# 🗄️table_of_contents
title: Entrepreneurial Decision Model with Uncertainty Minimization
two 🍾bottleneck breaking
- 🍾 first, between NAIL and SCALE where innovation's value is much higher than operational efficiency's value.
- 🍾🍾second, between actions within NAIL reaching to 1,1,1 meaning value proposition accepted by three stakeholders
mmi2023
| problem | | ![[Pasted image 20250501153901.png\|100]] |
| ---------------- | ------------------------------------------------------- | ----------------------------------------- |
| problem analysis | | ![[Pasted image 20250501153939.png\|200]] |
| solution1 | **bottleneck breaking to lower operational complexity** | ![[Pasted image 20250501154052.png\|200]] |
| solution2 | proactive testing to lower stakeholder complexity | ![[Pasted image 20250501154149.png\|100]] |
| solution3 | dynamic calibration | ![[Pasted image 20250501154204.png\|100]] |
| Section | Title | Theory/Phenomena | 🧱literature brick | 🙋♀️question to answer | corresponding components of 📝moon24_csv_ai_cofounderwith each section | 🎞️figure | Total page per section (progress %) |
| -------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------- | ----------------------------------- |
| 🕸️Introduction | | | | | | | **10** (90%) |
| | 1.1😵💫Entrepreneurial Decision Models (EDM) are Unusable by Entrepreneurs | EDMNO is defined as $\arg\min_{\textcolor{red}{a} \in \textcolor{red}{A}} \textcolor{violet}{W_d} \textcolor{#3399FF}{U_d} + \textcolor{violet}{W_s} \textcolor{#3399FF}{U_s} + \textcolor{violet}{W_i} \textcolor{#3399FF}{U_i}
lt;br>s.t.<br>1. $B\textcolor{green}{S} = [\textcolor{#3399FF}{U_d}, \textcolor{#3399FF}{U_s}, \textcolor{#3399FF}{U_i}]$, <br>2. $C\textcolor{red}{A} \leq \textcolor{#3399FF}R$, <br>3. $D(\textcolor{green}{S},\textcolor{red}{A}) = \textcolor{green}{S'}$ | 🗄️1Table of Contents (Q&A&B) | 🚨1. why did segway casestudy tackled uncertainty minimization rather than utility maximization? | | | 100$ |
| | 1.2🏳️🌈Complexity Spectrum of Entrepreneurial Decision Models | Thm.1 EDMNO is Np-complete | 🗄️2Comparison with Existing Theories | | | | |
| | 1.3🎞️Thesis Scope and Example Case | | | | | | |
| ⏰I Perceptual Learning to Relax Integrality | B | | | | | | **60** |
| | 2.Nature of problem | | | | | | |
| | 2.1 Why entrepreneurial decision models are difficult to use in practice | | | | | | |
| | 2.2 Mathematical formulation of decision space $\mathcal{D}$ with interdependent utilities | | | | | | |
| | 2.3 Empirical evidence from venture case studies | | | | | | |
| | | | | | | | |
| | 3. **Solution: Phase-Based Uncertainty Minimization** | | 🗄️🧠charlie | 🚨2. can nail vs scale be expressed with structural change in state transition matrix D? | | | |
| | 3.1💭Theorize solution: Phase-based learning framework (Nail, Scale + simplex) | | 📜Fine22_ops4ent | | | ![[🗄️table_of_contents 2025-04-29-8_0.svg]]<br>%%🖋 Edit in Excalidraw%% | |
| | 3.2📐Produce solution: Subpath formulation + Simplex algorithm for uncertainty reduction efficiency $\frac{\Delta\textcolor{#3399FF}{U}}{\textcolor{#3399FF}{C}}$ | | | | | <br> | |
| | 3.3💸Evaluate solution: Tesla/Betterpalce/Segway simulation | probabilistic program that replicates 📜Terwiesch09_innov_tourn's segway bottlneck breaking alternative operation sequence and 📜gans20_choose(tech)'s step-wise undertainty tackling | | | | | |
| | 3.4📜Related work | | | | | | |
| 👥II Proactive Testing to Lower Multi-stakeholder Complexity | C matrix | | | | | | **60** |
| | 4.Individual level of problem | | | | | | |
| | 4.1 Why entrepreneurs struggle with multi-stakeholder decisions | | 📜zhao09_shape-accom(pref) | 1. infer one's preference w + initial state $\textcolor{green}{S_0}lt;br>2. infer B, C each stakeholder's uncertainty decrease per state increase" | | | |
| | 4.2 Mathematical formulation of spatial complexity with stakeholder preferences $(\textcolor{violet}{W})$ | | | | | | |
| | 4.3 Cognitive barriers to stakeholder alignment | | | | | | |
| | | | | | | | |
| | 5. **Solution: Network of Business Model Hypotheses** | | | | | | |
| | 5.1💭Theorize solution: Proactive hypothesis testing framework for spatial complexity | | 🗄️🧠scott's value creation hypothesis of customer and technology and value capture hypothesis of organization and competition | | | ![[🗄️table_of_contents 2025-04-29-8.svg]]<br>%%🖋 Edit in Excalidraw%% | |
| | 5.2📐Produce solution: Estimate $B$ and $\textcolor{#3399FF}{C}$ in real-world applications | | 🗄️🧠vikash <br>📜Bernstein23_Abstractions for Probabilistic Programming to Support Model Development | | | | |
| | 5.3💸Evaluate solution | | | how to evaluate? 📝👻phantom rationalize meaning with jeff_dotson | | | |
| | 5.4📜Related work | | | | | | |
| ⏰👥III Expectation Propagation to Lower Operational Multi-Stakeholder Complexities | D matrix | | | | | | **60** |
| | 6.Institutional level of problem | 🏭venture_studio | 📜andrew_aki20_ep_wayoflife | | | | |
| | 6.1 Why entrepreneurs struggle with sequence-dependent decisions | | | | | | |
| | 6.2 Mathematical formulation of temporal uncertainty $\textcolor{#3399FF}{U_{t+1}}=f(\textcolor{#3399FF}{U_t},\textcolor{violet}{\Omega_t})$ | | | | | | |
| | 6.3 Information bottlenecks in venture planning | | | | | | |
| | | | | | | | |
| | 7. **Solution: Hierarchical (multi-level/nested) Uncertainty Modeling** | expectation-propagation | 🗄️🧠moshe | | | | |
| | 7.1💭Theorize solution: Federated learning framework for temporal complexity; Social planner's role in informing $D_{industry}$ | | clockspeed | | | | |
| | 7.2📐Produce solution: State transition tensor implementation $(D\in\mathbb{R}^{I\times\textcolor{red}{A}\times\textcolor{green}{S}\times\textcolor{green}{S}})$ defining $P(\textcolor{green}{S'}\mid\textcolor{green}{S},\textcolor{red}{A})$ | | | | | | |
| | 7.3💸Evaluate solution: Empirical validation of temporal complexity reduction | | | | | | |
| | 7.4📜Related work | | | | | | |
| IV Conclusion Integration and Evaluation | | | | | | | |
| | 8. **Integrated Framework for Entrepreneurial Decision Modeling** | | | | | | |
| | 8.1 Mathematical integration of the complete optimization framework:<br>$\arg\min_{\textcolor{red}{a} \in \textcolor{red}{A}} \textcolor{violet}{W_d} \textcolor{#3399FF}{U_d} + \textcolor{violet}{W_s} \textcolor{#3399FF}{U_s} + \textcolor{violet}{W_i} \textcolor{#3399FF}{U_i}lt;br>subject to $B\textcolor{green}{S} = [\textcolor{#3399FF}{U_d}, \textcolor{#3399FF}{U_s}, \textcolor{#3399FF}{U_i}]$, $C\textcolor{red}{A} \leq R$, $D(\textcolor{green}{S},\textcolor{red}{A}) = \textcolor{green}{S'}$ | | | | | | |
| | 8.2 Implementation roadmap for the unified model | | | | | | |
| | 8.3 Comprehensive usability evaluation and implications for practice | | | | | | |
can B, C, D product-market fit (nail to scale),
$\arg\min_{\textcolor{red}{a} \in \textcolor{red}{A}} \textcolor{violet}{W_d} \textcolor{#3399FF}{U_d} + \textcolor{violet}{W_s} \textcolor{#3399FF}{U_s} + \textcolor{violet}{W_i} \textcolor{#3399FF}{U_i}$
subject to $B\textcolor{green}{S} = [\textcolor{#3399FF}{U_d}, \textcolor{#3399FF}{U_s}, \textcolor{#3399FF}{U_i}]$, $C\textcolor{red}{A} \leq R$, $D(\textcolor{green}{S},\textcolor{red}{A}) = \textcolor{green}{S'}$
🗄️🧠scott
🗄️1Table of Contents (Q&A&B), 🗄️2Comparison with Existing Theories, 🗄️3Practical Implications
| Section | Title | | Page |
| -------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------ | ------- |
| **🕸️Introduction** | | | 15 |
| | 1.1 The Entrepreneurial Decision-Making Challenge | | 16 |
| | 1.2 Spectrum of Complexity in Entrepreneurial Models (⏰👥) | | 22 |
| | 1.3 Three Root Causes of Model Unusability | | 28 |
| | 1.4 Outline and Contributions | | 33 |
| | 1.5 Scope and Software Implementation | | 36 |
| ⏰👥<br>**I Phase-Based Learning for Bottleneck Breaking Operations** | | | **40** |
| | 2. **Problem: Decision Complexity and Bottlenecks** | | 41 |
| | 2.1 Why are entrepreneurial decision models difficult to use in practice? (⏰👥) | | 42 |
| | 2.2 Mathematical formulation of decision space $\mathcal{D}$ with interdependent utilities ($\textcolor{#3399FF}{U_d}$, $\textcolor{#3399FF}{U_s}$, $\textcolor{#3399FF}{U_i}$) | | 48 |
| | 2.3 Empirical evidence from venture case studies | | 54 |
| | 2.4 Related work | | 59 |
| | | | |
| | 3. **Solution: Phase-Based Learning with Nail, Scale, Sail** | | 64 |
| | 3.1 Mathematical framework for uncertainty minimization $\Delta\textcolor{#3399FF}{U}(\textcolor{green}{S},\textcolor{red}{A})$ | | 65 |
| | 3.2 Subpath formulation for breaking temporal complexity (⏰) | | 70 |
| | 3.3 Simplex algorithm for uncertainty reduction efficiency $\frac{\Delta\textcolor{#3399FF}{U}}{\textcolor{#3399FF}{C}}$ | | 76 |
| | 3.4 Related work | | 82 |
| | | | |
| | 4. **Implementation: Industry-Specific Parameter Estimation** | | 86 |
| | 4.1 Empirical analysis of stakeholder utilities $(B\in\mathbb{R}^{3\times3})$ | | 87 |
| | 4.2 Action cost estimation $(\textcolor{#3399FF}{C}\in\mathbb{R}^4)$ across venture phases | | 92 |
| | 4.3 Case studies in software and hardware ventures | | 97 |
| | 4.4 Evaluation and practical implications | | 103 |
| 👥<br>**II Proactive Hypothesis Testing for Spatial Complexity Reduction** | | | **108** |
| | 5. **Problem: Spatial Complexity in Multi-Stakeholder Decisions** | | 109 |
| | 5.1 Why do entrepreneurs struggle with multi-stakeholder decisions? (👥) | | 110 |
| | 5.2 Mathematical formulation of spatial complexity with stakeholder preferences $(\textcolor{violet}{W_d}, \textcolor{violet}{W_s}, \textcolor{violet}{W_i})$ | | 115 |
| | 5.3 Cognitive barriers to stakeholder alignment | | 120 |
| | 5.4 Related work | | 124 |
| | | | |
| | 6. **Solution: Value Creation and Capture Hypothesis Proposal Framework** | | 128 |
| | 6.1 Proactive model proposal for uncertainty reduction $\Delta\textcolor{#3399FF}{U_j}(\textcolor{green}{S},\textcolor{red}{A})$ | | 129 |
| | 6.2 Explicit uncertainty evaluation via stakeholder utilities $(B\in\mathbb{R}^{3\times3}, \textcolor{violet}{W}\in\mathbb{R}^3)$ | | 134 |
| | 6.3 Algorithms for hypothesis testing efficiency $\frac{\Delta\textcolor{#3399FF}{U_j}}{\textcolor{#3399FF}{C_j}}$ | | 139 |
| | 6.4 Related work | | 144 |
| | | | |
| | 7. **Implementation: Transition Models Between Venture Phases** | 🗄️🧠vikash<br><br>📜saad22_thesis | 148 |
| | 7.1 Practical application of hypothesis testing protocols | | 149 |
| | 7.2 Case studies of successful and failed hypothesis testing | | 154 |
| | | | 159 |
| | 7.4 Evaluation and practical implications | | 164 |
| ⏰<br>**III Federated Learning for Temporal Complexity Reduction** | | | **168** |
| | 8. **Problem: Temporal Complexity in Entrepreneurial Decisions** | | 169 |
| | 8.1 Why do entrepreneurs struggle with sequence-dependent decisions? (⏰) | | 170 |
| | 8.2 Mathematical formulation of temporal uncertainty $\textcolor{#3399FF}{U_{t+1}}=f(\textcolor{#3399FF}{U_t},\textcolor{violet}{W_t})$ | | 175 |
| | 8.3 Information bottlenecks in venture planning | | 180 |
| | 8.4 Related work | | 184 |
| | | | |
| | 9. **Solution: Hierarchical Uncertainty Modeling** | | 188 |
| | 9.1 Federated learning architecture for uncertainty reduction | | 189 |
| | 9.2 State transition tensor modeling $(D\in\mathbb{R}^{I\times\textcolor{red}{A}\times\textcolor{green}{S}\times\textcolor{green}{S}})$ defining $P(\textcolor{green}{S'}\mid\textcolor{green}{S},\textcolor{red}{A})$ | | 194 |
| | 9.3 Algorithms for distributed parameter estimation | | 199 |
| | 9.4 Related work | | 204 |
| | | | |
| | 10. **Implementation: Industry-Level Informing Function** | | 208 |
| | 10.1 Social planner's role in informing $D_{industry}$ | clockspeed | 209 |
| | 10.2 Implementation of interactive decision support tools | | 214 |
| | 10.3 Matrix $D$ estimation through federated data | 🗄️🧠moshe | 219 |
| | 10.4 Evaluation and practical implications | | 224 |
| **IV Conclusion** | | | **228** |
| | 11. **Unified Framework and Future Directions** | | 229 |
| | 11.1 Integration of phase-based, proactive, and federated approaches | | 230 |
| | 11.2 Limitations and future research | | 236 |
| | 11.3 Implications for entrepreneurship education and policy | | 240 |
---
