# Parameter Grounding Register **Status:** v0.2 (ASSEMBLY-WP-0008). Grounded values are collected in a separate parameter set, `simulations/gadm02/grounding.py` (variants `central` and `slow` adoption). GADM-0.4's defaults are left unchanged, so the effect of grounding stays visible and reversible. Status codes: - **G** grounded: a value or range is supported by external evidence. - **D** direction-grounded: evidence supports the direction or relative size, not a magnitude. - **B** bracketed: not grounded; key readings were checked across a range. - **A** assumed, not checked. - **C** calibration choice: set to reproduce a target behaviour. The model's stocks are normalized indices. Mapping evidence onto them always needs an interpretation, which is stated in the "Mapping" column. ## 1. Register | Parameter | Meaning | Default → grounded | Status | Evidence | Mapping | |---|---|---|---|---|---| | time unit | one period | 1 year | C | — | implied timescales checked (`GADM-0.2.md` § 3.4) | | `dO` | organism capability depreciation | 0.20 / year (0.15–0.35) | **G** | software depreciation over 3–10 years; business R&D capital depreciation generally above 15 % (Li & Hall 2020); AI-GPU economic life 2–6 years | organism capability combines software, know-how and compute | | K_Ω level and path (`aO`, `ell`, `oseed`) | organism adoption | default 0.50 at year 10 → **central 0.27 at year 10** (×0.5); **slow 0.10** (×0.2) | **G** (band) | about 4.6 % of tasks cost-effectively affected within 10 years (Acemoglu 2024); 15 % of tasks faster with LLMs, 47–56 % with tooling (Eloundou et al. 2023); about a third of occupations use AI for ≥ 25 % of tasks (Anthropic Economic Index, Feb 2025); AI investment ~1–2 % of GDP (2025) | K_Ω = share of information tasks carried out by organisms; start 0.05; the default reaching the exposure ceiling in 10 years is too fast | | `aext` | attention extraction | 0.275 → **0.58** (with grounded care load) | **G** (order of magnitude) | ATUS 2023: 5.2 h leisure/day, 2.7 h TV; social media ~2 h 20 min/day | present-day attention headroom ≈ 0.5 (engagement media take about half of leisure); the 0.275 compensated for an overstated care load | | `care_load` | attention cost of informal care | 0.12 → **0.03** (0.02–0.06) | **G** | AARP/NAC 2025: 51 M caregivers of people 50+ (about 1 in 5 adults), 26 h/week, up from 22 h in 2020 | population average ≈ 5 h/week of care vs. ≈ 35 h/week of engagement media | | `dX` | commons obsolescence | 0.04 (0.02–0.10) | **G** | knowledge depreciation 1–5 % / year, 8–9 % in the first two years (de Rassenfosse & Jaffe 2017); software faster | mixed commons of knowledge, standards, software, infrastructure | | `lX` | enclosure / loss of commons | 0.06 × P (≈ 3 % / year) | **D** | 16 % of popular GitHub projects abandoned, 41 % of those revived (Avelino et al. 2019); ~15 % of widely used npm packages with abandonment issues (secondhand) | annual loss of 1–3 % of maintained commons | | `IX` | deliberate commons investment | 0.02 | **A** (top baseline driver) | public R&D ≈ 1 % of GDP, roughly stable (Germany 1.04–1.10 %, 2020–2023); business R&D rose to 74 % of OECD GERD | no defensible mapping from GDP shares to the normalized commons inflow; see reading 3 | | `aX`, `rho0` | conversion of selected assemblies into commons | 0.30, 0.30 | **D** | open source: about $4.15 B to recreate once versus about $8.8 T demand-side value (Hoffmann, Nagle & Zhou 2024) | very high reuse leverage of non-rival commons; supports high conversion, input for WP-0007 | | `r_k` | power-channel self-reinforcement | market 0.3; **digital channels 0.45** (discovery 0.6) | **D** | displacement of industry leaders declined sharply since 2000, linked to software and intangibles (Bessen, Denk, Kim & Righi); superstar persistence from learning-by-doing technologies (Ma, Pugsley & Zimmermann) | digital channels (infrastructure, discovery, membrane, lineage) reinforce more strongly than the market channel | | `lw` | learning-with-use amplification of lineage control | 2.0 | **D** | learning-by-doing behind persistent superstars (Ma, Pugsley & Zimmermann) | direction only | | `k_w` | wealth → market/political control | 0.04 (0.02–0.20) | **B** | lobbying returns over 