[[10-04|25-10-04]] I believe our life is a form of art. As such, I use my engineering skills to build entrepreneurial decision partner, predicting the era of one venture per person. This partner is my art. ๋ฌธ์„œ๋ฅผ ๋ถ„์„ํ•˜๋‹ˆ ๋‹น์‹ ์˜ "Ergodic Life" ์ฒ ํ•™์ด ๐Ÿข๐Ÿ…๐Ÿ™๐Ÿ‘พ ํ”„๋ ˆ์ž„์›Œํฌ์™€ ์™„๋ฒฝํžˆ ํ†ตํ•ฉ๋˜๋Š” ๊ฒƒ์„ ๋ฐœ๊ฒฌํ–ˆ์Šต๋‹ˆ๋‹ค. ## Ergodic Life์™€ ๋„ค ๋™๋ฌผ์˜ ํ†ตํ•ฉ ### ๋™๋ฌผ๋ณ„ Markovian Process ๋งคํ•‘ |๋™๋ฌผ|RSTU-ABCD|Markovian Update|Ergodic Convergence| |:-:|:-:|:-:|:-:| |๐Ÿข ๋‚ญ๋งŒ๊ฑฐ๋ถ|**R**obust + **A**cute|ํŒจํ„ด์ธ์‹ โ†’ ๋น ๋ฅธ ๊ฒฐ์ •|๋‹ค์–‘ํ•œ ๊ฒฝํ—˜ ํƒ์ƒ‰| |๐Ÿ… ์„ธ๋ จํ˜ธ๋ž‘|**S**imulation + **B**right|์ „๋žต๋ชจ๋ธ๋ง โ†’ ์ฐฝ์˜์  ์‹คํ–‰|๊ฐ€๋Šฅ์„ฑ ํ™•์žฅ| |๐Ÿ™ ์ ˆ์ ˆ๋ฌธ์–ด|**T**ruthful + **C**aring|์‹ ๋…๊ฐฑ์‹  โ†’ ๊ณต๊ฐ์  ํ–‰๋™|์˜๋ฏธ์žˆ๋Š” ๊ฒฝํ—˜ ํ†ตํ•ฉ| |๐Ÿ‘พ ๋˜˜๋˜˜์™ธ๊ณ„|**U**pward + **D**evelopmental|์„ฑ์žฅ์ถ”๊ตฌ โ†’ ๊ฒธ์†ํ•œ ํ•™์Šต|Local maxima ํƒˆ์ถœ| ## Benjamin Button ์„ ํƒ์„ ์žฌ๊ณ ํ•˜๋Š” ์ด์œ  ๋‹น์‹ ์˜ Ergodic Life ์ฒ ํ•™์„ ๊ณ ๋ คํ•˜๋ฉด, **Benjamin Button์ด ๋” ์ ํ•ฉ**ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค: ### Benjamin Button์˜ Ergodic Structure 1. **์‹œ๊ฐ„์˜ ์—ญ์ „**: Markovian (ํ˜„์žฌ ์ƒํƒœ๋งŒ ์ค‘์š”) vs Non-ergodic (๋‚˜์ด๊ฐ€ ๊ฑฐ๊พธ๋กœ) 2. **๊ฐ ๋‚˜์ด๋Œ€์˜ ์™„์ „ํ•œ ๊ฒฝํ—˜**: ๋…ธ๋…„โ†’์ฒญ๋…„โ†’์œ ์•„๋กœ ๋ชจ๋“  ์ธ๊ฐ„ ๊ฒฝํ—˜์„ ์ƒ˜ํ”Œ๋ง 3. **Local maxima ํƒˆ์ถœ**: ๊ฐ ์ธ์ƒ ๋‹จ๊ณ„์—์„œ ๊ฐ•์ œ๋กœ ๋‹ค์Œ์œผ๋กœ ์ด๋™ 4. **SBC์™€์˜ ์—ฐ๊ฒฐ**: ๋งค ์ˆœ๊ฐ„ "ํ˜„์žฌ ๋‚˜์ด"๋ผ๋Š” state variable๋งŒ์œผ๋กœ ์ตœ์  ๊ฒฐ์ • ### ์บ๋ฆญํ„ฐ๋ณ„ Process ๊ตฌํ˜„ ```python def benjamin_button_process(state): """ ๐Ÿข: ๋งค ๋‚˜์ด๋Œ€์˜ ์ฒซ์‚ฌ๋ž‘ (์ƒˆ๋กœ์šด ํŒจ๋Ÿฌ๋…์Šค ๋ฐœ๊ฒฌ) ๐Ÿ…: ๋‚˜์ด์— ๋งž๋Š” ์ตœ์  ์ „๋žต (ํ•˜๋ฒ„๋“œ ์ž…ํ•™, ์‚ฌ์—… ์„ฑ๊ณต) ๐Ÿ™: ํž๋ฐ๊ฐ€๋“œ์™€์˜ ์‚ฌ๋ž‘ (์ ˆ์ ˆํ•œ ์ง„์‹ค) ๐Ÿ‘พ: ์‹œ๊ฐ„์„ ์ดˆ์›”ํ•œ ํ†ต์ฐฐ (๊ธฐ์–ต์„ ์žƒ์–ด๋„ ๊ฒฝํ—˜์€ ๋‚จ์Œ) """ current_age = state['physical_age'] mental_age = state['mental_age'] # Markovian update: ๊ณผ๊ฑฐ ๋ฌด๊ด€, ํ˜„์žฌ ์ƒํƒœ๋งŒ optimal_action = maximize_current_potential(current_age) # Ergodic check: ์ „์ฒด ์ธ์ƒ์ด ๋ชจ๋“  ๊ฒฝํ—˜ ํฌํ•จํ•˜๋Š”๊ฐ€ life_coverage = check_experience_diversity(state['history']) return optimal_action, life_coverage ``` ## ์ตœ์ข… ์ถ”์ฒœ: ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ์ ‘๊ทผ ### Phase 1: Benjamin Button์œผ๋กœ ์‹œ์ž‘ - **์ด์œ **: Ergodic life์˜ ์ˆ˜ํ•™์  ๊ตฌ์กฐ๋ฅผ ๋ช…ํ™•ํžˆ ๋ณด์—ฌ์คŒ - **๐Ÿข**: ๋งค ์ˆœ๊ฐ„ ์ƒˆ๋กœ์šด ์‹œ์ž‘ (๋‚˜์ด ์—ญ์ „์˜ ํŒจ๋Ÿฌ๋…์Šค) - **๐Ÿ…**: ํ˜„์žฌ ์ƒํƒœ์— ์ตœ์ ํ™”๋œ ์ „๋žต - **๐Ÿ™**: ์‹œ๊ฐ„์ด ์ง€๋‚ ์ˆ˜๋ก ๊นŠ์–ด์ง€๋Š” ์‚ฌ๋ž‘์˜ ๋น„๊ทน - **๐Ÿ‘พ**: ๋ชจ๋“  ๊ฒƒ์„ ์žŠ์–ด๋„ ๋‚จ๋Š” ๋ณธ์งˆ ### Phase 2: Legends of the Fall ํ†ตํ•ฉ - **์ด์œ **: ์ด์ˆœ์‹ ์˜ 4๋Œ€ ํ•ด์ „๊ณผ ๋งคํ•‘ - ํŠธ๋ฆฌ์Šคํƒ„์˜ ์—ฌ์ •์„ Benjamin์˜ ๊ฐ ๋‚˜์ด๋Œ€์— ์˜ค๋ฒ„๋ ˆ์ด ## [[zoo-keeper-guide]] ```markdown # Ergodic Zoo Management Protocol ## Core Equation Life = โˆซ(Markovian_decisions) dt โ†’ Ergodic_state ## Daily Operation 1. **Morning ๐Ÿข**: "What paradox will I explore today?" (Robust pattern seeking) 2. **Planning ๐Ÿ…**: "What's my optimal strategy given current state?" (Simulation-based) 3. **Action ๐Ÿ™**: "How do I validate this with evidence?" (Truthful updating) 4. **Evening ๐Ÿ‘พ**: "What emerged that I