diff --git a/.custodian-brief.md b/.custodian-brief.md index 8d39e85..eb5f0a2 100644 --- a/.custodian-brief.md +++ b/.custodian-brief.md @@ -2,22 +2,12 @@ # Custodian Brief — assembly-economics **Domain:** communication -**Last synced:** 2026-10-11 22:34 UTC +**Last synced:** 2026-10-11 22:36 UTC **State Hub:** http://127.0.0.1:8000 *(adjust if running on a remote machine)* ## Active Workstreams -### Promote exploration concepts into concepts/ -Progress: 2/9 done | workplan_id: `2af40c6f-7b17-5f78-be0c-e0d151bd8f38` - -**Open tasks:** -- · Promote Membrane Model `17d0fcac` -- · Promote Seed Kernel `d76809b7` -- · Promote Evolution Model `2ffe2f19` -- · Promote Metabolism Model `8b2a4b48` -- · Promote Resource Ontology `e6fbeef5` -- · Draft Network Object Model `ec8b650f` -- · Apply glossary recommendations `a2a2e121` +*(none — repo may need first-session setup)* ## Inbox Hygiene diff --git a/.gitignore b/.gitignore index e4e0199..f77afef 100644 --- a/.gitignore +++ b/.gitignore @@ -3,3 +3,7 @@ .claude/* !.claude/rules/ !.claude/rules/*.md + +# Python +__pycache__/ +*.pyc diff --git a/AGENTS.md b/AGENTS.md index bdbdeb3..5c14485 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -11,6 +11,19 @@ --- +## Verification + +Research repository: Markdown documents plus a standard-library Python +simulation. No install step. + +```bash +python3 -m unittest discover -s simulations/gadm02 # must pass before committing model changes +cd simulations/gadm02 && python3 run_scenarios.py && python3 sensitivity.py +``` + +After changing `simulations/` or `scenarios/`, regenerate the results and +update the affected readings in `models/GADM-0.2.md`. + ## State Hub Integration The Custodian State Hub tracks work across all domains. Codex uses HTTP REST and diff --git a/WORK-RECORDS.md b/WORK-RECORDS.md index 6522a18..e441c89 100644 --- a/WORK-RECORDS.md +++ b/WORK-RECORDS.md @@ -9,7 +9,7 @@ | Kind | ID | Status | Lane | Source | | --- | --- | --- | --- | --- | | workplan | ASSEMBLY-WP-0001 | finished | — | workplans/ASSEMBLY-WP-0001-statehub-bootstrap.md | -| workplan | ASSEMBLY-WP-0002 | active | — | workplans/ASSEMBLY-WP-0002-concept-promotion.md | +| workplan | ASSEMBLY-WP-0002 | finished | — | workplans/ASSEMBLY-WP-0002-concept-promotion.md | | workplan | ASSEMBLY-WP-0003 | finished | — | workplans/ASSEMBLY-WP-0003-seed-integration.md | | workplan | ASSEMBLY-WP-0004 | proposed | — | workplans/ASSEMBLY-WP-0004-gadm-0.2-modeling.md | | task | ASSEMBLY-WP-0001-T01 | done | — | workplans/ASSEMBLY-WP-0001-statehub-bootstrap.md | @@ -17,13 +17,13 @@ | task | ASSEMBLY-WP-0001-T03 | done | — | workplans/ASSEMBLY-WP-0001-statehub-bootstrap.md | | task | ASSEMBLY-WP-0002-T01 | done | — | workplans/ASSEMBLY-WP-0002-concept-promotion.md | | task | ASSEMBLY-WP-0002-T02 | done | — | workplans/ASSEMBLY-WP-0002-concept-promotion.md | -| task | ASSEMBLY-WP-0002-T03 | todo | — | workplans/ASSEMBLY-WP-0002-concept-promotion.md | -| task | ASSEMBLY-WP-0002-T04 | todo | — | workplans/ASSEMBLY-WP-0002-concept-promotion.md | -| task | ASSEMBLY-WP-0002-T05 | todo | — | workplans/ASSEMBLY-WP-0002-concept-promotion.md | -| task | ASSEMBLY-WP-0002-T06 | todo | — | workplans/ASSEMBLY-WP-0002-concept-promotion.md | -| task | ASSEMBLY-WP-0002-T07 | todo | — | workplans/ASSEMBLY-WP-0002-concept-promotion.md | -| task | ASSEMBLY-WP-0002-T08 | todo | — | workplans/ASSEMBLY-WP-0002-concept-promotion.md | -| task | ASSEMBLY-WP-0002-T09 | todo | — | workplans/ASSEMBLY-WP-0002-concept-promotion.md | +| task | ASSEMBLY-WP-0002-T03 | done | — | workplans/ASSEMBLY-WP-0002-concept-promotion.md | +| task | ASSEMBLY-WP-0002-T04 | done | — | workplans/ASSEMBLY-WP-0002-concept-promotion.md | +| task | ASSEMBLY-WP-0002-T05 | done | — | workplans/ASSEMBLY-WP-0002-concept-promotion.md | +| task | ASSEMBLY-WP-0002-T06 | done | — | workplans/ASSEMBLY-WP-0002-concept-promotion.md | +| task | ASSEMBLY-WP-0002-T07 | done | — | workplans/ASSEMBLY-WP-0002-concept-promotion.md | +| task | ASSEMBLY-WP-0002-T08 | done | — | workplans/ASSEMBLY-WP-0002-concept-promotion.md | +| task | ASSEMBLY-WP-0002-T09 | done | — | workplans/ASSEMBLY-WP-0002-concept-promotion.md | | task | ASSEMBLY-WP-0003-T01 | done | — | workplans/ASSEMBLY-WP-0003-seed-integration.md | | task | ASSEMBLY-WP-0003-T02 | done | — | workplans/ASSEMBLY-WP-0003-seed-integration.md | | task | ASSEMBLY-WP-0003-T03 | done | — | workplans/ASSEMBLY-WP-0003-seed-integration.md | diff --git a/metrics/MetricsCatalog.md b/metrics/MetricsCatalog.md new file mode 100644 index 0000000..7e7c5cc --- /dev/null +++ b/metrics/MetricsCatalog.md @@ -0,0 +1,184 @@ + + +# Metrics Catalog v0.1 + +**Status:** exploratory. Indicators are *candidates*: their availability, +validity and data sources still have to be checked before use. + +Each GADM-0.2 stock gets a **sensor bundle** of four kinds of measure: + +- **O** objective outcome +- **St** structural +- **B** behavioral +- **Su** subjective + +Each stock also gets its main **Goodhart risk**: how optimizing the measure +could hollow out the construct. + +Rules for using the catalog: + +1. No stock is reported through a single indicator. +2. When indicators in a bundle disagree, record the disagreement as a + finding. Do not average it away. +3. A stock's normalization to [0, 1] in GADM is a modeling convenience. The + catalog keeps the raw indicators. +4. Changes to indicators are governed like model changes: versioned, with a + rationale (WS-11, governance of metrics). + +--- + +## H — Human capability + +| Kind | Candidate indicators | +|---|---| +| O | healthy life expectancy; adult skills proficiency (literacy, numeracy, problem solving) | +| St | coverage of education, care and lifelong-learning provision; mentoring and apprenticeship capacity | +| B | participation in learning after formal education; skill use at work and outside it | +| Su | self-assessed capability to learn and to act on one's goals | + +**Goodhart risk:** credential inflation. Counting certificates instead of +usable capability. Organism-assisted performance can mask deskilling (link +#40), so measure unassisted capability as well. + +## X — Assembly commons + +| Kind | Candidate indicators | +|---|---| +| O | reuse rate of open components (dependents per open package, citations of open datasets) | +| St | share of critical infrastructure under open standards; share of openly licensed publicly funded research and software | +| B | contribution and maintenance activity in commons projects; maintainer continuity | +| Su | practitioners' perceived ease of building on existing components | + +**Goodhart risk:** publishing without maintenance. Counting released artifacts +rather than reusable, maintained ones. Enclosure through dependence on a +single steward. + +## K_phys / K_Ω — Productive capacity and organism capability + +| Kind | Candidate indicators | +|---|---| +| O | sustainable output per unit of physical throughput; task coverage of information organisms | +| St | installed automation and compute capacity; number of independent organism lineages | +| B | utilization rates; rate at which organisms are adopted into workflows | +| Su | users' assessment of organism usefulness and reliability | + +**Goodhart risk:** capacity without access. K can rise while effective +agency falls (automated-capitalism scenario). Always report K together with B +(claims breadth) and P. + +## F — Risk capacity + +| Kind | Candidate indicators | +|---|---| +| O | business