🗄️👁️🧠explain_away

🧱: [[🧠9.66 comp.cog.sci_exp_away_mach_illusion.txt]], [analyzing A2S time cost ratio cld](https://claude.ai/chat/6e2e7e0e-a37c-4988-80ce-3174365fcde6) 🏛️: [[📍focusing on action sample ratio]] | Component | Symbol ![[Pasted image 20241118145948.png\|100]] | 📍🧠A2S | Recovering Rationality | ⭐️🟩🔴Mach's Illusion | 📍🧠⚙️A2S Mechanisms | ⚙️Recovering Rationality Mechanism | ⚙️Mach Mechanisms ![[explain_away\|100]] | | --------------- | --------------------------------------------------- | ---------------------------- | ----------------------------- | -------------------------- | ---------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------ | -------------------------------------------------------------------------- | | ENV | 🌏 | Economic Uncertainty | Environment's A2S ratio | Lighting | - Market conditions affect relative costs - Industry dynamics impact strategy choices | - Industry structure (HFT vs PE) - Resource constraints (wealth vs poverty) | - Direction affects shadow patterns - Controls visibility of other cues | | AGT | 🔴 | Control Strategy | Irrationality | Surface Reflectance | - Patent-focused approach - Delayed market entry | - "Hasty" execution - "Over-careful" control | - Appears as step function - Coupled perceptually with lighting | | | 🟩 | A2S Ratio | Strategy (execution of idea) | 3D Shape | - Determines sampling vs action tradeoff - Varies by industry context | - Optimal response to A2S constraints - Strategic adaptation to environment | - Affects shadow creation - Creates distinctive contours | | OBS | 🔷 | TNC1 Behavior | Observed Behavior Patterns | Shading | - Observable testing patterns - Sample size variations | - Different testing frequencies - Speed to market variations | - Shows gradients - Influenced by all factors | | | ⭐️ | Strategic Evidence | Strategic Evidence | Contour | - Patent filings - Time to market metrics | - Performance outcomes - Market timing data | - Provides edge information - Critical for interpretation | | Causal Links | 🌏→🔷 | Uncertainty → Testing | Environment shapes behavior | Lighting → Shading | - Economic uncertainty shapes test patterns - Market conditions influence behavior | Environment's A2S ratio influences testing patterns | Light creates predictable patterns | | | 🔴→🔷 | Strategy → Testing | Irrationality causes behavior | Reflectance → Shading | - Control requires careful testing - Execution enables rapid testing | Perceived irrational choices drive differences | Surface modifies reflection | | | 🟩→🔷 | A2S → Testing | Strategy causes behavior | Shape → Shading | - Cost ratio determines sample size - Higher A2S needs more samples | Rational adaptation drives behavior | Geometry affects distribution | | | 🟩→⭐️ | A2S → Evidence | Strategy creates evidence | Shape → Contour | - Cost structure shows in choices - A2S predicts patterns | Rational choices show in patterns | Structure creates edges | | Explaining Away | ⭐️→🟩→🔴 | A2S explains "irrationality" | Evidence explains rationality | Shape explains reflectance | - Different sampling explained by A2S - Inefficient choices become rational | Evidence of constraints makes irrationality less likely | Contours make reflection less likely | ![[PRK_explain_away]] --- In the Mach illusion, our perception emerges from complex interactions between environmental lighting (🌏), physical properties (surface reflectance 🔴 and 3D shape 🟩), and observable features (shading 🔷 and contours ⭐️). While these factors are physically independent - like theater designers can independently control lighting, paint, and set geometry - they become coupled in our perception through explaining away. For example, when strong contour evidence (⭐️) suggests a 3D shape (🟩), our brain attributes shading patterns (🔷) to that shape rather than surface reflectance (🔴), even though both could cause similar patterns. This coupling can be manipulated through various experimental conditions like covering edges, using one-eye viewing, or changing lighting direction, demonstrating how our brain performs sophisticated probabilistic inference to interpret visual scenes. | | Symbol | Mach's Illusion | Key Mechanisms & Examples | | -------------------- | -------- | ------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | Environment (ENV) | 🌏 | Lighting | - Direction affects shadow patterns - Stronger effects with directional light - Controls visibility of other cues | | Agents (AGT) | 🔴 | Surface Reflectance | - Can appear as step function or constant - Interacts with lighting to create gradients - Independent physically but coupled perceptually | | | 🟩 | 3D Shape | - Can be flat (2D) or curved (3D) - Affects how lighting creates shadows - Creates distinctive contour patterns | | Observations (OBS) | 🔷 | Shading | - Shows brightness gradients - Actual pattern differs from perceived - Influenced by all three factors (🌏,🔴,🟩) | | | ⭐️ | Contour | - Provides edge information - Critical for 3D interpretation - Can override shading cues | | Causal Links | 🌏→🔷 | Lighting → Shading | Light direction creates predictable brightness patterns | | | 🔴→🔷 | Reflectance → Shading | Surface properties modify light reflection | | | 🟩→🔷 | Shape → Shading | Geometry affects light distribution | | | 🟩→⭐️ | Shape → Contour | 3D structure creates distinctive edges | | Explaining Away | ⭐️→🟩→🔴 | Strong contour evidence of shape explains away reflectance interpretation | When contours suggest 3D shape: - Shading attributed to shape rather than reflectance - Even though shape and reflectance are physically independent - Posterior beliefs become coupled | | Manipulation Effects | 🧐 | Experimental observations | - Covering edges affects interpretation - One-eye viewing changes depth perception - Squinting strengthens certain interpretations - Lighting direction affects perceived depth | [[📍focusing on action sample ratio]] **Unified Explanation:** Just as the Mach illusion shows how strong contour evidence (⭐️) of shape (🟩) explains away our interpretation of shading patterns (🔷), the A2S ratio structure explains away seemingly irrational execution speeds. When we observe different speeds across agents (🔷), what appears as irrational behavior (🔴) is actually explained by underlying cost constraints (🟩) shaped by environmental conditions (🌏). This explains why apparently "irrational" quick execution by some agents may be optimal given their specific A2S constraints. ### example of d()/d(A2S) | Cost Type | Example | A2S Change | Observed Execution Pattern | Explanation | |-----------|---------|------------|---------------------------|-------------| | **Action Cost Differences** |||| | 1. Private vs Public Equity | High A2S | Slower execution despite strong signals | High action costs (complex deals) force careful execution even with clear information | | 2. HFT vs Traditional Trading | Low A2S | Rapid execution despite uncertainty | Low action costs (automation) enable quick trades even with limited information | | **Sampling Cost Differences** |||| | 3. Wealth vs Poverty | Varies | Wealthy: slower, deliberate Poor: faster, decisive | Sampling costs higher for poor (limited access) explaining quicker but less-informed decisions | | 4. Desert vs City | High vs Low | Desert: quicker, less precise City: measured, informed | High sampling costs in desert explain faster execution despite equal action costs | | 5. Voting with/without Misinformation | Varies | More rapid decisions in high misinformation | Increased sampling costs from misinformation lead to faster, less-researched choices | **Unified Pattern:** Like how lighting (🌏) and shape (🟩) in Mach's illusion create shading patterns (🔷) that appear paradoxical, variations in A2S ratios create execution patterns that might seem irrational. When action costs dominate (PE/HFT), behavior follows action constraints. When sampling costs vary (wealth/location/information), execution speed inversely tracks sampling difficulty. This explains away apparently irrational quick execution as optimal responses to cost structures. ---- figure in 🟩quality of idea and 🔴execution observed as 🔴(🟩), given 🌏lighting, 🔴reflectance and 🟩shape affects observed 🔷shading and ⭐️contour, Explaining away occurs when multiple independent causes of an observed effect become correlated in our posterior beliefs after observing evidence. In the Mach illusion, when we observe both 🔷 shading and ⭐️contour, our brain has to attribute these patterns to either 🔴reflectance or 🟩 