💠integ(process-product) - amoon.world🌙

[[📜Integrating explanation and prediction in computational social science]] [[🗄️🧍‍♀️🌏🧭🗺️]] 2025-03-02 Integrated Entrepreneurial Cognition and Modeling Framework | Process / Product | MOD (🧍‍♀️Founder) | INTEG (🌏Founder+Investor) | | ------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | INTEG (🧭, 🗺️ How,What) | **3. BPE\|D - Predictive Modeling** Belief, meaning, judgment, and environment functions are integrated while desire function remains separate. Environment and belief are synchronized to forecast outcomes in similar situations, but system still permits variable desire/preference structures. Focuses on predicting market behaviors without sacrificing unique value propositions. | **4. BPDE - Integrative Modeling** Complete integration of belief, meaning, judging, desire, and environment functions. Individual entrepreneurial cognition fully aligns with societal-level uncertainty dynamics. Enables prediction of outcomes under changing conditions and interventions. Represents mature entrepreneurial cognition that can adapt to complex market changes. | | MOD (🧭\|🗺️ How\|What) | **1. BP\|DE - Descriptive Modeling** Belief, meaning, and judging functions operate independently from desire and environment. This represents the basic entrepreneurial state before any integration with external stakeholders. Limited to describing past/present observations without causal linkages or market validation. | **2. BPD\|E - Explanatory Modeling** Belief, meaning, judging, and desire functions are integrated while environment remains separate. System can estimate causal effects within the controlled founder-investor relationship but not yet full market dynamics. Creates internal coherence and causal understanding without external validation. | The [[📝moon25_mvt_gmt]] research aligns best with the Integrative Modeling (BPDE) quadrant because it combines mathematical decision frameworks with complete integration of belief systems and market dynamics. It transcends the other quadrants by formalizing relationships between different testing approaches rather than treating them as competing methodologies. This research represents mature entrepreneurial cognition capable of predicting outcomes under changing conditions while balancing internal decision processes with external market realities. 2025-02-06 | | No intervention or distributional changes | Under interventions or distributional changes | | ------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Focus on specific features or effects | **Quadrant 1: Descriptive modeling** [[📜Felin23_disrupt_evol]], [[📜Mastrogiorgio22_more_thumbs]] Evolution/adaptation patterns (Felin) - documenting how capabilities emerge | **Quadrant 2: Explanatory modeling** [[benakiva rationality]] - Discrete Choice Analysis - estimating how features affect choices - Capability transfer (Fine, Tushman) - analyzing how skills transfer across contexts [[📜Tushman96_ambidextrous_org]] | | Focus on predicting outcomes | **Quadrant 3: Predictive modeling** - One-and-done sampling (Tenenbaum) - optimal sampling under fixed conditions - Test-two-choose-one (Stern) - comparing fixed options | **Quadrant 4: Integrative modeling** - Dynamic Hypothesis Testing (Sterman) - testing and updating system models - Bayesian Workflow (Gelman) - iterative model testing and improvement | | assuming rationality is constructed \ | No distribution change from | Distribution Change from Intervention/Interaction | | ---------------------------------------------------------------------------------------- | -------------------------------------------------------------------- | ---------------------------------------------------------------------------- | | feature matter **Ex-ante** (knowledge is transferrable across body) | Using forward-looking models in static conditions | Predicting outcomes under planned changes/interventions | | | Examples: Gelman's Bayesian workflow, most empirical analysis in ENT | Examples: Scott's stopping rule | | prediction **Ex-post** (capability is transferable given body) embodied rationaity | Analyzing historical patterns without causal claims | Understanding effects of past interventions/changes | | | Examples: Moshe's discrete choice | Examples: Tushman's ambidexterity, Fine's evolutionary, teppo's