formation rate; survival-adjusted re-entry rate after failure | +| St | availability of staged finance, insurance and public risk pools; bankruptcy regime leniency | +| B | share of first-time founders without inherited wealth | +| Su | perceived personal downside of a failed venture | + +**Goodhart risk:** subsidized waste. Raising experiment counts while +externalizing harm. Pair with externalized-harm measures (constitutional +floor in GADM-0.1 § 21). + +## G — Effective agency + +| Kind | Candidate indicators | +|---|---| +| O | restartability (time × cost × re-entry restrictions, GADM-0.1 § 12); economic mobility | +| St | exit options: provider switching costs, data and organism portability, membrane control held by users | +| B | actual switching, relocation and career-change rates | +| Su | perceived agency and perceived range of reachable futures | + +**Goodhart risk:** formal options without real reach. Measures of legal +freedom that ignore material reachability. Organism "access" counted without +asking who controls the membrane (link #41). + +## N — Diversity & composability + +| Kind | Candidate indicators | +|---|---| +| O | diversity of firms, products and lineages (entropy measures); interoperability test pass rates | +| St | standards coverage; market concentration per component class | +| B | cross-vendor recombination: products built from components of several sources | +| Su | developers' and founders' perceived lock-in | + +**Goodhart risk:** fragmentation counted as diversity. Variety without +composability. Report both halves; GADM treats them jointly for this reason. + +## Q — Epistemic capacity + +| Kind | Candidate indicators | +|---|---| +| O | correction latency (time from error to public correction); forecast accuracy; reproducibility rates | +| St | independent audit coverage; public access to evidence; concentration of information channels | +| B | share of AI-mediated decisions with retrievable provenance and explanation | +| Su | trust calibration: does trust track actual reliability? | + +**Goodhart risk:** measuring trust instead of trustworthiness. High trust in +unreliable sources scores well on naive measures. Use calibration, not +levels. + +## P — Domination concentration + +| Kind | Candidate indicators | +|---|---| +| O | ownership and market concentration (e.g. Herfindahl–Hirschman indices); concentration of lineage ownership | +| St | infrastructure, discovery and identity dependency; share of membranes controlled by a few operators | +| B | rate of self-preferencing and exclusion events | +| Su | perceived ability to contest decisions by dominant actors | + +**Goodhart risk:** measuring wealth instead of power. GADM models power +separately from wealth: control of discovery, membranes and infrastructure can +be extreme at moderate wealth concentration. + +## E — Ecological headroom + +| Kind | Candidate indicators | +|---|---| +| O | position relative to planetary boundaries; material and energy throughput | +| St | share of circular material flows; energy mix | +| B | compute energy use per unit of organism output (the metabolic draw of K_Ω) | +| Su | — (deliberately empty: ecological headroom is not a perception) | + +**Goodhart risk:** monetary decoupling claimed as physical decoupling. +GADM-0.1 § 14 requires physical throughput, not value, as the basis. + +## S — Social cohesion + +| Kind | Candidate indicators | +|---|---| +| O | incidence of violent conflict; legitimacy of institutions in contested decisions | +| St | channels for voice and appeal; plurality of recognition systems | +| B | civic participation; peaceful resolution rates | +| Su | generalized trust; expectation of future participation | + +**Goodhart risk:** suppressed disagreement read as cohesion. GADM defines +cohesion as continued legitimate participation *under* conflict. + +## A — Attention headroom + +| Kind | Candidate indicators | +|---|---| +| O | self-directed time from time-use surveys (time not claimed by work, care obligations or engineered engagement) | +| St | share of attention-facing services funded by engagement; default notification and interruption regimes | +| B | interruptions per hour; share of attention spent in self-chosen versus recommended activities | +| Su | perceived control over one's own attention; reported time scarcity | + +**Goodhart risk (critical):** counting *captured* attention as a resource. +Engagement metrics measure the opposite of A. Including A in VFAC rewards +extraction unless A measures **unclaimed, self-directed** attention +(`models/InformationOrganismsInGADM.md` § 4.1). In GADM-0.2, omitting A +understates attention harm (`models/GADM-0.2.md`, reading 5). + +--- + +## Derived diagnostics + +| Diagnostic | Definition | Caveat | +|---|---|---| +| VFAC | geometric mean of H, X, G, N, Q, S, E, 1 − P, A | Research instrument. Equal exponents damp near-collapse of one stock, so it never replaces floor checks. | +| Claims breadth B | share of output reaching the broad population | Says nothing about agency (dividend-abundance reading). | +| Floor status | share of periods in which a constitutional floor is violated | Floors are non-tradeable; do not aggregate them into VFAC. | diff --git a/models/GADM-0.2.md b/models/GADM-0.2.md new file mode 100644 index 0000000..18915c1 --- /dev/null +++ b/models/GADM-0.2.md @@ -0,0 +1,264 @@ + + +# Generative Assembly Dynamics Model — GADM-0.2 + +**Status:** exploratory hypothesis generator. Not calibrated to any real +economy. All numbers are in normalized, unitless model space. + +GADM-0.2 extends GADM-0.1 (`GenerativeAssemblyDynamicsModel.md`) in three +ways: + +1. It specifies every flow with signs (see `GADM-CLD.md`). +2. It adds the information-organism extensions selected below. +3. It is implemented as a runnable toy simulation. + +--- + +## 1. Decision: which organism extensions enter GADM-0.2 (T03) + +Candidates from `InformationOrganismsInGADM.md` § 4: + +| Candidate | Decision | Rationale | +|---|---|---| +| Split K into K_phys + K_Ω | **in** | Learning with use (R7) and lineage control (R8) are the main new loops. Without the split, they cannot be expressed. | +| Attention headroom A | **in** | Reference scarce resource of the program. Needed to test whether VFAC without A misses attention harm. | +| Organism selection channel O | **in** | Cheap to add. Needed to examine hypothesis M3 (O's quality and its contribution to P share a cause). | +| Membrane control and lineage concentration in Ṗ | **in**, as levers `m` and `L` | Main institutional levers of the organism lens. | +| Participation claims C_p | **in**, as lever `cp` | Lets scenarios test a post-work claim channel. It is treated as a share of capital return, which is a simplification (see § 6). | +| Compute C as its own stock | **deferred to 0.3** | Represented for now as a direct draw of K_Ω on E (`e2`). A separate stock only pays off once energy classes are modeled (GADM-0.4). | +| Age cohorts | **deferred to 0.3** | See `GADM-0.3-Candidates.md`. | + +--- + +## 2. State + +Twelve stocks, all in [0, 1]: + +| Symbol | Code | Stock | New in 0.2 | +|---|---|---|---| +| H | `H` | Human capability | | +| X | `X` | Assembly commons | | +| K_phys | `Kp` | Physical / conventional productive capacity | split | +| K_Ω | `Ko` | Information-organism capability | ✓ | +| F | `F` | Risk capacity | | +| G | `G` | Effective agency | | +| N | `N` | Diversity & composability | | +| Q | `Q` | Epistemic capacity | | +| P | `P` | Domination concentration | | +| E | `E` | Ecological headroom | | +| S | `S` | Social cohesion | now a stock relaxing to a target | +| A | `A` | Attention headroom: unclaimed, self-directed attention | ✓ | + +**Bounding convention.