by charlie-scott | Culturate-Collaborate-Capitalize | Microeconomics, innovation economics ([[🗄️🧠scott]])Operations strategy ([[🗄️🧠charlie]])Program synthesis ([[🗄️🧠vikash]]) | [[🗄️🧠charlie]]-[[🗄️🧠vikash]]-[[🗄️🧠scott]] | - Align internal/external quality perceptions- Strategic capital allocation- Optimal collaborative structures | - Culture- Capital- Segment- Collaborate | Dynamic Quality Improvement, Program Synthesis | Operational tools for scaling | Complexity of Entrepreneurial Decision Making and Strategy | UrbanVolt builds an internal culture emphasizing sustainable innovation while externally showcasing pilot projects and customer satisfaction metrics, strategically partnering with Panasonic for battery tech and local cities for charging infrastructure pilots. | | **2. Social Planner's Perspective Evaluate-Capitalize** | Social Planner | Social planner's perspective evaluate-capitalize | Evaluate-Capitalize | Behavioral Science, AI, Urban Mobility ([[🗄️🧠jinhua]])Discrete Choice Analysis ([[🗄️🧠moshe]])System Dynamics ([[🗄️🧠charlie]], [[tom_fiddaman]], [[hazhir_rahmandad]], [[john_sterman]]) | [[🗄️🧠jinhua]] | - Proactive utility shaping- Concurrent engineering integration- Bayesian experimentation | - Behavioral Science- System Dynamics- Choice Analysis | Probabilistic Program for Social Planner | Equity Proposal as Action Converging towards Optimal Term Sheets with Conversational Inference | | UrbanVolt works with cities to develop proactive metrics such as cost-per-mile and carbon reductions, applying discrete choice models to shape public transit electrification and integrating concurrent engineering in infrastructure rollout. | | **4. Mobility Innovation Ecosystem** | Entrepreneur + Society | Mobility Innovation Ecosystem Dynamics | Evaluate-Capitalize-Collaborate | MIT heritage in automotive thinking, dynamics of innovation mobility ecosystem | MIT Leadership | - Ecosystem-level collaboration- Concurrent engineering for market adoption- Probabilistic assessment of systemic impact | - Heritage & Evolution | Probabilistic Program for Ecosystem Modeling | MIT Automotive Legacy | AI Co-founder: A Computational Cognitive Approach to Entrepreneurial Paradox | UrbanVolt collaborates within MIT's innovation ecosystem, using concurrent engineering to speed up prototyping. They systematically assess systemic impacts on local traffic and energy grids through probabilistic simulations, supporting iterative policy engagement and infrastructure scaling. | 2025-04-20 | # | User | Sections | 🛠️ NSS Tool | Basis | Collaborate | Three Bullet Point Summary | Subsections | On Probabilistic Program | By Product | Relevant Paper | | --- | ---------------------- | ----------------------------------------------- | ------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------ | ---------------------------------------------- | --------------------------------------------------- | -------------- | | 0 | Entrepreneur + Society | Probabilistic Program | Probabilistic Reasoning | Probabilistic frameworks, hierarchical Bayes, exchangeability, generative + inference programs | [[🗄️🧠vikash]] | - Formalize nested uncertainties<br>- Dynamic inference from subjective beliefs-<br>Probabilistic reasoning for irreversible decisions | - Inference | Hierarchical Bayesian, Generative Programs | | | | 1 | Entrepreneur | Culturate-Collaborate-Segment-Capitalize | Entrepreneurial Operations | Microeconomics, innovation economics ([[🗄️🧠scott]]) Operations strategy ([[🗄️🧠charlie]]) Program synthesis ([[🗄️🧠vikash]]) | [[🗄️🧠charlie]]-[[🗄️🧠vikash]] | - Align internal/external quality perceptions<br>- Strategic capital allocation<br>- Optimal collaborative structures | - Culture<br>- Capital<br>- Segment<br>- Collaborate | Dynamic Quality Improvement, Program Synthesis | Operational tools for scaling | | | | | | | [[🗄️🧠scott]]: Economics of ideas, innovation, entrepreneurship | | | Microeconomics, Innovation Economics | | | | | | | | | [[🗄️🧠charlie]]: Operations, manufacturing strategy, dynamic quality | | | Operations Strategy | | | | | | | | | [[🗄️🧠vikash]]: Program synthesis | | | Program Synthesis | Probabilistic computing for decision support | | | | 2 | Social Planner | Meaningful Mobility Innovation Ecosystem Metric | Evaluate-Capitalize | Behavioral Science, AI, Urban Mobility ([[🗄️🧠jinhua]])Discrete Choice Analysis ([[🗄️🧠moshe]])System Dynamics ([[🗄️🧠charlie]], [[tom_fiddaman]], [[hazhir_rahmandad]], [[john_sterman]]) | [[🗄️🧠jinhua]]-[[🗄️🧠scott]] | - Proactive utility shaping- Concurrent engineering integration- Bayesian experimentation | - Behavioral Science- System Dynamics- Choice Analysis | Probabilistic Program for Social Planner | [[15_357_proposal_MoonZhang_angie25_annotated.pdf]] | social prior | | | | | | [[🗄️🧠jinhua]]: Frontier of transportation research | | | Behavioral Science, Urban Mobility | | | | | | | | | [[🗄️🧠charlie]]: Value chain clockspeed, system dynamics | | | System Dynamics | | | | | | | | | [[🗄️🧠moshe]]: Discrete choice analysis, stated and revealed preference | | | Choice Analysis | | | | | 3 | Entrepreneur + Society | Bayesian Experiment | Probabilistic Computing | Bayesian experimentation, decision under uncertainty, exchangeability, structural learning | [[🗄️🧠scott]]-[[🗄️🧠vikash]] | - Adaptive experimentation strategies- Structural model-based decision making- High variance parallel testing | - Bayesian Methods | Bayesian inference frameworks | Generative inference | | | 4 | Entrepreneur + Society | Mobility Innovation Ecosystem | Evaluate-Capitalize-Collaborate | MIT heritage in automotive thinking, dynamics of innovation mobility ecosystem | MIT Leadership | - Ecosystem-level collaboration- Concurrent engineering for market adoption- Probabilistic assessment of systemic impact | - Heritage & Evolution | Probabilistic Program for Ecosystem Modeling | MIT Automotive Legacy | | [[📝moon24_csv_ai_cofounder]] | # | User | Sections | 🛠️ NSS Tool | Basis | Collaborate | Three Bullet Point Summary | Subsections | On Probabilistic Program | By Product | Relevant Paper | Example (EV Startup: "UrbanVolt") | | --- | ---------------------- | ----------------------------------------------- | ------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------ | ---------------------------------------------- | ----------------------------- | --------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | 0 | Entrepreneur + Society | Probabilistic Program | Probabilistic Reasoning | Probabilistic frameworks, hierarchical Bayes, exchangeability, generative + inference programs | [[🗄️🧠vikash]] | - Formalize nested uncertainties<br>- Dynamic inference from subjective beliefs<br>- Probabilistic reasoning for irreversible decisions | - Inference | Hierarchical Bayesian, Generative Programs | | | UrbanVolt uses a Bayesian program to dynamically infer market entry timing based on uncertain urban EV policy changes. They regularly update market-entry probabilities using policy monitoring and customer sentiment analysis. | | 1 | Entrepreneur | Culturate-Collaborate-Segment-Capitalize | Entrepreneurial Operations | Microeconomics, innovation economics ([[🗄️🧠scott]])Operations strategy ([[🗄️🧠charlie]])Program synthesis ([[🗄️🧠vikash]]) | [[🗄️🧠charlie]]-[[🗄️🧠vikash]] | - Align internal/external quality perceptions<br>- Strategic capital allocation<br>- Optimal collaborative structures | - Culture- Capital- Segment- Collaborate | Dynamic Quality Improvement, Program Synthesis | Operational tools for scaling | | UrbanVolt builds an internal culture emphasizing sustainable innovation while externally showcasing pilot projects and customer satisfaction metrics, strategically partnering with Panasonic