# 🎹 Scale: Conceptual Nodes and Narrative Templates using [[🔴sun/thesis/🐢🐢promise_vendor/🎹scale]] template, updated on [[2025-07-14|25-07-14]], [[2025-07-24|25-07-24-21]] # main | Module | Label | Description | Full Analysis | | ----------- | ------ | ------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | [[🟣alert]] | 🟣0 | **Bold promises: spectacular success or spectacular failure** | **Point**: The same bold promise strategy leads to both unicorns and prison sentences. Elon Musk promised 300-mile range EVs when batteries cost $1,000/kWh—Tesla is now worth $800B. Elizabeth Holmes promised instant blood tests from a single drop—she's serving 11 years. **Evidence**: Among bold promisers, winners include Tesla (300-mile EV), Apple (1,000 songs in pocket), Amazon (one-day delivery). Losers include Theranos (instant diagnostics), Fyre Festival (luxury experience), FTX (revolutionary trading). **Explanation**: This pattern suggests bold promises aren't inherently good or bad—success depends on calibrating promise level φ to the fundamental tradeoff. **Transition**: Understanding this tradeoff requires examining how promises affect both sellability and deliverability. | | | 🟣⏰ | **The fundamental tradeoff: sellable versus deliverable** | **Point**: Bolder promises are more sellable to customers but less deliverable with capability. **Evidence**: φ=0.3 (150-mile EV): Only 30% of customers buy it, but 70% chance of delivery. φ=0.7 (300-mile EV): 70% of customers want it, but only 30% chance of delivery. The mathematics: as φ increases, P(Sell=1\|φ)=φ rises while P(Deliver=1\|φ)=1-φ falls. **Explanation**: This inverse relationship creates the entrepreneur's dilemma—you can make promises customers love OR promises you can keep, rarely both. The sweet spot φ* must balance sellability against deliverability. **Transition**: This tradeoff manifests differently across time, creating systematic biases. | | | 🟣⏰↕️ | **Promise creates the market it serves** | **Point**: Unlike newsvendor reacting to demand, promise vendor creates demand through the promise itself. **Evidence**: Before Tesla's 300-mile promise, there was no "luxury EV market." The promise created the category, attracted early adopters, and established price points. Similarly, iPhone's "1,000 songs" didn't respond to existing demand—it created new behavior patterns. **Explanation**: Promise level φ doesn't predict market readiness; it manufactures it. This endogeneity—where your decision variable creates the uncertainty—transforms optimization from matching supply-demand to generating both simultaneously. **Transition**: The temporal structure of promise-then-deliver amplifies certain cost asymmetries. | | | 🟣⏰↕️⏰ | **The paradox of entrepreneurial boldness** | **Point**: Why do rational entrepreneurs systematically overpromise when failure leads to lawsuits, bankruptcy, even prison? **Evidence**: Pattern analysis shows successful entrepreneurs (Musk, Jobs, Bezos) and failed ones (Holmes, McFarland, SBF) all made similarly bold promises—the difference was execution, not promise level. **Explanation**: This suggests systematic forces push entrepreneurs toward bold promises regardless of individual rationality. The promise vendor framework will show this isn't bias but optimal response to asymmetric costs and endogenous uncertainty. **Transition**: These patterns demand rigorous theoretical examination through three lenses: time, cost, and uncertainty. | | [[♻️dig]] | ♻️0 | **⏰Time: Promise-then-deliver inverts traditional flow** | **Point**: News vendor observes demand then stocks inventory; promise vendor promises first then develops capability—reversing the information flow from past→present to future→present. **Evidence**: Traditional sequence: observe market signals → decide inventory → meet demand. Promise sequence: declare future state → attract resources → create capability. Tesla promised 300-mile range before having the technology, using the promise to attract resources that made delivery possible. **Explanation**: This temporal inversion means entrepreneurs must optimize under fundamental uncertainty—not about what customers want (that's created by the promise) but