** Every stock follows +\(\dot x = \text{inflow}\cdot(1-x) - \text{outflow}\cdot x\), so stocks stay in +[0, 1] without clipping. Clipping is applied only as a numerical guard. + +**Institutional levers** (scenario axes, all in [0, 1]): + +| Lever | Meaning | +|---|---| +| `m` | membrane control held by users and communities rather than operators | +| `L` | lineage diffusion: 0 = concentrated, 1 = diffuse ownership of organism lineages | +| `lin_open` | share of lineages that are openly inheritable (commons rather than enclosure) | +| `std` | open standards and interoperability | +| `disc` | discovery concentration | +| `expl` | explainability and provenance enforcement | +| `x_att` | attention regime: 0 = protected, 1 = extraction-funded | +| `auto`, `dshare`, `cp`, `od` | automation intensity, social dividend share, participation claims share, ownership diffusion of private returns | + +--- + +## 3. Equations + +### 3.1 Auxiliaries + +\[ +K = 1-(1-K_{phys})(1-K_\Omega), \qquad Y^* = K\sqrt{E} +\] + +Claims (GADM-0.1 § 9, extended with C_p): + +\[ +w_L = w_{L0}(1-auto\cdot K_\Omega),\quad c = 1-w_L,\quad +\Pi_p = c\,(1-dshare-cp) +\] +\[ +B = w_L + c\,(dshare+cp) + \Pi_p\cdot od +\] + +\(B\) is the **breadth of claims**: the share of output reaching the broad +population as wages, dividends, participation claims or diffused ownership. +Demand factor: \(dem = (0.4+0.6B)(0.6+0.4S)\). + +Experimentation (GADM-0.1 § 5, plus a Q-gated organism term, hypothesis M2): + +\[ +V = \min\!\left(1,\; v_0 H^{.5}X^{.5}F^{.3}G^{.4}N^{.3}Q^{.3}(1-P)^{.5} + v_o K_\Omega Q\right) +\] + +Selection (GADM-0.1 § 6, plus organism channel O weighted by prevalence): + +\[ +M=\sqrt{A\cdot dem},\quad O = Q(1-disc) +\] +\[ +\sigma = \frac{w_m M + w_s Q + w_c N(1-P) + w_p S + w_o K_\Omega O}{w_m+w_s+w_c+w_p+w_o K_\Omega}, +\qquad V_{sel} = V\sigma +\] + +Reuse: \(\rho = \min(1, \rho_0 + \rho_O K_\Omega\, lin\_open)\). + +### 3.2 Stocks + +\[ +\begin{aligned} +\dot X &= (a_X\rho V_{sel} + I_X)(1-X) - (\delta_X + l_X P + l_{Xo}K_\Omega(1-lin\_open))X\\ +\dot K_{phys} &= a_K V_{sel}\,dem\,(1-K_{phys}) - \delta_K K_{phys}\\ +\dot K_\Omega &= (a_O V_{sel}\,dem + \ell K_\Omega\,dem\,Q + o_{seed})(1-K_\Omega) - \delta_O K_\Omega\\ +\dot H &= (h_0(\tfrac{S+B}{2}) + h_A A + h_T X)(1-H) - (\delta_H + desk\cdot K_\Omega(1-m))H\\ +\dot F &= (f_0 + f_Y Y^* B)(1-F) - \delta_F F\\ +\dot G &= (g_1B + g_2X(1-P) + g_3F + g_4K_\Omega m)(1-G) - (\delta_G + b_1P + b_2K_\Omega(1-m))G\\ +\dot N &= (n_1V(1-P) + n_2X\,std)(1-N) - (\delta_N + n_3P + n_4K_\Omega(1-L))N\\ +\dot Q &= (q_0 + q_1X + q_oK_\Omega\,expl)(1-Q) - (\delta_Q + q_2P + q_xK_\Omega(1-expl) + q_3\max(0,V-Q))Q\\ +\dot P &= (p_1V_{sel}(1-B) + p_2\,disc + p_3K_\Omega(1-m) + p_4K_\Omega(1-L))(1-P) - (p_5QS + p_6X\,std + p_7B)P\\ +\dot E &= e_{reg}(1-E) - (e_1K_{phys}\,dem\,thr + e_2K_\Omega)E\\ +\dot S &= s_{rate}\left(G^{.4}(1-P)^{.3}B^{.2}A^{.1} - S\right)\\ +\dot A &= a_{rec}(0.4+0.6B)(1-A) - a_{ext}\,x\_att\,(0.2+K_\Omega)A +\end{aligned} +\] + +Parameter values are in `simulations/gadm02/gadm02.py` (`PARAMS`, `INIT`). + +### 3.3 Diagnostics + +\[ +VFAC = \left(H\,X\,G\,N\,Q\,S\,E\,(1-P)\,A\right)^{1/9},\qquad +VFAC_{0.1} = \left(H\,X\,G\,N\,Q\,S\,E\,(1-P)\right)^{1/8} +\] + +These use equal exponents. VFAC is 0 if any factor is 0. As in GADM-0.1, VFAC +is a research instrument, not a welfare function. + +Constitutional floors, checked separately: \(G \ge 0.2,\; E \ge 0.2,\; P \le 0.7,\; A \ge 0.2\). + +### 3.4 Calibration + +There is no empirical calibration. Three coefficients were adjusted so that +the **baseline** stays near its initial state, instead of trivially collapsing +or saturating: + +- `v0` = 2.5: the seven-factor product is about 0.14 at initial values. +- `aK` = 0.35 and `n1` = 0.30: otherwise K_phys and N decay to about 0.12. + +Everything else is a first guess, chosen so that rates fall roughly in 0.03–0.3 +per period. + +--- + +## 4. Scenario results (T05) + +Full table: `simulations/gadm02/results/summary.md`. Scenario definitions: +`scenarios/`. Final values at t = 100: + +| Scenario | VFAC | VFAC_0.1 | P | G | A | B | Notes | +|---|---|---|---|---|---|---|---| +| Baseline | 0.46 | 0.44 | 0.52 | 0.44 | 0.65 | 0.59 | slow erosion of X and N, rising P | +| Automated capitalism | 0.32 | 0.29 | **0.73** | 0.28 | 0.58 | 0.33 | P ceiling breached in 69 % of periods; demand and cohesion fall | +| Dividend abundance | 0.45 | 0.43 | 0.54 | 0.45 | 0.62 | 0.69 | demand stabilized, but VFAC ≈ baseline | +| Universal venture access | 0.49 | 0.47 | 0.52 | 0.57 | 0.64 | 0.58 | agency up via restartability | +| Commons-rich | 0.54 | 0.54 | 0.48 | 0.47 | 0.62 | 0.57 | X 0.27 → 0.66; second best overall | +| Attention oligopoly | 0.37 | 0.36 | 0.69 | 0.38 | 0.55 | 0.61 | P approaches the ceiling; N collapses to 0.17 | +| Generative regime | **0.65** | 0.64 | 0.31 | 0.64 | 0.78 | 0.70 | stable; lowest ecological headroom (E 0.48) | +| — with concentrated lineages | 0.53 | 0.50 | 0.50 | 0.46 | 0.79 | 0.71 | loses about two thirds of the gain over baseline | +| — with attention extraction | 0.58 | 0.59 | 0.41 | 0.60 | 0.45 | 0.71 | VFAC_0.1 understates the loss | + +### Readings + +These describe the model's behavior under its assumed signs. They are +candidate hypotheses, not claims about economies. + +1. **Abundance without breadth fails** (automated capitalism). Output Y* is + close to the baseline (0.50 vs. 0.53), but domination crosses its ceiling + and claims breadth B drops from 0.59 to 0.33. This reproduces GADM-0.1's primary question in + model form. +2. **Dividends stabilize demand, not agency.** Dividend abundance raises B + from 0.59 to 0.69, but G, P and VFAC barely move. In this model, claims + alone do not counter domination or commons erosion. +3. **Reuse works as a bundle.** The commons-rich regime is the strongest + single-lever regime. However, `rho0` alone is not a top driver in the + sensitivity analysis (rank 20 of 80 at baseline). The effect comes from + ρ together with investment I_X, open lineages and standards. +4. **Organism governance dominates the generative regime.** Concentrating + lineages (`L`, `lin_open`, `m` low) costs about two thirds of the + generative regime's VFAC gain over baseline (0.65 → 0.53, baseline 0.46). + An extraction-funded attention regime costs about 40 % (0.65 → 0.575). +5. **VFAC without attention understates attention harm.** Moving from the + generative regime to its attention-extraction variant, VFAC falls by 0.075, + but VFAC_0.1 falls by only 0.042 (0.636 → 0.594). In the extraction + variant, VFAC_0.1 even ranks above VFAC, the reverse of every other + scenario. This supports including A, and also the Goodhart warning in + `InformationOrganismsInGADM.md` § 4.1. +6. **The generative regime has an ecological cost.** It has the lowest E of + all regimes. It stays above the floor, but `ereg` ranks third in its + sensitivity. Abundance needs throughput decoupling (GADM-0.1 § 14) to stay + generative. + +--- + +## 5. Sensitivity (T06) + +Full tables: `simulations/gadm02/results/sensitivity.md` +(one-at-a-time, ±20 %, effect on final VFAC). + +| Rank | Baseline | Generative regime | +|---|---|---| +| 1 | `dO` organism depreciation (+) | `m` membrane control (+) | +| 2 | `ell` organism learning (−) | `L` lineage diffusion (+) | +| 3 | `p7` claims breadth checking P (+) | `ereg` ecological regeneration (+) | +| 4 | `wL0` labor share (+) | `expl` explainability (+) | +| 5 | `IX` commons investment (+) | `v0` experimentation (+20 % → −) | + +Readings: + +- **Under baseline governance, faster organism learning lowers VFAC.