# Title: Entrepreneurial Decision Model with Uncertainty Minimization
vision: theorize, produce, evaluate bayesian decision making tool for entrepreneurs
# 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 preferences toward multiple stakeholder (customer, investor, operational resource partner, employee). 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.
## 🧠Angie's perception of evaluator's evaluation of Angie's vision state
| Professor | Perceived Evaluation of Vision | Key Interests/Concerns | Opportunity for Alignment |
| --------------------- | ------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Charlie Fine** | Appreciates the operational focus but may want more concrete examples of action sequencing in real ventures | Interested in how your model connects to "nail it, scale it, sail it" phases and real entrepreneurial operations | Frame your uncertainty reduction model as a formal method for implementing Charlie's phase-based framework with clear operational tools |
| **Scott Stern** | Sees potential in the Bayesian approach but may question whether it's formally rigorous enough for economic theory | Interested in how your model connects to his Bayesian entrepreneurship framework and economic modeling | Position your model as bridging theoretical Bayesian entrepreneurship with practical decision-making tools that entrepreneurs can actually implement |
| **Vikash Mansinghka** | Values the probabilistic approach but may want more technical depth in the computational implementation | Interested in how you'll implement advanced probabilistic programming concepts in your model | Emphasize how your approach can leverage probabilistic programming to model complex entrepreneurial decisions under uncertainty |
| **Moshe Ben-Akiva** | Likely appreciates the discrete choice dimension but wants more rigorous validation of preference weights | Interested in how you'll measure and validate stakeholder preferences and weights empirically | Discuss how his discrete choice modeling expertise could help validate the preference weights in your model |
| **Jinhua Zhao** | Probably values the behavioral aspects but wants more focus on implementation and adoption | Interested in how your model could be implemented in practical mobility venture contexts | Highlight how your model can be applied to mobility ventures and incorporate behavioral science insights |
# 🗄️table_of_contents
| Section | Title | Total page per section (progress %) |
| ---------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------- |
| 🕸️Introduction | | **10pg** (90%) |
| | 1.1😵💫Entrepreneurial Decision Models (EDM) are Unusable by Entrepreneurs | |
| | 1.2🏳️🌈Complexity Spectrum of Entrepreneurial Decision Models | |
| | 1.3🎞️Thesis Scope and Outline | |
| <br>⏰I Phase-Based Learning to Lower Operational Complexity | | **60pg** (75%) |
| | 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, Sail) | |
| | 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 Hypothesis Testing for multi-stakeholder Complexity Reduction | | **60pg** (50%) |
| | 4.Individual level of problem | |
| | 4.1 Why entrepreneurs struggle with multi-stakeholder decisions | |
| | 4.2 Mathematical formulation of multi-stakeholder 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 multi-stakeholder complexity | |
| | 5.2📐Produce solution: Estimate $B$ and $\textcolor{#3399FF}{C}$ in real-world applications | |
| | 5.3💸Evaluate solution | |
| | 5.4📜Related work | |
| ⏰👥III Federated Learning for Spatio-operational Complexity Reduction | | **60pg** (20%) |
| | 6.Institutional level of problem | |
| | 6.1 Why entrepreneurs struggle with sequence-dependent decisions | |
| | 6.2 Mathematical formulation of operational 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 operational 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 operational complexity reduction | |
| | 7.4📜Related work | |
| IV Conclusion Integration and Evaluation | | 10pg |
| | 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 | |
# 🕸️Introduction
## 1.1😵💫Entrepreneurial Decision Models (EDM) are Unusable by Entrepreneurs
### The Fundamental Challenge
Entrepreneurial decision-making occurs in environments characterized by extreme uncertainty, multi-stakeholder interactions, and sequence-dependent outcomes. Despite decades of academic research developing entrepreneurial decision models (EDMs), practicing entrepreneurs rarely adopt these models in their actual decision processes. This creates a troubling gap between theory and practice that undermines both entrepreneurial success and scholarly advancement.