about what they can deliver. Time becomes a resource to be managed, not just a constraint. **Transition**: This time structure interacts with specific cost parameters. | | | ♻️⏰ | **💰Cost: Asymmetric penalties for over vs under promising** | **Point**: Overpromise cost Co (salvage value of sold promise that fails) differs fundamentally from underpromise cost Cu (opportunity cost of unsold promise that could succeed)—and this asymmetry drives behavior. **Evidence**: News vendor: Co = price - salvage value (inventory loss), Cu = price - cost (margin loss). Promise vendor: Co = reputation damage + refunds when Sell=1 but Deliver=0, Cu = entire venture value when Sell=0 despite Deliver=1. **Explanation**: The asymmetry is profound: overpromising risks concrete losses (lawsuits, reputation), while underpromising risks opportunity that never materializes. This maps to the classic newsvendor insight but with existential stakes—unsold newspapers lose money, unsold promises kill ventures. **Transition**: These costs interact with the endogenous nature of the sellability and deliverability. | | | ♻️⏰↕️ | **🎲Uncertainty: Endogenous variables change the game** | **Point**: Unlike newsvendor's exogenous demand D, promise vendor faces endogenous uncertainty—your decision (promise φ) creates the randomness (Sell\|φ and Deliver\|φ) you must navigate. **Evidence**: News vendor: demand D is random, inventory q is decision, but q doesn't affect D. Promise vendor: promise φ is decision that directly affects both P(Sell=1\|φ)=φ and P(Deliver=1\|φ)=1-φ. Your boldness determines your challenge. **Explanation**: This endogeneity transforms the problem from "predict and match" to "create and survive." The optimization min Co·P(Sell=1\|φ)·P(Deliver=0\|φ) + Cu·P(Sell=0\|φ) requires understanding how your own choices shape the probability landscape. Traditional stochastic optimization assumes independence; entrepreneurship violates this fundamentally. **Transition**: The full model integrates these three elements. | | | ♻️x | **Self-fulfilling promise is outside our scope** | **Point**: We exclude reinforcement loops where funding enables delivery, creating endogeneity. **Evidence**: While F\|P /⊥ D\|P in reality (funding affects delivery capability), we assume F\|P ⊥ D\|P for tractability. **Explanation**: Self-fulfilling prophecies where bold promises attract resources that enable delivery would require dynamic analysis beyond our static framework. This simplification allows deriving closed-form solutions while acknowledging that "deep pocket effect" existed in Tesla's case. **Transition**: Within these boundaries, we can derive optimal promise formulas. | | [[🟧grow]] | 🟧0 | **News vendor baseline: P* = Cu/(Co+Cu)** | **Point**: Classical newsvendor with uniform [0,1] demand yields simple critical ratio. **Evidence**: Standard result where optimal inventory balances underage and overage costs. **Explanation**: This baseline formula shows that higher underage cost Cu drives higher optimal quantity, while overage cost Co moderates it. For entrepreneurs, this translates to promise level instead of inventory quantity, setting foundation for extended models. **Transition**: Adding value V changes the optimal formula. | | | 🟧⏰ | **Promise vendor with reward: (2Cu + V)/2(Co + Cu + V)** | **Point**: Adding value V to temporal model shifts optimal promise upward. **Evidence**: When PF = P and PD = 1-P (linear probabilities), first-order condition yields closed form. **Explanation**: The formula reveals dual effects: both higher underage cost Cu and value V push toward bolder promises, while overage cost Co moderates them. The factor of 2 emerges from probability derivatives, capturing how value creation amplifies the underage cost effect. **Transition**: Relaxing linearity assumptions yields different functional form. | | | 🟧↕️ | **Nonlinear promise vendor: ln((2Cu + V)/(2Co + V))** | **Point**: Logit probability models yield logarithmic optimal promise formula. **Evidence**: Using PD(P) = e^V/(1+e^V) captures diminishing returns in delivery probability. **Explanation**: The log structure emerges from S-shaped probability curves—as promises become