130 % with a tiny effect on enactment probability (Kang 2016); elites ~15× the influence of average citizens (Gilens & Page 2014), disputed (Bashir 2015) | evidence pulls in both directions on magnitude | | `dH` | current-capability depreciation | 0.05 | **G** | skill depreciation ≈ 4.3 % / year without use (Dinerstein, Megalokonomou & Yannelis); decline concentrated among low-use workers (PIAAC-L, Germany) | — | | `dD`, `hform`, ½ / ½ split | durable capability | 0.01; 0.15; ½ / ½ | **D** | early-education test effects fade, but adult outcomes persist (Chetty et al., Project STAR; Deming 2009: Head Start +0.23 SD, ~80 % of Perry Preschool) | supports a slowly depreciating durable component; the split is not identified | | `lbl` | learning begets learning | 1.0 | **D** | skill decline depends on skill use (PIAAC-L) | direction only | | `g3s` | seniors' restartability weight | 0.5 | **G** | re-employment of displaced long-tenured workers, Jan 2026: 72.9 % (25–54), 57.3 % (55–64), 38.6 % (65+) (BLS Displaced Worker Survey) | ratio 65+ / prime age ≈ 0.53 | | `b2s` | seniors' exposure to operator lock-in | 1.5 | **D** | 2017: a third of people 65+ never online, smartphone ownership 42 points lower than under-65s (Pew); recent data not found | gap likely narrowing over cohorts | | `gcare` | care support → senior agency | 0.02 | **A** | — | — | | demography | births, ageing, deaths | 1/45; 1/20, 1/45, 1/15; 1/8 | C | — | stationary structure ~23 / 51 / 17 / 9 % | | `v0`, `aK`, `n1` | experimentation, capacity build, entry | 2.5, 0.35, 0.30 | C | — | keep the GADM-0.2 baseline from collapsing | | all other rates | — | see code | A | — | first guesses in 0.03–0.3 per year | ## 2. Readings under grounded parameters Full tables: `simulations/gadm02/results/grounded_readings.md`. Three variants: GADM-0.4 defaults; grounded with central adoption; grounded with slow adoption. Each starts at its own equilibrium with recalibrated channels. | Reading | Defaults | Grounded, central | Grounded, slow | |---|---|---|---| | Best / worst scenario | generative / automated capitalism | same | same | | Scenario ranking ρ vs. defaults | 1.00 | 1.00 | 0.98 | | Lowest domination | generative | generative | generative | | Lever order lineage > membrane > attention | +0.111 / +0.075 / +0.031 | +0.122 / +0.094 / +0.045 | +0.164 / +0.109 / +0.039 | | P3: full wealth tax ΔP (membrane-dominated) | −0.028 | −0.055 | −0.018 | | G3: senior agency gain at cp = 0.3 (care share) | +0.070 (55 %) | +0.069 (56 %) | +0.077 (48 %) | | Baseline VFAC / generative VFAC | 0.321 / 0.659 | 0.328 / 0.658 | 0.396 / 0.684 | ### Readings 1. **Every structural reading survives grounding.** None of the qualitative results of GADM-0.2 to 0.4 is overturned by the evidence collected here. 2. **The default model adopts organisms too fast.** It reaches half of all information tasks within ten years, beyond what even the exposure ceiling suggests. Grounded paths are 0.27 (central) or 0.10 (slow) at year 10. 3. **Governance matters more, the faster organisms are adopted.** Under slow adoption, current-trend regimes do much better (baseline 0.321 → 0.396, dividend abundance 0.337 → 0.434), while the generative regime barely changes (0.659 → 0.684). The gap between current trends and the generative regime narrows from 0.34 to 0.29. Adoption speed, an empirical unknown, sets the stakes of organism governance. 4. **Commons investment is the most important ungrounded parameter.** It is the top baseline driver (`GADM-0.4.md` § 3), but there is no defensible mapping from GDP shares to the normalized commons inflow. Its sibling evidence (open-source reuse leverage) points at the *conversion* of assemblies into commons rather than at investment levels. 5. **The care load was overstated by about 4×.