for battery tech and local cities for charging infrastructure pilots. | | 2 | Social Planner | Meaningful Mobility Innovation Ecosystem Metric | Evaluate-Capitalize | Behavioral Science, AI, Urban Mobility ([[🗄️🧠jinhua]])Discrete Choice Analysis ([[🗄️🧠moshe]])System Dynamics ([[🗄️🧠charlie]], [[tom_fiddaman]], [[hazhir_rahmandad]], [[john_sterman]]) | [[🗄️🧠jinhua]]-[[🗄️🧠scott]] | - Proactive utility shaping<br>- Concurrent engineering integration<br>- Bayesian experimentation | - Behavioral Science- System Dynamics- Choice Analysis | Probabilistic Program for Social Planner | | [[15_357_proposal_MoonZhang_angie25_annotated.pdf]] | UrbanVolt works with cities to develop proactive metrics such as cost-per-mile and carbon reductions, applying discrete choice models to shape public transit electrification and integrating concurrent engineering in infrastructure rollout. | | 3 | Entrepreneur + Society | Bayesian Experiment | Probabilistic Computing | Bayesian experimentation, decision under uncertainty, exchangeability, structural learning | [[🗄️🧠scott]]-[[🗄️🧠vikash]] | - Adaptive experimentation strategies<br>- Structural model-based decision making<br>- High variance parallel testing | - Bayesian Methods | Bayesian inference frameworks | Generative inference | | UrbanVolt implements adaptive A/B testing of charging station locations and battery leasing models, structurally updating their decisions based on Bayesian inference of consumer adoption data. | | 4 | Entrepreneur + Society | Mobility Innovation Ecosystem | Evaluate-Capitalize-Collaborate | MIT heritage in automotive thinking, dynamics of innovation mobility ecosystem | MIT Leadership | - Ecosystem-level collaboration<br>- Concurrent engineering for market adoption<br>- Probabilistic assessment of systemic impact | - Heritage & Evolution | Probabilistic Program for Ecosystem Modeling | MIT Automotive Legacy | | UrbanVolt collaborates within MIT's innovation ecosystem, using concurrent engineering to speed up prototyping. They systematically assess systemic impacts on local traffic and energy grids through probabilistic simulations, supporting iterative policy engagement and infrastructure scaling. | | | user | sections | 🛠️nss tool | basis | collaborate | three bullet point summary | subsections | on probabilistic program | by product | relevant paper | | | -------------------------------------------------- | ---------------------- | ----------------------------------------- | -------------------------------- | ---------------------------------------------------------------------------------------------------------------------- | -------------------------------- | -------------------------- | ----------- | ------------------------ | ---------- | -------------------------------------------------------------------- | --- | | 0. probabilistic program | entrepreneur + society | ![[Pasted image 20250420210138.png\|200]] | | | | | | | | | | | 1. culturate-collaborate-segmentc-apitalize- | entrepreneur | ![[Pasted image 20250420210231.png\|300]] | culturate-collaborate-capitalize | | [[🗄️🧠charlie]]-[[🗄️🧠vikash]] | | | | | | | | | | ![[Pasted image 20250420215109.png\|100]] | | [[🗄️🧠scott]] Micro Economics, Economics of idea, innovation, entrepreneurship | | | | | | | | | | | ![[Pasted image 20250420143556.png\|100]] | | [[🗄️🧠charlie]] operations for entrepreneurs, manufacturing strategy, dynamic quality improvement<br> | | | | | | | | | | | ![[Pasted image 20250420214958.png\|100]] | | [[🗄️🧠vikash]] program synthesis | | | | | | | | | 2. meaningful mobility innovation ecosystem metric | social planner | ![[Pasted image 20250420210158.png\|300]] | evaluate-capitalize | | [[🗄️🧠jinhua]]-[[🗄️🧠scott]] | | | | | | | | | | ![[Pasted image 20250420140904.png\|30]] | | [[🗄️🧠jinhua]] Behavioral Science, AI, Urban Mobility, Frontier of Transportation Research | | | | | | | | | | | ![[Pasted image 20250420215446.png\|200]] | | [[🗄️🧠charlie]] clockspeed (value chain), system dynamics<br>[[tom_fiddaman]], [[hazhir_rahmandad]], [[john_sterman]] | | | | | | | | | | | ![[Pasted image 20250420215702.png\|200]] | | [[🗄️🧠moshe]] discrete choice analysis, stated and revealed preference | | | | | | | | | | | ![[Pasted image 20250420214910.png\|100]] | | probabilistic program for mobility social planner | | | | | | [[15_357_proposal_MoonZhang_angie25_annotated.pdf]]<br>social prior, | | | 4. mobility innovation ecosystem | | ![[Pasted image 20250420214640.png]] | | | | | | | | | | meaning of "self-consistency for learning rule" - calibration - conscientious contrarian (strategy / computation), bottleneck breaking (operation / algorithmic) - [[🌕lunar society]]'s david mentioned connection between industry + mobility + transportation - ![[Pasted image 20250420045945.png|100]] 2025-04-19 todo: 1. synthesize quality evaluation (replace abstracted "generative ai technology" with ) 2. [[Environmental Performance of New Mobility]] [[mmi_bhuvan_altauri]] https://youtu.be/iF36kcjxQrA https://www.mmi.mit.edu/_files/ugd/29d096_bda3b06073204ed0901ec3394afbffac.pdf https://youtu.be/pvvQgO-WPwU https://www.mmi.mit.edu/_files/ugd/29d096_0ff9c04ac8b046d7a3dcfa3d68fa4ee5.pdf [[narrow the gap of observation and intervention unit]], can the logic of trade and probabilistic [[mmi2024]], [[cul_collab_cap_seg_eval]] 2025-04-14 | Section | Question | Answer | Literature | | ------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------ | ---------- | | Abstract | What is the motivation, objectives, methods, and key results of your research? | | | | Introduction | Why is this research important and how does it connect to broader societal problems? What existing literature relates to your work, and what gaps does your research fill? | | | | Methods | How did you approach your research, and how do you address uncertainty? | | | | Results | What specific outcomes were observed and how was uncertainty quantified and addressed? | | | | Discussion | How do your results compare with other studies, and how have you addressed the long-term applications mentioned in your introduction? | | | - by broadening that lens (for example, by **augmenting the test quantity vector** to higher dimensions or making it depend on data), we effectively shine a light on the difference between what is merely random variation and what is correctible ignorance - converges to continuous calibration as simulations grow dense - 2025-04-12 | Section | Question | Answer | Literature | | ------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Abstract | What is the motivation, objectives, methods, and key results of your research? | The research addresses the need for a systematic, scalable method of entrepreneurial experimentation, motivated by inconsistencies and inefficiencies in traditional trial-and-error approaches within mobility startups. The primary objective is developing a Bayesian-style parallel experimentation framework, using probabilistic program synthesis and cognitive model synthesis techniques. Methods involve segmentation, societal evaluation, and iterative decision-making cycles, employing computational cognitive models and probabilistic inference. Key results indicate that startups using this structured framework significantly improve their adaptability, decision quality, and societal alignment, with demonstrably reduced strategic uncertainty and enhanced resource allocation efficiency. | Afeyan & Pisano (2024); Stern et al. (2023); Fine (2024); Mansinghka (2024); Startup Strategy Presentation (2024); Angie AI Cofounder (2024); CogSci Model Synthesis (2025). | | Introduction | Why is this research important and how does it connect to broader societal problems? What existing