### Mathematical Formulation of the Problem
At its core, the Entrepreneurial Decision Model for Navigating Outcomes (EDMNO) can be mathematically represented as:
**Definition (EDMNO):** Given rational matrices $A_t$ ($N \times M$) and $R_t$ ($N \times P$), rational vectors $b_t$ ($M$) and $c_t$ ($P$) for $t \in 1, \ldots, T$, a set of opportunity states $\textcolor{blue}{\Omega} = {\textcolor{blue}{\omega_1}, \ldots, \textcolor{blue}{\omega_Q}}$, a non-additive uncertainty function $\textcolor{blue}{U}: \mathbb{R}^{P \times T} \times \textcolor{blue}{\Omega} \rightarrow \mathbb{R}$, and a rational number $L$, does there exist a sequence of integral vectors $x_1, \ldots, x_T$ (each of length $N$) such that $A_tx_t \leq b_t$ for all $t$, and $\textcolor{blue}{U}({R_tx_t - c_t}_{t=1}^T, \textcolor{blue}{\omega}) \leq L$ for some $\textcolor{blue}{\omega} \in \textcolor{blue}{\Omega}$, where $\textcolor{blue}{U}$ is non-additive in its first argument and represents uncertainty to be minimized?
**Theorem 1:** EDMNO is NP-complete, making it computationally intractable in its full form.
### The Usability Paradox
This mathematical formulation reveals the fundamental paradox: as EDMs increase in reality fit (capturing more of the complex multi-stakeholder, multi-period dynamics entrepreneurs actually face), they decrease in computational tractability. Entrepreneurs find themselves caught between models that are:
1. **Too simple**: Single-stakeholder, static models with high tractability but poor reality fit
2. **Too complex**: Multi-stakeholder with multiple operational variables that have high reality fit but intractable computation requirements
![[1.1😵💫Entrepreneurial Decision Models (EDM) are Unusable by Entrepreneurs 2025-04-28-10.svg]]
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This creates a "tractability-reality gap" in the middle where entrepreneurs abandon formal modeling altogether, reverting to intuition, imitation, or simplified heuristics that fail to capture critical decision dynamics instead of developing their own entrepreneurial style by experimenting with their unique operating environment. Without appropriate modeling tools, entrepreneurs cannot systematically learn from their context, preventing the emergence of personalized decision approaches adapted to their specific venture and market conditions.
## 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|operational Complexity|multi-stakeholder 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 operational complexity (uncertainty unfolding over time) and multi-stakeholder 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.
### 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 operational uncertainty that compounds with multi-stakeholder 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 operational complexity through modularized approaches that adapt to different venture development stages
2. **Proactive hypothesis proposal** to address multi-stakeholder complexity through probabilistic programming that navigates stakeholder interdependencies
3. **Calibrated federated learning** to address spatio-operational 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 **operational complexity** (time/horizon) and **multi-stakeholder 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 operational complexity) and **proactive proposal testing** (to manage multi-stakeholder complexity).
| Model Type | operational Complexity | multi-stakeholder 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 operational changes, but still one perspective only | Scenario planning, forecasting models, dynamic programming | Low – conventional methods handle moderate operational 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 operational 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 Outline
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 operational (<span style="color:red">red</span>), multi-stakeholder (<span style="color:green">green</span>), and spatio-operational (<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 operational 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 multi-stakeholder 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 operational and multi-stakeholder 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.
# 2.Nature of problem
![[🗄️table_of_contents 2025-04-29-8_0.svg|400]]
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| 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>- multi-stakeholder complexity from recurrent social reasoning and causal inference on high dimension parameter space<br>- operational 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 |
todo: tesla_betterplace.png from 📜gans20_choose(tech)
## 3.1💭Theorize solution
EDMNO can be simplified to our working notation:
$\arg\min_{\textcolor{red}{a} \in \textcolor{red}{A}} \textcolor{violet}{W_d} \textcolor{#3399FF}{U_d} + \textcolor{violet}{W_s} \textcolor{#3399FF}{U_s} + \textcolor{violet}{W_i} \textcolor{#3399FF}{U_i}$
s.t.
1. $B\textcolor{green}{S} = [\textcolor{#3399FF}{U_d}, \textcolor{#3399FF}{U_s}, \textcolor{#3399FF}{U_i}]$
2. $C\textcolor{red}{A} \leq \textcolor{#3399FF}R$
3. $\textcolor{blue}{D}(\textcolor{green}{S},\textcolor{red}{A}) = \textcolor{green}{S'}$
Where:
- $\textcolor{red}{A}$ represents the action space (entrepreneur's decisions)
- $\textcolor{green}{S}$ represents the state space (venture context)
- $\textcolor{violet}{W}$ represents stakeholder (on demand, supply, capital side - customer, operational resource partner, investor) preference weights
- $\textcolor{#3399FF}{U}$ represents uncertainty metrics to be minimized
- $B$ maps states to uncertainty measures
- $C$ maps actions to resource requirements
- $D$ is the state transition function
This formulation aligns with the "de-risking by milestones" mindset that entrepreneurs typically employ, where the goal is to minimize weighted uncertainties across different dimensions (development, stakeholder, institutional) rather than maximizing abstract utility.