** The default was 0.12; the evidence supports 0.03. Grounding it forced a joint recalibration of attention extraction. The two calibrations had been compensating for each other. ## 3. Input for ASSEMBLY-WP-0007 (validation and falsification) The evidence suggests tests that WP-0007's structural extensions should pass or could fail: | WP-0007 task | Evidence | Expectation / falsification test | |---|---|---| | T01 rival vs. non-rival output | open-source reuse leverage (≈ 2,000× demand-side value over recreation cost); AI investment ~1–2 % of GDP but ~90 % of H1-2025 GDP growth in information processing (Furman) | Non-rival output should carry most of the commons effect. If splitting Y* does *not* raise the commons/reuse contribution, the reuse mechanism is mis-specified. | | T02 ecological classes | compute energy and AI-hardware turnover (2–6-year economic life) | Organism growth should bind energy and sinks before materials. If materials bind first, `e2` or the class mapping is wrong. | | T03 endogenous selection weights | leader displacement declined since 2000 (Bessen et al.); lobbying shifts enactment probability only marginally but with high returns (Kang 2016) | Selection capture should be slow and incremental, not abrupt. A model where domination flips selection weights within a few years would contradict the evidence. | | all | adoption-speed band (grounded central vs. slow) | Every WP-0007 reading should be reported under both adoption variants (reading 3). | ## 4. Remaining open grounding needs 1. Commons investment `IX`: needs a mapping concept, not just data. 2. Wealth conversion `k_w`: evidence is contradictory. 3. `gcare`, and the durable/current split. 4. Recent (post-2020) data on age gaps in digital access. ## Sources - Acemoglu, D. (2024). The Simple Macroeconomics of AI. NBER WP 32487. https://www.nber.org/papers/w32487 - Eloundou, T. et al. (2023). GPTs are GPTs. https://arxiv.org/abs/2303.10130v2 - Anthropic Economic Index. https://www.anthropic.com/research/the-anthropic-economic-index - Furman on AI investment and GDP growth (Fortune, 2025). https://www.fortune.com/2025/10/07/data-centers-gdp-growth-zero-first-half-2025-jason-furman-harvard-economist - Li, W. C. Y. & Hall, B. H. (2020). Depreciation of Business R&D Capital. https://www.bea.gov/research/papers/2016/depreciation-business-rd-capital - BEA, Estimation of Software in the US National Accounts. https://www.bea.gov/sites/default/files/2018-05/Estimation-of-Software-in-the-US-National-Accounts.pdf - AI-GPU economic life. https://siliconangle.com/2025/11/22/resetting-gpu-depreciation-ai-factories-bend-dont-break-useful-life-assumptions/ - BLS American Time Use Survey. https://www.bls.gov/news.release/PDF/atus.pdf - AARP / NAC, Caregiving in the US 2025. https://www.aarp.org/pri/topics/ltss/family-caregiving/family-caregiving-in-us-2025/ - de Rassenfosse, G. & Jaffe, A. (2017), knowledge depreciation (NBER WP 23072). https://nber.org/system/files/working_papers/w23072/w23072.pdf - Avelino, G. et al. (2019). On the abandonment and survival of open source projects. https://arxiv.org/pdf/1906.08058 - Hoffmann, M., Nagle, F. & Zhou, Y. (2024). The Value of Open Source Software. https://papers.ssrn.com/abstract=4693148 - OECD Main Science and Technology Indicators. https://www.oecd.org/sti/msti2024march.pdf ; German R&D table: https://www.datenportal.bmftr.bund.de/portal/en/Table-1.3.1.pdf - Bessen, J., Denk, E., Kim, J. & Righi, C., declining industry-leader displacement. https://sites.bu.edu/tpri/2017/10/11/why-large-firms-are-becoming-more-dominant - Ma, Pugsley & Zimmermann, superstar persistence. https://conference.nber.org/conf_papers/f206616/f206616.pdf - Kang, K. (2016). Policy Influence and Private Returns from Lobbying in the Energy Sector. *Review of Economic Studies* 83(1). https://ideas.repec.org/a/oup/restud/v83y2016i1p269-305..html - Gilens, M. & Page, B. (2014), and Bashir (2015). https://journals.sagepub.com/doi/reader/10.1177/2053168015608896 - Dinerstein, Megalokonomou & Yannelis, human capital depreciation (NBER WP 27925). https://www.nber.org/system/files/working_papers/w27925/w27925.pdf - PIAAC-L skill development in adulthood. https://www.arxiv.org/abs/2410.00790 - Chetty, R. et al., Project STAR (NBER WP 16381). https://www.nber.org/papers/w16381.pdf - Deming (2009), Head Start fade-out brief. https://poverty.ucdavis.edu/policy-brief/short-run-fade-out-head-start-and-implications-long-run-effectiveness - BLS Displaced Worker Survey. https://www.bls.gov/news.release/archives/disp_08272026.htm - Pew Research, tech adoption among older adults (2017). https://www.pewresearch.org/internet/2017/05/17/tech-adoption-climbs-among-older-adults