literature relates to your work, and what gaps does your research fill? | Entrepreneurial experimentation significantly impacts society through economic growth and innovation, especially within the mobility sector (e.g., electric vehicles, micro-mobility). Current trial-and-error methods are inefficient, creating unnecessary financial and operational risks. This research addresses these issues by proposing a structured Bayesian parallel experimentation framework, allowing startups to systematically explore strategic alternatives with reduced risks. Existing literature on Bayesian entrepreneurship (Afeyan & Pisano, 2024), transportation systems analysis (Ben-Akiva, 2024), cognitive modeling (Mansinghka, 2024), and operational frameworks (Fine, 2024) provide foundational insights, but a comprehensive method integrating these into entrepreneurial decision-making processes remains absent. This research fills that gap by synthesizing these perspectives into a practical, scalable decision-making model. Short-term goals include developing and testing the Bayesian framework in simulated mobility startup scenarios, while long-term goals involve widespread practical deployment in real-world entrepreneurship contexts to foster sustainable innovation and robust economic growth. | Afeyan & Pisano (2024); Ben-Akiva (2024); Fine (2024); Mansinghka (2024); Stern et al. (2023); Angie AI Cofounder (2024); CogSci Model Synthesis (2025). | | Methods | How did you approach your research, and how do you address uncertainty? | The approach employs iterative Bayesian methods through probabilistic program synthesis combined with computational cognitive modeling. It begins with segmenting market contexts and societal values, followed by synthesizing societal utility functions based on policy incentives and societal preferences. The decision-making strategy employs probabilistic program synthesis to create scenario-specific action plans, continuously updated via Bayesian inference as experimental outcomes emerge. Uncertainty is explicitly managed through Bayesian updating, quantifying uncertainties and systematically reducing them with data-driven feedback loops. Robustness of the probabilistic approach was validated through sensitivity analyses and simulation-based calibration checks, ensuring the model's predictions remain valid under varying levels of informational uncertainty and scenario complexities. | Ben-Akiva (2024); Mansinghka (2024); Angie AI Cofounder (2024); CogSci Model Synthesis (2025); Stern et al. (2023). | | Results | What specific outcomes were observed and how was uncertainty quantified and addressed? | Results show significant improvements in decision clarity, resource allocation, and strategic alignment between startups and societal goals. Specifically, scenarios demonstrated that startups using Bayesian parallel experimentation frameworks achieved higher expected utility (market traction, profitability, societal impact) compared to traditional single-path experimentation. Uncertainty quantification, achieved through Bayesian posterior distributions, indicated progressive reduction in critical decision parameters (e.g., market adoption rates, resource availability) throughout experimentation cycles. Uncertainty handling involved repeated scenario testing and sensitivity analyses, ensuring model robustness and practical reliability. Quantitative metrics showed consistent improvement in predictive accuracy and decision effectiveness, directly correlated with iterative Bayesian updates driven by real-world feedback data. | Angie AI Cofounder (2024); CogSci Model Synthesis (2025); Afeyan & Pisano (2024); Mansinghka (2024). | | Discussion | How do your results compare with other studies, and how have you addressed the long-term applications mentioned in your introduction? | Comparative analysis shows this Bayesian framework notably outperforms conventional entrepreneurial decision frameworks (Afeyan & Pisano, 2024; Stern et al., 2023), particularly regarding adaptability and robustness under uncertainty. Unlike traditional models which often neglect explicit uncertainty quantification or rely on ad-hoc experimentation methods, this structured Bayesian approach demonstrates clear empirical advantages, especially in systematically reducing decision uncertainty over multiple experimentation cycles. Addressing long-term application goals, this research developed a scalable and computationally efficient decision-making tool (the "AI co-founder"), validated in realistic mobility venture scenarios. Long-term implications include facilitating sustainable, scalable entrepreneurial strategies widely applicable across various sectors, significantly enhancing innovation ecosystems by aligning startup strategies closely with societal utilities and improving economic resilience through informed decision-making. Future research involves empirical field testing with real-world startups, further enhancing model validity and practical applicability. | Afeyan & Pisano (2024); Stern et al. (2023); Fine (2024); Mansinghka (2024); Angie AI Cofounder (2024). | 2025-04-10 | three solution module development in parallel | evaluators | efforts to gravitate mental models | | ---------------------------------------------------------------------------------------------------- | ---------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | 1. quality measure for mobility innovation ecosystem [[📐]] | [[🗄️🧠jinhua]]<br>[[🗄️🧠scott]]<br> | | | | | | | 2. simulation-based experimentation for bayesian entrepreneurship [[💭bayes(ent)]] | [[🗄️🧠moshe]]<br>[[🗄️🧠charlie]]<br>[[🗄️🧠scott]] | <br> | | | | [[session5 - frontiers of bayesian decision making]]<br>- Bayesian entrepreneurship and decision-making<br>- Novice vs expert reasoning in new domains<br>- Intuitive theory development<br>- Evaluation of novel systems/opportunities<br>- Recursive/social reasoning about what others don't know | | 3. entrepreneurial operations using rational meaning construction implemented with program synthesis | [[🗄️🧠vikash]]<br>[[🗄️🧠charlie]]<br> | | --- [mapping gulliver and angie's travel cld](https://claude.ai/chat/d1c80997-5b76-48bd-afd2-9d130e9f27d3) - cow's food optimized with nutrition and cost tasted too awful so cows didn't eat - 🧱[[📜swift_guilliver's travel]] - [[🛠️selling probabilistic program to innovation]] and [[🌙nsp1(sbc, be)]] Simulation-Based Calibration (SBC), paired with Bayesian Entrepreneurship (BE), addresses critical limitations across practical limitations of applying four methods. SBC mitigates Optimization method's tendency toward narrow metrics by validating these metrics with real-world feedback, aligning decisions with broader entrepreneurial goals. For instance, entrepreneurs should regularly check if lowering supply chain costs actually leads to market success (tesla overestimating cost labor to supply chain cost). For Agent-Based Simulation, SBC reduces calibration complexity by reliably calibrating detailed agent parameters despite uncertainty at broader levels, uncovering emergent system behaviors. For instance, such as accurately modeling customer adoption patterns even without complete data (moshe's stratified sampling + iterative population fitting). In Differential Equation Simulation, SBC supports flexible recalibration of model parameters, counteracting rigid structures that might diverge from observed realities. For instance, "?" Lastly, SBC helps Bayesian methods handle novel conditions effectively by maintaining reliable detailed decisions even when broader entrepreneurial assumptions or priors remain uncertain, enhancing practical adaptability and responsiveness. For instance, "?" Bayes in