# manuscript1
# 🕸️Introduction
# Abstract Versions
## 1. Supply-Development Focus (for Charlie Fine and Moshe Ben-Akiva)
**Abstract: Entrepreneurial Decision Model with Phase-Based Operational Uncertainty Reduction**
This thesis develops an operational framework for entrepreneurial decision-making that minimizes uncertainty rather than maximizing utility. We introduce a structured approach to sequence critical operational actions (Collaborate, Segment, Capitalize) based on their uncertainty reduction efficiency across venture development phases. Using state transition modeling, we demonstrate how different action sequences create distinct uncertainty reduction paths, allowing entrepreneurs to optimize resource allocation. Our model integrates operations management principles with discrete choice modeling to create actionable decision tools that adapt to different venture phases. Case studies in mobility ventures validate our approach, showing how entrepreneurs can systematically reduce operational uncertainties while respecting resource constraints. This work bridges the gap between theoretical models and practical implementation, providing entrepreneurs with structured methods to navigate complex decision environments.
## 2. Demand-Development Focus (for Scott Stern and Jinhua Zhao)
**Abstract: Bayesian Entrepreneurial Decision-Making Under Multi-Stakeholder Uncertainty**
This thesis develops a Bayesian approach to entrepreneurial decision-making under uncertainty, focusing on how entrepreneurs can systematically reduce demand-side uncertainty through strategic action sequencing. We formulate entrepreneurial decisions as uncertainty minimization problems where stakeholder preferences and initial venture states determine optimal action paths. The model accounts for how different actions (Collaborate, Segment, Capitalize) affect stakeholder uncertainties and demonstrates that market-focused actions early in the venture lifecycle can dramatically improve efficiency. Our empirical analysis of mobility ventures validates the model's predictive power for understanding entrepreneurial choices and outcomes. This work contributes to entrepreneurship theory by providing a mathematically rigorous framework for analyzing the previously tacit knowledge of uncertainty reduction, while offering practical guidance for entrepreneurs navigating complex market environments.
## 3. Technical Implementation Focus (for Vikash Mansinghka)
**Abstract: Probabilistic Programming for Entrepreneurial Decision Support Under Uncertainty**
This thesis develops a probabilistic programming approach to entrepreneurial decision-making under uncertainty. We formulate entrepreneurial choices as sequential decisions aimed at minimizing weighted uncertainties across demand, supply, and investor dimensions. Our model leverages advanced probabilistic programming techniques to represent the state transition dynamics between different uncertainty states based on entrepreneurial actions. We demonstrate how this approach enables entrepreneurs to reason systematically about complex decision spaces, accounting for resource constraints and stakeholder preferences. Implementation using probabilistic programming allows for efficient simulation of alternative action sequences and their uncertainty reduction effects. Case studies in mobility ventures showcase how different initial conditions and preference weights lead to distinct optimal action sequences. This work bridges theoretical entrepreneurship models with computational decision support tools, providing a foundation for AI-assisted entrepreneurial decision-making.
# 1.2🏳️🌈Complexity Spectrum of Entrepreneurial Decision Models
## Progressive Spectrum of Model Complexity
The unusability of entrepreneurial decision models becomes evident when examining the progressive spectrum of model complexity:
|Model Type|Temporal Complexity|Spatial Complexity|Tractability|Reality Fit|Need for New Approach|
|---|---|---|---|---|---|
|**Strategy-Only, Single Stakeholder**|Low|Low|High|Poor|❌ No|
|**Strategy + Time Steps, Single Stakeholder**|Medium|Low|Medium-High|Moderate|⬇️ Low|
|**Strategy + Multi-Stakeholder (Static)**|Low|Medium|Medium|Moderate|⬆️ Medium|
|**Strategy + Operations + Multi-Stakeholder (Dynamic)**|High|High|Low|High|✅ Yes|
|**Full Operational Scaling + Multi-Stakeholder**|Very High|Very High|Very Low|Very High|🚨 Critical|
As temporal complexity (uncertainty unfolding over time) and spatial complexity (number of interacting stakeholders/variables) increase, the model better represents reality but becomes increasingly intractable. At the highest complexity levels—precisely where real entrepreneurial decisions exist—traditional models become unusable.
- **Temporal Complexity** = How much uncertainty unfolds over time.
- **Spatial Complexity** = How many stakeholders/variables interact.
- **Reality Fit** = How closely the model matches real entrepreneurial conditions.
- **Need for New Approach** for integrated phase-based learning + + calibrated federated learning
## Three-Dimensional Complexity Analysis
The unusability of entrepreneurial decision models stems from three interconnected dimensions of complexity:
1. **System Design Issues**: The fundamental tractability-reality tradeoff creates structural barriers to model adoption, as evidenced by the progression from strategy-only to operational-scaling EDMs.
2. **Individual Cognitive Barriers**: Entrepreneurs face overwhelming cognitive load attempting to infer both their own preferences and stakeholder responses, leading to ineffective causal reasoning about multi-dimensional decision spaces.
3. **Institutional Coordination Gaps**: Misalignment between entrepreneur pace and institutional/societal evolution creates temporal uncertainty that compounds with spatial complexity, particularly when ventures require coordination with broader ecosystem stakeholders.
## Toward Integrated Solutions
This thesis proposes that entrepreneurial decision models can become usable through three integrated solutions that address these complexity dimensions:
1. **Phase-based learning** to address temporal complexity through modularized approaches that adapt to different venture development stages
2. **Proactive hypothesis proposal** to address spatial complexity through probabilistic programming that navigates stakeholder interdependencies
3. **Calibrated federated learning** to address spatio-temporal complexity through entrepreneur-social planner coordination
The challenge of making entrepreneurial decision models usable isn't just academic—it directly impacts innovation capacity, resource efficiency, and entrepreneurial success rates across the economy. By developing frameworks that balance complexity and tractability while maintaining reality fit, we can bridge the theory-practice gap and empower entrepreneurs with decision tools that match the actual challenges they face.
### 🎯 Why this structure works:
- Quickly shows that as we move **right →**, reality fit increases but **tractability collapses**.
- Builds intuitive reason **why smart uncertainty minimization methods** are necessary.
- Shows **where** your methods kick in (middle-high complexity) without overwhelming readers.
The table below compares various entrepreneurial decision models, progressing from a simple single-stakeholder strategy to a highly detailed multi-stakeholder operational model. It shows how **temporal complexity** (time/horizon) and **spatial complexity** (breadth of stakeholders/variables) increase along this spectrum, leading to higher dimensionality, reduced tractability, and changes in phenomenological accuracy. At the extreme end, traditional optimization and heuristic methods become insufficient – underscoring the need for new approaches like **federated learning** (to manage temporal complexity) and **proactive proposal testing** (to manage spatial complexity).
| Model Type | Temporal Complexity | Spatial Complexity | Dimensionality | Tractability | Phenomenological Accuracy | Typical Methods Used | Need for New Approach |
| ------------------------------------------------------------------ | ----------------------------------------------------- | ---------------------------------------------------------- | ----------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------ |
| **Basic Single-Stakeholder Strategy (Static)** | Low – static (single time-point decision) | Low – single stakeholder (one perspective) | Low – very few variables | High – highly tractable (simple; optimal solution easily found) | Low – oversimplified (misses key dynamics and interactions) ([When models are wrong, but useful | Mathematical Institute](https://www.maths.ox.ac.uk/node/34245#:~:text=other%20hand%2C%20simple%20models%20are,So%20how | Intuition, basic ROI/cost-benefit analysis, simple spreadsheets |
| **Single-Stakeholder Dynamic Planning (Multi-period)** | Moderate – incorporates a timeline or multiple stages | Low – single stakeholder focus (limited scope) | Moderate – more variables introduced by time steps | High – still tractable with standard methods (e.g. dynamic programming, scenario analysis) | Moderate – captures temporal changes, but still one perspective only | Scenario planning, forecasting models, dynamic programming | Low – conventional methods handle moderate temporal complexity |
| **Multi-Stakeholder Strategic Model (Static Multi-criteria)** | Low – static decision (single period) | Moderate – multiple stakeholders or criteria considered | Moderate – higher dimensionality (several objectives/constraints) | Medium – must balance conflicting objectives; no single optimum (trade-offs via MCDA) (Introduction - Multicriteria Analysis for Environmental Decision-Making | Moderate – accounts for diverse perspectives at one time, but no dynamics | Multi-criteria analysis (AHP, weighted scoring), stakeholder negotiations | Medium – complexity grows with stakeholders; advanced support tools increasingly useful |
| **Integrated Multi-Stakeholder Dynamic Model (Moderate Detail)** | High – multiple decision stages or time steps | High – multiple stakeholders and functional areas included | High – many variables across time and subsystems | Low – computationally difficult; relies on heuristics or approximate optimization | High – captures dynamic interactions and stakeholder influences (more realistic) | System dynamics models, agent-based simulations, heuristic optimization (e.g. genetic algorithms) | High – traditional methods strained; benefit from federated learning (to divide temporal scope) and proposal testing (to explore scenario space) |
| **Full High-Dimensional Multi-Stakeholder Model (Maximal Detail)** | Very High – fine-grained long-horizon dynamics | Very High – many stakeholders & all operational variables | Very High – extremely large state space (myriad variables) | Very Low – intractable for exact optimization (combinatorial explosion); even simulation is hard ([When models are wrong, but useful | Mathematical Institute](https://www.maths.ox.ac.uk/node/34245#:~:text=but%20some%20are%20useful,On%20the | Very High (in theory) – includes most real-world phenomena (highest fidelity) but nearly unmanageable due to complexity | Massive-scale simulations (digital twins), exhaustive scenario exploration, AI-driven search (e.g. reinforcement learning) |
---
# 1.3🎞️Thesis Scope and Example Case
Three interrelated factors contribute to the unusability of current EDMs, each with significant consequences at a different level of analysis. At the fundamental **nature of the problem** level, the inherent trade-off between model tractability and reality-fit means that formal models tend to be either overly simplistic or overwhelmingly complex; consequently, entrepreneurs often **abandon formal modeling** in favor of intuition, imitation, or ad hoc heuristics. At the **individual** level, entrepreneurs’ idiosyncratic initial conditions and the cognitive difficulty of inferring both their own and others’ preferences render one-size-fits-all models ineffective – leading individuals to revert to copying others’ strategies and hindering the development of a personalized decision-making style. At the **institutional** level, insufficient modeling education for entrepreneurs and weak coordination between ventures and public stakeholders result in **fragmented, non-cumulative learning** and planning failures on a broader scale. Each of these problem dimensions is examined in depth in Section 2.Nature of problem, Section 4.Individual level of problem, and Section 6.Institutional level of problem, respectively, underscoring the need for new approaches to bridge this usability gap.