** Higher + `ell` and lower `dO` both reduce VFAC: K_Ω grows under operator-controlled + membranes (`m` = 0.3) and concentrated lineages, and this feeds P, lock-in + and deskilling. In the generative regime, the same organism growth is + beneficial, and the governance levers `m` and `L` become the top drivers. + This is the model form of hypothesis M3. Caveat: it follows directly from + the assumed moderation of links #39–#41 by `m` and `L`. The model shows that + these assumptions matter most, not that they are true. +- **More experimentation can hurt** in the generative regime (+20 % `v0` → + lower VFAC). This happens through the epistemic brake B3 (link #33, a + contested link). +- **One-at-a-time analysis misses interactions.** Finding 3 (reuse as a + bundle) is an interaction effect. A global method (e.g. Sobol, or Morris + screening) is a GADM-0.3 candidate. + +--- + +## 6. Known limitations + +- No units, no calibration. Comparisons between scenarios are meaningful; + absolute values are not. +- Every structural sign comes from `GADM-CLD.md`. The contested links (#33, + #40, #41, #46, #49) drive several readings. +- Participation claims `cp` are a fixed share of capital return. A real + mechanism would tie them to contributions (attention, judgment, governance), + which is WS-14 work. +- A single population with no cohorts, sectors or regions. +- Attention dynamics are weak: in the attention oligopoly, A falls only to + 0.55. `aext` may be underestimated. +- Euler integration, dt = 0.1. No stiffness issues were observed at these + rates. + +## 7. Reproduce + +```bash +python3 -m unittest discover -s simulations/gadm02 +cd simulations/gadm02 && python3 run_scenarios.py && python3 sensitivity.py +``` diff --git a/models/GADM-0.3-Candidates.md b/models/GADM-0.3-Candidates.md new file mode 100644 index 0000000..ca1a156 --- /dev/null +++ b/models/GADM-0.3-Candidates.md @@ -0,0 +1,125 @@ + + +# GADM-0.3 Candidates — Life-Cycle and Power Extensions + +**Status:** outline. Nothing here is implemented. + +GADM-0.1 § 26 plans for GADM-0.3: age cohorts, household distribution, wealth +separated from power, common versus private ownership, and adoption modeled +separately from attention. GADM-0.2 already added attention headroom A. This +note outlines the two extensions INTENT.md asks for, plus the items GADM-0.2 +deferred. + +--- + +## 1. Life-cycle extension + +### 1.1 Cohorts + +Replace the single population with four cohorts \(c \in\) {children, +working-age, seniors, very old/dependent}. Each cohort carries its own +\(H_c\), \(G_c\) and \(A_c\). Commons, productive capacity, P and E stay +society-wide. + +### 1.2 Flows between cohorts + +| Flow | From → to | Mechanism | +|---|---|---| +| Ageing | c → c+1 | demographic transition rates | +| Capability formation | society → children | education and care. Care is generative infrastructure (concept § 9). | +| Intergenerational transfer \(T_g\) | seniors → working-age, children | mentoring, tacit knowledge, archives (GADM-0.1 § 18) | +| Dependency | very old/dependent ← society | care demand draws on working-age attention \(A\) | +| Organism mediation | K_Ω → transfer | organisms can preserve tacit knowledge (inheritance) or displace mentoring (deskilling) | + +### 1.3 Hypotheses to test + +- **L1.** Strong \(T_g\) lets an ageing society keep H constant (GADM-0.1 § 11 + interpretation). +- **L2.** Care demand competes with other uses of attention headroom. + Without recognized care participation (C_p), A falls fastest in the + working-age cohort. +- **L3.** Children's capability formation responds with a lag of 15–20 + periods. Effects of policy on H appear only beyond the current 100-period + horizon unless the time scale is fixed. + +### 1.4 Prerequisite + +Fix the time unit: one period = one year is the natural choice once cohorts +exist. + +--- + +## 2. Power extension + +### 2.1 Separate wealth from power + +Introduce wealth concentration \(W_c\) as a stock distinct from P: + +\[ +\dot W_c = \text{private return concentration} - \text{taxation/diffusion} +\] + +P then gains a conversion term from wealth into control, with a coefficient +that depends on institutional checks: + +\[ +\dot P \mathrel{+}= \kappa(Q, S)\,W_c(1-P) +\] + +This allows regimes with high wealth inequality but bounded power (strong +checks), and with moderate wealth but extreme power (platform or membrane +control). GADM-0.2's sensitivity results suggest the second kind matters +most for information organisms. + +### 2.2 Channels of P as sub-stocks + +Split P into channel sub-stocks, each with its own drivers and counters: + +| Channel | Drivers | Counters | +|---|---|---| +| Market | network effects, scale | competition, entry | +| Infrastructure | control of essential facilities | interoperability, public provision | +| Discovery / attention | ranking, recommendation | plural discovery, attention protection | +| Membrane | operator control of organism membranes | user/community control (`m`), portability | +| Lineage | ownership of high-use organism lineages | lineage diffusion (`L`), open inheritance | +| Political / institutional | lobbying, capture | transparency, checks, revisability | + +Aggregate \(P = 1-\prod_k (1-P_k)\), so any single dominant channel suffices +for high P. + +### 2.3 Hypotheses to test + +- **P1.** The success → control loop (R3) becomes self-sustaining above a + threshold of channel concentration. Find that threshold per channel. +- **P2.** Membrane and lineage channels interact with R7 (learning with use) + to form a natural-monopoly regime unless `L` exceeds a critical value + (mapping note, open question 1). +- **P3.** Wealth taxation alone does not bound P when the membrane channel + dominates. + +--- + +## 3. Items deferred from GADM-0.2 + +| Item | Source | Note | +|---|---|---| +| Compute C as a stock | `GADM-0.2.md` § 1 | Needed once energy classes are modeled (GADM-0.4). | +| Participation claims tied to contributions | `GADM-0.2.md` § 6 | Requires the WS-14 participation model; currently a fixed share. | +| Global sensitivity analysis | `GADM-0.2.md` § 5 | Morris screening first, then Sobol indices on the top parameters. | +| Stronger attention dynamics | `GADM-0.2.md` § 6 | `aext` appears underestimated; calibrate against time-use evidence. | +| Organism-aware scenario grid | `InformationOrganismsInGADM.md` § 5 | Full 2 × 2 × 2 grid of lineage, membrane and attention regime. GADM-0.2 runs two corners. | + +--- + +## 4. Suggested order + +1. Fix the time unit, and add global sensitivity analysis to the existing + model (cheap, and it improves every later step). +2. Power extension § 2.2 (channel sub-stocks), because GADM-0.2's + sensitivity points there. +3. Wealth separated from power (§ 2.1). +4. Cohorts (§ 1), the largest structural change. diff --git a/models/GADM-CLD.md b/models/GADM-CLD.md new file mode 100644 index 0000000..ed71d57 --- /dev/null +++ b/models/GADM-CLD.md @@ -0,0 +1,158 @@ + + +# GADM Causal Structure — Causal-Loop Diagram and Link Table + +**Status:** v0.1 for GADM-0.1 plus the GADM-0.2 additions, exploratory. + +## 