holes Simulation-Based Calibration (SBC), paired with Bayesian Entrepreneurship (BE), addresses critical limitations across diverse methodological approaches. SBC mitigates Optimization's tendency toward narrow metrics by validating these metrics with real-world feedback, aligning decisions with broader entrepreneurial goals—for instance, . For Agent-Based Simulation, SBC reduces calibration complexity by reliably calibrating detailed agent parameters despite uncertainty at broader levels, uncovering emergent system behaviors—such as accurately modeling customer adoption patterns even without complete data. In Differential Equation Simulation, SBC supports flexible recalibration of model parameters, counteracting rigid structures that might diverge from observed realities—for example, quickly adjusting forecasting models when unexpected market disruptions occur. Lastly, SBC helps Bayesian methods handle novel conditions effectively by maintaining reliable detailed decisions even when broader entrepreneurial assumptions or priors remain uncertain, enhancing practical adaptability and responsiveness—such as rapidly adapting priors after encountering unprecedented market shifts. Simulation-Based Calibration (SBC), paired with Bayesian Entrepreneurship (BE), addresses critical limitations across diverse methodological approaches. SBC mitigates Optimization's tendency toward narrow metrics by validating these metrics with real-world feedback, aligning decisions with broader entrepreneurial goals—for instance, regularly checking if optimized supply chain costs lead to practical market success. For Agent-Based Simulation, SBC reduces calibration complexity by reliably calibrating detailed agent parameters despite uncertainty at broader levels, uncovering emergent system behaviors—such as accurately modeling customer adoption patterns without complete data. In Differential Equation Simulation, SBC supports flexible recalibration of model parameters, counteracting rigid structures that might diverge from observed realities—for example, quickly adjusting forecasting models when unexpected market disruptions occur. Lastly, SBC helps Bayesian methods handle novel conditions effectively by maintaining reliable detailed decisions even when broader entrepreneurial assumptions or priors remain uncertain, enhancing practical adaptability and responsiveness—such as rapidly adapting priors after encountering unprecedented market shifts. | **Four Countries**(Methodological School) | **Origin & Vision** | **Gulliver’s Symbol and Lesson** | **Practical Limitation** | **Simulation-Based Calibration Improvements** | | --------------------------------------------- | -------------------------------------------------------------------------------------- | ---------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------- | | **[[Lilliput (Optimization)]]** | - Origin: War (Operations/Management)<br><br>- Vision: Minimize uncertainty to “win” | [[🥚Egg-breaking controversy]]<br><br>Missing the forest for the trees | - Over-focus on one metric (e.g. cost; optimized cow's food that tastes awful)<br>- Trivial optimization ignoring bigger-picture constraints | - Periodic Bayesian re-estimation ensures metrics align with reality- Simulation validates optimized solutions for hidden trade-offs | | [[Brobdingnag (Agent-Based simulation)]] | - Origin: Society, human behavior<br><br>- Vision: Modeling emergence and complexity | [[🔍Magnifying glass on human flaws]] | - High calibration burden for agent rules<br><br>- Complexity can hide emergent patterns when data is scarce | - Calibrate agents with real data at intervals (e.g., 2-week cycles)- Use Monte Carlo simulations to reveal system-level dynamics | | [[Laputa (Differential Equation simulation)]] | - Origin: Relation-based math<br><br>- Vision: Abstract structures & relationships | [[👡Flappers to jolt abstract thinkers]] | - Equations can drift from real-world realities<br><br>- Rigid structure makes it slower to adjust to new variables and emergent feedback loops | - Re-estimate parameters regularly with fresh data- Posterior predictive checks to detect model drift from observed behavior | | [[Houyhnhnms (Bayesian)]] | - Origin: Statistics, cognition<br><br>- Vision: update belief given belief and desire | [[🐴Logical debates among horses]] | - Can overlook unknown unknowns<br><br>- Strict reliance on prior data struggles with truly novel events | - Integrate simulation-based posterior checks to spot inconsistencies- Use adaptive priors that learn in real-time to handle novel conditions | --- ### Tips for Using the Table 1. **Parallel Format:** Each row covers the same points, so it’s easy to compare methods. 2. **Concrete Bullet Points:** Brief statements clarify each method’s strengths, flaws, and simulation-based fixes. 3. **Real-World Relevance:** The final column shows _how_ Bayesian-simulation-optimization synergy addresses each limitation. Feel free to adjust wording or add examples specific to your thesis context. Let me know if you need any further refinements! ## Key Implications for Thesis Structure: 1. **Integration**: Each methodological approach reveals different aspects of truth, just as each of Swift's countries highlights different human foibles 2. **Complementarity**: The weaknesses of each approach can be offset by strengths of others: - Optimization's granularity + Agent-Based Simulation's emergence - DE's elegant abstraction + Bayesian calibration - etc. 3. **GPT Agent Role**: The agent can serve as: - Translator between approaches - Pattern identifier across methods - Bridge between theory and application - Guard against each method's characteristic pitfalls [[💠integ(process-product)]] can be used to pivot one's way to one's destination. I show rejection sampling using self consistency equation is equivalent to generating cut in a lifted matching polyhedron. This has implications on individual and societal level. First using local balance between meaning p(s|o) and judging p(a|s). Second using which can be operationalized as # Bayesian Calibrated Choice (BCC) ## Hypotheses This thesis examines two fundamental hypotheses about Bayesian Calibrated Choice (BCC). First, BCC provides an effective framework through its self-consistent learning rule, which we demonstrate through simulation-based calibration. Second, BCC offers computational efficiency by reducing the dimensionality of the exploration problem, analyzed from both individual founders' and society's perspectives. ## Chapter 1: Research Objective 🚨🚨🚨🚨🚨 could you make research objective more coherent with hypothesis? also, "a framework for understanding the sources of belief heterogeneity, and algorithms for both societal matching and individual pathfinding in entrepreneurial decision-making." seems less relevant with what comes before. In business and economics, calibration serves as a crucial foundation for value-based action. While Bayesian theorem provides a philosophical foundation for calibration, traditional sequential decision-making applications assume state and actions are given. However, in entrepreneurial contexts, states that affect actions can always be decomposed further, making the distinction between state and action fluid. This thesis addresses this challenge by developing two key components: a framework for understanding the sources of belief heterogeneity, and algorithms for both societal matching and individual pathfinding in entrepreneurial decision-making. 