🗄️table_of_contents - amoon.world🌙

2025-05-13 | Section | Problem Definition | 💭 Theorize Solution | 📐 Produce Solution | 💸 Evaluate Solution | 📜 Related Work | | --------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------ | | [[I.🧭Agents Perceive to Act]] | [[1.🧭entrepreneur's perception and action]] Entrepreneurs lack structured ways to handle stakeholder uncertainty, leading to inefficient decisions. | [[1.1💭Theorize solution(🧭)]] Propose STRAP to systematically reduce uncertainty via Bayesian logit models and entropy measures | [[1.2📐Produce solution(🧭)]] Implement primal-dual optimization to select experiments maximizing information gain per resource unit | [[1.3💸Evaluate solution(🧭)]] Demonstrate STRAP's effectiveness by reducing weighted uncertainty and satisfying stakeholder thresholds better than traditional methods | [[1.4📜Related work(🧭)]] Builds upon Bayesian entrepreneurship, cognitive theories of decision-making, and optimization approaches | | [[II.🗺️Society Dualize to Distribute]] | [[2.🗺️social planner's perception and action]] | [[2.1💭Theorize solution(🗺️)]] | [[2.2📐Produce solution(🗺️)]] | [[2.3💸Evaluate solution(🗺️)]] | [[2.4📜Related work(🔄💸)]] | | [[III.🧬Agents Act to Perceive]] | [[3.🧬Agents Act to Perceive]] | [[3.1💭Theorize solution(🧬)]] | [[3.2📐Produce solution(🧬)]] | [[3.3💸Evaluate solution(🧬)]] | [[3.4📜Related work(🧬)]] | --- | Section | Problem Definition | 💭 Theorize Solution | 📐 Produce Solution | 💸 Evaluate Solution | 📜 Related Work | as is -> to be | | --------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------ | | [[I.🧭Agents Perceive to Act]] | Entrepreneurs struggle to abstract complex stakeholder uncertainties into clear, actionable strategies. [[1.🧭entrepreneur's perception and action]] | We propose a framework for entrepreneurs to model stakeholder uncertainties as abstracted representations that guide decision-making. [[1.1💭Theorize solution(🧭)]] | We implement a Bayesian decision tool that allows entrepreneurs to generate actionable strategies from these abstracted models. [[1.2📐Produce solution(🧭)]] | Simulation experiments test whether decisions guided by the abstracted model outperform intuitive strategies under uncertainty. [[1.3💸Evaluate solution(🧭)]] | This approach builds on cognitive theories of entrepreneurial decision-making and Bayesian methods for handling uncertainty. [[1.4📜Related work(🧭)]] | ![[Pasted image 20250512204143.png]] | | [[II.🗺️Society Dualize to Distribute]] | Ventures struggle to allocate limited resources across stakeholders with competing needs and constraints. Society should report $\beta_{js}$mean p, f, mu, c so [[2.🗺️social planner's perception and action]] | We propose dualizing the resource allocation problem to derive shadow prices that guide fair distribution of resources among stakeholders. [[2.1💭Theorize solution(🗺️)]] | We implement an optimization mechanism that uses dual variables to dynamically distribute resources while satisfying stakeholder thresholds. [[2.2📐Produce solution(🗺️)]] | Comparative experiments assess whether dual-driven allocation yields higher stakeholder satisfaction and resource efficiency than traditional methods. [[2.3💸Evaluate solution(🗺️)]] | This approach builds on research in multi-objective optimization, market-based resource allocation, and stakeholder management. [[2.4📜Related work(🔄💸)]] | perception | | [[III.🧬Agents Act to Perceive]] | Entrepreneurial ecosystems struggle to optimize collective outcomes without harnessing iterative experiments by individual entrepreneurs. [[3.