extreme, natural resistance emerges. This creates bounded optimal promises based on cost-value ratios, preventing infinite promises even when Cu >> Co. The nonlinearity captures real-world saturation effects. **Transition**: Speed and scale parameters further modify this formula. | | [[🔴core]] | 🔴0 | **Rational promise from founder's perspective** | **Point**: Individual entrepreneurs rationally optimize P*\|(Co,Cu,V) given their cost-value structure. **Evidence**: Our model rationalizes observed overpromising when Cu > Co, common in early ventures. **Explanation**: What appears as systematic bias is actually systematic adaptation. Each founder faces unique (Co,Cu,V) parameters and rationally chooses promise level P* that minimizes expected cost minus value. Early-stage founders with high Cu/Co rationally overpromise. **Transition**: This individual rationality aggregates differently over time. | | | 🔴⏰ | **Temporal: mix now and later within an agent** | **Point**: Individual founders balance present and future through promise medium over time sequences P*_t\|(Co,Cu,V)^n_t. **Evidence**: Each funding round shifts parameters—success increases Co (more to lose), reduces Cu (proven model), affecting optimal P*_t+1. **Explanation**: Temporal mixing occurs within each entrepreneur's journey. Early bold promises (high Cu/Co) naturally moderate as ventures mature (lower Cu/Co). This isn't inconsistency but rational adaptation to changing parameters over time. Fast clockspeed ventures compress this evolution. **Transition**: Ecosystems shape these parameter trajectories. | | | 🔴↕️ | **Spatial: mix confidence and carefulness among agents** | **Point**: Ecosystems optimize population mix through parameter design (Co,Cu,V)*\|P^{1..N}_{1..T}. **Evidence**: VC ecosystems encourage "fail fast" (low Co), accelerators provide resources (reduce Cu), prizes increase V. **Explanation**: System designers act as "promise choreographers," using three levers to shape agent behavior. Low Co encourages experimentation, high Cu drives urgency, large V attracts ambition. The ecosystem cultivates optimal diversity—some careful (high Co/Cu), some bold (high Cu/Co). **Transition**: Both mixing mechanisms create system-level rationality. | | | 🔴⏰↕️ | **Rational entrepreneurial ecosystem** | **Point**: System rationality emerges from dual mixing: temporal (within agents) and spatial (across agents). **Evidence**: (P,Co,Cu,V)*_T represents equilibrium where individual and system rationality align. **Explanation**: The ecosystem achieves dynamic balance through two mechanisms: (1) temporal—each entrepreneur evolves from bold to careful as parameters shift, (2) spatial—population maintains diversity through parameter heterogeneity. This dual mixing sustains innovation: bold entrepreneurs explore, careful ones exploit, with natural succession maintaining vitality. **Transition**: This framework explains entrepreneurial overpromising as feature, not bug. | # [[2025-07-13|25-07-13]] [[🍔PEER]] A0----A1-A12---A121---A1212 D0---D1-D12---Dx G0---G1---G2-G12 C0---C1---C2-C12 🟣0----🟣⏰-🟣⏰↕️---🟣⏰↕️⏰---🟣⏰↕️⏰↕️ ♻️0---♻️⏰-♻️⏰↕️---♻️x 🟧0---🟧⏰---🟧↕️-🟧⏰↕️ 🔴0---🔴⏰---🔴↕️-🔴⏰↕️ | Category | Code | Narrative | Template Pattern | | ------------- | --------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------- | | **[[🟣alert]] | 🟣 | Why do the most successful entrepreneurs consistently overpromise? Jobs promised 1,000 songs in your pocket when flash memory cost $400/GB. Musk promised sub-$35k EVs when batteries cost $1,000/kWh.<br><br>The puzzle: systematic overpromising by winners suggests hidden rationality, not cognitive bias. | The puzzle: [Pattern] suggests [Hidden rationality] | | | 🟣⏰ | Difficult as future informs present. <br><br>**Lack‑of‑data tension**: immediate unfunded death (Cu) vs delayed funded failure (Co). Co >> Cu (e.g. spacex safety), Co << Cu (e.g. IT winner takes all), | First insight: [Cost asymmetry drives behavior] | | | 🟣↕️ | Difficult as new state variables are added and existing variables are aggregated in state space. | Second insight: [Triple complexity expansion] | | | 🟣⏰↕️ | We argue overpromise is rational choice in this data lacking situation, especially