neural reuse | **Ex-ante** (Symbolic representations) Knowledge encoded in transferable symbols [[📜🟦_fine+22_integrate(om-theory, ent-practice)]] | Rationality Construction | No Distribution Change (Analysis/Sampling) | Distribution Change (Intervention/Interaction) | | --------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Disembodied Rationality | Forward-looking models in static conditions Examples: 1️⃣Tenenbaum's one and done, 2️⃣Gelman's Bayesian workflow, 3️⃣Moshe's discrete choice modeling (given target customer segment whose behavior can be modeled) | Predicting outcomes under planned changes Examples: 4️⃣Scott's stopping rule | | Embodied Rationality: Capabilities emerge through use | x | Understanding effects of past interventions Examples: 5️⃣Tushman's ambidexterity, 6️⃣Teppo's neural reuse, 7️⃣Szilárd's information isn't free, 8️⃣Charlie's evolutionary approach | The framework organizes rationality along two dimensions: embodied vs. disembodied rationality (rows) and whether distribution changes through intervention (columns). This captures how rationality can be constructed either through symbolic manipulation or emergent capabilities, and whether the context involves static analysis or active intervention. In the No Distribution Change column: The disembodied approach applies symbolic models to static conditions, exemplified by 1️⃣ Tenenbaum's "one and done" sampling strategies, 2️⃣ Gelman's iterative Bayesian model refinement, and 3️⃣ Moshe's discrete choice analysis of stable customer segments. The embodied cell is marked 'x', suggesting pure analysis without intervention may require some symbolic representation. The Distribution Change column involves active interventions: The disembodied approach is represented by 4️⃣ Scott's stopping rule for entrepreneurial testing, using symbolic optimization for intervention decisions. The embodied cell shows how capabilities emerge through intervention, demonstrated by 5️⃣ Tushman's organizational learning to balance competing demands, 6️⃣ Teppo's theory of cognitive functions emerging through neural pathway reuse, 7️⃣ Szilárd's analysis of how information processing requires physical embodiment with inherent energy costs, and 8️⃣Charlie's view of capabilities evolving through practical experience. ---- lasynthesizing - defining three functional units for scientist (understand what will happen), artist (imagine what might happen), judge (evaluate utility of what happens) and how they are synergized to create three functions (skill, discovery, vision) and objects (meaning, possibilities, hope) [[🧠👁️🤜scientist_artist_judge]]. - decomposition of causes into three customers and solvers in lower level to solve upper level problem in [[🧠🤜1331need_sol]] - angie's research example in [[🗺️eval(1234💠)]] 2025-01-13 recognized charlie's interest in real option valuation from the past. <img width="706" alt="image" src="https://github.com/user-attachments/assets/4795e0af-e59e-4c41-ad15-800f93101ec6" /> ended up in [[⭐️thesis]] ## 📦research product | Process / Product | MOD (🧍‍♀️Founder) | INTEG (🌏Founder+Investor) | | ------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | INTEG (🧭, 🗺️ How,What) | **3. BPE\|D** Belief, meaning, judgment, and environment functions are integrated while desire function remains separate. Environment and belief are synchronized but system still permits variable desire/preference structures. | **4. BPDE** Complete integration of belief, meaning, judging, desire, and environment functions. Individual entrepreneurial cognition fully aligns with societal-level uncertainty dynamics. | | MOD (🧭\|🗺️ How\|What) | **1. BP\|DE** Belief, meaning, and judging functions are variable while desire and environment are fixed. This represents the basic entrepreneurial state before any integration with external stakeholders. | **2. BPD\|E** Belief, meaning, judging, and desire functions are integrated while environment remains separate. System can model joint founder-investor preferences but not yet full market dynamics. | 2025-01-06 | | 🧍‍♀️Agent (A) Probabilistic Inference and Action (Bayesian) | 🧍‍♀️🌏Agent+Environment (E) Co-adaptation (Evolutionary) | | ---------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | 🧭 BELIEF (B) How to Allocate resource? | 1. AB *Theory:* Bayesian decision