### The Entrepreneurial Decision-Making Challenge
$\arg\min_{\textcolor{red}{a} \in \textcolor{red}{A}} \textcolor{purple}{W_d} \textcolor{#3399FF}{U_d} + \textcolor{purple}{W_s} \textcolor{#3399FF}{U_s} + \textcolor{purple}{W_i} \textcolor{#3399FF}{U_i}$
subject to $B\textcolor{green}{S} = [\textcolor{#3399FF}{U_d}, \textcolor{#3399FF}{U_s}, \textcolor{#3399FF}{U_i}]$, $C\textcolor{red}{A} \leq R$, $D(\textcolor{green}{S},\textcolor{red}{A}) = \textcolor{green}{S'}$
### Step 1: Labeling and Understanding the Equations
The provided formulation is:
$\min [\textcolor{purple}{W_D} \cdot \textcolor{#3399FF}{U_D} + \textcolor{purple}{W_S} \cdot \textcolor{#3399FF}{U_S} + \textcolor{purple}{W_C} \cdot \textcolor{#3399FF}{U_C}]$
subject to the constraints:
$\begin{aligned}
B \, \textcolor{green}{S} &= \textcolor{#3399FF}{U} \\
C \, \textcolor{red}{A} &= R \\
D \, \textcolor{red}{A} \, \textcolor{green}{S} &= 0
\end{aligned}$
Here's what each component represents:
* **Objective (minimize total weighted uncertainty):**
* $\textcolor{purple}{W_D}$, $\textcolor{purple}{W_S}$, $\textcolor{purple}{W_C}$: weights for demand-side ($\textcolor{#3399FF}{U_D}$), supply-side ($\textcolor{#3399FF}{U_S}$), and capital-side ($\textcolor{#3399FF}{U_C}$) uncertainty.
* The goal is minimizing total uncertainty across three stakeholder dimensions.
* **Constraints:**
* $B \, \textcolor{green}{S} = \textcolor{#3399FF}{U}$: Relates the current **state** $\textcolor{green}{S}$ to the level of uncertainty $\textcolor{#3399FF}{U}$. (**B:** How the current state directly translates into uncertainty levels.)
* $C \, \textcolor{red}{A} = R$: Relates chosen **actions** $\textcolor{red}{A}$ to **resources or costs** $R$. (**C:** Cost or resource implications of actions taken.)
* $D \, \textcolor{red}{A} \, \textcolor{green}{S} = 0$: Captures dynamics ensuring actions and states are consistent (no internal contradictions in decision-state combinations). (**D:** Dynamic consistency—ensuring chosen actions make sense given the current states.)
### Step 2: Mapping Operational and Stakeholder Uncertainty with Matrices (B, C, D)
| Dimension | Mathematical Structure | Meaning (10-year-old) | Examples (Top 3 ordered by helpfulness) | Matrix Mapping (Intuition) |
|-----------|------------------------|------------------------|----------------------------------------|----------------------------|
| **Operational Uncertainty** | $D[i,\textcolor{red}{a},\textcolor{green}{s},\textcolor{green}{s'}]$: uncertainty about **when** and **how fast** states change. | "Not knowing when or how fast things will happen." | 1. Battery improvement speed (Tesla)<br>2. EV adoption timing (Better Place, Tesla)<br>3. Regulatory timing (Segway) | **D (Dynamic consistency)**<br>Ensures actions chosen now correctly match and evolve with future states. |
| **Stakeholder Uncertainty** | Non-linear, discrete interactions in $\Delta\textcolor{#3399FF}{U_j}$, and $C(\textcolor{red}{a})$: uncertainty about **which actions fit together best**, and their interactions at a given moment. | "Not knowing exactly where things fit or which actions go well together." | 1. Battery swapping vs. plug-in infrastructure (Better Place)<br>2. Choosing initial market segment (Tesla)<br>3. Selecting initial supply-chain partners (Segway) | **B (State-uncertainty linkage)**<br>Links current state directly to uncertainty; captures uncertainty arising from specific configurations.<br><br>**C (Action-resource linkage)**<br>Actions impact resources directly; captures uncertainty about interactions of actions and their immediate costs. |
**Why these mappings make sense:**
* **Operational Uncertainty ↔ D Matrix:**
* The **D matrix** captures how states evolve dynamically. It ensures chosen actions at any point correctly transition into future states without conflicts. Operational uncertainty is explicitly about how states evolve over time and how quickly or slowly transitions happen.