🧬Agents Act to Perceive]] | We propose that treating each agent’s action as an experiment (sample) provides valuable feedback to improve collective outcomes. [[3.1💭Theorize solution(🧬)]] | We implement a sequential learning framework where data from each agent’s experiment updates a global strategy to iteratively improve outcomes. [[3.2📐Produce solution(🧬)]] | Experiments measure how quickly and effectively the iterative, experiment-driven approach improves outcomes compared to a non-iterative baseline. [[3.3💸Evaluate solution(🧬)]] | This approach draws on lean startup experimentation, multi-armed bandit algorithms for sequential learning, and collective innovation theory. [[3.4📜Related work(🧬)]] | | # 2025-05-12 I asked [gpt](https://chatgpt.com/share/6821ecc6-6804-8002-b46c-4f094f7a6e4d) how the structure below helped me organized [[_ref/Moon25_propose(thesis).pdf]] i.e. projecting from 1million dimension to 10pg :) The reorganization of Section 2 transformed a fragmented treatment of stakeholders into a unified decision-making core, which in turn enabled the STRAP framework to emerge in the thesis. Originally, Section 2’s “Multi-Stakeholder Decision Matrices” was refocused to explicitly incorporate stakeholder thresholds and a dual optimization perspective. This clarity bridged the gap between Section 1’s perceptual modeling (how an entrepreneur **perceives** uncertainties) and Section 3’s action sequencing (how an entrepreneur **acts** on uncertainties). In practice, the revised Section 2 provided the logical glue: it formalized how reducing uncertainty and satisfying stakeholders can be balanced in one **Bayesian** decision rule. This logical integration allowed the disparate ideas to synthesize into **STRAP (Strategic Threshold Resolution for Actionable Priorities)** – a coherent perceive-act cycle. Thus, by restructuring Section 2, the thesis could formalize STRAP’s theorize–produce–evaluate process, unifying operational omissions, stakeholder coordination, and decision metrics into the actionable framework presented in the PDF. # 2025-05-05 updating from [[0.2💭Need Analysis]], [[1.🏳️‍🌈Entrepreneurial Decision-Making Reimagined]], [[0.3😵‍💫Thesis Scope and Example Case]] to [[0.1📽️🔄⚡ Entrepreneurial Decision Complexity Framework]], [[0.3📐Solution Design]], [[0.3🎞️ Thesis Scope and Example Case]] | Mechanism | Key Intuition | Complexity-Lowering Approach | Mathematical Framework | Application Level | | -------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------- | | 🕸️Introduction | [[0.2💭Need Analysis]] [[1.🏳️‍🌈Entrepreneurial Decision-Making Reimagined]] [[0.3😵‍💫Thesis Scope and Example Case]] | | | | | [[I.📽️Perceptual Decision Framework]] | Project observable startup signals onto stakeholder perceptual spaces to decode decisions | Map observables to perceptions using primal-dual Bayesian inference | Models perception as $\textcolor{purple}{w_j} H(p_j\|\textcolor{red}{a})$ subject to $\sum_j c_j \textcolor{red}{a_j} \leq \textcolor{#8B0000}{R}$ | Individual stakeholder decision-making | | [[II.🔄Multi-Stakeholder Decision Matrices]] | Handle simultaneous interdependent decisions across multiple stakeholders | Create parallel decision models capturing cross-stakeholder spillovers | Models joint decisions as $\sum_{j \in {i,c,o}} \textcolor{purple}{w_j} \textcolor{#3399FF}{U_j}$ with state transitions | Multi-stakeholder coordination | | [[III.⚡Bottleneck-Driven Action Sequencing]] | Optimize resource allocation by targeting highest information value per cost | Use LP approximation of POMDP value function for tractable solutions | Minimizes $\sum_j \textcolor{purple}{w_j} \textcolor{#3399FF}{U_j}$ subject to $\sum_j c_j \textcolor{red}{a_j} \leq \textcolor{#8B0000}{R}$ | Resource-optimized implementation | # Complexity Reduction Mechanisms in Entrepreneurship | Chapter | Mechanism | Key Intuition | Complexity-Lowering Approach | Mathematical Framework | Application Level | | -------------- | --------------------------------------- | ------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------ | | **I. 🤜🙋‍♀️** | **Bottleneck Breaking** | Focus on the single biggest knowledge gap at each step for highest learning benefit per cost | Identify and prioritize actions by uncertainty-reduction-to-cost ratio using primal-dual optimization | Minimizes weighted sum of stakeholder uncertainties ($\textcolor{#3399FF}{U}$) subject to resource constraints ($\textcolor{#8B0000}{R}$) using action-to-cost matrix $C$ | Individual operational complexity | | **II. 