1. Causal-loop diagram + +Solid arrows are GADM-0.1. Dotted arrows are the GADM-0.2 organism +additions. Edge labels give the polarity: `+` means same direction, `−` means +opposite direction. + +```mermaid +flowchart LR + H[H Human capability] -->|+| V + X[X Assembly commons] -->|+| V + F[F Risk capacity] -->|+| V + G[G Effective agency] -->|+| V + N[N Diversity & composability] -->|+| V + Q[Q Epistemic capacity] -->|+| V + P[P Domination] -->|−| V + V((V Experimentation)) -->|+| VS((V_sel Selected)) + SIG((σ Selection quality)) -->|+| VS + VS -->|+ via ρ| X + VS -->|+| K[K Productive capacity] + K -->|+| Y((Y* Output)) + E[E Ecological headroom] -->|+| Y + Y -->|+| I((I Claims)) + I -->|+| D((D Demand)) + S[S Social cohesion] -->|+| D + D -->|+| SIG + I -->|+ existence security| G + F -->|+ restartability| G + P -->|−| G + VS -->|+ private return| P + P -->|+ network effects| P + Q -->|− checks| P + X -->|− interoperability| P + K -->|− throughput| E + G -->|+| S + P -->|−| S + X -->|+| H + S -->|+| H + X -->|+| Q + P -->|−| Q + V -->|− if V > Q| Q + V -->|+ entry| N + P -->|−| N + X -->|+ standards| N + + Ko[K_Ω Organism capability] -.->|+| V + Ko -.->|+ learning with use| Ko + VS -.->|+| Ko + Ko -.->|+ membrane & lineage control| P + Ko -.->|− deskilling| H + Ko -.->|− lock-in| G + Ko -.->|− monoculture| N + Ko -.->|− labor share| I + Ko -.->|− compute draw| E + Ko -.->|− extraction| A[A Attention headroom] + A -.->|+ adoption| SIG + A -.->|+| H + A -.->|+| S + I -.->|+ recovery| A +``` + +## 2. Loops + +| ID | Type | Path | Reading | +|---|---|---|---| +| R1 | reinforcing | X → V → V_sel → X | **Generative reuse.** Commons enable experiments, and selected experiments become commons. Strength depends on ρ. | +| R2 | reinforcing | V_sel → K → Y* → I → D → σ → V_sel | **Demand-backed growth.** Works only while claims stay broad. | +| R3 | reinforcing | V_sel → P (private return) → P (network effects) | **Success becomes control** (GADM-0.1 § 13). Path closure when unchecked. | +| R4 | reinforcing | Y* → I → G → V → V_sel → K → Y* | **Agency-backed growth.** Existence security widens who can attempt. | +| R5 | reinforcing | G → S → D → σ → V_sel → K → Y* → I → G | **Cohesion.** Legitimate participation sustains demand and selection. | +| R6 | reinforcing | X → Q → V → V_sel → X | **Epistemic commons.** Open evidence improves the experiments that feed the commons. | +| R7 (0.2) | reinforcing | K_Ω → K_Ω (learning with use) | **Organism learning.** Organisms improve fastest where they are used most. | +| R8 (0.2) | reinforcing | K_Ω → P → G, S → D → V_sel → K_Ω | **Lineage concentration.** Combined with R7, this is the natural-monopoly loop of the mapping note's open question 1. | +| B1 | balancing | P → V → V_sel → P (private return) | **Domination throttles its own source.** High P shrinks experimentation, which slows the success that feeds P. Weak relative to R3. | +| B2 | balancing | K → E → Y* → I → D → V_sel → K | **Ecological limit.** Throughput erodes headroom and caps output. | +| B3 | balancing | V → Q → V (when V > Q) | **Epistemic brake.** Experimentation that outruns model correction degrades Q, which slows V (GADM-0.1 § 15). | +| B4 | balancing | Q → P → Q (checks) | **Accountability.** Epistemic capacity checks domination, and domination captures information. In the model, a reinforcing pair in disguise: whichever wins compounds. | +| B5 (0.2) | balancing | K_Ω → A → σ → V_sel → K_Ω | **Attention limit.** Extraction depletes the attention that adoption and selection need. | +| B6 (0.2) | balancing | K_Ω → I (labor share) → D → V_sel → K_Ω | **Demand erosion.** Automation without broad claims undermines its own demand. | + +## 3. Link table + +`Conf.` is confidence in the sign: **H** (stated in GADM-0.1 or definitional), +**M** (plausible, inferred from GADM text), **L** (contested). `Src.` is the +source section in GADM-0.1, or the mapping note (`MN`). + +| # | Source | Target | Sign | Mechanism | Conf. | Src. | +|---|---|---|---|---|---|---| +| 1 | H | V | + | capability to attempt | H | § 5 | +| 2 | X | V | + | reusable components | H | § 5 | +| 3 | F | V | + | risk-bearing capacity | H | § 5 | +| 4 | G | V | + | effective freedom to attempt | H | § 5 | +| 5 | N | V | + | recombination potential | H | § 5 | +| 6 | Q | V | + | model quality | H | § 5 | +| 7 | P | V | − | gatekeeping | H | § 5 | +| 8 | V | V_sel | + | definitional | H | § 6 | +| 9 | σ | V_sel | + | definitional | H | § 6 | +| 10 | V_sel | X | + | preservation via ρ | H | § 7 | +| 11 | P | X | − | enclosure L_X | M | § 7 | +| 12 | V_sel | K | + | commercial conversion | H | § 8 | +| 13 | K | Y* | + | definitional | H | § 8 | +| 14 | E | Y* | + | ecological constraint f(E) | H | § 8 | +| 15 | Y* | I | + | claims on output | H | § 9 | +| 16 | I | D | + | purchasing power | H | § 10 | +| 17 | S | D | + | social confidence | H | § 10 | +| 18 | D | σ (market) | + | adoption | M | § 19 | +| 19 | I | G | + | existence security | H | § 12 | +| 20 | F | G | + | restartability | M | § 12 | +| 21 | P | G | − | domination term | H | § 12 | +| 22 | V_sel | P | + | private return → control | H | § 13 | +| 23 | P | P | + | network effects, capture | H | § 13 | +| 24 | Q | P | − | checks (audit, evidence) | M | § 13 | +| 25 | X | P | − | interoperability | M | § 13 | +| 26 | K | E | − | physical throughput | H | § 14 | +| 27 | G | S | + | agency, future opportunity | H | § 17 | +| 28 | P | S | − | domination, lack of voice | H | § 17 | +| 29 | X | H | + | education, transfer T_g | M | §§ 11, 18 | +| 30 | S | H | + | care, participation | M | § 11 | +| 31 | X | Q | + | open evidence, research infrastructure | M | § 15 | +| 32 | P | Q | − | information capture | H | § 15 | +| 33 | V | Q | − (if V > Q) | experimentation outruns correction | L | § 15 | +| 34 | V | N | + | entry | M | § 16 | +| 35 | P | N | − | monoculture, lock-in | H | § 16 | +| 36 | X | N | + | open standards | M | § 16 | +| 37 | K_Ω | V | + | cheap experiments, gated by Q | M | MN § 3.1 | +| 38 | K_Ω | K_Ω | + | learning with use | M | MN § 1 | +| 39 | K_Ω | P | + | membrane control, lineage concentration (moderated by m, L) | M | MN § 3.5 | +| 40 | K_Ω | H | − | deskilling (moderated by m) | L | MN § 2 | +| 41 | K_Ω | G | ± | access (+, with m) vs. lock-in (−, without m) | L | MN § 2 | +| 42 | K_Ω | N | − | monoculture (moderated by L) | M | MN § 2 | +| 43 | K_Ω | I (labor share) | − | automation | H | MN § 3.4 | +| 44 | K_Ω | E | − | compute draw | M | MN § 4 | +| 45 | K_Ω | A | − | extraction (moderated by x_att) | M | MN § 4 | +| 46 | K_Ω | Q | ± | audit/research (+, with expl) vs. opacity (−) | L | MN § 2 | +| 47 | A | σ (market) | + | adoption needs attention | M | MN § 4 | +| 48 | A | H, S | + | self-directed development, participation | M | MN § 4 | +| 49 | I | A | + | economic security frees attention | L | new | + +### Contested links (Conf. L) + +- **#33.** GADM-0.1 states the damage hypothesis qualitatively. GADM-0.2 + implements it as a degradation of Q proportional to `max(0, V − Q)`. The + threshold form is a modeling choice. +- **#40, #41, #46.** The sign depends on institutional levers. GADM-0.2 makes + this explicit: membrane control `m` and explainability `expl` switch the + dominant sign. +- **#49.