theory of founder (mover) - [[📜yoo16_alloc(scaler, time)]] - [[📜yoo21_theor(lean)]] - literature charlie pointed me to (meaning of pivot in business, pivot_real option theory, real option valuation, optimal stopping rule) marginnote3app://note/7DA073E9-1EE6-4E21-9F45-47ED0BC3FB26 *Algorithm:* ? *Implementation:* inference engines with online learning (e.g. Stan) | 3. ABD + E *Theory:* meaning construction of investor (external evaluator) *Algorithm:* compare opportunity with high uncertainty (zero to one) VS medium/low uncertainty (one to many) , partial vs full exchangeability ; *Implementation:* ? | | 🧭 🗺️ BELIEF + DESIRE (D) What does it mean? | 2. ABD *Theory:* meaning construction of investor (observer) *Algorithm:* *Implementation:* investor archetype as ChatGPT API | 4. ABDE *Theory:* ? *Algorithm:* programmable inference ; active inference (which includes both bayesian decision and inference algorithm) *Implementation:* MIT inference stack | ⭐️learned ABD+E is more constructive than ABE+D to reach ABDE=ABED | | a (Agent: Bayesian Single-Model) | ae (Agent+Environment: Evolutionary Multi-Model) | |-----------|------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | **b** | **ab (🧠, partial)** - *Theory:* Resource-rational inference with partial pooling across similar contexts - *Algorithm:* Weighted Bayesian updating (hierarchical: agent-level hyperparameters) - *Implementation:* e.g. Stan/Gen with partial exchangeability assumptions | **abe (🧠🤜, partial→full)** - *Theory:* Resource re-use across agent + environment (some partial, some full pooling) - *Algorithm:* Hierarchical bandit that tests local (partial) vs. global (full) resource configurations - *Implementation:* Evolutionary comp platforms with agent-level + environment-level pools | | **bd** | **abd (🧠👓, partial)** - *Theory:* Rational meaning construction among investor/founder with partial sharing of mental models - *Algorithm:* Active inference with hierarchical prior (individual-level beliefs + partial overlap) - *Implementation:* MIT inference stack + “Judge” for partial alignment testing | **abde (🧠👓🤜, full)** - *Theory:* Full-blown world-model bridging multi-agent meaning + environment co-adaptation - *Algorithm:* Program synthesis engine supporting partial→full exchangeability. Expands “Judge” role for multi-level fitness tests - *Implementation:* Non-parametric Bayesian + program-synthesis synergy | | | a (Agent-Focus) | ae (Agent+Environment-Focus) | |------------------|---------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | **b (Resource)** | **ab (🧠)** - *Theory:* Resource-rational inference (Bayesian) - *Algorithm:* Single-agent partial pooling - *Implementation:* Stan or Gen for agent-level resource optimization | **abe (🧠🤜)** - *Theory:* Co-creative resource adaptation (agent+world) - *Algorithm:* Mixed Bayesian + evolutionary search - *Implementation:* Parallel “artist” expansions + “scientist” checks with environment feedback | | **bd (Meaning)** | **abd (🧠👓)** - *Theory:* **Rational Meaning Construction** among founder/investor - *Algorithm:* Active inference capturing *why* - *Implementation:* Natural language interface bridging “scientist” and “judge” interpretations | **abde (🧠👓🤜)** - *Theory:* **Multi-level rational meaning** (team, environment, emergent outcomes) - *Algorithm:* Program synthesis with triadic roles - *Implementation:* Full synergy among “scientist” for analysis, “artist” for novel frames, “judge” for normative evaluation at system scale | --- updated to | | a | ae | | --- | ----------- | ------------- | | b | ab (🧠) | abe (🧠🤜) | | bd | abd (🧠👓) | abde (🧠👓🤜) | - Individual 👤: Focus on single model/problem - Population 👥: Consider multiple agents/contexts - HOW 🧭: Focus on method/execution - HOW+WHY 🗺️: Consider both method & purpose - 2024-11-15 after josh's inverse planning class, i updated the y axis from compass to map to known states to joint state inference [[📝product-process]] | Nature of Mental State Inference \ Level of Analysis | Individual (Micro) | Multi-agent / Population (Macro) | | ------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------- | | Fixed Planning with Belief Inference (answering how) | [[⏰industry clockspeed with action sample ratio]] [[📝🧭Vectorizing Adaptation]] [[🎲Sampling with and without