🧐👥** | **Proactive Multi-Stakeholder Testing** | Test value propositions with all stakeholders in parallel to break cycle of mutual dependency | Simultaneously run hypotheses by engaging all key stakeholders at once | Minimizes collective entropy across stakeholder network while maximizing probability of joint stakeholder buy-in using state-transition model $D(\textcolor{green}{S}, \textcolor{red}{A})=\textcolor{green}{S'}$ | Individual multi-stakeholder complexity | | **III. 🤜👥** | **Expectation Propagation** | Align beliefs about venture progress across ecosystem to improve individual outcomes and collective learning | Federated learning approach that iteratively calibrates individual and shared beliefs | Minimizes divergence among stakeholder expectations (e.g., $U_d$, $U_s$, $U_i$) weighted by $\textcolor{violet}{W}$ while maximizing likelihood of coordinated understanding | Institutional operational multi-stakeholder complexity | title: Usable Entrepreneurial Decision Model entrepreneurial uncertainty minimization and data-driven learning are mathematically dual problems, providing the first unified optimization framework for entrepreneurial decision-making | WHO | section and subsections | intuition | HOW (lower complexity for realistic tractability) | WHAT | paper abstract | | literature brick | pg | | ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --- | | 🕸️Introduction | [[0.2💭Need Analysis]] [[1.🏳️‍🌈Entrepreneurial Decision-Making Reimagined]] [[0.3😵‍💫Thesis Scope and Example Case]] | | | | | ![[Pasted image 20250501153901.png\|100]] ![[Pasted image 20250501153939.png\|200]] ![[Pasted image 20250503112149.png\|200]] | | 10 | | 1. Individual (🤜🙋‍♀️) | [[I.📽️Perceptual Decision Framework]] [[1.🧭entrepreneur's perception and action]] [[1.1💭Theorize solution(🧭)]] [[1.2📐Produce solution(🧭)]] [[1.3💸Evaluate solution(🧭)]] | wU/C theory as hyperprior ϕ and data as y 1. **Phase Bottleneck**: Early-stage success requires balancing stakeholder input for innovation (customers vs collaborators vs investors), while growth-stage success demands optimizing between market segmentation and collaboration for operational efficiency. 2. **Operations Bottleneck**: Simplex algorithm provides a solution by framing decisions as linear programming problems, where actions are selected by maximizing utility-to-cost ratios guided by the gradient of U(a,s)/c(a). | **Lower operational complexity** by decomposing the value chain and applying bottleneck breaking subpaths under cost constraints. | Addressing operational complexity at the individual level through bottleneck breaking (uncertainty minimization per cost), connecting theoretical strategy with data-driven approaches | This section presents a decision-theoretic framework for reducing operational complexity in early-stage entrepreneurship by identifying and sequencing actions that yield the highest uncertainty reduction per unit of cost. Using material startups as a running case, we model decision-making as minimizing residual uncertainty UC(a)/C(a), subject to deterministic state transitions Da(Ae,Se)=S'e. The framework decomposes uncertainty per cost into three estimable components: uncertainty per state (B), state transitions per action (D), and action cost (C). By applying LP formulation to bottleneck identification, entrepreneurs can make tractable myopic decisions rather than solving intractable sequential optimization. Case studies demonstrate how this guides phase-wise execution and accelerates operational learning. | ![[Pasted image 20250501154052.png\|200]] | WU/A focus on #segment, #collaborate - [[📝⛰️scale_seg_collab]] [[💜🟩seg_collab]] uncertainty per cost _(w × U)_= uncertainty per state _(B)_× state per action _(D)_× action per cost _(C)_ | 60 | | 2. Individual(🧐👥) | [[II.🔄Multi-Stakeholder Decision Matrices]] [[2.🗺️social planner's perception and action]] [[2.1💭Theorize solution(🔄)]] [[2.2📐Produce solution(🔄)]] [[2.3💸Evaluate solution(🔄)]] | 1. 