for early stage founders in fast clockspeed industry | Integration: [Discrete states + amplified alignment value] | | [[♻️dig]] | ♻️ | Promise vendor optimizes promise level P for uncertain delivery capability D<br>—inverting uncertainty source and decision variable from newsvendor optimizes inventory Q for uncertain demand D; | Framework comparison: [Classical vs entrepreneurial] | | | ♻️⏰ | In promise vendor, future informs present e.g. future promise affects today's funding<br>— inverting newsvendor's past→present flow<br><br>⚖️define Co, Cu, V | Temporal gap: [Backward causality] | | | ♻️↕️ | In promise vendor, new state variables are created (e.g. luxury EV, model T) and exisitng variables are aggregated (e.g. (D-Q)^+ to I(D>Q) and (Q-D)^+ to I(D<Q))<br>— compared to newsvendor where variables are known (albeit its value) <br><br>⚖️define P (promise level), F\|P =1 (funded given promise), D\|P =1 (delivered given promise), | Spatial gap: [Continuous→discrete transformation] | | | ♻️⏰↕️<br> | The two transformations compound complexity exponentially: <br><br>1. Without historical data (temporal), every promise is a leap of faith<br>2. With discrete value jumps (spatial), small errors have catastrophic consequences<br>3. Combined effect: O(n) newsvendor complexity becomes O(2^n) promise vendor complexity<br><br>This multiplicative interaction explains why entrepreneurial decisions feel qualitatively different from operational ones—it's not just harder, it's a different category of problem requiring new mathematical tools. | Complexity multiplication: [Linear→exponential] | | [[🟧grow]] | 🟧 | Extend E[Cost] = Cu·P(understock) + Co·P(overstock) to E[π] = Co · Pc(q)·[1-Pr(q)] -Cu · [1-Pc(q)]·Pr(q)- V·Pc(q)·Pr(q) with matching value V<br><br>⚖️define P (promise level), F\|P =1 (funded given promise), D\|P =1 (delivered given promise), <br><br>⚖️define $C_u$ (cost to founder for funded promise, not delivered), $C_o$ (cost to founder for not funded promise, delivered), $V$ (reward to founder when funded promise is delivered) | Model foundation: [Cost minimization→value maximization] | | | 🟧⏰ | unit to nonunit scale: Pc(q) probability of being funded increases with promise level, while probability to deliver decreases. this decrease and increase is scaled with $\mu$ | Dual probabilities: [Opposing sensitivities] | | | 🟧⏰↕️ | linear to nonlinear: from q*= (2Cu+V) / (2Cu+V) + (2Co+V) to ln[(2Cu+V)/(2Co+V)] | Closed-form solution: [The promise formula] | | | 🟧♻️ | 1/mu Value V damps or amplifies boldness depending on cost ratios—binding time & space levers<br><br>Acknowledges the limitation that endogeneity makes precise prediction difficult | Value moderation: [Context-dependent amplification] | | [[🔴core]] | 🔴 | Explain 🟣alerted problem of entrepreneur's promise level setting as decision making under uncertainty. | Central insight: [Rationality under asymmetry] | | | 🔴⏰ | Early ventures (high Cu/Co) should promise boldly; later ventures (high Co/Cu) should moderate—each funding round naturally shifts optimal promise levels<br><br>Fast clockspeed ventures (high Cu/Co) should promise boldly; slow clockspeed ventures (high Co/Cu) should moderate—new environment naturally shifts optimal promise levels | Stage prescriptions: [Lifecycle calibration] | | | 🔴⏰↕️ | Ecosystem designers can modulate V to dampen extremes: increase V to encourage timid founders (when Cu > Co) or discipline over-promisers (when Co > Cu)<br> | Temporal insight: [Bidirectional causality] | | | 🔴♻️ | Entrepreneurial choice under uncertainty uses priors drawn from both past evidence and imagined futures. | System design: [Value as control mechanism] | 🟣---🟣⏰-🟣↕️---🟣⏰↕️ ♻️---♻️⏰-♻️↕️---♻️⏰↕️ 🟧---🟧⏰---🟧⏰↕️-🟧♻️ 🔴---🔴⏰---🔴⏰↕️-🔴♻️ - Base (0): Core paradox/gap/model/insight - ⏰: Temporal aspect (time reversal) - ↕️: Spatial aspect (state expansion) - ⏰↕️: Spatio temporal aspect - **♻️: Interaction (endogenous emergence) ### Core Insight The most successful entrepreneurs systematically overpromise not from bias but from hidden rationality—when the cost of dying unfunded (Cu) exceeds the cost of failing while funded (Co), bold promises become mathematically optimal.