To address these challenges, this thesis proposes a three-pronged framework of solutions, summarized in the table below.
| Solution | Symbols | Complexity Addressed | As-is → To-be | How | Why |
| ---------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- | ---------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **<span style="color:red">Phase-based learning by Entrepreneur</span>** | <span style="color:red">![wavy red line] D(a,s)=s'</span> | <span style="color:red">Time</span> | Too complex/too simple strategy only, one stakeholder model → Modularized, not too simple but not too complex, containing multiple operational variables with multiple stakeholders | Sub-path based formulation with simplex algorithm | Entrepreneurs need phases to learn and change operational modes (experiment first); mobility ventures show D-shape differs between phases (from innovation/idea/value creation to value capturing with precision/operational efficiency) |
| **<span style="color:green">Proactive hypothesis proposal by Entrepreneur</span>** | <span style="color:green">UC/Cost = UC/State × State/Act × Act/Cost (B,D,C)</span> | <span style="color:green">Space</span> | Causal inference (inferring preference, initial state, and stakeholders' perception) → Synthesizing probabilistic programs (aligning explainability, participatory modeling of value creation/capture) | Multi-model probabilistic program and simplex algorithm | Entrepreneurs need to understand boundaries of acceptable regions and find the fastest path toward those regions |
| **<span style="color:violet">Calibrated federated learning by Entrepreneur & Social Planner</span>** | <span style="color:violet">D mapped to D-bar interconnected equations, s'=E[s\|a], D-MDP</span> | <span style="color:violet">Time & Space</span> | City without vision or strategy → Bounded probability distribution on width and height of S-curve; dynamic consistency leading to tighter solution set | D-bar sharing through milestones (time and performance metrics in form of test quantities) | Need coordinated vision with milestones (e.g., "2030: 50% EV for California"); shift in performance measures (mile per intervention to cost per mile, range-based to efficiency-based) |
Table 1.3 A three-solution framework addressing temporal (<span style="color:red">red</span>), spatial (<span style="color:green">green</span>), and spatio-temporal (<span style="color:violet">violet</span>) complexities in entrepreneurial decision models through phase-based learning, proactive hypothesis testing, and calibrated federated learning approaches.
This table presents a comprehensive framework for addressing key complexities in entrepreneurial decision models through three integrated solutions. The <span style="color:red">red-coded phase-based learning approach</span> tackles temporal complexity by transforming overly simplistic or overly complex strategies into modularized operational models with sub-path formulations. The <span style="color:green">green-coded proactive hypothesis proposal methodology</span> addresses spatial complexity by evolving causal inference into participatory value modeling through probabilistic programming techniques. Finally, the <span style="color:violet">violet-coded calibrated federated learning system</span> combines both temporal and spatial dimensions by replacing unstructured approaches with bounded probability distributions that enable dynamic consistency through milestone-based coordination between entrepreneurs and social planners. Together, these color-coded solutions form a robust toolkit for enhancing model usability across the different complexity dimensions of entrepreneurial decision-making.
## NEED
# Markovian Entrepreneurial Decision Framework: Structured Database Table
| Concept | Mathematical Formulation | Phase Transition Bottleneck | Stakeholder Acceptance Bottleneck | Application to Sublime Systems |
| ---------------------------- | ----------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **State Vector (S)** | $S = [S_1, S_2, S_3]$ where $S_i \in {0,1}$ | $S_{NAIL} \rightarrow S_{SCALE}$ transition occurs when $\sum_{i=1}^{3} S_i \geq 2$ | Full acceptance requires $S = [1,1,1]$ | $S_1$: Testing facility approval<br>$S_2$: Eco-builder adoption<br>$S_3$: Investor commitment |
| **Uncertainty Matrix (B)** | $B \cdot S = U$ where $B \in \mathbb{R}^{3 \times 3}$ | Critical values of $B$ determine phase transition threshold: $\frac{\partial U}{\partial S}\big\vert_{S_{NAIL} \rightarrow S_{SCALE}}$ | $B_{ij}$ coefficients reveal cross-stakeholder dependencies | $B = \begin{bmatrix} 0.8 & 0.6 & 0.4 \ 0.7 & 0.9 & 0.5 \ 0.3 & 0.6 & 0.8 \end{bmatrix}$ where $B_{21} = 0.7$ means testing approval reduces builder uncertainty by 70% |
| **Resource Constraints (C)** | $C \cdot A \leq R$ where $C \in \mathbb{R}^{3 \times 3}$ | Resource allocation shifts at transition: $\frac{R_{SCALE}}{R_{NAIL}} > 1$ | Resources must be strategically allocated to reach $S = [1,1,1]$ | $C = \begin{bmatrix} 0.4 & 0.3 & 0.7 \ 0.5 & 0.6 & 0.4 \ 0.7 & 0.5 & 0.3 \end{bmatrix}$ mapping segment/collaborate/capitalize actions to resources |
| **State Transition (D)** | $D(S,A) = S'$ | Transition rates increase exponentially after inflection: $\frac{dS}{dt}\big\vert_{SCALE} > \frac{dS}{dt}\big\vert_{NAIL}$ | Optimal path to $[1,1,1]$ given by $\arg\max_A P(S' = [1,1,1] \| S,A)$ | $D(S,A) = S + \begin{bmatrix} A_1 \cdot (1-S_1) \ A_2 \cdot (1-S_2) \ A_3 \cdot (1-S_3) \end{bmatrix} \cdot P_A$ |
| **Optimization Objective** | $\min (W_1 \cdot U_1 + W_2 \cdot U_2 + W_3 \cdot U_3)$ | At transition: $\frac{\Delta U}{\Delta C} = \frac{W_1 \cdot \Delta U_1 + W_2 \cdot \Delta U_2 + W_3 \cdot \Delta U_3}{\Delta C}$ | Minimizing stakeholder-weighted uncertainty | For Sublime: $W = [0.3, 0.4, 0.3]$ balancing testing, builder, and investor concerns |
| **Proactive Testing** | $D_{proactive}(S, A_{multi}) = D(S, A_1) \cup D(S, A_2) \cup D(S, A_3)$ | Accelerates transition by parallelizing validation: $T_{NAIL-SCALE}^{proactive} < T_{NAIL-SCALE}^{sequential}$ | Enables simultaneous state changes across multiple stakeholders | Sublime can simultaneously validate with testing facilities while demonstrating to builders and investors |
| **Value Creation** | $V(S) = V_{innovation}(S) + V_{operation}(S)$ | Phase transition occurs when $\frac{dV_{innovation}}{dS} = \frac{dV_{operation}}{dS}$ | Total value maximized at $S = [1,1,1]$: $V([1,1,1]) > V(S)$ for all $S \neq [1,1,1]$ | Sublime's value creation shifts from innovation (CO₂ reduction) to operational efficiency (cost parity) |
| **Decision Sequencing** | $\pi^*(S) = \arg\max_A \sum_{S'} P(S' \|S,A) \cdot V(S')$ | | Optimal policy switches at transition: $\pi^__{NAIL} \neq \pi^__{SCALE}$ | Sequence that maximizes probability of reaching $[1,1,1]$ |
## SOL
## FULFILLMENT
from moshe_benAkiva
- you mentioned "1.Experimental (proactive)" is impractical or infeasible, but this is very usual in entrepreneurship where previous collected data hints the next experiment. I believe ultimate outcome of every model should be an action that triggers data based on which next action is chosen. Incidentally I don't trust any data collected by others (measurement issues) so my preference is experimental > hypothetical > observational
---
# manuscript23
# 2.Nature of problem
![[🗄️table_of_contents 2025-04-29-8_0.svg|400]]
%%🖋 Edit in Excalidraw%%
| Perspective | Causes of the problem | Effects of the problem (As-Is)<br>(Why we NEED to solve this) | NEED-Solution (To-Be) | Evaluation Method<br>(Functionality/adoption by entrepreneurs) |
| ---------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------- |
| Nature of the Problem | - Models are either too simple or too complex<br>- Spatial complexity from recurrent social reasoning and causal inference on high dimension parameter space<br>- Temporal complexity in stakeholder choices | - Imitative rather than experimental behavior<br>- Non-cumulative optimal solutions<br>- Abandonment of modeling and measurement | Phase-based learning <br>- 3.1💭Theorize solution, <br>- 3.2📐Produce solution | 3.3💸Evaluate solution |
| Individual's Attribution of the Problem | - Personalized initial states and preferences<br>- Difficulty inferring own/others' states and preferences<br>- Lack of tools for personalized entrepreneurial development | - Reliance on imitation without developing personal style<br>- Inability to build on observed behaviors<br>- Giving up on scientific approaches to entrepreneurship | 🎁Model hypothesis network<br>- 5.1💭Theorize solution<br>- 5.2📐Produce solution<br>Personalized modeling tools<br>- Systems that account for individual differences<br>- Educational frameworks for individual growth | 5.3💸Evaluate solution |
| Institution's Attribution of the Problem | - Insufficient modeling education for entrepreneurs<br>- Poor coordination between entrepreneurs and local government<br>- Lack of systematic approach to entrepreneurial development | - Uncumulative learning at societal level<br>- Ineffective planning processes<br>- Knowledge gaps between theory and practice | - 7.1💭Theorize solution<br>- 7.2📐Produce solution <br>Enhanced entrepreneurial education <br>- (Inverse) planning coordination systems<br>- Institutional frameworks for knowledge accumulation | 7.3💸Evaluate solution |
todo: tesla_betterplace.png from 📜gans20_choose(tech)
# 3.2📐Produce solution
| 🕸️Introduction | |
| -------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| | 1.1😵💫Entrepreneurial Decision Models (EDM) are Unusable by Entrepreneurs |
| | 1.2🏳️🌈Complexity Spectrum of Entrepreneurial Decision Models |
| | 1.3🎞️Thesis Scope and Example Case |
| ⏰I Perceptual Learning to Relax Integrality | B |
| | 2.Nature of problem |
| | 2.1 Why entrepreneurial decision models are difficult to use in practice |
| | 2.2 Mathematical formulation of decision space $\mathcal{D}$ with interdependent utilities |
| | 2.3 Empirical evidence from venture case studies |
| | |
| | 3. **Solution: Phase-Based Uncertainty Minimization** |
| | 3.1💭Theorize solution: Phase-based learning framework (Nail, Scale + simplex) |