🕸️from statistical model fitting **Business Model Fitting Interpretation:** The entrepreneur minimizes weighted uncertainty $\sum_j \textcolor{purple}{w_j} \textcolor{#3399FF}{U_j}$ by strategically choosing actions $\textcolor{red}{a}$ within resource constraints $C\textcolor{red}{a} \leq \textcolor{#8B0000}{R}$. The dual reveals this is equivalent to maximizing predictive power (log-likelihood) while respecting the same resource limitations. 2. 📦from inventory optimization Supply chain math helps startups test efficiently. Just as inventory managers balance overstocking vs. stockouts, entrepreneurs must balance over-investing in bad ideas vs. missing good opportunities. The optimal testing strategy minimizes total error costs while maximizing learning through spillover effects. | | Addressing multi-stakeholder complexity at the individual level through abstraction and proactive testing Abstraction (blessings in disguise) via transition matrix _(D)_ and proactive hypothesis testing _(U)_ to model accept/reject probabilities as simultaneous latent preferences Contrast proactive testing with causal inference-based decision-making and develop tools to expose model network and trace bottleneck interactions | This section introduces proactive hypothesis testing to navigate multi-stakeholder complexity where actions affect stakeholders with interdependent, uncertain decisions. Using the dynamic transition model D'e(S'e,A'e)=S'et+1, we show how strategic experimentation triggers cross-stakeholder spillovers. Stakeholder preferences are abstracted as binary accept/reject responses, enabling simultaneous testing rather than sequential negotiation. The framework minimizes residual weighted uncertainty WBS'e/C(A'e), where the entrepreneur acts as central coordinator selecting actions that create positive network effects. Platform design applications demonstrate how this approach breaks circular dependencies and accelerates coordinated commitments.Stakeholder responses are modeled as binary latent variables over a hypothesis space structured by the transition matrix _(D)_ and utility projections _(U)_. By contrasting this with causal-inference-based reasoning, we reveal how entrepreneurs can simulate stakeholder acceptance via test quantities and resolve preference entanglements through integer-based reasoning. We introduce tools for visualizing model structure and apply our framework to platform design and product feature allocation decisions in early-stage ventures. | ![[Pasted image 20250501154149.png\|200]] ![[🗄️table_of_contents 2025-04-29-8.svg\|200]] %%[[🗄️table_of_contents 2025-04-29-8\|🖋 Edit in Excalidraw]]%% | B uncertainty per state (different interpretation between different stakeholder) | 60 | | 3. Institutions(💭) | [[III.⚡Bottleneck-Driven Action Sequencing]] [[3.⚡Individual level of resource optimization problem]] [[3.1💭Theorize solution(🧬)]] [[3.2📐Produce solution(🧬)]] [[3.3💸Evaluate solution(🧬)]] | D | **Lower spatiotemporal complexity** through expectation propagation: federated updates of beliefs and priors enable tractable inference from distributed signals | Addressing multi-stakeholder complexity at the institutional level through dynamic calibration and shared priors, establishing evaluation metrics for testing and learningMulti-stakeholder complexity (deep uncertainty) arises from heterogeneity in evaluation; solution involves dynamic calibration and federated shared priors, integrating both ILP (for stakeholder coupling) and LP (for system-wide metrics)Institutional structure aggregates uncertainty across agents and time via test statistics | This section addresses institutional-level complexity through expectation propagation between individual ventures and social planners. We introduce a two-step calibration process where planners propose state transitions s's=D̂s(as,ss) and entrepreneurs generate transitions D̃e~N(D̂e,σ²), leading to actual transitions Da(ae,se)=s'e that update planners' expectations E[s'e]=s's. This federated learning mechanism enables bi-directional updates: entrepreneurs' experiences refine institutional models while aggregated patterns provide better priors for new ventures. Applications to grant making and regulatory coordination show how this framework improves ecosystem-wide resource allocation and cumulative learning. | ![[Pasted image 20250501154204.png\|200]] | D _D_, _U_, _R_ (shared test statistics)_w_ (shared weights)_state transitions → federated belief updates_ | 60 | | 4. conclusion | [[IV. 