🚨🚨🚨🚨🚨 ## Chapter 2: Theoretical Background on Bayesian Calibrated Choice ### 2.1 Foundations of BCC #### 2.1.1 Optimization Approach The optimization approach integrates insights from decision science and statistical decision theory as developed by Charlie, alongside discrete choice analysis from Mosche. This foundation provides the mathematical framework for understanding how agents optimize their choices under uncertainty. #### 2.1.2 Bayesian Approach The Bayesian approach combines Bayesian statistics, pioneered by Andrew, with Bayesian cognition developed by Josh. This integration encompasses SBC/prior/posterior re-calibration, Bayes factor analysis, rational meaning construction, and inverse planning for mental model inference, providing a comprehensive framework for understanding how agents update their beliefs and make decisions. #### 2.1.3 Simulation Approach System dynamics, as developed by John, along with dynamic modeling and calibration techniques from Tom, provide the foundation for our simulation approach. This framework incorporates MCMC methods to simulate and analyze complex decision-making processes in entrepreneurial contexts. #### 2.1.4 Bayesian Calibrated Choice Integration BCC synthesizes optimization, Bayesian, and simulation approaches into a unified framework that emphasizes context-dependent calibration. This integration leverages system dynamics concepts of flows and stocks to model the interplay between actions and states in entrepreneurial decision-making. ### 2.2 Application in Entrepreneurship #### 2.2.1 Bayesian X Optimization The integration of Bayesian methods with optimization theory provides a novel framework for understanding entrepreneurial decision-making. This approach specifically addresses how entrepreneurs handle the duality between states and actions while pursuing opportunities without regard to currently controlled resources. #### 2.2.2 Bayesian X Simulation Simulation-based calibration reveals the advantages of non-centered parameterization over centered parameterization in entrepreneurial contexts. This framework helps explain how entrepreneurs can effectively navigate uncertainty through iterative learning and adaptation. #### 2.2.3 BCC Framework The complete BCC framework synthesizes optimization and simulation approaches while emphasizing context-dependent calibration. This integration provides a robust foundation for analyzing entrepreneurial decision-making under uncertainty. ## Chapter 3: Hypothesis Verification ### Section 1: Simulation-based calibration [[🌒📐Test quantities shape sensitivity]] ### Section 2: Entrepreneurs Are Not Secretaries [[🌓⌨️Entrepreneurs Are Not Secretaries_Decision Making when State and Action are Indistinguishable]] ### Section 3: [[🌔🌊Bayesian Calibrated Choice_Balancing Individual and Market-Level Uncertainty]] ## Chapter 4: Conclusion The conclusion synthesizes our findings supporting both hypotheses, demonstrating how BCC provides both an effective self-consistent learning rule and efficient dimensionality reduction in entrepreneurial exploration. We discuss implications for both theory and practice, suggesting directions for future research in entrepreneurial decision-making. | Section | Content | Instruction | | -------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Title** | Bayesian Calibrated Choice (BCC) | | | **Hypothesis** | 1. BCC is effective as it provides `self-consistent learning rule`. I implement this using simulation-based calibration.<br>2. BCC is efficient as it reduces the dimensionality of the exploration problem. I analyze this dimension reduction process in both individual (founder) and society's perspective. | | | **Chapter 1: Research Objective** | • In business and economics, calibration is crucial for value-based action<br>• Bayesian theorem provides philosophical foundation for calibration,<br>• sequential decision making applications assumes state and actions are given. however in reality, state that affects action can always be found (robot's repeated action of bumping into the wall can help it go outside the box). For entrepreneurs who naturally explore uncharted territory, state and action becomes both action. what's needed is 1) understanding the source of belief heterogeneity and 2) developing 2s) society's 👥matching algorithm (between beliefs encoding (meaning) and preferences (judging)) 2i) individual's 🚗path finding algorithms. | | | **Chapter 2: Theoretical Background** | 2.1 Foundations of Bayesian Calibrated Choice <br>2.1.1 Optimization approach: Decision science/Statistical Decision Theory (charlie), Discrete Choice analysis (mosche)<br><br>2.1.2 Bayesian approach: Bayesian Statistics (andrew) and Bayesian Cognition (josh) - SBC/prior/posterior re-calibration in bayes factor, rational meaning construction<br><br>2.1.3 Simulation approach: System dynamics (john): Dynamic modeling and Calibration (tom) - mcmc <br><br>2.1.4 Bayesian Calibrated Choice <br><br>2.2 Application of Bayesian Calibrated Choice in Entrepreneurship<br>entrepreneurship: pursuit of opportunity without regard to resource controlled.<br>we focus on how (among what, why, "how"): 1. how the action to relax constraint affect ??, 2. how the society should balance uncertainties, <br><br>2.2.1. Bayesian X Optimization : decision theory in Entrepreneurship<br>2.2.2. Bayesian X Simulation: simulation-based calibration explain the benefit of entrepreneurship by connecting non centered parameterization is beneficial than centered parameterization <br>2.2.3 Bayesian Calibrated Choice | Should comprehensively cover related theories from three fields: Bayesian approach, Simulation approach, Optimization approach.<br>[[📝rational meaning construction]]<br><br>2.1.1. <br>reinforcement learning, MDP | | **Chapter 3: Hypothesis Verification** | Case studies including:<br>• Section 1: [[🌒📐Test quantities shape sensitivity]]<br>• Section 2: [[🌓⌨️Entrepreneurs Are Not Secretaries_Decision Making when State and Action are Indistinguishable]]<br>• Section 3: [[🌔🌊Bayesian Calibrated Choice_Balancing Individual and Market-Level Uncertainty]]<br><br>[[📝moon24_csv_ai_cofounder]]<br>draft<br>- From society's perspective, this becomes a 🔄matching problem where belief flows between origin (observation of founders) to destination (invest action). I interpret the meaning of flow balance condition in founder-investor matching and use this to design flow maximization alg.1 that elicits state. I explain its implication for institution and governance.<br><br>- From founder's perspective, this becomes a 🚗path finding problem in hypothesis (state) space. <br>-- When states are finite, we use rejection sampling which is interpreted as separating cutting hypothesis using rejection sampling (lift and project cut). <br>-- When states are in-definite (e.g. aware of unaware) and online algorithm is appreciated. I use metropolis hasting's propose and accept algorithm which maintains local balance between one's meaning and judging function.