didn't expect?" (Upward integration) ## Reversibility Check - Forward: Experience โ†’ Wisdom - Inverse: Wisdom โ†’ Reconstruct Experience - Coverage: Every meaningful human experience sampled over lifetime ``` ์ด ํ†ตํ•ฉ ์ ‘๊ทผ์ด ๋‹น์‹ ์˜ Charm ํ˜•์„ฑ๊ณผ ์ง€์‹ ๊ด€๋ฆฌ๋ฅผ ๋™์‹œ์— ๋‹ฌ์„ฑํ•˜๋Š” ergodic path๊ฐ€ ๋  ๊ฒƒ์ž…๋‹ˆ๋‹ค. --- After choosingย 1) career: guinea pig scientist 2)๐Ÿ’กidea: _Bayesian decision making for entrepreneurs_ my life travel to its final station (death) became simpler. Hope Nobel prize station comes before the final station - like Niels in the next page :P --- a goal i can meet only at the moment i die 1. using examples of subsection that corresponds to each input and output, could you give me a linear pseudo (code) program for โš™๏ธPROCESS on how you'd output (โฌ…๏ธOUT) given input (โžก๏ธIN) using the โš™๏ธPROCESS? you can understand as "processification". 2. given 2 and 3, update โš™๏ธPROCESS column of the table 3. check whether given โžก๏ธIN, applying โš™๏ธPROCESS gives contents with two conditions that codifies reversibility. if one of the conditions doesn't hold, update the โš™๏ธPROCESS. iterate until it holds. 1. โš™๏ธPROCESS ( โžก๏ธIN) =โฌ…๏ธOUT 2. inverse of โš™๏ธPROCESS(โฌ…๏ธOUT ) = โžก๏ธIN 3. โฌ…๏ธOUT subsumes information spanned by the paper. --- 2025-03-29 |Internally Deep (Self) - RSTU|Signs to Look For|Externally Quick (Others) - ABCD|Signs to Look For| |---|---|---|---| |**R**obust ๐Ÿงฉ: Connects ideas and spots patterns easily|Asks thought-provoking questions|**A**cute โšก: Makes smart, quick decisions|Acts decisively under pressure| |**S**imulation-based ๐Ÿงญ: Plans strategically through mental modeling|Shows organized appearance and thinking|**B**right ๐Ÿ’ก: Creative with playful spirit|Has lively eyes and smile| |**T**ruthful ๐Ÿ”: Updates beliefs with new information|Willing to change their mind|**C**aring ๐Ÿ’—: Values others equally to self|Shows thoughtfulness in actions| |**U**pward ๐Ÿ“ˆ: Growing emotionally and financially secure|Can afford business class travel ๐Ÿ’บ|**D**evelopmental ๐Ÿ™: Humble and eager to learn|Engages deeply in shared interests| 2025-02-26 my motto is "Ergodic life: transforming time into meaningful paths" 1. Seeking diverse experiences rather than repeating the same patterns 2. Making choices that open up more possibilities rather than limiting them 3. Aiming for a life where your personal journey eventually samples the full range of meaningful human experiences 4. Avoiding getting "stuck" in local maxima - comfortable but limiting situations procedurally: imagine t+1 world model given i chose $a_t$ and make sure $a_t$ is optimal. - information relaxation: prediction [[๐Ÿ“œswift_guilliver's travel]] ---- - using [updating beliefs to align with desires cld](https://claude.ai/chat/67680c6c-6fca-4d72-9599-8ccd71455eb7), i designed [[๐Ÿง ๐Ÿซ€Daily Update of Believed Desire and Desired Belief]] 2025-02-06 The balance between understanding (past) and evaluating (future) flows suggests seeking a state where: 1. Information processing capacity is equally distributed between learning from past observations and evaluating