| | 3.2📐Produce solution: Subpath formulation + Simplex algorithm for uncertainty reduction efficiency $\frac{\Delta\textcolor{#3399FF}{U}}{\textcolor{#3399FF}{C}}$ |
| | 3.3💸Evaluate solution: Tesla/Betterpalce/Segway simulation |
| | 3.4📜Related work |
| 👥II Proactive Testing to Lower Multi-stakeholder Complexity | C matrix |
| | 4.Individual level of problem |
| | 4.1 Why entrepreneurs struggle with multi-stakeholder decisions |
| | 4.2 Mathematical formulation of spatial complexity with stakeholder preferences $(\textcolor{violet}{W})$ |
| | 4.3 Cognitive barriers to stakeholder alignment |
| | |
| | 5. **Solution: Network of Business Model Hypotheses** |
| | 5.1💭Theorize solution: Proactive hypothesis testing framework for spatial complexity |
| | 5.2📐Produce solution: Estimate $B$ and $\textcolor{#3399FF}{C}$ in real-world applications |
| | 5.3💸Evaluate solution |
| | 5.4📜Related work |
| ⏰👥III Expectation Propagation to Lower Operational Multi-Stakeholder Complexities | D matrix |
| | 6.Institutional level of problem |
| | 6.1 Why entrepreneurs struggle with sequence-dependent decisions |
| | 6.2 Mathematical formulation of temporal uncertainty $\textcolor{#3399FF}{U_{t+1}}=f(\textcolor{#3399FF}{U_t},\textcolor{violet}{\Omega_t})$ |
| | 6.3 Information bottlenecks in venture planning |
| | |
| | 7. **Solution: Hierarchical (multi-level/nested) Uncertainty Modeling** |
| | 7.1💭Theorize solution: Federated learning framework for temporal complexity; Social planner's role in informing $D_{industry}$ |
| | 7.2📐Produce solution: State transition tensor implementation $(D\in\mathbb{R}^{I\times\textcolor{red}{A}\times\textcolor{green}{S}\times\textcolor{green}{S}})$ defining $P(\textcolor{green}{S'}\mid\textcolor{green}{S},\textcolor{red}{A})$ |
| | 7.3💸Evaluate solution: Empirical validation of temporal complexity reduction |
| | 7.4📜Related work |
| IV Conclusion Integration and Evaluation | |
using optimizing startup operations cld
![[3.2📐Produce solution 2025-05-01-21.svg]]
%%🖋 Edit in Excalidraw%%# Sublime Systems Stakeholder Decision Matrices
| | Operational Partner Decision Matrix ($B_o$) | Customer Decision Matrix ($B_c$) | Investor Decision Matrix ($B_i$) |
| -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Observable Attributes:** | - Technical Performance (unproven → lab validated → field validated)<br>- Compliance with Standards (non-compliant → partial compliance → full compliance)<br>- Testing Scale (lab samples → pilot scale → production scale)<br>- Financial Backing (pre-seed → major funding → government backing)<br> | - Performance (inferior → equivalent → superior)<br>- Carbon Reduction (30-50% → 50-80% → 80-100%)<br>- Cost Premium (over 50% → 20-50% → under 20%)<br>- Regulatory Status (experimental → limited approval → full approval) | - Technology Maturity (lab prototype → pilot scale → commercial)<br>- Carbon Reduction Potential (incremental → significant → revolutionary)<br>- Market Traction (interest only → initial orders → paying customers)<br>- Team Qualifications (academic only → mixed team → industry veterans) |
| **Perceptual Frameworks:** | Technical Validation: "Is this cement proven safe?"<br>Industry Advancement: "Will this advance standards?" | - Risk Assessment: "Is this cement safe to use?"<br>- Value Proposition: "Is the green premium worth it?" | - Market Impact: "Will this disrupt the cement industry?"<br>- Execution Capability: "Can this team scale production?"<br> |
## Operational Partner Decision Matrix ($B_o$)
This matrix maps observable startup attributes to partnership decisions for material testing facilities, construction material suppliers, and other operational partners.
|Observable Attribute|Level 1|Level 2|Level 3|Decision Impact (0-1)|
|---|---|---|---|---|
|**Technical Performance**|Unproven|Lab validated|Field validated|0.9|
|**Manufacturing Readiness**|Theoretical process|Working prototype|Scalable process|0.8|
|**Compliance with Standards**|Non-compliant|Partially compliant|Fully compliant|0.9|
|**Integration Complexity**|Major changes needed|Moderate adaptation|Drop-in replacement|0.7|
|**Market Demand Signals**|Speculative|Early adopter interest|Confirmed demand|0.6|
|**Financial Stability**|Pre-seed funding|Major funding secured|Revenue generating|0.5|
|**IP Protection**|Provisional patents|Filed patents|Granted patents|0.4|
|**Partnership Terms**|Exclusive license|Joint development|Open collaboration|0.3|
**Partnership Decision Function:** $P(Partner) = \sigma(\sum_{i=1}^{n} w_i \cdot Attribute_i - \theta)$
Where:
- $w_i$ is the Decision Impact weight for attribute $i$
- $\theta$ is the partnership threshold (currently 3.0)
- $\sigma$ is the sigmoid function: $\sigma(x) = \frac{1}{1+e^{-x}}$
**Sublime's Current Position (May 2025):**
- Technical Performance: Level 2 (Lab validated, first commercial application)
- Manufacturing Readiness: Level 2 (Working prototype at pilot scale)
- Compliance with Standards: Level 2 (Partially compliant with ASTM standards)
- Integration Complexity: Level 3 (Claimed to be drop-in replacement)
- Market Demand Signals: Level 2 (Early adopter interest from eco-builders)
- Financial Stability: Level 2 (DOE funding secured)
- IP Protection: Level 2 (Patent applications filed)
- Partnership Terms: Level 3 (Open collaboration model)
**Current $P(Partner)$ = 0.68**
## Customer Decision Matrix ($B_c$)
This matrix maps observable startup attributes to purchase decisions for construction companies, developers, and other potential cement buyers.
| Observable Attribute | Level 1 | Level 2 | Level 3 | Decision Impact (0-1) |
| ---------------------------- | --------------------- | ---------------------- | --------------------- | --------------------- |
| **Performance Metrics** | Inferior to Portland | Equivalent to Portland | Superior to Portland | 0.9 |
| **Cost Premium** | >50% | 20-50% | <20% | 0.8 |
| **ESG Certification Value** | No certification | Standard certification | Premium certification | 0.7 |
| **Supply Chain Reliability** | Unproven | Limited capacity | Robust capacity | 0.8 |
| **Regulatory Approval** | Experimental use only | Limited approval | Full code approval | 0.9 |
| **Case Studies/References** | None | Early projects | Multiple references | 0.6 |
| **Technical Support** | Limited | Standard | Comprehensive | 0.4 |
| **Market Differentiation** | None | Moderate | Significant | 0.5 |
**Purchase Decision Function:** $P(Purchase) = \sigma(\sum_{i=1}^{n} w_i \cdot Attribute_i - \theta)$
Where:
- $w_i$ is the Decision Impact weight for attribute $i$
- $\theta$ is the purchase threshold (currently 3.2)
- $\sigma$ is the sigmoid function: $\sigma(x) = \frac{1}{1+e^{-x}}$
**Sublime's Current Position (May 2025):**
- Performance Metrics: Level 2 (Claimed to be equivalent to Portland)
- Cost Premium: Level 1 (Currently >50% higher than Portland)
- ESG Certification Value: Level 3 (Premium "zero-carbon" certification potential)
- Supply Chain Reliability: Level 1 (Unproven at scale)
- Regulatory Approval: Level 2 (Limited approval for non-structural applications)
- Case Studies/References: Level 2 (One Boston Wharf Road project)
- Technical Support: Level 2 (Standard support)
- Market Differentiation: Level 3 (Significant "true zero" carbon claim)
**Current $P(Purchase)$ = 0.56**
## Investor Decision Matrix ($B_i$)
This matrix maps observable startup attributes to investment decisions for Sublime Systems' cement technology.
|Observable Attribute|Level 1|Level 2|Level 3|Decision Impact (0-1)|
|---|---|---|---|---|
|**Testing Facility Approval**|None|Preliminary tests|Full certification|0.7|
|**Production Scale**|Lab scale (<1 ton)|Pilot plant (250 TPY)|Commercial (30,000+ TPY)|0.8|
|**Customer Commitments**|Interest only|Letters of intent|Binding contracts|0.9|
|**Team Experience**|Academic only|Academic + startup|Industry veterans|0.6|
|**Regulatory Status**|Unknown|Under review|Approved for construction|0.8|
|**Capital Requirements**|>$200M|$87-200M|<$87M|0.5|
|**Carbon Reduction**|<50%|50-80%|>80%|0.4|
|**Production Cost**|>2x Portland|1.3-2x Portland|≤1.3x Portland|0.7|
**Investment Decision Function:** $P(Invest) = \sigma(\sum_{i=1}^{n} w_i \cdot Attribute_i - \theta)$
Where:
- $w_i$ is the Decision Impact weight for attribute $i$
- $\theta$ is the investment threshold (currently 2.5)
- $\sigma$ is the sigmoid function: $\sigma(x) = \frac{1}{1+e^{-x}}$
**Sublime's Current Position (May 2025):**
- Testing Facility Approval: Level 2 (Preliminary tests completed)
- Production Scale: Level 2 (250 TPY pilot plant)
- Customer Commitments: Level 2 (Letters of intent for 45,000 tons)
- Team Experience: Level 3 (Founded by MIT researchers with industry connections)
- Regulatory Status: Level 2 (DOE funding secured, permitting in progress)
- Capital Requirements: Level 2 ($87M from DOE, additional private investment needed)
- Carbon Reduction: Level 3 (>90% reduction claimed)
- Production Cost: Level 1 (Currently >2x Portland cement)
**Current $P(Invest)$ = 0.73**
## Interpretation and Strategy Implications
These decision matrices reveal Sublime Systems' key strategic challenges:
1. **Investors** are most likely to engage (P=0.73) due to strong climate tech interest, but are concerned about production costs and capital requirements.
2. **Operational Partners** show moderate engagement likelihood (P=0.68), with technical validation and standards compliance being critical barriers.
3. **Customers** have the lowest current engagement probability (P=0.56), primarily constrained by cost premium, supply chain reliability, and regulatory approval.
The optimal strategy involves:
1. **Sequential Validation**: First focus on improving technical validation and reducing production costs to strengthen operational partner relationships.
2. **Proactive Testing**: Simultaneously engage early-adopter customers with strong ESG priorities to build case studies while production scales.
3. **Dynamic Calibration**: Shift focus from technical validation to cost reduction and supply reliability as production scales, to broaden customer adoption beyond early adopters.
This mathematical approach identifies actionable priorities for Sublime Systems while balancing the interdependent stakeholder concerns in the conservative construction materials industry.