shape. When strong ⭐️contour evidence suggests a particular 🟩 shape interpretation, it "explains away" the 🔷 shading variations as being caused by 🟩shape rather than 🔴reflectance changes, even though physically these properties are independent. | | symbol | Recovering Rationality | Recovering Rationality Mechanism | entrepreneurial straetgy | mach | eg(es) | | ------------ | --------------------------------------------------------------------- | -------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------- | | ENV | 🌏 | Environment's A2S ratio | - Industry structure (HFT vs PE) - Resource constraints (wealth vs poverty) - Information access (city vs desert) - Institutional context (faculty vs student) | env | lighting | | | | | Apparent Irrationality | - "Hasty" execution - "Over-careful" control - Seemingly inefficient testing patterns - Quick vs delayed market entry | | | | | AGT | 🔴 | True Rational Strategy | - Optimal response to A2S constraints - Strategic adaptation to environment - Cost-minimizing behavior - Resource-appropriate sampling | execution capa | surface reflectance ![[Pasted image 20241118092021.png\|50]] | | | | 🟩 | Observed Behavior Patterns | - Different testing frequencies - Varying sample sizes - Speed to market variations - Patent vs no-patent choices | idea quality | 3D shape ![[Pasted image 20241118092037.png\|50]] | | | | | Strategic Evidence | - Performance outcomes - Success rates - Market timing data - Resource allocation patterns | | | | | OBS | 🔷 | Environment shapes behavior | Environment's A2S ratio directly influences observed testing and execution patterns | capitalization situation | shading | funding situation | | | ⭐️ | Apparent irrationality causes behavior | Initially perceived "irrational" choices seem to drive behavioral differences | founder's confidence | contour | angie's confidence | | new obs | | True strategy causes behavior | Rational adaptation to A2S constraints actually drives behavior | When strong ⭐️founder's confidence suggests a particular 🟩 idea quality interpretation | When strong ⭐️contour evidence suggests a particular 🟩 shape interpretation, | angie argues a2s is underlying cause of exec-ctrl with confidence | | explain away | | Strategy creates evidence | Rational choices manifest in observable strategic patterns | "explains away" 🔷 capitalization situation variations as being caused by 🟩idea quality rather than 🔴execution capa, even though physically these properties are independent | "explains away" 🔷 shading variations as being caused by 🟩shape rather than 🔴reflectance changes, even though physically these properties are independent | | | | 🌏->🔷 | Evidence explains rationality | When we observe strategic evidence (⭐️) supporting environmental constraints (🟩), apparent irrationality (🔴) becomes less likely explanation for behavior | env affects capitalization situation | lighting affects shading | | | | 🔴->🔷 | | | execution capa affects capitalization situation | surface reflectance affects shading | | | | 🟩->🔷 | | | idea quality affects capitalization situation | 3D shape affects shading | | | | 🟩->⭐️ | | | idea quality affects founder's confidence | 3D shape affects contour | | | | discovering ⭐️symptom supporting 🟩cause2, i think 🔴 is less likely | | | | | | examples | Component | Symbol | Recovering Rationality | Recovering Rationality Mechanism | | --------------- | -------- | -------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | ENV | 🌏 | Environment's A2S ratio | - Industry structure (HFT vs PE) - Resource constraints (wealth vs poverty) - Information access (city vs desert) - Institutional context (faculty vs student) | | AGT | 🔴 | Apparent Irrationality | - "Hasty" execution - "Over-careful" control - Seemingly inefficient testing patterns - Quick vs delayed market entry | | | 🟩 | True Rational Strategy | - Optimal response to A2S constraints - Strategic adaptation to environment - Cost-minimizing behavior - Resource-appropriate sampling | | OBS | 🔷 | Observed Behavior Patterns | - Different testing frequencies - Varying sample sizes - Speed to market variations - Patent vs no-patent choices | | | ⭐️ | Strategic Evidence | - Performance outcomes - Success rates - Market timing data - Resource