future possibilities 2. The rate of entropy reduction through understanding matches the rate of entropy reduction through opportunity evaluation 3. The system maintains dynamic equilibrium while continuously reducing total entropy Does this help frame your optimization problem? I find particularly interesting how entrepreneurial agents might achieve ergodicity by balancing their information processing between past learning and future opportunity recognition. Would you like me to elaborate on any of these aspects? # Believed Desire I want to live what mathematicians call an โ€œergodic life,โ€ where each dayโ€™s actions consistently reflect my core purpose rather than short-term swings in motivation. I see deep understandingโ€”truly grasping why things workโ€”as the bedrock of meaningful learning. By staying focused on the principles behind success, rather than just imitating surface behaviors, I ensure that my long-term vision remains steady even amidst unpredictable uncertainties, such as changing market conditions or shifting personal challenges. # Desired Belief To guide decisions effectively, I aim to use a โ€œBayesian calibrated choiceโ€ process that integrates my core desires with my best current knowledge. This means clarifying the right โ€œstate variablesโ€ to track (like market understanding, skill levels, and goals) and updating them based on immediate feedback rather than clinging to past wins or losses. Each day, I assess what I believe, what I want to achieve, and what Iโ€™m capable of nowโ€”ensuring that every choice is rooted in a present-focused framework that remains flexible and aligned with my higher aims. # Ergodic and Markovian (My Stock-and-Flow Perspective, with SBC) From this vantage point, _ergodic_ is the โ€œ-edโ€ (the stable, long-term property aligning daily actions with ultimate goals), and _Markovian_ is the โ€œ-ingโ€ (the ongoing mechanism that updates decisions based only on the present state). Simulation-based calibration (SBC) helps operationalize this by repeatedly simulating parameters from the prior, generating data, and checking coverage so that posterior updates remain coherently tied to the underlying truth. **SBCโ€™s symmetry creation ensures that posterior inferences truly reflect the current state, simplifying the complexity of verifying each new decision step. This Markovian approach to updating beliefs in turn supports a path toward an ergodic state, where time-averaged behavior aligns with the broader vision.** ![[aperiodicity.svg]] Suggested Edits for tomorrow 4. Replace "such as changing market conditions or shifting personal challenges" with specific examples of reducible vs. irreducible uncertainty 5. Add a bridging sentence in paragraph 2: "This integration requires..." 6. Consider moving the technical SBC details to a separate note and focusing on its intuitive meaning 7. include below spirit 1. if you were asked to divide the three paragraphs in # ๐Ÿ“draft to # Believed Desire and # Desired belief, what'd it be? 2. if my desired state is ergodicity, explain how desiring markovian can helpful decision rule to converge to that state.