---
# 3.3💸Evaluate solution
2025-04-27
todo:
---
# 3.4📜Related work
---
# manuscript45
# 4.Individual level of problem
| Perspective | Causes of the problem | Effects of the problem (As-Is)<br>(Why we NEED to solve this) | NEED-Solution (To-Be) | Evaluation Method<br>(Functionality/adoption by entrepreneurs) |
| --------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------- |
| Individual's Attribution of the Problem | - Personalized initial states and preferences<br>- Difficulty inferring own/others' states and preferences<br>- Lack of tools for personalized entrepreneurial development | - Reliance on imitation without developing personal style<br>- Inability to build on observed behaviors<br>- Giving up on scientific approaches to entrepreneurship | 🎁Model hypothesis network<br>- 5.1💭Theorize solution<br>- 5.2📐Produce solution<br>Personalized modeling tools<br>- Systems that account for individual differences<br>- Educational frameworks for individual growth | 5.3💸Evaluate solution |
# 5.2📐Produce solution
2025-05-01
C matrix
![[5.2📐Produce solution 2025-05-01-22.svg]]
%%🖋 Edit in Excalidraw%%
using concept mapping btw max flow and min uncertainty cld
building on business model 3d cld and code, gradient of $\frac{d \textcolor{red}{a^*}}{d \textcolor{purple}{w}}$, stakeholder utility or uncertainty evaluation $(B\textcolor{green}{S} = [\textcolor{#3399FF}{U_d}, \textcolor{#3399FF}{U_s}, \textcolor{#3399FF}{U_i}])$
---
# 5.3💸Evaluate solution
---
# 5.4📜Related work
---
# 👥II Proactive Testing to Lower Multi-stakeholder Complexity
---
# manuscript67
# ⏰👥III Expectation Propagation to Lower Operational Multi-Stakeholder Complexities
# 7.1💭Theorize solution
|Paper Title|Reason for Classification (Federated Learning & Spatio-Temporal Complexity)|Optimization Component (3.1 Theoretical)|
|---|---|---|
|**Probabilistic Programming for Entrepreneurial Decision Support Under Uncertainty**|Offers a unified probabilistic programming framework that integrates distributed insights across stakeholders and time, systematically reducing spatio-temporal complexity by synthesizing multiple information sourcesoaicite:2.|Establated state-transition functions and uncertainty weights, explicitly structuring collective uncertainty reduction across spatio-temporal dimensions ($U_d, U_s, U_i$)oaicite:3.|
|**Mining Test Quantities with Exchangeability: Bayesian Reversibility**|Introduces altructures ensuring reversible learning from sequential experiments, transforming path-dependent complexity into cumulative federated knowledge that reduces spatio-temporal uncertainty .|Clarifies federated learning strategies via reversible test quantity metrics, systematically shaping spatio-temporal state transitions ($D(S,A)=S'$) into a coordinated federated learning process .|
|**Complexity Analysis of Entrepreneurial Decision-Making (NP-Hardness Proof)**|Provides foundational complexity theory justifying the necessity for federated approximation strategies by demonstrating intrinsic computational complexity of comprehensive spatio-temporal optimization, motivating federated learning as necessary due to NP-hardness .|Establishes theoretical justification for federated learning approaches due to computational complexity, defining why exact spatio-temporal uncertainty optimization ($U_d, U_s, U_i$) is intractable without federated strategies .|
|**Calibrated Federated Learning via Entrepreneur–Social Planner Coordination**|Extends federated learning to ecosystem-level coordination, defining collaborative milestone-driven benchmarks that federate spatio-temporal insights from individual ventures and social planners, systematically reducing ecosystem-wide complexityoaicite:4.|Clarifies theoretical structures for collaborative, federated optimization across ventures, explicitly aligning multi-venture state transitions ($D(S,A)=S'$) into unified spatio-temporal uncertainty reduction strategies ($U_d, U_s, U_i$)oaicite:5.|
federated_learning
📜gans20_choose(tech)
---
# 7.2📐Produce solution
---
# 7.3💸Evaluate solution
---
# 7.4📜Related work
tan_zhixuan's recommendation preparing session7_social planner:
- 📜planning with theory of mind for few shot adaptation in sequential social dilemmas
---
# IV Conclusion Integration and Evaluation
---
# 🌙simulated collaboration based on observed belief and goal of role model charlie, scott, vikash, moshe, jinhua
2025-04-29
### five abstracts -> three abstracts
motivation: exchanging influence with investment: making five versions of abstract that each leverages the optimism of each professors - think of this as selfish gene logic where human's nature is to desire for influence (whether biological or spiritual) and invest money, time, effort to satisfy that desire. i.e. my business model is to create and capture value using optimism of the five professors. several success examples is implementing the advice of mentor/investors have increase the startup quality as measured by one research using creative destruction lab venture accelerator database (description attached). using charlie's advice - you want others to feel excited about your research, and the recipe for others to get excited about something is, first find a shared vision (as you go up the ladder of abstraction, you can find shared vision with everyone e.g. from optimism of small scope like entrepreneurship to bigger scope of world peace). then given the shared vision, help them understand what they can contribute to they would find a "how" themselves.
2025-04-29
1. logging disagreeed -> persuaded
2. predictability of their reaction (experiencing the surprise in advance)
For an EV startup founder, engaging with a diverse array of advisors is crucial for refining their understanding of the complex landscape they navigate. Each conversation sheds light on different facets—whether it’s the trajectory of battery technology, the expansion of charging infrastructure, evolving regulatory policies, or patterns in customer adoption. While no single piece of advice may perfectly align with the founder
2025-04-26
under random utility model, the modeler builds model to group the choices that share unobserved heterogeneity.
- 🗄️🧠moshe's "features are chosen because they explain variation, not because they cause variation"
- 🗄️🧠vikash's " probabilistic models are highly simplified, stochastic approximations of reality that capture patterns phenomenologically without true causal mechanisms, yet can be semantically rich despite their syntactic simplicity.
- ethical considerations - propaganda
| Professor | Key Expertise | Contribution to Your Research | Optimism of the Professor I'm Betting On | Connection to Tesla Case Study |
| --------------------- | ----------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------- |
| **Vikash Mansinghka** | Probabilistic programming (GEN platform, SPPL, rational semantic frameworks) | Technical foundation for your Domain-Specific Language development; expertise in rational alternatives to deep learning | Highly optimistic about probabilistic programming as a human-like, rational alternative to deep learning for complex decision environments | Could enhance modeling of Tesla's "cowboy engineer" culture through probabilistic programming principles |
| **Moshe Ben-Akiva** | Discrete choice modeling, random utility theory, latent class analysis | Rigorous methods to enhance your stakeholder utility structures and decision-making primitives | Confident that precise quantitative modeling of decision-making behavior provides essential foundations for strategic planning | Can help formalize Tesla's evolving market conditions and stakeholder utility functions |
| **Scott Stern** | Bayesian economic frameworks, innovation ecosystem mapping, Entrepreneurial Compass | Entrepreneurial economics lens and theoretical positioning for your work; aligns with your Bayesian structure learning | Optimistic about Bayesian approach (inference and or decision making) as foundational for entrepreneurial decision-making; advocates for systematic testing in innovation | Valuable for modeling Tesla's innovation ecosystem and entrepreneurial decision-making approach |
| **Charlie Fine** | Value chain models, industry clockspeed, "nail it, scale it, sail it" framework | Operational/value-chain perspective to validate your five primitives within industry contexts | Believes in sustainable entrepreneurial success through adaptive operations and understanding industry evolution patterns | Direct application to Tesla's scaling challenges and operational evolution |
| **Jinhua Zhao** | Behavioral science implementation, field experiments, transportation focus | Knowledge for translating your framework into practical applications, especially in mobility | Optimistic about behavioral science-based interventions for effective policy and operations in transportation and mobility | Insights on behavioral aspects of Tesla's market approach and transportation industry transformation |
2025-04-01
| Component | Moshe Ben-Akiva | Charlie Fine | Scott Stern | Jinhua Zhao | Vikash Mansinghka |
| ----------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| Key Contrarian Idea🤔 | Discrete choice and random utility modeling as rigorous frameworks essential for precisely understanding and predicting decision-making behavior | "Nail it, scale it, sail it": Framework emphasizing the dynamic evolution of entrepreneurial operations and industry value chains | Bayesian inference as foundational framework in entrepreneurial decision-making; openness and systematic testing ("test-two-choose-one") in innovation | Behavioral science-based interventions for effective policy and operations, emphasizing real-world human behavior as critical for successful implementation | Probabilistic programming as human-like, rational alternative to deep learning approaches for modeling open-ended decision environments |
| Toolbox🛠️ | - Discrete choice modeling- Random utility theory- Latent class and probabilistic decision analysis | - Value chain model (Supplier-OEM-Distributor)- Double helix (modular-integral)- Industry clockspeed and gear models- Triple s-curve evolution patterns<br><br>charlie24🛠️_clockspeed🗣️ | - Entrepreneurial Compass Framework- Idea Production Function ($\dot{A} = f(L_A, K_A, A; Z_A, δ)$)- Bayesian economic frameworks for innovation economics- Innovation ecosystem mapping<br><br>scott23🛠️_econ_idea_innov_ent.pdf | - Behavioral transportation frameworks and qualitative/quantitative integration- Field-based behavioral experiments- Practical design and policy implementation approaches | - GEN probabilistic programming platform- Rational semantic interpretation frameworks- Automated differentiation of expected value (ADEV)- SPPL for symbolic probabilistic inference <br><br>vikash24🛠️_ AI_understands_world_pp.pdf |
| Long-Term Vision👓 | High-precision modeling and prediction of human decision-making behavior; integration of rigorous quantitative methods into practically relevant decision-making contexts | Sustainable entrepreneurial success through continuously adaptive operations and deep understanding of industry evolution patternscharlie24👓_nss🗣️ | Sustained long-term economic growth through innovation, driven by clear theoretical and practical bridges in entrepreneurship research; alignment of innovation ecosystems and policy incentivesscott24👓_Bayesian_Entrepreneurship.pdf | Practical, behaviorally-informed transportation solutions that drive sustainable urban mobility transformation; real-world feasibility and measurable behavioral outcomes | Human-like rational reasoning in artificial intelligence for handling complex decision-making environments; integrated frameworks for language and thoughtvikash24👓_rational_meaning_prob_lang_thought🗣️ |
| value create⚙️ | Quantitative rigor in modeling that precisely predicts choices and clarifies strategic decisions, providing reliable foundations for strategic and operational planning | Clear operational frameworks enabling entrepreneurs to successfully scale and adapt in changing industry dynamics, delivering actionable tools for value creation | Entrepreneurial strategy frameworks that drive effective innovation, improving clarity and structure of entrepreneurial decision-making and fostering broader academic-practitioner value creation | Integration of behavioral insights into practical operations and policy, enabling effective, human-centered, behaviorally aligned implementation that creates measurable improvements in transportation systems | Advanced probabilistic programming tools enabling rational human-like decision-making and inference in complex, uncertain entrepreneurial environments, improving decision quality and interpretability |
| value capture🥍 | Intellectual leadership and influence within discrete-choice modeling and rigorous quantitative analysis communities; shaping critical methodological practices and influencing decision-making approaches widely adopted in academia and industry | Capture entrepreneurial interest and practitioner engagement by establishing widely applicable operations tools that demonstrate practical effectiveness in real-world entrepreneurial scenarios, becoming authoritative reference points for operational strategies | Control key theoretical resources and frameworks (such as entrepreneurial compass, Bayesian inference methods) that establish high trust among scholars and practitioners; influential theoretical concepts capture scholarly and practical influence | Position as authoritative voice in behavioral transportation innovation; capture value from policy and implementation consulting, practical tool adoption, and influential networks within urban and transportation innovation communities | Leading intellectual position in rational probabilistic reasoning as a powerful alternative to opaque deep learning approaches; capturing influential roles in AI and decision-support tool communities |
| table | 🗄️🧠moshe | 🗄️🧠charlie | 🗄️🧠scott | 🗄️🧠jinhua | 🗄️🧠vikash |
| dissertation | | | | 📜zhao09_shape-accom(pref) | 📜mansinghka09_natively_prob_comp |