allocation patterns | | Causal Links | 🌏→🔷 | Environment shapes behavior | Environment's A2S ratio directly influences observed testing and execution patterns | | | 🔴→🔷 | Apparent irrationality causes behavior | Initially perceived "irrational" choices seem to drive behavioral differences | | | 🟩→🔷 | True strategy causes behavior | Rational adaptation to A2S constraints actually drives behavior | | | 🟩→⭐️ | Strategy creates evidence | Rational choices manifest in observable strategic patterns | | Explaining Away | ⭐️→🟩→🔴 | Evidence explains rationality | When we observe strategic evidence (⭐️) supporting environmental constraints (🟩), apparent irrationality (🔴) becomes less likely explanation for behavior | **Core Mechanism:** Just as strong contour evidence in Mach's illusion explains away surface reflectance interpretation, evidence of environmental A2S constraints explains away apparent entrepreneurial irrationality. When we understand how A2S ratios vary across contexts (high for PE, low for HFT), seemingly irrational behavior patterns (quick vs delayed execution) are revealed as optimal strategic responses. explain away: information flow in the posterior The key insight is that while rationality and environmental structure (R) are independent a priori, they become coupled in our posterior beliefs when we observe sampling behavior (K), creating the explaining away effect. First, industry clockspeed (the rate at which products, processes, and organizational structures change) appears to have multiple independent root causes: modularity of product architecture (📦), R&D investment levels (💰), competitive pressure (📍), industry architecture (🏛️), development capabilities (💪), and revenue dynamics (💹). However, just as in the Mach illusion where apparent surface properties can be explained by either reflectance or shape, observed industry clockspeed could be driven by either technological-structural factors (modularity, R&D, pressure) or organizational-economic factors (industry architecture, capabilities, revenue). The challenge is that these factors, while theoretically independent, become deeply coupled in our observations through explaining away effects. This explaining away mechanism suggests a novel approach to untangling causality: rather than trying to isolate individual factors, we should look for situations where new evidence about one set of factors (e.g., observing changes in industry architecture) systematically changes our interpretation of the role of other factors (e.g., the apparent impact of modularity on clockspeed). Just as understanding lighting conditions in the Mach illusion helps disambiguate shape from reflectance, understanding how changes in organizational-economic factors alter the apparent influence of technological-structural factors (and vice versa) may reveal the true causal structure driving industry clockspeed. This could involve studying natural experiments where one set of factors shifts while others remain relatively constant. | "Lighting" Factor | What It Illuminates | How It Helps Explain Away | | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------- | ----------------------------------------------------------------------------------------------------------------- | | 🌐 **Tech Standards** | 📦 Modularity vs 📍 Pressure | When open APIs exist: - Fast + Easy Integration → Modularity wins - Fast + Hard Integration → Pressure wins | | 📜 **Regulations** | 🏛️ Architecture vs 💪 Capabilities | After new rules: - Everyone changes same way → Architecture wins - Different responses → Capabilities win | | 📊 **Market Data** | 💪 Capabilities vs 💹 Revenue | In transparent markets: - Similar patterns → Revenue dynamics win - Different patterns → Capabilities win | | 🌍 **Trade Rules** | 📦 Modularity vs 🏛️ Architecture | During trade shifts: - Varied adaptation → Modularity wins - Uniform adaptation → Architecture wins | | 💻 **Digital Infrastructure** | 💰 R&D vs 💪 Capabilities | In digital-mature markets: - Distributed innovation → R&D wins - Centralized innovation → Capabilities win | | 💡 lighting in Mach illusion: - Easy to observe (like seeing if it's sunny ☀️) - Sets context for everything else - Helps decide between competing explanations | | |