<br><br>- 🔄local balance (frequency matching) between updating encoded belief and updating utility | • Each section should be equivalent to a publishable paper (compose of 3 papers published in top-tier journals) | | **Chapter 4: Conclusion** | Summary and findings | | | **Note** | From the previous 12 papers, create 3 papers that can be woven together in this structure and approach them one by one | | | Dimension | Ben-Akiva et al. (2012) | Moon (2025) | | ------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Process-Context Framework** | Models choice as interaction between decision processes (perception, planning) and social context (networks, family). Uses explicit mathematical formulations of utility and field effects. | Frames choice as interaction between meaning (interpreting context) and judging (decision process). Focuses on conditional probability distributions p(state\|observation) and p(action\|state). | | **Latent States and Inference** | Proposes using subjective data (surveys, experiments) and structural models to infer latent psychological states affecting choices. Emphasizes need for richer data collection. | Suggests using GPT to directly measure latent states through systematic prompting of hypothetical venture profiles. Focuses on two key states: execution capability and idea quality. | | **Social Network Effects** | Models "field effects" where peer group choices influence individual decisions. Considers both tight networks (family) and loose networks (market). Addresses endogeneity through BLP procedure. | Examines how entrepreneurs navigate different investor "archetypes" with varying interpretations of signals. Shows how actors can strategically pivot between different social interpretations (meaning construction). | Cases in startup investment Cases in strategic decision-making First Paragraph: PayPal's founding story illustrates a fundamental challenge in entrepreneurial theory-building. The founders developed a causal theory where θ (encryption technology working) was hypothesized as critical for Y (company success). They operated under two interconnected beliefs: P(Y|θ=1) suggested a high probability of success with working encryption, while P(Y|θ=0) indicated near-zero chance of success without it. This theoretical framework wasn't just about predicting success given encryption capability (forward prediction); it fundamentally shaped their strategic decisions about resource allocation and technology development priorities. Second Paragraph: The need for calibration emerges when we recognize that entrepreneurial theories can be tested bidirectionally. While PayPal's founders focused on P(Y|θ) (probability of success given encryption works), equally important is P(θ|Y) (probability that encryption was crucial given success). This bidirectional relationship introduces a natural symmetry in belief validation - if encryption is truly critical, we should observe both that companies with encryption tend to succeed AND that successful companies tend to have strong encryption. However, founders often focus primarily on the forward direction (θ → Y) while underutilizing the valuable information contained in the reverse direction (Y → θ). This asymmetric attention to causal relationships can lead to miscalibrated beliefs about what truly drives success. 🗄️Table 1: PayPal probability | Probability Notation | Description | PayPal's Context | | -------------------- | ---------------------------------------------------- | -------------------------------------------------------------------------- | | P(θ) | Prior probability of encryption technology working | Founders' initial belief about likelihood of developing working encryption | | P(Y\|θ=1) | Probability of success given encryption works | Founders' belief that working encryption would lead to success | | P(Y\|θ=0) | Probability of success given encryption doesn't work | Founders' belief that success was unlikely without encryption | | P(θ\|Y=1) | Probability encryption was crucial given success | Validation measure: among successful companies, how many needed encryption | | P(Y) | Overall probability of success | Weighted average: P(Y\|θ=1)P(θ) + P(Y\|θ=0)(1-P(θ)) | Third Paragraph: The symmetry between θ (actual encryption capability) and θ̃ (founders' prior belief about encryption) in PayPal's case reveals a crucial aspect of Bayesian calibration. Just as founders can simulate forward from their beliefs about encryption to predict success, we can work backward from observed successes to validate those beliefs. This symmetry suggests that if the founders' theory about encryption's importance is well-calibrated, then both forward prediction (P(Y|θ)) and backward inference (P(θ|Y)) should yield consistent probabilities. The four key equations from SBC framework provide a systematic way to check this consistency: the self-consistency equation ensures the prior maintains its structure through data averaging, the joint distributions (both population and MP2 decompositions) verify the symmetry between believed and actual importance of encryption, and the three-step procedure offers a practical way to validate these beliefs through simulation. 🗄️Table 2: Equations and PayPal Example | Equation Name | Mathematical Form | PayPal Example | |---------------|------------------|----------------| | 🔄Self Consistency (1) | πprior(θ) = ∫∫ πpost(θ\|y)πobs(y\|θ̃)πprior(θ̃) dy dθ̃ | The overall belief about encryption's importance should remain consistent whether assessed directly or through observing multiple startups' outcomes | | 🍾Population Decomposition (2) | πSBC(y, θ, θ̃) = πprior(θ̃)πobs(y\|θ̃)πpost(θ\|y) | PayPal's prior belief about encryption (θ̃), observed market success given encryption (y\|θ̃), and updated beliefs about encryption's importance (θ\|y) should form a coherent story | | 📖MP2 Decomposition (3) | πSBC(y, θ, θ̃) = πmarg(y)πpost(θ\|y)πpost(θ̃\|y) | If encryption truly matters, successful companies (y) should show both high prior belief in encryption (θ̃) and actual encryption capability (θ) | | 🪜Three-step Procedure (4) | θ̃ ~ πprior(θ̃)<br>y ~ πobs(y\|θ̃)<br>θ1,...,θM ~ πpost(θ\|y) | 1. Start with belief about encryption's importance<br>2. Observe market outcomes<br>3. Update beliefs about encryption's role in success | pre-Bayesian exchangeability-based surprise. This idea relates to the unexpected discovery that two seemingly unrelated things work well together, leading to the suspicion of a hidden or latent parameter connecting them. You used the mRNA platform as an example to illustrate this concept. In this context, you likely explained how researchers or companies might have been surprised to find that mRNA technology, initially developed for one purpose, proved effective in multiple, apparently unrelated applications. This surprising effectiveness across different domains led to the hypothesis that there might be an underlying, previously unrecognized parameter or principle (the latent parameter) that explains why the mRNA platform works so well in various contexts. Your comment seems to have been exploring how this type of unexpected discovery can lead to new insights and potentially reshape our understanding of a technology or scientific principle. It also touches on how such surprises can guide further research and development efforts, as investigators try to identify and understand the latent parameters that might explain the observed successes. This idea of "exchangeability-based surprise" appears to be a novel or less common concept that you were introducing or explaining in the context of hierarchical modeling and the development of platform technologies like mRNA. --- 내가 생각하는 창업은 교환가치에 굴복하지 않고, 끊임없이 사용가치를 찾아가는 것이다. The actor and critic relation is shown between founder and investor. Network flow and local balance of path finding By casting founder's journey of innovation as network flow problem where belief flow is conserved as 1, I operationalize the flow balance as an algorithm to with low uncertainty origin and (from idea inception to impact), we interpreting the meaning of flow balance from both individual (founder and investor) and social welfare planner's perspective. Startup investing is about more than just providing capital for the highest ROI. Startup investors who believe in founders, support their journey, and strategize on transforming vision into reality, are abundance-mindset leaders who embrace investing for impact and returns. Investors today focus on building relationships, offering strategic guidance, and creating lasting