🫠limitation future work]] | | | | | | | | | Section | Title | Theory/Phenomena | 🧱literature brick | 🙋‍♀️question to answer | corresponding components of [[📝moon24_csv_ai_cofounder]]with each section | 🎞️figure | Total page per section (progress %) | | --------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------- | ----------------------------------- | | [[🕸️Introduction]] | | | | | | | **10** (90%) | | | [[0.2💭Need Analysis]] | 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. 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. $D(\textcolor{green}{S},\textcolor{red}{A}) = \textcolor{green}{S'}$ | [[🗄️1Table of Contents (Q&A&B)]] | 🚨1. why did segway casestudy tackled uncertainty minimization rather than utility maximization? | | | 100$ | | | [[1.🏳️‍🌈Entrepreneurial Decision-Making Reimagined]] | Thm.1 EDMNO is Np-complete | [[🗄️2Comparison with Existing Theories]] | | | | | | | [[0.3😵‍💫Thesis Scope and Example Case]] | | | | | | | | I🤜🙋‍♀️ Bottleneck Breaking to Relax Operational Complexity | B | | | | | | **60** | | | [[1.🧭entrepreneur's perception and action]] | | | | | | | | | 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? | | | | | | [[1.1💭Theorize solution(🧭)]]: Phase-based learning framework (Nail, Scale + simplex) | | [[📜🟦_fine+22_integrate(om-theory, ent-practice)]] | | | ![[🗄️table_of_contents 2025-04-29-8_0.svg]] %%[[🗄️table_of_contents 2025-04-29-8_0\|🖋 Edit in Excalidraw]]%% | | | | [[1.2📐Produce solution(🧭)]]: Subpath formulation + Simplex algorithm for uncertainty reduction efficiency $\frac{\Delta\textcolor{#3399FF}{U}}{\textcolor{#3399FF}{C}}$ | | | | | | | | | [[1.3💸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 | | | | | | | | [[1.4📜Related work(🧭)]] | | | | | | | | [[II.🔄Multi-Stakeholder Decision Matrices]] | C matrix | | | | | | **60** | | | [[2.🗺️social planner's perception and action]] | | | | | | | | | 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** | | | | | | | | | [[2.1💭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 | | | | | | | [[2.2📐Produce solution(🔄)]]: Estimate $B$ and $\textcolor{#3399FF}{C}$ in real-world applications | | [[🗄️🧠vikash]] [[📜Bernstein23_Abstractions for Probabilistic Programming to Support Model Development]] | | | | | | | [[2.3💸Evaluate solution(🔄)]] | | | how to evaluate? [[📝👻phantom rationalize meaning]] with [[jeff_dotson]] | | | | | | [[2.4📜Related work(🔄)]] | | | | | | | | [[III.⚡Bottleneck-Driven Action Sequencing]] | D matrix | | | | | | **60** | | | [[3.⚡Individual level of resource optimization 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]] | | | | | | | [[3.1💭Theorize solution(🧬)]]: Federated learning framework for temporal complexity; Social planner's role in informing $D_{industry}$ | | [[clockspeed]] | | | | | | | [[3.2📐Produce solution(🧬)]]: State transition tensor implementation $(D\in\mathbb{R}^{I\times\textcolor{red}{A}\times\textcolor{green}{S}\times\textcolor{green}{S}})$ defining $P(\textcolor{green}{S'}\mid\textcolor{green}{S},\textcolor{red}{A})$ | | | | | | | | | [[3.3💸Evaluate solution(🧬)]]: Empirical validation of temporal complexity reduction | | | | | | | | | [[3.4📜Related work(🧬)]] | | | | | | | | [[IV Conclusion Integration and Evaluation]] | | | | | | | | | | 8. **Integrated Framework for Entrepreneurial Decision Modeling** | | | | | | | | | 8.1 Mathematical integration of the complete optimization framework: $\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 | | | | | | | [[🗄️1Table of Contents (Q&A&B)]], [[🗄️2Comparison with Existing Theories]], [[🗄️3Practical Implications]]