** Not in either source. Introduced so that precarity reduces + attention headroom. Needs empirical grounding (WS-14). diff --git a/models/README.md b/models/README.md index 775232f..a3b3925 100644 --- a/models/README.md +++ b/models/README.md @@ -9,6 +9,9 @@ exploratory: they generate hypotheses and are not established results. |----------|--------|--------| | [GenerativeAssemblyDynamicsModel.md](GenerativeAssemblyDynamicsModel.md) | GADM-0.1 (promoted verbatim) | `seed/GenerativeAssemblyDynamicsModel.md` | | [InformationOrganismsInGADM.md](InformationOrganismsInGADM.md) | mapping note v0.1 | ASSEMBLY-WP-0003-T03; input for GADM-0.2 (ASSEMBLY-WP-0004) | +| [GADM-CLD.md](GADM-CLD.md) | v0.1 | causal-loop diagram, loops, signed link table (ASSEMBLY-WP-0004-T01/T02) | +| [GADM-0.2.md](GADM-0.2.md) | GADM-0.2 | extensions decision, equations, scenario and sensitivity findings (ASSEMBLY-WP-0004) | +| [GADM-0.3-Candidates.md](GADM-0.3-Candidates.md) | outline | life-cycle and power extensions, deferred items (ASSEMBLY-WP-0004-T08) | ## Conventions diff --git a/references/LiteratureComparison.md b/references/LiteratureComparison.md new file mode 100644 index 0000000..7109868 --- /dev/null +++ b/references/LiteratureComparison.md @@ -0,0 +1,102 @@ + + +# Literature Comparison v0.1 + +**Status:** first orientation map. **Bibliographic details are unverified.** +Check each entry against the original before citing it. + +Purpose: place the Generative Assembly Society framework, GADM and the +information-organism lens relative to existing work, so that the project +(a) does not reinvent existing results, (b) borrows established methods, and +(c) states clearly what is new. + +Each entry gives the overlap, the difference from this project, and what to +borrow. + +--- + +## 1. Assembly and complexity + +| Work | Overlap | Difference | Borrow | +|---|---|---|---| +| **Assembly Theory** (L. Cronin, S. I. Walker and colleagues; e.g. Sharma et al., *Nature* 2023) | Objects as assemblies built from reusable components; selection as the source of complex objects | A physical and chemical theory with a measurable assembly index. INTENT.md explicitly does not require accepting it. | The idea of quantifying "assembly depth" as a reuse measure for X | +| **The adjacent possible** (S. Kauffman, *Investigations*, 2000) | Future combinations reachable from current components ≈ viable future assembly space | Biological and theoretical, not institutional | Vocabulary for reachable futures; combinatorial growth arguments | +| **Technology as combination** (W. B. Arthur, *The Nature of Technology*, 2009) | Technologies built from existing technologies; recursive composition | Descriptive history of technology, no institutional model | Evidence for R1 (generative reuse) | +| **Economic complexity** (C. Hidalgo & R. Hausmann, e.g. *PNAS* 2009; *The Atlas of Economic Complexity*) | Capabilities as recombinable building blocks; diversity × ubiquity | Measures national capability structures from trade data | Empirical proxies for N and X | + +## 2. Agency, capability and freedom + +| Work | Overlap | Difference | Borrow | +|---|---|---|---| +| **Capability approach** (A. Sen, *Development as Freedom*, 1999; M. Nussbaum) | Effective agency as reachable functionings, not formal liberty | Normative development framework, no dynamic model | Operationalization of G; lists of central capabilities | +| **Real freedom / basic income** (P. Van Parijs, *Real Freedom for All*, 1995) | Existence principle; participation decoupled from labor | Focused on income; does not model domination or commons | Arguments for D_s and the Existence principle | +| **Republican non-domination** (P. Pettit, *Republicanism*, 1997) | Domination as capacity for arbitrary interference, separate from wealth | Political philosophy | Conceptual grounding for modeling P separately from wealth | + +## 3. Commons, institutions and power + +| Work | Overlap | Difference | Borrow | +|---|---|---|---| +| **Governing the commons** (E. Ostrom, 1990) | Commons as institutions; polycentric governance | Natural-resource commons. Assembly commons are mostly non-rival. | Design principles for commons governance (WS-06, WS-11) | +| **Commons-based peer production** (Y. Benkler, *The Wealth of Networks*, 2006) | Non-rival knowledge commons; open source | Network economy, pre-AI | Evidence for ρ and I_X mechanisms | +| **Inclusive vs. extractive institutions** (D. Acemoglu & J. Robinson, *Why Nations Fail*, 2012) | Institutional configurations determine whether success broadens or closes access | Historical, macro-institutional | Framing for the R3 / B1 loop (success becomes control) | +| **Capital concentration** (T. Piketty, *Capital in the Twenty-First Century*, 2014) | Concentration of returns when r > g | Wealth-focused | Data and dynamics for W_c in the GADM-0.3 power extension | +| **Entrepreneurial state** (M. Mazzucato, 2013) | Public risk-bearing in innovation | Focused on the state | Evidence for public risk pools (F) | + +## 4. Automation and distribution + +| Work | Overlap | Difference | Borrow | +|---|---|---|---| +| **Task-based automation** (D. Acemoglu & P. Restrepo, e.g. "Automation and New Tasks", *Journal of Economic Perspectives*, 2019) | Automation displaces tasks, new tasks reinstate labor | Labor-market focus, assumes wage-based participation | Labor share dynamics for w_L | +| **AI and income distribution** (A. Korinek & J. Stiglitz, NBER working paper, 2017) | Redistribution needs when automation concentrates returns | Standard welfare economics | Distribution regimes for claims I | + +## 5. Systems, cybernetics and viability + +| Work | Overlap | Difference | Borrow | +|---|---|---|---| +| **System dynamics** (J. Forrester, *Industrial Dynamics*, 1961; D. Meadows et al., *The Limits to Growth*, 1972) | Stock-and-flow modeling, feedback loops | GADM is a direct descendant in method | Method: causal-loop diagrams, stock-flow conventions, validation practice | +| **Requisite variety** (W. R. Ashby, *An Introduction to Cybernetics*, 1956) | A regulator needs as much variety as what it regulates; the membrane as variety filter | Abstract cybernetics | Theoretical basis for membrane design | +| **Viable System Model** (S. Beer, *Brain of the Firm*, 1972) | Viability, recursion, regulation of organizations | Organizational cybernetics, not learning systems | Recursion and viability vocabulary for the AIPN and the organism lens | +| **Resilience and panarchy** (C. S. Holling; L. Gunderson & C. S. Holling, *Panarchy*, 2002) | Adaptive cycles, release and reorganization ≈ disassemble/reassemble | Ecological systems | Adaptive-cycle framing for transition dynamics (WS-12) | +| **Planetary boundaries** (J. Rockström et al., *Nature*, 2009) | Non-tradeable ecological floors | Earth-system science | Grounding for the E floor | +| **Doughnut economics** (K. Raworth, 2017) | Social floor plus ecological ceiling as a viability corridor | No dynamic model of generativity | Corridor framing for constitutional constraints | + +## 6. Attention + +| Work | Overlap | Difference | Borrow | +|---|---|---|---| +| **H. A. Simon**, "Designing Organizations for an Information-Rich World" (1971) | "A wealth of information creates a poverty of attention" | Organizational design | Founding statement of attention as the scarce resource | +| **Attention economy** (T. Davenport & J. Beck, *The Attention Economy*, 2001) | Attention as an economic resource | Managerial, treats attention as something to capture | Contrast case for x_att | +| **Surveillance capitalism** (S. Zuboff, 2019) | Extraction-funded attention, discovery concentration | Critical, not modeled | Scenario grounding for the attention-oligopoly regime | +| **Goodhart's law / Campbell's law** | Metric gaming | — | Basis for the metrics catalog's Goodhart risks | + +--- + +## 7. What appears new in this project + +To be confirmed by an actual literature search: + +1. **Generativity as the objective.