| disagreement with angie | - entrepreneur’s reasoning seems to require probability but not statistics<br>- bayesian gives prior for statistical model + regularization -> prior serves regularization for statistical model. <br>- what’s probability and statistics? doesn’t entrepreneur do both theory-driven (model based) and date-driven (model-free) decision making? <br>- scenario discovery tool <br>- business model’s log likelihood (+ alpha) can be founder’s objective function | ✅nothing is purely digital -> changed digital product management course to product management course, analyzed the effect of digital and physical in management | resource-rational model is both normative and positive () | jinhua(mobility venture)<br><br>- inverse reinforcement learning can be learned for career choice (kids don’t know utility)<br><br>- observer aware learning | - to implement human’s scaling behavior, we need two: dna (base) and federated learning and planning (btom group prior)<br>- automatic differentiation of expected value’s performance should be compared with automated routines like integer optimization (cutting plane, colum generation) which can help interpretation - jaxopt |
mail threads to maintain
- to Charlie and Vikash (initiated by Charlie's question to Vikash on whether there can be any "experts" or "rules to learn" in entrepreneurship game
- to Charlie and Scott's mental model with Bayesian statistics and cognition ideas absent from current Bayesian entrepreneurship (e.g. hierarchical bayesian)
2025-02-23
collab(josh, scott)
three collaboration
| three solution module development in parallel | evaluators | efforts to gravitate mental models of my evaluators |
| ---------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 1. quality measure for mobility innovation ecosystem | 🗄️🧠jinhua<br>🗄️🧠scott<br> | |
| | | |
| 2. simulation-based experimentation for bayesian entrepreneurship | 🗄️🧠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 🥚egg2chicken | 🗄️🧠vikash<br>🗄️🧠charlie<br> | |
2025-02-11
behanvior science, decision scieence, 📜swift_guilliver's travel
1. pshycological inventory - when making choice Angie Charlie Scott on simulation in entrepreneurship.txt
2024-10-05
| Component | Charlie Fine | Jinhua Zhao | Moshe Ben-Akiva | Scott Stern | Vikash Mansinghka | Angie Moon |
| --------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------- | --------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Key Contrarian Idea🤔 | "Nail it, scale it, sail it" framework for dynamic capability based entrepreneurial operations | | | - Bayesian approach to entrepreneurship and innovation economics<br>- role of scientific openness in scientific research | Probabilistic programming as alternative to deep learning for AI that understands the world like humans do | (to be enhanced) Conversational inference of startup valuation and equity allocation using language model and probabilistic programming |
| Toolbox🛠️ | - Value chain model (supplier-oem-distributor)<br>- Double helix between modular and integral<br>- Clockspeed across value chain (+bull whip)<br>- Gear model<br>- Triple s-curve<br>charlie24🛠️_clockspeed🗣️<br><br> | | | - Entrepreneurial compass<br>- Map for Innovation ecosystem across U.S. with Quality Measure <br>- Idea Production Function $\dot{A} = f(L_A, K_A, A; Z_A, δ)$ where $A, \dot{A}$ : knowledge stock, flow<br>- scott23🛠️_econ_idea_innov_ent.pdf<br> | - Gen probabilistic programming platform<br>- Rational meaning construction<br>- Automated differentiation of expected values (ADEV) optimization algorithm<br>- SPPL: Probabilistic Programming with Fast Exact Symbolic Inference<br>- vikash24🛠️_ AI_understands_world_pp.pdf<br>- andrew(bayesian cringe)<br> | - Probabilistic programs with equity valuation game rule world model<br>- Language model probabilistic programs to answer user's natural language question <br>- ADEV-based queries for equity allocation optimization<br>- Rational meaning construction of SAFE, term sheet document<br>🛠️selling probabilistic program to innovation from vikash<br>🧭🗺️selling entrepreneurial choice and map as Bayes.Entrep from scott<br>- 🧬⚙️selling value chain tools as evolutionary entrepreneurship from charlie |
| Long-Term Vision👓 | Sustainable entrepreneurial success through adaptive operations and understanding of industry evolution<br>charlie24👓_nss🗣️ | | | Long-term economic growth through innovation; Alignment of economic policies with innovation goals<br>- scott24👓_Bayesian_Entrepreneurship.pdf | Human-like reasoning in AI for complex, open-ended environments; unifying different approaches to meaning in language and thought<br>- vikash24👓_rational_meaning_prob_lang_thought🗣️<br> | AI-assisted, rational design of startup financial structures that amplify cooperation among stakeholders |
| value create⚙️ | | | | general component innovations entrepreneurial strategy can deliver value for existing users entrepreneurship scholars and practitioners | | |
| value capture🥍 | | | | controlling key functional resource trust among entrepreneurship scholars can capture value | | |
| table | 🗄️🧠charlie | | | 🗄️🧠scott | 🗄️🧠vikash | |
Jan
----
| Taylor's Principles of Scientific Management | Steps for Scientific Entrepreneurial Management | Development Plan with Charlie, Vikash, Scott |
| :------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| Develop scientific methods by applying measurement and statistics to existing work practices | Human and machine performance can be compared for current entrepreneurial management | **Charlie**: World model across nail and scale phase (evaluation)<br>**Vikash**: API for Human-AI comparison in entrepreneurial contexts<br>**Scott**: measurement tools for `value create` given four choices on customer (existing vs new) x technology (system vs component) -> `value capture` given four choices on (integrated vs functional) x (leverage resource vs build capabilities) |
| Select workers suited for tasks, then train them to become top performers | Machines can be trained to be the best partners of humans for entrepreneurial management | **Charlie**: Processification and automation tools for entrepreneurial best practices<br>**Vikash**: API for modular learning with human-scale data |
| Management and workers collaborate to implement scientific work methods | Entrepreneurs and machines can collaborate to implement scientific work methods | **Vikash**: CHI conference showcasing probabilistic programming in entrepreneurial decision-making<br>**Scott**: Bayesian Entrepreneurship conference demonstrating AI-enhanced entrepreneurial theory and practice<br>**Charlie**: Engage NSS followers in testing AI-powered tools for entrepreneurial operations |
| (Integration) | Unified approach to AI-assisted entrepreneurship | - Explore intersection of operations management, entrepreneurial strategy, and probabilistic programming<br>- Adapt scientific approach across industries and entrepreneurial phases<br>- Develop guidelines for responsible AI-assisted entrepreneurial decision-making |
2024-09-03
| Strategic/Operations Tool | Primary Entrepreneurial Function | How Probabilistic Programming Helps |
| ------------------------- | -------------------------------- | ------------------------------------------------------------------------------------------------------------------------------ |
| Segmentation | Simplifying | Helps model complex market structures and customer behaviors, allowing entrepreneurs to identify distinct segments more easily |
| Automation | Calibrating | Enables simulation of automated processes, helping calibrate the right balance between efficiency and flexibility |
| Optimization | Choosing | Facilitates decision-making under uncertainty by modeling various scenarios and their probabilities |
| Revenue Management | Probabilistic Reasoning | Allows for modeling of pricing strategies and demand fluctuations, incorporating uncertainty |
| Evaluation (KPIs) | Calibrating | Helps in creating robust metrics that account for uncertainty and noise in measurements |
| Processification | Simplifying | Models complex business processes, helping identify core components and potential improvements |
| Professionalization | Calibrating | Simulates impact of bringing in specialized skills, helping calibrate the right mix of generalists and specialists |
| Culturalization | Probabilistic Reasoning | Models how cultural factors might influence business outcomes, accounting for uncertainties |
| Platformization | Choosing | Helps in decision-making about platform strategies by modeling network effects and scaling dynamics |
| Collaboration | Probabilistic Reasoning | Models potential outcomes of partnerships, accounting for uncertainties in partner behaviors |
| Capitalization | Choosing | Assists in making funding decisions by modeling various scenarios and their probabilities |
| Replication | Simplifying | Helps in modeling how processes can be replicated across different contexts, simplifying scaling decisions |
| Project Title | Collaborators | Timeline | Paper Titles | Abstract | keywords |
| -------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------ | --------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------- |
| R1 x E1. Entrepreneurial Learning vs Decision Making on choice and growth architecture | R1: choice and growth architecture (Charlie, Scott)<br><br><br>E1: Vikash, JB | 2024-2025 | 1. "Integrating Strategy and Operations: A Bayesian Framework for Entrepreneurial Decision Making" <br><br> 2. "Causal Complexity in Entrepreneurial Learning: Bridging Bayesian and Evolutionary Approaches"<br><br> 2. "Probabilistic Reasoning for Entrepreneurial Decision Support: A Human-AI Collaboration Framework" | This project explores the intersection of entrepreneurial learning and decision-making processes. The first paper develops a Bayesian framework that integrates strategic and operational decision-making in entrepreneurial contexts. The second paper investigates the causal complexity in entrepreneurial learning, synthesizing Bayesian and evolutionary learning approaches. Both papers contribute to a deeper understanding of how entrepreneurs navigate uncertainty and make decisions across different stages of venture development. | strategy vs operations<br><br>causal vs complex approaches <br><br>bayesian vs evolutionary learning |
| E1. AI-Assisted Entrepreneurial Thinking and Decision Partnership | | 2024-2026 | 1. "Cognitive Augmentation in Entrepreneurship: AI as a Thinking and Decision Partner" <br><br> | This project focuses on developing AI systems that can serve as both thinking and decision partners for entrepreneurs. The first paper explores the concept of cognitive augmentation in entrepreneurship, examining how AI can enhance entrepreneurial thinking processes. The second paper presents a framework for human-AI collaboration in entrepreneurial decision-making, emphasizing probabilistic reasoning approaches. The research aims to advance the field of AI-assisted entrepreneurship and provide practical tools for entrepreneurs. | |
| | | | | | |
| R2. Applying machine learning concepts to systemize entrepreneurial learning<br><br> | Abdullah, Matt<br><br>Classification of Entrepreneurial Uncertainties, Dilemmas (tradeoff) | 2025-2026 | 1. "A Taxonomy of Entrepreneurial Uncertainties: Mapping the Landscape of Choice and Growth Dilemmas" <br><br> 2. "Value Chain Dynamics in Entrepreneurship: Integrating Capability Development and Supply Chain Fulfillment" | This project develops a comprehensive classification system for entrepreneurial uncertainties and dilemmas. The first paper proposes a taxonomy that maps the landscape of choice and growth dilemmas faced by entrepreneurs. The second paper examines the dynamic interplay between capability development and supply chain fulfillment in entrepreneurial value creation and capture. The research aims to provide a structured approach to understanding and managing the complex uncertainties inherent in entrepreneurship. | |
| E2. Probabilistic Programming Language for Entrepreneurial Nailers | Vikash Mansinghka, Charlie Fine | 2025-2026 | 1. "A Domain-Specific Language for Modeling Entrepreneurial Strategies: Integrating Bayesian and Evolutionary Learning" <br><br> 2. "Pivoting in Uncertainty: A Probabilistic Approach to Idea and Strategy Evolution in Early-Stage Ventures" | This project develops a probabilistic programming language specifically designed for early-stage entrepreneurs ("nailers"). The first paper introduces the language, demonstrating how it integrates Bayesian and evolutionary learning to model entrepreneurial strategies. The second paper focuses on applying this language to model pivoting decisions, providing a probabilistic framework for idea and strategy evolution under uncertainty. The research aims to provide entrepreneurs with powerful tools for decision-making in the early stages of venture creation. | |
| E3. Scalable Probabilistic Programming for Growth-Stage Ventures | Charlie Fine, Scott Stern | 2026-2027 | 1. "Scaling Entrepreneurial Decision Systems: A Probabilistic Programming Approach to Strategy and Operations" <br><br> 2. "Optimizing Value Creation and Capture: A Computational Framework for Scaling Entrepreneurial Ventures" | This project extends the probabilistic programming approach to address the unique challenges of scaling entrepreneurial ventures. The first paper presents a scalable decision system that integrates strategic and operational considerations for growth-stage companies. The second paper focuses on optimizing value creation and capture processes, providing a computational framework for entrepreneurs to navigate the complexities of scaling. The research aims to bridge theoretical insights with practical tools for entrepreneurs managing rapid growth. | |
| Entrepreneurial Uncertainty | Machine Learning Analogy | Key ML References |
| --------------------------- | ------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------- |
| Technological uncertainty | Inductive bias in algorithm selection | Mitchell (1997), "Machine Learning"; Domingos (2015), "The Master Algorithm" |
| Customer uncertainty | Generalization to unseen data | Vapnik (1998), "Statistical Learning Theory"; Goodfellow et al. (2016), "Deep Learning" |
| Organizational uncertainty | Bias-variance tradeoff | Geman et al. (1992), "Neural Networks and the Bias/Variance Dilemma"; James et al. (2013), "An Introduction to Statistical Learning" |
| Competition uncertainty | Adversarial examples | Szegedy et al. (2013), "Intriguing properties of neural networks"; Goodfellow et al. (2014), "Explaining and Harnessing Adversarial Examples" |
| Regulatory uncertainty | Domain adaptation | Ben-David et al. (2010), "A theory of learning from different domains"; Csurka (2017), "Domain Adaptation in Computer Vision Applications" |
### 2024 Aug
I met with vikash, charlie said he'd reply to vikash (but not sure he has yet)
moving from compete to collaborate
f \in F1 vs F2 (F1 < F2)
f(x) - f(x*)
conditional distribution can be competed - cost of commiting to competition?