impact, transforming the traditional approach of 1 in 10 of startup surviving to a more mindful 9 out of 10 survival rate for customer-centric startups. Why is probabilistic reasoning not well adopted in our lives? First goal of this thesis is to find the root cause of this problem. Categorizing failure modes with value chain process (design, develop, launch, produce, distribute) suggest actionable solutions for each. Second goal is to suggest tools that address root cause. cannot consider different usecase context. Using the chemical equation below, I show end to end value chain of `Probabilistic Reasoning TOOLS` . Chemical equation of this goal `Probabilistic Reasoning SOL + Probabilistic Reasoning NEED -> usable Probabilistic Reasoning TOOLS` The value chain includes five process (design, develop, launch, produce, distribute) which can be catalyzed by five stakeholders (algorithm builder, tool builder, model builder, policy designer, stakeholder). Instance of this equation class is: ``` Bayesian Reasoning SOL + Bayesian Entrepreneurship NEED - (catalyze) -> usable Entrepreneurial Operation TOOLS ``` In entrepreneurial system, Entrepreneurial Operation TOOLS is low (Fine et. al. 2022). I'd like to catalyze chemical equation. In 2016, I was shocked from an interview with bakery owners that the simplest regression based demand forecasting and inventory optimization algorithms are not used. This led me to start a startup that develop smart and simple analytics tool. Three lessons I learned: - prioritize goal (primary: learn why analytics tools are not used, secondary: add startup to my career which is helpful for studying abroad) and communicate - understand startup and CEO are different agents - understand existence of different entrepreneurial strategies: architectural (compete and control) vs value chain strategy (collaborate and execute) - massive adoption of probabilistic reasoning needs Table 1: Problems with Probabilistic Reasoning Not Being Used in Entrepreneurship | Stakeholder | Problem | |-------------|---------| | Algorithm builders | May focus on technical improvements over usability | | Tool builders | Difficulty bridging algorithm development and real-world applications | | Model builders | Tendency to assume deterministic situations for simplicity | | Policy/curriculum designers | Lack tools to incorporate probabilistic reasoning | | Entrepreneurs | Skeptical of probabilistic approaches due to perceived randomness | Table 2: Enablers to solve the problem | Stakeholder | Potential Tool/Approach | | --------------------------- | --------------------------------------------------------------------- | | Algorithm builders | User feedback mechanisms to identify usability bottlenecks | | Tool builders | Frameworks to integrate real-world use cases into designs | | Model builders | Tools to incorporate probabilistic elements into deterministic models | | Policy/curriculum designers | Ready-to-use probabilistic reasoning modules for curricula | | Entrepreneurs | User-friendly tools demonstrating value of probabilistic approaches | Here is the updated table with more specific examples and detailed questions for interviewing experts: Here is the updated table with blended content and specific examples derived from the reading list: | **Business Model** | **Market/Regulation** | **Technology Development** | **Technology Adoption/Scaling** | | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | How can we select and modify the best business models to offer clean energy in both developing and developed countries, while making them attractive for investment and reducing the cost of microgrid systems? <br> **Example:** Investigate models like pay-as-you-go solar in Kenya and large-scale solar farms in the US. <br> **Approach:** Pair countries based on geographic proximity, technological capabilities, and CO2 emission levels (e.g., Singapore with Indonesia, US with Mexico). Observe and update clusters based on their speed in lowering CO2 and increasing GDP. <br> **Example from Reading:** Leveraging private sector participation in microgrid projects in Kenya and reviewing successful public-private partnership models in the US【26†source】. | How can we categorize and prioritize potential populations and regions for clean energy projects, ensuring local government participation and addressing regulatory differences between countries? <br> **Example:** Identify key regions in Southeast Asia and Sub-Saharan Africa, comparing their regulatory environments. <br> **Approach:** Pair countries such as Malaysia and Indonesia for geographic proximity and regulatory efficiency. Update clusters based on their speed in project implementation and regulatory adaptation. <br> **Example from Reading:** Implementing transparency initiatives and community engagement programs in Malaysian municipalities【26†source】. | How can we create and implement effective digital tools and processes to aid the deployment of microgrids in areas without electricity, while accessing grants and evaluating project feasibility? <br> **Example:** Develop a grant management platform tailored for rural electrification projects in India. <br> **Approach:** Pair countries based on digital infrastructure and CO2 emission levels (e.g., South Korea with Vietnam, US with Mexico). Observe and update clusters based on the effectiveness of digital tools in lowering CO2 and increasing GDP. <br> **Example from Reading:** Developing microgrid management platforms for rural electrification in Ethiopia and Chile【26†source】. | How can we generalize the design and adoption of microgrids to serve diverse electrical needs across different regions, while understanding and addressing local entrepreneurs' risk aversion? <br> **Example:** Case studies of microgrid implementations in Tanzania and Indonesia. <br> **Approach:** Pair countries based on technological capabilities and geographic proximity (e.g., South Korea with Vietnam, US with Mexico). Observe and update clusters based on the speed of adoption and lowering CO2 emissions. <br> **Example from Reading:** Examining the scalability of community-based microgrid systems in Sub-Saharan Africa and Southeast Asia【26†source】. | | How can we identify regions with policy compatibility for clean energy adoption, and convince authorities and investors of the importance of developing clean energy research to balance profitability and investment attractiveness? <br> **Example:** Compare policy frameworks in the EU and US for renewable energy incentives. <br> **Approach:** Pair countries based on policy compatibility and CO2 emission levels (e.g., Singapore with Indonesia, US with Mexico). Update clusters based on policy adoption speed and CO2 reduction. <br> **Example from Reading:** Reviewing renewable energy policy frameworks in Singapore and Indonesia to understand their impact on investment attractiveness【26†source】. | How can we induce large-scale behavioral changes in local governments, such as Malaysia, to reduce corruption and incentivize clean energy infrastructure development? <br> **Example:** Implement transparency initiatives and community engagement programs in Malaysian municipalities. <br> **Approach:** Pair countries based on governance challenges and CO2 emission levels (e.g., Singapore with Indonesia, US with Mexico). Observe and update clusters based on behavioral changes and CO2 reduction. <br> **Example from Reading:** Implementing community engagement programs to reduce corruption in Malaysia and incentivize clean energy projects【26†source】. | How can we generalize the technology development for microgrids across different countries, ensuring scalability and