### in prompt i used to synthesize and [[📝👻phantom rationalize meaning]] on [🚰TAP], [🗺️MAP], [💰RR] > 1. we will synthesize two models (🚰TAP and 🗺️MAP) with the goal of synthesizing two theories by matching the parameters. > 2. we will synthesize two models (🚰TAP and 🗺️MAP) with the goal of corresponding the parameters. > 3. 🚰TAP is explained in ###🚰TAP. read kauffman theory of adjacent possible.pdf carefully and make sure it is consistent with ###🚰TAP. > 4. 🗺️MAP is explained in ### 🗺️MAP. read 📝👻phantom rationalize meaning.md and 👻jeff_angie_profile.txt carefully and make sure it is consistent with ### 🗺️MAP. > 5. understand 🚰TAP and 🗺️MAP better using eg(🚰TAP) and eg(🗺️MAP). speak out loud as you reason the theory (###🚰TAP and ### 🗺️MAP) using eg(🚰TAP) and eg(🗺️MAP). explain this to me. > 6. based on 4, make a table that compares 🚰TAP and 🗺️MAP. --- ### out | Emoji Code | Name | Explanation | | ---------- | ------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | 🚰TAP | Theory of Adjacent Possibility | A statistical model describing growth as a function of decay rate (1-μ) and combination efficiency (α). Models how new possibilities emerge through combinations of existing elements with time decay. | | 🗺️MAP | Meaning and Planning | A lay theory framework that breaks down information processing into two stages: meaning construction (observation to perception) and planning (perception to action). | | ⚡️RR | Resource Rationality | A framework that optimizes reward rate by balancing decision quality against time costs. Considers both perceiving (k samples) and planning (r cost ratio) resources. | | 👁️o | Observable | Raw observable information from environment (e.g., startup metrics, market data). | | 🧠p | Perceptual programmatic theory | Processed information after meaning construction (e.g., assessment of execution capability). | | 🤜c | Commitment | Final decision or choice one commit to after planning (e.g., invest/don't invest). | | 👓l() | Meaning Construction Function | Maps observations to perceptions (👁️o → 🧠p). Efficiency similar to TAP's combination rate α. | | 👆u() | Planning/Utility Function | Maps perceptions to actions (🧠p → 🤜a). Time cost reflected in RR's planning ratio r. | | 💨d() | Diffusing | | | 📉μ | Decay Rate | How quickly knowledge/value deteriorates with time in TAP model. | | 🧩α | Combination Efficiency | How effectively elements combine to create new possibilities in TAP. | | 📊k | Number of Samples | Number of perceiving events in RR model (e.g., due diligence meetings). | | ⚖️r | Planning/Perceiving Ratio | Ratio of planning to perceiving time costs in RR model. | | 📈Q | Decision Quality | Quality of decision as function of samples k and true probability p. | | ⏱️T | Total Time Cost | Combined cost of perceiving (k) and planning (r) events. | | 💫R | Reward Rate | Final optimization target: decision quality per unit time (Q/T). | detailed in [[🗺️abD.agent's belief and desire to equity valuation]] | Type | Name | Formula/Value | Unit | Meaning | Process Role | |------|------|---------------|-------|----------|--------------| | 📦 Stocks ||||| || 👁️ Observable | INTEG(diffusing_commitment - testing, 100) | unit | Raw information database | Input source | || 🧠 Perceptual_Programmatic_Theory | INTEG(testing - implementing - decaying_usefulness, 50) | unit | Theory repository | Knowledge base | || 🤜 Commitment | INTEG(implementing - diffusing_commitment - decaying_effectiveness, 0) | dmnl | Decision repository | Action state | | 🔄 Flows ||||| || 📍 testing | Theory/testing_time | unit/Week | 👁️→🧠 Theory development | R1: Theory reinforcement | || 📈 implementing | Commitment/implementing_time | unit/Week | 🧠→🤜 Theory application | R2: Action reinforcement | || 💫 diffusing_commitment | Commitment/diffusing_time | dmnl/Week | 🤜→👁️ Impact realization | Feedback to environment | | 🔀 Decay Flows ||||| || 📉 decaying_usefulness | Theory × mu_p | unit/Week | 🧠↓ B1: Theory obsolescence | Knowledge entropy | || 📉 decaying_effectiveness | Commitment × mu_c | dmnl/Week | 🤜↓ B2: Impact erosion | Action entropy | | ⏱️ Time Parameters ||||| || testing_time | 0.5 | Week | Theory development delay | Learning cost | || implementing_time | testing_time × implement_to_test_time_cost | Week | Application delay | Action cost | || diffusing_time | 2 | Week | Environmental response delay | Feedback delay | | 🎚️ Decay Rates ||||| || mu_p | 0.1 | dmnl/Week | Theory decay rate | Knowledge deterioration | || mu_c | 0.1 | dmnl/Week | Commitment decay rate | Impact diminishing | | ⚙️ Constants ||||| || implement_to_test_time_cost | 10 | dmnl | Action/learning cost ratio | Resource allocation | Feedback Loops: 1. R1: Observable → testing(+) → Theory → testing(+) [Theory building reinforcement] 2. R2: Theory → implementing(+) → Commitment → implementing(+) [Action reinforcement] 3. B1: Theory → decaying_usefulness(-) [Theory balancing] 4. B2: Commitment → decaying_effectiveness(-) [Impact balancing] | Component | System Dynamics Formula | TAP Interpretation | Combined Formula | |-----------|------------------------|-------------------|------------------| | 📊 Theory Growth | Theory = INTEG(testing - implementing - decaying_usefulness, 50) | M_t+1^p = M_t^p(1-μ_p) + Σ(i=2 to M_t^p)(M_t^p choose i)(1/T_T) | dTheory/dt = testing - implementing - μ_p×Theory + Σ(Theory choose i)/T_T | | 🤜 Commitment Growth | Commitment = INTEG(implementing - diffusing - decaying_effectiveness, 0) | M_t+1^c = M_t^c(1-μ_c) + Σ(i=2 to M_t^c)(M_t^c choose i)(1/T_I) | dCommit/dt = implementing - diffusing - μ_c×Commit + Σ(Commit choose i)/T_I | | 👥 Total System | Observable = INTEG(diffusing - testing, 100) | M_t+1^a = M_t^a(1-(μ_p+μ_c)) + Σ(i=2 to M_t^a)(M_t^a choose i)(1/T_D) | dSystem/dt = diffusing - testing - (μ_p+μ_c)×System + Σ(System choose i)/T_D | | ⏱️ Time Parameters |||| || testing_time | Basic delay | 1/rate of successful combinations | T_T | || implementing_time | testing_time × cost_ratio | 1/rate of action creation | T_I | || diffusing_time | Feedback delay | 1/rate of system response | T_D | | 📉 Decay Parameters |||| || mu_p | Theory decay | Knowledge entropy | Theory obsolescence rate | || mu_c | Commitment decay | Action entropy | Impact diminishing rate | Key Synthesis Points: 1. SD flows (testing, implementing, diffusing) map to TAP combination rates (1/T_T, 1/T_I, 1/T_D) 2. SD decay rates (mu_p, mu_c) map to TAP's extinction rate μ 3. Combined formulas show both continuous flow and discrete combinatorial aspectsfff