given that we committed to execution (me):
given that we committed to control (vikash):
![[Pasted image 20240410094259.png|200]]
Based on my interaction with each of you, I think we'd make a lovely organization with Artist Scott, judge Charlie, scientist Vikash (from Cronin_SAJ.pdf)
### 2024 July
Last Tuesday Vikash said he can only fund research that is directly aligned with his lab's vision. Not to scare him away, I shared how responsible you have been about my funding and said funding is not my primary goal of approaching him. Without financial constraints, Vikash became more open. We brainstormed how to develop a meditech case and he asked me to deliver meditech's summary by Wednesday so that we can have deeper conversations on Thursday dinner. I stayed up all Tuesday night & Wednesday to deliver this slide, which Vikash was satisfied with.
During Thursday dinner, I met Joshua Tenenbaum (Vikash's advisor) and asked for advice on planning to reach my goal - bridging CHI (Josh and Vikash's Focused Research Organizations and Bayesian Entrepreneurship. I shared my hardship in eliciting active support from you or Vikash due to my lack of communication skill.
He was interested in Bayesian Entrepreneurship and shared his theory on why Vikash might be interested in my research: Reid Hoffman and David Siegel are important CHI's funders (for AGI) so the business use case of CHI's technology would be valuable. Joshua and Vikash have been discussing game theoretic multi agent business strategy tools, which I think would be an extension of tug of war (p.13) from CHI's architecture paper (From Word Models to World Models). Joshua advised connecting these dots for you + having you and Vikash to have a brief chat would be helpful.
After carefully sharing Joshua's advice with Vikash, he thankfully updated his mode of help and gave more actionable advice. We brainstormed how to pitch this project to you. Attached is my first draft which Vikash offered to revise before sharing with you. I'm waiting for his feedback.
ai accountant - pitch to charlie (with vikash's help).pdf with case2 on case2_pivoting and case2_pivoting
charlie's reaction to our `ai accountant project` was being clear on “who is proposing what to whom and what we all hope to contribute and accomplish?” to choose my doctoral committee member. This requires probabilistic reasoning on
- why `Vikash` is interested in my research (`joshua` shared important CHI investors are entrepreneurs, so business application can be valuable)
- what `Charlie` meant when he said “can `Vikash` partially fund you”?
- can `Scott (theorist)` be a reliable distribution channel for our research to entrepreneurship community, than `Bill (practitioner, director of martin trust center)`?
----
### Before 2024 summer
Gerpott (2005) and Brem and Voigt (2008) reported high and low ‘newness’ of the innovation and thus between radical innovations (‘technology push’) and incremental innovations (‘market pull’)
- CONTRANIAN dynamics e.g. no is more informative than yes, can have clearer goal in infecting majority with idea; persuasion -> BRIDGING dynamics (higher quantity leading to higher quantity (Terweich 2015) - evolutionary)
- M3S DIRL team didn't have plans on interpretability. incremental research.
- angie is betting on charlie's clockspeed and gear model, scott's innovation ecosystem and bayesian entrepreneurship, vikash's probablistic programming language architecture, starting with `ai accountant` product
| BE: Bayes.Entrepreneurship<br>PPL: Probablistic Programming Language | Charlie (C) - Angie's advisor | Scott (S) | Vikash (V) | Joshua |
| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| why interested in angie's research at all | **value creation**: entrepreneurial operations can discover value for entrepreneurship practitioners (startup founders, corporate innovators) <br>**value capture**: executing on operations and innovation management can capture value | value creation: entrepreneurial strategy can deliver value for entrepreneurship scholars and practitioners<br>value capture: controlling functional resource innovation ecosystem’s trust can capture value<br> | **value creation**: natural intelligence reverse-engineer-based AI can deliver value for entrepreneurship practitioners and scholars<br> **value capture**: controlling integrated entrepreneurial decision support system can capture value | |
| tribe | analogizer | | symbolist + bayesian + connectionist | |
| 1. Charlie, Scott, Vikash's vision relevant to Angie | contributing to entrepreneurial theory and practice with operations management | Bayesian entrepreneurship, endogenous appropriability | developing new generation of probabilistic computing system as building blocks of software and hardware, CHI - language based probabilistic programming | mentioned the possibility of having him as committee member. vikash is more on probabilistic inference, joshua is on cognitive science (sanjay: ai winter from hubris of knowing human knowledge/cognitive sci) |
| 2. Charlie, Scott, Vikash's entrepreneurial strategy | `disruption` -> `value chain` in S's market using V's product (persuading C) | `value chain` | `architectural` | |
| 3. Charlie, Scott, Vikash's entrepreneurial phase (nail, scale, sail , tools of strength & weakness | `nail` (published comic book) | about to `scale` (40 people invited to the first BE conference on March) | about to `scale` (40 people invited to the first CHI conference on June) | |
| 4. bottleneck action for their goal | platformize, capitalize | automate | segment - finding CHI's business application (multiagent, game theoretic) | |
| action proposal to Charlie, Scott, Vikash | collaborate (need to decompose altruism and obj. val. of research) | collaborate | collaborate | |
| | | | | |
| **Skill** | | | | |
| 1. google scholar research keyword | supply chain management operations management innovation and entrepreneurship | EconomicsInnovationEntrepreneurship | artificial intelligence statistics probabilistic programming machine learning | |
| 2. Charlie, Scott, Vikash's skill Angie chose to learn | improvising examples to ground abstract thoughts | producing excitement by vision-driven + choice-based framing | tool-based persuasion with visual (3D + video) expression | |
| 3. what Angie chose not to absorb yet | connection to operations management literature | connection to entrepreneurial literature, inclination to comparative statics | connection to intelligence literature, inclination to Gen language | |
| 4. skill Angie wish to be more appreciated CSV | | inclination to visual expression | analogical skills (prototypes can be poetic) | |
| 5. main contribution to Angie's knowledge production value chain | - distributor to operations journals<br>- evaluator for our organization's operation<br>- [market acceptance, usability, usefulness, desirability] of our product2 (PC-based expert system for startups) targeted for entrepreneurship scholars<br><br>- informative prior on business school job market in operations | - distributor to entrepreneurship journals<br>- evaluator for [market acceptance, usability, usefulness, desirability] of our product1 (entrepreneurial theory) targeted for entrepreneurship scholars<br><br>- informative prior on business school job market in entrepreneurship | - probabilistic computation educator<br>- production feasibility evaluator<br>- PC module supplier<br>- connection to prob.comp ecosystem | |
| role proposal to Charlie, Scott, Vikash within our org. | judge | artist | scientist | |
| | | | | |
| **Product** | | | | |
| collaboration | | "PC-based entrepreneurial decision theory for educators" PRODUCT with Scott and Charlie | "PC-based expert system for startups" PRODUCT with Vikash and Charlie | |
| misalignment to be aligned between Charlie, Scott, Vikash | Charlie disagreed to Vikash's<br>"cognitive aspects are probably not part of the normal operations research paradigm" and suggested behavioral operations | some practitioners think Scott's approach is too theoretical. | | |
| Angie's defense | prob.comp is more multiple steps | entrepreneurial theory's users need not be practitioner , but educator<br>multi-step, comparative statics | | |
| **Expectation and Action** | | | | |
| Charlie, Scott, Vikash's expectation (by 2024 summer) | writeup of vision that is communicable to all SCV | equip graduate level of micro economics and econometrics | - learn POMDP and formulate strategic business decisions within that framework to develop hierarchical Bayesian / structure learning aspect<br>- equip informative prior knowledge on entrepreneurial finance for gen-finance | |
| Angie's reaction to each | | took micro-econ 1 graduate course (grade: A) and empirical operations management | - taking "money for startups", "reinforcement learning" class | |
from conscientious contrarian cld
| Researcher | File Name | Content Summary |
| ----------------- | -------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------ |
| Charlie Fine | charlie24 nss class.txt | Transcript discussing value chain dynamics, innovation, and the "nail it, scale it, sail it" framework |
| Charlie Fine | charlie24 clockspeed class.txt | Transcript covering industry clockspeed, value chain evolution, and innovation strategies |
| Charlie Fine | Fine22_OM4Entrep.pdf | Paper on "Operations for Entrepreneurs" discussing how OM can contribute to entrepreneurship |
| Vikash Mansinghka | VKM scaleAI.pdf | Slides on probabilistic programming for AI that understands the world |
| Vikash Mansinghka | pc7_vkm_ scaleAI.txt | Transcript explaining the slides on probabilistic programming and AI |
| Scott Stern | scott15_357__Economics_of_Ideas__Innovation_and_Entrepreneurship.pdf | Lecture summary on the economics of ideas, innovation, and entrepreneurship |
| Scott Stern | Bayesian_Entrepreneurship WP Updated.pdf | Paper on Bayesian approach to entrepreneurship and decision-making under uncertainty |
| Criteria | Scott Stern | Charlie Fine | Vikash Mansinghka | Angie Moon |
| ----------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------ | ---------- |
| 🤔 Contrarian Thinking | Emphasizes the role of ideas and innovation in economic growth; focuses on the economics of science | Challenges traditional view of operations management in entrepreneurship; proposes "nail it, scale it, sail it" framework | Advocates for probabilistic programming as an alternative to deep learning for AI | |
| 🎯 Incentive Analysis | Studies incentives in scientific research and innovation ecosystems | Analyzes incentives in value chains and how they affect innovation and entrepreneurship | Examines incentives in AI development and research funding | |
| 🧠 Rational Thesis | Proposes a Bayesian approach to understand optimal design given different belief and incentive in entrepreneurship and innovation context | ⭐️Argues for a more dynamic view of operations and value chains in entrepreneurship | Proposes a natural intelligence reverse-engineering approach to AI development using probabilistic programming | |
| 💹 Market Inefficiency Exploitation | Explores inefficiencies in the market for ideas and innovation | Identifies opportunities in understanding and managing industry clockspeed | Targets inefficiencies in current AI approaches (intelligence as large-scale neural model), proposing alternatives | |
| 🔄 Systematic Approach | Builds theoretical and empirical frameworks for studying innovation | Develops frameworks for analyzing value chains and industry dynamics | Creates systematic methods for developing AI using probabilistic programming | |
| 🎭 Goal Alignment | Aligns economic theory with practical innovation and entrepreneurship | 🔺Aligns operations management with entrepreneurial goals | Seeks to align AI development with human-like understanding | |
| 🕰️ Long-Term Perspective | Studies long-term economic growth through innovation and ideas | Considers long-term industry evolution and value chain dynamics | Focuses on long-term AI development that aligns human cognition (decision partner) | |
| Criteria | Charlie Fine | Vikash Mansinghka | Scott Stern |
| ------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Thoughtfully Challenging Norms | Challenges traditional views of operations management in entrepreneurship. Proposes the "nail it, scale it, sail it" framework, questioning the static approach to operations. Introduces the concept of "industry clockspeed" to propose benchmarking across industry (Clockspeed) | Challenges the dominant end-to-end deep learning paradigm in AI. Proposes probabilistic programming as an alternative approach to AI that better mimics human-like understanding and reasoning. Questions the effectiveness of current AI methods in truly understanding the world. | Questions established economic theories when he sees gaps or flaws. Challenges conventional wisdom on the economics of ideas and innovation. Proposes new frameworks for understanding entrepreneurship and innovation ecosystems. |