measuring the effectiveness of community-based microgrid systems? <br> **Example:** Standardize microgrid components to facilitate deployment in diverse environments like Ethiopia and Chile. <br> **Approach:** Pair countries based on technological capabilities and CO2 emission levels (e.g., South Korea with Vietnam, US with Mexico). Observe and update clusters based on scalability and CO2 reduction. <br> **Example from Reading:** Standardizing microgrid components in Sub-Saharan Africa and Southeast Asia for scalable deployment【26†source】. | How can we measure and encourage the use of digital transformation tools for better scalability and adoption of microgrids in both developed and developing countries? <br> **Example:** Develop metrics for evaluating the impact of digital tools in microgrid projects in South Korea and Brazil. <br> **Approach:** Pair countries based on digital transformation readiness and CO2 emission levels (e.g., South Korea with Vietnam, US with Mexico). Observe and update clusters based on the adoption speed and CO2 reduction. <br> **Example from Reading:** Evaluating digital transformation tools in microgrid projects in South Korea and Brazil to enhance scalability【26†source】.c | | **Technology Development**                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  | **Technology Adoption/Scaling** | | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Question:** How can we create and implement effective digital tools and processes to aid the deployment of microgrids in areas without electricity, while accessing grants and evaluating project feasibility? <br> **Example:** Develop a grant management platform tailored for rural electrification projects in India. <br> **Approach:** Pair countries based on digital infrastructure and CO2 emission levels (e.g., South Korea with Vietnam, US with Mexico). Observe and update clusters based on the effectiveness of digital tools in lowering CO2 and increasing GDP. <br> **Example from Reading:** Developing microgrid management platforms for rural electrification in Ethiopia and Chile . | **Question:** How can we generalize the design and adoption of microgrids to serve diverse electrical needs across different regions, while understanding and addressing local entrepreneurs’ risk aversion? <br> **Example:** Case studies of microgrid implementations in Tanzania and Indonesia. <br> **Approach:** Pair countries based on technological capabilities and geographic proximity (e.g., South Korea with Vietnam, US with Mexico). Observe and update clusters based on the speed of adoption and lowering CO2 emissions. <br> **Example from Reading:** Examining the scalability of community-based microgrid systems in Sub-Saharan Africa and Southeast Asia .                | | **Answer:** <br> **1. Needs Assessment:** Conduct thorough resource mapping and community engagement to understand local energy needs and renewable resources. <br> **2. Digital Tool Development:** Create a comprehensive grant management platform that includes: <br> - **Grant Identification:** Database of grants specific to rural electrification. <br> - **Application Tracking:** Monitor application status and deadlines. <br> - **Proposal Writing Assistance:** Templates and AI-powered suggestions. <br> - **Budgeting Tools:** Create and track budgets. <br> **3. Feasibility Studies:** Utilize tools like HOMER Pro for techno-economic analysis. <br> **4. Partnerships:** Collaborate with local and international NGOs, government agencies, and private sector. <br> **Example:** Implementing these tools in rural India to streamline funding and deployment.                                                                                                                                                          | **Answer:** <br> **1. Standardization:** Develop standardized microgrid components to ensure compatibility across different regions. <br> **2. Case Studies:** Document and analyze successful microgrid projects, focusing on regions like Tanzania and Indonesia. <br> **3. Risk Management:** Create risk mitigation strategies tailored to local entrepreneurs’ concerns. <br> **4. Training Programs:** Develop training for local stakeholders on microgrid technology and business models. <br> **5. Monitoring & Evaluation:** Implement continuous monitoring to gather data on performance and impact. <br> **Example:** Conducting workshops and training sessions in Tanzania and Indonesia to share knowledge and build local capacity.                                                                                                                | | **Question:** How can we generalize the technology development for microgrids across different countries, ensuring scalability and measuring the effectiveness of community-based microgrid systems? <br> **Example:** Standardize microgrid components to facilitate deployment in diverse environments like Ethiopia and Chile. <br> **Approach:** Pair countries based on technological capabilities and CO2 emission levels (e.g., South Korea with Vietnam, US with Mexico). Observe and update clusters based on scalability and CO2 reduction. <br> **Example from Reading:** Standardizing microgrid components in Sub-Saharan Africa and Southeast Asia for scalable deployment .                      | **Question:** How can we measure and encourage the use of digital transformation tools for better scalability and adoption of microgrids in both developed and developing countries? <br> **Example:** Develop metrics for evaluating the impact of digital tools in microgrid projects in South Korea and Brazil. <br> **Approach:** Pair countries based on digital transformation readiness and CO2 emission levels (e.g., South Korea with Vietnam, US with Mexico). Observe and update clusters based on the adoption speed and CO2 reduction. <br> **Example from Reading:** Evaluating digital transformation tools in microgrid projects in South Korea and Brazil to enhance scalability . | | **Answer:** <br> **1. Component Standardization:** Develop a set of standardized components and protocols for microgrids that can be adapted to local conditions. <br> **2. Collaborative R&D:** Facilitate partnerships between countries to co-develop and test scalable microgrid solutions. <br> **3. Pilot Projects:** Implement pilot projects in diverse environments to test and refine standardized components. <br> **4. Scalability Metrics:** Create metrics to evaluate scalability, such as cost per kWh, ease of deployment, and local adaptability. <br> **5. CO2 Reduction Tracking:** Develop tools to monitor and report on CO2 emissions reductions achieved through microgrid deployments. <br> **Example:** Launching pilot projects in Ethiopia and Chile using standardized components and evaluating their performance and scalability.                        | **Answer:** <br> **1. Impact Metrics:** Develop specific metrics to evaluate the impact of digital tools on microgrid projects, including cost savings, time efficiency, and CO2 reductions. <br> **2. Case Study Analysis:** Conduct detailed case studies in countries like South Korea and Brazil to assess the effectiveness of digital tools. <br> **3. Feedback Mechanisms:** Implement systems for continuous feedback from users to improve digital tools. <br> **4. Adoption Incentives:** Provide incentives for the adoption of digital tools, such as subsidies or technical support. <br> **5. Training and Support:** Offer training programs to ensure users are proficient in using digital tools. <br> **Example:** Evaluating digital transformation tools in microgrid projects in South Korea and Brazil, focusing on metrics like adoption speed and CO2 reduction. |