* **Stakeholder Uncertainty ↔ B, C Matrices:**
* The **B matrix** connects specific states to resulting uncertainty levels directly, reflecting immediate uncertainty due to Stakeholderly complex configurations (e.g., market positions, stakeholder alignments).
* The **C matrix** explicitly connects each action to its resource impact, reflecting Stakeholder uncertainty about action interactions, immediate implications, and costs at a single moment in time.
This structured mapping clearly separates Operational uncertainty (dynamics of state changes) from Stakeholder uncertainty (complex immediate interactions), aligned neatly with your provided mathematical formulation.
### Bottleneck Breaking Operations for Uncertainty Reduction
The entrepreneurial decision model can be significantly enhanced by incorporating principles from bottleneck breaking operations, a concept detailed in innovation management literature (Terwiesch & Ulrich, 2009). This approach aligns directly with our EDMNO's focus on uncertainty minimization rather than traditional utility maximization.
At its core, bottleneck breaking operations provides a structured methodology for optimizing the sequence of actions to maximize uncertainty reduction at minimal cost. When applied to our model:
$\arg\min_{\textcolor{red}{a} \in \textcolor{red}{A}} \textcolor{violet}{W_d} \textcolor{#3399FF}{U_d} + \textcolor{violet}{W_s} \textcolor{#3399FF}{U_s} + \textcolor{violet}{W_i} \textcolor{#3399FF}{U_i}$
The key insight is that different action sequences (Collaborate, Segment, Capitalize) create dramatically different uncertainty reduction paths. The optimal sequence depends on:
1. **Initial state**: Where the venture begins in the uncertainty space
2. **Preference weights**: Which stakeholder uncertainties are prioritized
3. **Resource constraints**: Available capital and operational bandwidth
This approach transforms the state transition function $D(\textcolor{green}{S},\textcolor{red}{A}) = \textcolor{green}{S'}$ from a simple mapping into a strategic decision tool that entrepreneurs can leverage to efficiently navigate the uncertainty landscape. Different venture phases (e.g., nail, scale, sail) require different operational modes, and the bottleneck breaking operations framework provides the analytical structure to determine the most effective action sequence for each phase.
By analyzing the uncertainty reduction efficiency (ΔU/C) of different action sequences, entrepreneurs can identify which actions deliver the most uncertainty reduction per unit of resources invested. This allows for the construction of optimal action paths that minimize total weighted uncertainty while respecting resource constraints.
### Phase-based learning
![[eval(charlie, manuscript) 2025-04-29-14_0.svg]]
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# 3.2📐Produce solution
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## 3.3💸Evaluate solution
2025-04-27
todo:
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## 3.4📜Related work
![[eval(charlie, manuscript) 2025-04-29-14_6.svg]]
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# 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 |
![[eval(charlie, manuscript) 2025-04-29-14_1.svg]]
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## 5.2📐Produce solution
building on business model 3d cld and code, gradient of $\frac{d \textcolor{red}{a^*}}{d \textcolor{purple}{w}}$, stakeholder utility or uncertainty evaluation $(B\textcolor{green}{S} = [\textcolor{#3399FF}{U_d}, \textcolor{#3399FF}{U_s}, \textcolor{#3399FF}{U_i}])$
![[eval(charlie, manuscript) 2025-04-29-14_4.svg]]
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## 5.3💸Evaluate solution
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## 5.4📜Related work
![[eval(charlie, manuscript) 2025-04-29-14_3.svg]]
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# 👥II Proactive Hypothesis Testing for multi-stakeholder Complexity Reduction
# 6.Institutional 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) |
| ---------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------- |
| 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 |
## 7.1💭Theorize solution
![[eval(charlie, manuscript) 2025-04-29-14_5.svg]]
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## 7.2📐Produce solution
## 7.3💸Evaluate solution
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## 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
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# ⏰👥III Federated Learning for Spatio-operational Complexity Reduction
# IV Conclusion Integration and Evaluation
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# appendix
## 🌙simulated evaluation-collaboration-investment based on observed belief and goal of role model charlie, scott, vikash, moshe, jinhua
2025-04-29
![[eval(charlie, manuscript) 2025-04-29-14.svg]]
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| 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 | 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 | 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 | - 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 | 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 | 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 | 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 | 🗄️🧠vikash |
| dissertation | | | | 📜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 () | - 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 |