Replacement]] | [[📝🪶Sequential Evolutionary and Parallel Bayesian Startup Adaptations]] | | Inverse Planning with Belief-Desire Joint Inference (answering (how, why)) | [[📝👻phantom rationalize meaning]] [[🐸Breast Stroke Model of Innovation]] | [[📝🔴💜physical-digital-institution]] | ## ⚙️Scientist, Artist, Judge Process | | 🧍‍♀️ | 🌏 | | --- | ----- | ------- | | 🧭 | 1🧠 | 2🧠🤜 | | 🗺️ | 3🤜👓 | 4🧠🤜👓 | #### 1 $\overset{🧠}{\leftarrow}{\underset{🤜👓}{\rightarrow}}$ 3 - [[📝🧭Vectorizing Adaptation]] and [[📝🤝Conversational Inference of Equity Valuation Agreement]] are two sides of one coin. The former fixes resource then optimizes business choices (supply chain and market), while the latter fixes business choices then optimize resource. In reality, optimization happens in the joint space of resource, supply chain, market, with the `capitalize`, `collaborate`, `segment` process. We attribute the difficulty to play 3D game as our finite memory i.e. cognitive constraint. #### 1 $\underset{🤜}{\rightarrow}$ 2 = 3 $\underset{🤜}{\rightarrow}$ 4 - From [[📝🧭Vectorizing Adaptation]] to [[📝🪶Sequential Evolutionary and Parallel Bayesian Startup Adaptations]] is addition of 👓judge. This can help move [[📝🤝Conversational Inference of Equity Valuation Agreement]] to [[📝🌳🌊Startup Lifecycle World modeling with Program Synthesis]]. - [[📝🌳🌊Startup Lifecycle World modeling with Program Synthesis]] relaxes cognitive constraint using program synthesis. This offers automated data modeling in a flexible manner, hence scalable. With stochastic world models evolving (hence non-stationary) across nail, scale, sail stage from [[📝🌳🌊Startup Lifecycle World modeling with Program Synthesis]], we revisit the known conclusion [behavior of bayesian inference with finite memory emerges from evolution under non-stationary stochastic environments](https://www.sciencedirect.com/science/article/pii/S258900422100821X) #### 👓Judge - evaluated by [[🗺️eval(1234💠)]] - Outlet: candidate journal that appreciates modeling + where I have access to evaluators for research-journal fit are logged in [segment: journal characteristics](https://github.com/Data4DM/BayesSD/discussions/255). Segmenting too early is costly so I didn’t add outlet for individual articles yet. Collaborators are also tentative as I didn’t get any commitment from my investors (except JB). #### 🧠Scientist 3. AB → AEB based on my skill3 on meta cognition, with jb (artist) and charlie (judge), i'd like to imagine how evolution and bayesian can be synthesized (artist) on my model (scientist). this discovery can be then evaluated (judge) 4. AB → ABD based on my skill2 on hierarchical bayesian, with jeff (artist) and teppo (judge). jeff and i would discover archetype of investors, and meaning of knowing this will be evaluated by judge 2024-11-13 🧍‍♀️🌏🧭🗺️ | Symbol | Core Meaning | Behavioral (Purple) | Bayesian (Green) | Evolutionary (Red) | | ------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------- | | 🧍‍♀️ | **Agent** Represents individuals operating with capacities for scientific understanding, artistic imagination, and normative judgment. Highlights the ability of agents to balance these modes in decision-making. | Quick, pattern-based individual decisions • Relies on intuition • Uses available means | Systematic individual analysis • Uses memory • Fixed goal pursuit | Part of population • Local experimenter • Adaptation unit | | 🌏 | **World (Agent + Environment)**: Represents the broader context that includes both agents and the environment. It emphasizes the dynamic interaction between agents and their surroundings, capturing both discrete actions by agents and the continuous influences of the environment. | Immediate feedback source • Local environment • Direct interactions | Problem space to analyze • Variables to control • System to model | Selection landscape • Fitness determiner • Emergence source | | 🧭 | **How (Process/Skill/Discovery/Inference/Yang)**: Represents the directional actions and processes related to exploration, skill-building, discovery, and causal reasoning. It captures the "how" aspect of producing outcomes through structured exploration. | Fast pattern matching • Quick pivots • Immediate actions | Structured exploration • Systematic testing • Method following | Multiple parallel trials • Population learning • System adaptation | | 🗺️ | **Why (Conceptual Structure/Vision/Meaning/Representation/Ying)**: Represents the reasons behind actions, the articulation of meaning, and the construction of representations. It captures the "why" aspect of decisions, focusing on vision, meaning, and the representational structures that guide thinking. | Immediate needs • Local meaning • Quick understanding | Clear goals • Prior knowledge • Explicit models | Emergent purposes • Novel meanings • System patterns | | 🧠 | **Scientist (Understanding/Prediction/Control)**: Represents analytical, evidence-based thinking. Focuses on understanding how the world works, predicting outcomes, and controlling variables systematically. Accuracy and verification are central. | Quick pattern recognition • Intuitive understanding • Fast learning | Analytical thinking • Evidence-based • Systematic control | Population-level insights • System understanding • Emergence recognition | | 🤜 | **Artist (Imagination/Creation/Hope)**: Represents creative processes and the imagination of alternative states. It brings expansion, novel insights, and the hope needed to inspire new directions, often without immediate feasibility constraints. | Intuitive creation • Quick improvisation • Immediate solutions | Structured innovation • Goal-directed creation • Planned exploration | Novel combinations • Multiple variations • Emergent creation | | 👓 | **Judge (Evaluation/Appropriateness/Value)**: Represents evaluative processes, assessing the desirability or appropriateness of actions based on norms, values, and ethics. Ensures alignment with moral frameworks and societal standards, judging both actions and their outcomes. | Quick assessment • Good-enough choices • Fast feedback use | Evidence evaluation • Clear criteria • Systematic assessment | Survival testing • Population selection • Fitness evaluation | ### 2024-11-11 🧍‍♀️🌏 view - [Time] x [Scale] x [Integration Type] matrix showing: - Temporal stages (initial → shift → new) - Scale levels (micro → meso → macro) - Integration types (process ↔ product) - Each cell now better captures: multiple scales of operations, temporal evolution, creative repurposing, emergent properties | | **Agent** (🧍‍♀️) | **Agent + Environment** (🌏) | | ----------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Process Integration (🧭 How) | **1. Resource Repurposing** - Theory (led by 🧠scientist): Scientist analyzes existing resources, artist imagines novel applications, judge evaluates fitness for new context - Algorithm: Multi-objective optimization with adaptation feedback - Implementation: Resource repurposing recommendation system | **3. Multi-level Adaptation** - Theory (led by 🧠scientist-👓judge): Evolution across individual, group, and system levels with parallel exploration - Algorithm: Hierarchical multi-armed bandit with cross-level learning - Implementation: Multi-scale experimentation platform | | Product Integration (🗺️ Why) | **2. Function Evolution** - Theory (led by 🤜artist-👓judge): Track how artifacts evolve from original to emergent functions through creative reinterpretation - Algorithm: Temporal pattern recognition with semantic mapping - Implementation: Function evolution tracking system | **4. Knowledge Ecosystem** - Theory: Integrate individual insights, group practices, and system-level knowledge - Algorithm: Multi-agent program synthesis with emergence detection - Implementation: Collaborative knowledge evolution platform | | | agent | agent+environment | | -------------------------------------------------------------------------------------------- | ----- | ------------------------------------------------------------------------------------------------------------------------------------------------- | | **🔧 Operational Domain (1,2)** *Resource Optimization* "Working within constraints" | | Strategic Experimentation [[📝🪶Sequential Evolutionary and Parallel Bayesian Startup Adaptations]] [[🐸Breast Stroke Model of Innovation]] | | **🌱 Evolutionary Domain (3,4)** *Environmental Adaptation* "Changing the constraints" | | | Key Updates: 5. "Resource Allocation" → "Resource Repurposing": Emphasizes creative reuse 6. "Constructed Meaning" → "Function Evolution": Captures dynamic meaning shifts 7. "Adapting Evolutionarily" → "Multi-level Adaptation": Adds hierarchical perspective 8. "Chained