| Rigorous Methodology | Develops systematic frameworks for analyzing value chains and industry dynamics (value chain tools). Uses case studies and empirical analysis to support his theories. Applies a multidisciplinary approach, combining operations management with strategic management and innovation studies. | Creates systematic methods for developing AI using probabilistic programming. Builds rigorous mathematical models and frameworks for AI development. Conducts empirical studies to demonstrate the effectiveness of his approach. | Ensures his contrarian views are backed by systematic research and robust analysis (Do scientist pay to be scientist?, Of mice and academia). Develops theoretical models and conducts empirical studies to support his hypotheses. Uses a Bayesian approach to study entrepreneurship and innovation. |
| Aligning Incentives and Goals | Aligns operations management principles with entrepreneurial goals and strategies. Considers how value chain dynamics and industry clockspeed affect incentives for innovation. Explores how entrepreneurs can exploit market inefficiencies by understanding industry evolution. | Seeks to align AI development goals with human-like understanding and reasoning. Examines incentives in AI research funding and development. Proposes methods that could lead to more robust and generalizable AI systems. | Analyzes incentives within the innovation ecosystem to ensure his contrarian perspectives are not just theoretically sound but also practically relevant. Studies how different incentive structures affect innovation and entrepreneurship. Explores how to align economic policies with innovation goals. |
| Criteria | Charlie Fine | Vikash Mansinghka | Scott Stern |
| ------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Thoughtfully Challenging Norms | Challenges static view of operations with "nail it, scale it, sail it" framework. Introduces "industry clockspeed" concept, exemplified by the evolution of the computer industry from IBM to Microsoft to Apple dominance. | Questions deep learning dominance in AI. Proposes probabilistic programming as an alternative, demonstrated through real-time 3D scene inference examples. | Challenges conventional innovation economics. Develops "Ideas Production Function" to model innovation, incorporating factors like existing knowledge and institutional effects. |
| Rigorous Methodology | Analyzes value chains across industries (e.g., computers, autos). Uses case studies like Zappos to illustrate entrepreneurial operations strategies. | Develops Gen probabilistic programming platform. Conducts empirical studies like inference on medical data for the Gates Foundation. | Employs Bayesian approaches to entrepreneurship. Uses natural experiments, e.g., studying the impact of NIH agreements on research mouse accessibility. |
| Aligning Incentives and Goals | Aligns operations with entrepreneurial goals through "10 scaling tools" framework. Analyzes how value chain position affects innovation incentives, e.g., in the automotive industry's shift to electric vehicles. | Designs AI systems that balance exploration and exploitation, exemplified by language model steering techniques. Examines incentives in AI research funding. | Studies how different funding mechanisms (e.g., prizes, patents, grants) affect innovation. Analyzes the impact of patent policies on follow-on innovation, as in the case of human gene patents. |
My goal of making this table was to persuade each of you.
2. Charlie can appreciate Vikash's challenge to deep learning norms and Scott's novel approach to modeling innovation.
3. Scott can appreciate Charlie's dynamic view of operations and Vikash's alternative AI development methods.
4. Vikash can appreciate Scott's analysis of innovation incentives and Charlie's industry evolution concepts.
I apologize for the error regarding Charlie Fine's examples. You're right, and I'll correct that. Based on your feedback and the suggested rows, I'll update the table. Here's the revised version:
| Component | Charlie Fine | Vikash Mansinghka | Scott Stern |
| ----------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 🤔 Contrarian Thinking | Challenges static view of operations with "nail it, scale it, sail it" framework. Introduces "industry clockspeed" concept to rethink how industries evolve. | Questions deep learning dominance in AI. Proposes probabilistic programming as an alternative approach to AI that better mimics human-like understanding. | Challenges conventional innovation economics. Develops "Ideas Production Function" to model innovation, incorporating factors like existing knowledge and institutional effects. |
| 🎯 Incentive Analysis | Analyzes how value chain dynamics and industry clockspeed affect incentives for innovation. Examines incentives in entrepreneurial operations, as seen in the Tesla Roadster and Asia School of Business (ASB) cases. | Examines incentives in AI research funding and development. Analyzes incentives in language model steering techniques. | Studies how different funding mechanisms (e.g., prizes, patents, grants) affect innovation incentives. Analyzes the impact of patent policies on follow-on innovation. |
| 🧠 Rational Thesis | Develops the "10 scaling tools" framework for entrepreneurial operations. Proposes a dynamic view of value chains and industry evolution, supported by his observation from systematic analysis of >10 startups (Angularity, ASB, Tesla) | Develops Gen probabilistic programming platform as a rational alternative to deep learning for certain AI tasks. | Employs Bayesian approaches to entrepreneurship. Develops theoretical models for innovation ecosystems. |
| 💹 Market Inefficiency Exploitation | Identifies opportunities in understanding and managing industry clockspeed. Explores how entrepreneurs can exploit market inefficiencies by understanding value chain dynamics. | Targets inefficiencies in current AI approaches, proposing alternatives that could lead to more robust and generalizable AI systems. | Explores inefficiencies in the market for ideas and innovation. Studies how to optimize innovation policy to address market failures. |
| 🎭 Goal Alignment | Aligns operations management principles with entrepreneurial goals and strategies. Demonstrates this alignment through case studies like ASB and Tesla. | Seeks to align AI development goals with human-like understanding and reasoning. Proposes methods for aligning language models with human intent. | Analyzes how to align economic policies with innovation goals. Studies the alignment of scientist incentives with societal goals in "Do Scientists Pay to Be Scientists?" |
| 🕰️ Long-Term Perspective | Considers long-term industry evolution and value chain dynamics. Analyzes how companies like Angularity adapt to changing industry landscapes over time. | Focuses on long-term AI development that mimics human cognition. Works on AI systems that can handle long-term uncertainty and complex reasoning. | Studies long-term economic growth through innovation and ideas. Examines the long-term impacts of innovation policies and institutional structures. |
| Component | Charlie Fine | Vikash Mansinghka | Scott Stern | Angie Moon |
| --------------------- | --------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- |
| Key Contrarian Idea | "Nail it, scale it, sail it" framework for entrepreneurial operations | Contrary to Industry's bet (predictive modeling with large neural networks), betting on scaling world modeling & decision making with probabilistic programs | Bayesian and choice-based approach to entrepreneurship and innovation economics | Demonstrates how to challenge established paradigms with novel, well-reasoned frameworks |
| Focus of Analysis | Value chain dynamics and industry clockspeed | New generation of probabilistic computing systems that integrate probability and randomness into the basic building blocks of software and hardware. | Entrepreneurial decision-making under uncertainty | Shows how to identify misaligned incentives and inefficiencies in current systems |
| Innovative Method | Dynamic view of operations and industry evolution | Gen probabilistic programming platform; rational meaning construction using language models | Entrepreneurial strategy (axioms and test two choose one stopping rule) | Illustrates development of new methodologies to support contrarian viewpoints |
| Practical Application | Case studies (e.g., Tesla, Asia School of Business) | Automated data modeling, 3D scene perception, steering large language models | Informing innovation policy and entrepreneurial strategies | Emphasizes importance of linking contrarian ideas to real-world impact |
| Long-Term Vision | Sustainable entrepreneurial success through adaptive operations | Human-like reasoning in AI for complex, open-ended environments; unifying different approaches to meaning in language and thought | Long-term economic growth through innovation | Encourages pursuit of high-risk, high-reward research with potential for significant breakthroughs |
#### 1. key accomplishments pre-PhD
| | role | goal and belief | evaluation, online proof |
| -------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------- |
| NextOpt (2018-2021) | startup founder | provide advanced and affordable analytics for all | revenue and customer evaluation in def(nextopt) |
| Stan (2019-) | developer <br> - implement ADVI diagnostics<br>- test latent gaussian Laplace approximation with SBC<br>- casestudy on hierarchical gaussian process<br>- develop stanify which translates dynamic model in Vensim to Stan | - diagnostics- based testing is a key for Bayesian workflow<br>- hierarchical + dynamic (ODE-based) model is where HMC shines | |
| | educator<br>- textbook BDA translator<br>- StanKorea founder<br>- StanCon chair on SBC | - build Bayes knowledge production ecosystem<br>- allowing individuals to self-educate can facilitate pairing need and solution, testing theories, routing knowledge to user | - 244 copies sold (Korean BDA)<br>- Andrew's promotion |
| Bayes to Business (2023-) | knowledge sharing community | disseminate Bayes idea to management domain | - AOM conference members (7 professors) |
#### 2. PhD goals
- persuasion between tribes with different language and tool - contributed to convergence of `connectionist`, `symbolist`, `bayesian`
- connectionists, symbolists are being combined on top of Bayesian (as was predicted in the book). Analogizes (learning with kernel; Fourier) and evolutionaries (algorithmic reasoning) are in line to be unified.
- experience science of scale: need homogenous and visionary organization to scale
- smooth connection of inference and decision
- stan tribe likes code, sd tribe likes diagram
- probabilistic programming for two markets: i) entrepreneurship education, ii) startup
#### 3. summarizing interactions with prob.comp lab
| | met and discussed | mail correspondance | connect to next member | detail |
| ------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------------- |
| McCoy | met in PROBPROG 2021 Bob Carpenter's What do we need from a PPL to support Bayesian workflow? | 1 | connected me to Xuan | |
| Xuan | 1. explore "Bayes in MIT" (Apr.2024) <br>2. discuss bayesian theory of mind + get feedback on Stan based demo model (Apr.2024). <br>I persuaded the possibility of modeling startup decision making<br> | > 15 | - connected me to prob.comp lab by inviting to give seminar on SBC<br>- encouraged attending prob.comp mini school | ![[Pasted image 20240410224407.png\|100]] |
| Vikash | - taught me prob.comp in mini school (Aug.2023)<br>- warned my phd thesis topic is meaningful but risky<br>- showed gen-finance demo (Dec.2023) | > 10 | | |
| Nishad | introduced his research on computer vision | | | |
| Alex | what it is like to be part of prob.comp (Oct.2022) + learn prob.comp's progress (Oct.2023) | | | |
| Feras | Jun.2023<br>- why BNP models is helpful for entrepreneurial decision making which includes "adapts to novelty"<br> | | | ![[Pasted image 20240410224736.png\|100]] |
| George | Oct.2022 | | | |
| Yoni | Apr.2023<br>- interested in understanding procrastination | | | |
| Matthew | Apr.203<br> - vectorization more than parallelization, to do more efficient computations on the GPU<br>- ADEV: automatic differentiation | | | |
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this prompted 🌙simulated collaboration based on observed belief and goal of role model charlie, scott, vikash, moshe, jinhua which I'll send out to sync "goal and role prior" on Apr.11