Knowledge" → "Knowledge Ecosystem": Emphasizes emergent properties ### 2024-11-6 🧍‍♀️ view | | **Agent** (🧍‍♀️) | **Agent + Environment** (🌏) | | -------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Process Integration (🧭 How) | **1. Allocating Resource Rationally** - Theory (led by 🧠scientist): While the artist imagines novel resource allocation strategies, the scientist verifies their feasibility, and the judge evaluates their appropriateness - Algorithm: Resource-rational sequential optimization with feedback loops - Implementation: Real-time resource planning dashboard | **3. Adapting Evolutionarily** - Theory (led by 🧠scientist-👓judge): The artist generates adaptation possibilities, the scientist tests - their viability, and the judge validates their fitness - Algorithm: Multi-armed bandit with parallel/sequential testing Implementation: Adaptive experimentation platform | | Product Integration (🗺️ Why) | **2. Constructed Meaning** - Theory (led by 🤜artist-👓judge): The scientist uncovers patterns in term sheets, the artist envisions novel interpretations, and the judge assesses their value implications - Algorithm: Probabilistic programming for term meaning extraction - Implementation: Conversational term sheet negotiation interface | **4. Chained Knowledge Production** - Theory: The 🤜artist imagines future scenarios, the 🧠scientist validates their plausibility, and the 👓judge evaluates their desirability - Algorithm: Program synthesis for world simulation - Implementation: Interactive scenario planning system | --- application examples in [[📝recovering rationality of venture's adaptation]] | | 🧍‍♀️Agent Probabilistic Inference and Action (Bayesian) | 🌏Agent-Environment Co-adaptation (Evolutionary) | | ----------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **🧭 How** *Allocating resource* | 🧍‍♀️'s 🧭**1. Allocating Resource Rationally** - Role (led by 🧠scientist): While the artist imagines novel resource allocation strategies, the scientist verifies their feasibility, and the judge evaluates their appropriateness - e.g. [[🪵(📝product1)]] *Theory:* Maximize return on resource investment (RRR) between ops/market *Algorithm:* Resource-rational inference with industry-specific adaptation *Implementation:* Stan/Gen inference engines with online learning | 🌏's 🧭**3. Adapting Evolutionarily** - Role (led by 🧠scientist-👓judge): The artist generates adaptation possibilities, the scientist tests - their viability, and the judge validates their fitness - e.g. [[📝🪶Sequential Evolutionary and Parallel Bayesian Startup Adaptations]] *Theory:* Balance exploration/exploitation through exaptation *Algorithm:* Test-two-choose-one with parallel/sequential search *Implementation:* Evolutionary computation platforms | | **🗺️ Meaning** *Understanding/Persuading meaning* | 🧍‍♀️'s 🗺️**2. Constructed Meaning** - Role (led by 🤜artist-👓judge): The scientist uncovers patterns in term sheets, the artist envisions novel interpretations, and the judge assesses their value implications - e.g. [[📝🤝Conversational Inference of Equity Valuation Agreement]] *Theory:* Optimize founder-investor utility through proposal convergence *Algorithm:* Sequential optimization via post-money SAFE model *Implementation:* MIT inference stack with natural language interface | 🌏's 🗺️**4. Chained Knowledge Production** - Role: The 🤜artist imagines future scenarios, the 🧠scientist validates their plausibility, and the 👓judge evaluates their desirability - e.g. [[📝🌳🌊Startup Lifecycle World modeling with Program Synthesis]] *Theory:* Flexible world modeling through non-parametric inference *Algorithm:* PP for scenario generation (as opposed to Bayes nets) *Implementation:* Program synthesis engines for world simulation | 9. 🎲scenario modeling in/output, result: balanced (reference) VS operations-heavy VS marketing-heavy 10. 🗺️🧭 meaning2moving - 🗺️ meaning: $\color{SkyBlue}{why}$ 🫀desire (utility) & 🧠belief (constraints) - 🗺️2🧭 operationalizing : $\color{Green}{what}$ 🎯target by $\color{Green}{when}$⏰ - 🧭 moving: $\color{Red}{how}$📍act 11. 🎯target by🧍‍♀️agent, 🎲scenario e.g.🧍‍♀️agent: resource governance, power, mobility, buildings, industry, land, global developments (for bob's e.g.) 4.🎯target by ⏰time,🧍‍♀️agent, 🎲scenario e.g. for the next five years (longer for physical, shorter for digital)