** Using *viable future assembly space* + (rather than welfare, output or capability sets alone) as the target of + institutional design. +2. **Information organisms as a distinct capital class**, with learning + with use, lineage inheritance, membranes and metabolism, coupled into a + macro model. Related work treats AI as automation capital or as a labor + substitute. +3. **Membrane and lineage control as domination channels**, separate from + wealth. +4. **Attention headroom as a stock inside a macro diagnostic**, with an + explicit guard against counting captured attention. + +## 8. Next steps + +- Run a real literature search (WS-01, WS-07, WS-14 priorities) and verify + the bibliographic details above. +- Look specifically for prior macro models that treat AI systems as + learning, inheritable capital; any such work would affect claim 2. +- Review system-dynamics validation practice (e.g. structure-verification + and extreme-condition tests) and apply it to GADM-0.2. diff --git a/scenarios/README.md b/scenarios/README.md new file mode 100644 index 0000000..341f03b --- /dev/null +++ b/scenarios/README.md @@ -0,0 +1,13 @@ +# Scenarios + +Scenario definitions for GADM simulations, one JSON file per scenario. + +| Field | Meaning | +|-------|---------| +| `name` | identifier; must match the file name | +| `title`, `perturbation`, `question` | from the GADM-0.1 § 23 scenario table, or the organism axes in `models/InformationOrganismsInGADM.md` § 5 | +| `model` | model version the parameters belong to | +| `extends` | optional; inherit `params` and `init` from another scenario (one level) | +| `params`, `init` | overrides of `PARAMS` / `INIT` in `simulations/gadm02/gadm02.py` | + +Unknown parameter names are rejected by the simulation. diff --git a/scenarios/attention_oligopoly.json b/scenarios/attention_oligopoly.json new file mode 100644 index 0000000..aa3332f --- /dev/null +++ b/scenarios/attention_oligopoly.json @@ -0,0 +1,14 @@ +{ + "name": "attention_oligopoly", + "title": "Attention oligopoly", + "model": "GADM-0.2", + "perturbation": "discovery concentrated, P up", + "question": "Can productive abundance coexist with declining effective assembly access?", + "params": { + "disc": 0.9, + "x_att": 0.9, + "m": 0.1, + "p2": 0.1 + }, + "init": {} +} diff --git a/scenarios/automated_capitalism.json b/scenarios/automated_capitalism.json new file mode 100644 index 0000000..46c4d22 --- /dev/null +++ b/scenarios/automated_capitalism.json @@ -0,0 +1,20 @@ +{ + "name": "automated_capitalism", + "title": "Automated capitalism", + "model": "GADM-0.2", + "perturbation": "K up, labor share down, ownership concentrated", + "question": "Can abundance coexist with collapsing agency and demand?", + "params": { + "auto": 0.95, + "oseed": 0.04, + "aO": 0.25, + "dshare": 0.0, + "cp": 0.0, + "od": 0.05, + "m": 0.1, + "L": 0.1, + "lin_open": 0.1, + "disc": 0.6 + }, + "init": {} +} diff --git a/scenarios/baseline.json b/scenarios/baseline.json new file mode 100644 index 0000000..6a61d8c --- /dev/null +++ b/scenarios/baseline.json @@ -0,0 +1,9 @@ +{ + "name": "baseline", + "title": "Baseline (current trends)", + "model": "GADM-0.2", + "perturbation": "none", + "question": "Reference trajectory for all comparisons.", + "params": {}, + "init": {} +} diff --git a/scenarios/commons_rich.json b/scenarios/commons_rich.json new file mode 100644 index 0000000..2d519b8 --- /dev/null +++ b/scenarios/commons_rich.json @@ -0,0 +1,15 @@ +{ + "name": "commons_rich", + "title": "Commons-rich economy", + "model": "GADM-0.2", + "perturbation": "high rho, high X", + "question": "How powerful is reuse compared with raw investment?", + "params": { + "rho0": 0.6, + "IX": 0.06, + "lin_open": 0.8, + "std": 0.8, + "lX": 0.03 + }, + "init": {} +} diff --git a/scenarios/dividend_abundance.json b/scenarios/dividend_abundance.json new file mode 100644 index 0000000..c1e896b --- /dev/null +++ b/scenarios/dividend_abundance.json @@ -0,0 +1,15 @@ +{ + "name": "dividend_abundance", + "title": "Dividend abundance", + "model": "GADM-0.2", + "perturbation": "automation plus broadly distributed claims", + "question": "Does demand stabilize without suppressing useful merit feedback?", + "params": { + "auto": 0.95, + "oseed": 0.04, + "aO": 0.25, + "dshare": 0.5, + "od": 0.2 + }, + "init": {} +} diff --git a/scenarios/generative_attention_extraction.json b/scenarios/generative_attention_extraction.json new file mode 100644 index 0000000..4a78e41 --- /dev/null +++ b/scenarios/generative_attention_extraction.json @@ -0,0 +1,13 @@ +{ + "name": "generative_attention_extraction", + "title": "Generative regime, attention extraction", + "model": "GADM-0.2", + "extends": "generative_regime", + "perturbation": "organism axis: extraction-funded attention regime", + "question": "Does the generative regime survive an extraction-funded attention economy?", + "params": { + "x_att": 0.9, + "disc": 0.7 + }, + "init": {} +} diff --git a/scenarios/generative_concentrated_lineages.json b/scenarios/generative_concentrated_lineages.json new file mode 100644 index 0000000..67769df --- /dev/null +++ b/scenarios/generative_concentrated_lineages.json @@ -0,0 +1,14 @@ +{ + "name": "generative_concentrated_lineages", + "title": "Generative regime, concentrated lineages", + "model": "GADM-0.2", + "extends": "generative_regime", + "perturbation": "organism axis: lineage ownership concentrated, membranes operator-controlled", + "question": "Does the generative regime survive concentrated control of information organisms?", + "params": { + "L": 0.1, + "lin_open": 0.1, + "m": 0.2 + }, + "init": {} +} diff --git a/scenarios/generative_regime.json b/scenarios/generative_regime.json new file mode 100644 index 0000000..7e088b6 --- /dev/null +++ b/scenarios/generative_regime.json @@ -0,0 +1,26 @@ +{ + "name": "generative_regime", + "title": "Generative regime", + "model": "GADM-0.2", + "perturbation": "high X, G, Q, N; distributed claims; bounded P", + "question": "Does the hypothesized generative feedback loop remain stable?", + "params": { + "auto": 0.95, + "oseed": 0.04, + "aO": 0.25, + "dshare": 0.35, + "cp": 0.15, + "od": 0.3, + "m": 0.8, + "L": 0.8, + "lin_open": 0.8, + "rho0": 0.5, + "IX": 0.05, + "std": 0.8, + "expl": 0.8, + "x_att": 0.2, + "f0": 0.06, + "disc": 0.2 + }, + "init": {} +} diff --git a/scenarios/universal_venture_access.json b/scenarios/universal_venture_access.json new file mode 100644 index 0000000..376d55a --- /dev/null +++ b/scenarios/universal_venture_access.json @@ -0,0 +1,14 @@ +{ + "name": "universal_venture_access", + "title": "Universal venture access", + "model": "GADM-0.2", + "perturbation": "F up, restartability up", + "question": "Does experimentation rise sustainably or mainly increase waste and externalities?", + "params": { + "f0": 0.08, + "fY": 0.15, + "g3": 0.1, + "dF": 0.04 + }, + "init": {} +} diff --git a/simulations/README.md b/simulations/README.md new file mode 100644 index 0000000..8258b47 --- /dev/null +++ b/simulations/README.md @@ -0,0 +1,19 @@ +# Simulations + +Runnable implementations of the models in `models/`. Each simulation names the +model version it implements. + +| Directory | Model | Entry points | +|-----------|-------|--------------| +| `gadm02/` | GADM-0.2 (`models/GADM-0.2.md`) | `run_scenarios.py`, `sensitivity.py`, `test_gadm02.py` | + +Python 3.10+, standard library only. + +```bash +python3 -m unittest discover -s simulations/gadm02 # tests +cd simulations/gadm02 && python3 run_scenarios.py # → results/summary.md + CSVs +cd simulations/gadm02 && python3 sensitivity.py # → results/sensitivity.md +``` + +Results under `results/` are committed so that model documents can cite them. +Regenerate them after any change to the model or the scenarios. diff --git a/simulations/gadm02/gadm02.py b/simulations/gadm02/gadm02.py new file mode 100644 index 0000000..488d375 --- /dev/null +++ b/simulations/gadm02/gadm02.py @@ -0,0 +1,175 @@ +"""GADM-0.2 toy simulation (normalized stocks, Euler integration). + +Implements models/GADM-0.2.md. Every stock lives in [0, 1]. Inflows are scaled +by headroom (1 - x) and outflows by level (x), so stocks stay bounded without +clipping. This is a hypothesis generator, not a forecast (GADM-0.1 § 27). + +Standard library only, deterministic. +""" + +from __future__ import annotations + +import math + +MODEL_VERSION = "GADM-0.2" + +STOCKS = ("H", "X", "Kp", "Ko", "F", "G", "N", "Q", "P", "E", "S", "A") + +INIT = { + "H": 0.50, # human capability + "X": 0.40, # assembly commons + "Kp": 0.50, # physical / conventional productive capacity + "Ko": 0.05, # information-organism capability (K_Omega) + "F": 0.40, # risk capacity + "G": 0.50, # effective agency + "N": 0.50, # diversity & composability + "Q": 0.50, # epistemic capacity + "P": 0.40, # domination concentration + "E": 0.50, # ecological headroom + "S": 0.50, # social cohesion + "A": 0.50, # attention headroom (unclaimed, self-directed attention) +} + +PARAMS = { + # experimentation V (GADM-0.1 § 5) + organism term gated by Q (M2) + "v0": 2.5, "eH": 0.5, "eX": 0.5, "eF": 0.3, "eG": 0.4, "eN": 0.3, + "eQ": 0.3, "eP": 0.5, "vo": 0.4, + # selection channel weights (§ 6) + organism channel O + "wm": 0.35, "ws": 0.20, "wc": 0.15, "wp": 0.15, "wo": 0.30, + # claims on output (§ 9) + participation claims C_p + "wL0": 0.60, "auto": 0.6, "dshare": 0.10, "cp": 0.0, "od": 0.20, + # commons (§ 7) + "aX": 0.30, "rho0": 0.30, "rhoO": 0.40, "IX": 0.02, "dX": 0.04, + "lX": 0.06, "lXo": 0.06, + # productive capacity (§ 8) and organism capability (mapping note § 1) + "aK": 0.35, "dK": 0.05, "aO": 0.15, "ell": 0.25, "oseed": 0.01, "dO": 0.06, + # human capability (§ 11) + "h0": 0.08, "hA": 0.04, "hT": 0.03, "dH": 0.05, "desk": 0.06, + # risk capacity + "f0": 0.03, "fY": 0.08, "dF": 0.06, + # effective agency (§ 12) + "g1": 0.08, "g2": 0.05, "g3": 0.04, "g4": 0.04, "dG": 0.04, + "b1": 0.08, "b2": 0.06, + # diversity & composability (§ 16) + "n1": 0.30, "n2": 0.05, "dN": 0.04, "n3": 0.06, "n4": 0.08, + # epistemic capacity (§ 15) + "q0": 0.04, "q1": 0.05, "qo": 0.06, "dQ": 0.03, "q2": 0.06, "qx": 0.08, + "q3": 0.10, + # domination (§ 13) + membrane control and lineage concentration + "p1": 0.10, "p2": 0.06, "p3": 0.06, "p4": 0.06, + "p5": 0.10, "p6": 0.05, "p7": 0.06, + # ecology (§ 14) + "ereg": 0.05, "e1": 0.06, "e2": 0.03, "thr": 1.0, + # cohesion (§ 17), relaxation rate + "srate": 0.20, + # attention headroom (mapping note § 4) + "arec": 0.10, "aext": 0.12, + # institutional / organism-regime levers (scenario axes) + "m": 0.30, # membrane control held by users/communities (0..1) + "L": 0.30, # lineage diffusion (0 concentrated .. 1 diffuse) + "lin_open": 0.30, # openly inheritable lineages (commons vs enclosure) + "std": 0.50, # open standards / interoperability + "disc": 0.40, # discovery concentration + "expl": 0.40, # explainability / provenance enforcement + "x_att": 0.50, # attention-extraction regime (0 protected .. 1 extraction) +} + +FLOORS = {"G": (">=", 0.20), "E": (">=", 0.20), "P": ("<=", 0.70), "A": (">=", 0.20)} + + +def auxiliaries(s: dict, p: dict) -> dict: + """Flows and auxiliaries for state s under parameters p.""" + H, X, Kp, Ko, F, G, N, Q, P, E, S, A = (s[k] for k in STOCKS) + K = 1 - (1 - Kp) * (1 - Ko) + ystar = K * math.sqrt(E) + + wl = p["wL0"] * (1 - p["auto"] * Ko) + cap = 1 - wl + priv = cap * max(0.0, 1 - p["dshare"] - p["cp"]) + B = min(1.0, wl + cap * (p["dshare"] + p["cp"]) + priv * p["od"]) + dem = (0.4 + 0.6 * B) * (0.6 + 0.4 * S) + + v_h = p["v0"] * (H ** p["eH"]) * (X ** p["eX"]) * (F ** p["eF"]) * (G ** p["eG"]) \ + * (N ** p["eN"]) * (Q ** p["eQ"]) * ((1 - P) ** p["eP"]) + V = min(1.0, v_h + p["vo"] * Ko * Q) + + M = math.sqrt(A * dem) + O = Q * (1 - p["disc"]) + w = [p["wm"], p["ws"], p["wc"], p["wp"], p["wo"] * Ko] + q = [M, Q, N * (1 - P), S, O] + sigma = sum(a * b for a, b in zip(w, q)) / sum(w) + vsel = V * sigma + rho = min(1.0, p["rho0"] + p["rhoO"] * Ko * p["lin_open"]) + return {"K": K, "Ystar": ystar, "B": B, "dem": dem, "V": V, "sigma": sigma, + "Vsel": vsel, "rho": rho, "wl": wl} + + +def derivatives(s: dict, p: dict) -> tuple[dict, dict]: + H, X, Kp, Ko, F, G, N, Q, P, E, S, A = (s[k] for k in STOCKS) + a = auxiliaries(s, p) + B, dem, V, vsel, rho = a["B"], a["dem"], a["V"], a["Vsel"], a["rho"] + m, L, lo, std = p["m"], p["L"], p["lin_open"], p["std"] + + d = {} + d["X"] = (p["aX"] * rho * vsel + p["IX"]) * (1 - X) \ + - (p["dX"] + p["lX"] * P + p["lXo"] * Ko * (1 - lo)) * X + d["Kp"] = p["aK"] * vsel * dem * (1 - Kp) - p["dK"] * Kp + d["Ko"] = (p["aO"] * vsel * dem + p["ell"] * Ko * dem * Q + p["oseed"]) * (1 - Ko) \ + - p["dO"] * Ko + d["H"] = (p["h0"] * (0.5 * S + 0.5 * B) + p["hA"] * A + p["hT"] * X) * (1 - H) \ + - (p["dH"] + p["desk"] * Ko * (1 - m)) * H + d["F"] = (p["f0"] + p["fY"] * a["Ystar"] * B) * (1 - F) - p["dF"] * F + d["G"] = (p["g1"] * B + p["g2"] * X * (1 - P) + p["g3"] * F + p["g4"] * Ko * m) * (1 - G) \ + - (p["dG"] + p["b1"] * P + p["b2"] * Ko * (1 - m)) * G + d["N"] = (p["n1"] * V * (1 - P) + p["n2"] * X * std) * (1 - N) \ + - (p["dN"] + p["n3"] * P + p["n4"] * Ko * (1 - L)) * N + d["Q"] = (p["q0"] + p["q1"] * X + p["qo"] * Ko * p["expl"]) * (1 - Q) \ + - (p["dQ"] + p["q2"] * P + p["qx"] * Ko * (1 - p["expl"]) + p["q3"] * max(0.0, V - Q)) * Q + d["P"] = (p["p1"] * vsel * (1 - B) + p["p2"] * p["disc"] + p["p3"] * Ko * (1 - m) + + p["p4"] * Ko * (1 - L)) * (1 - P) \ + - (p["p5"] * Q * S + p["p6"] * X * std + p["p7"] * B) * P + d["E"] = p["ereg"] * (1 - E) - (p["e1"] * Kp * dem * p["thr"] + p["e2"] * Ko) * E + s_target = (G ** 0.4) * ((1 - P) ** 0.3) * (B ** 0.2) * (A ** 0.1) + d["S"] = p["srate"] * (s_target - S) + d["A"] = p["arec"] * (0.4 + 0.6 * B) * (1 - A) - p["aext"] * p["x_att"] * (0.2 + Ko) * A + return d, a + + +def vfac(s: dict, with_attention: bool = True) -> float: + """Geometric-mean VFAC (equal exponents). Diagnostic, not a welfare function.""" + f = [s["H"], s["X"], s["G"], s["N"], s["Q"], s["S"], s["E"], 1 - s["P"]] + if with_attention: + f.append(s["A"]) + if min(f) <= 0.0: + return 0.0 # a collapsed foundational subsystem collapses the diagnostic + return math.exp(sum(math.log(x) for x in f) / len(f)) + + +def simulate(params: dict | None = None, init: dict | None = None, + T: float = 100.0, dt: float = 0.1, record_every: int = 10) -> list[dict]: + p = {**PARAMS, **(params or {})} + s = {**INIT, **(init or {})} + unknown = set(p) - set(PARAMS) + if unknown: + raise KeyError(f"unknown parameters: {sorted(unknown)}") + rows = [] + steps = int(round(T / dt)) + for i in range(steps + 1): + d, a = derivatives(s, p) + if i % record_every == 0: + row = {"t": round(i * dt, 6), **s, **{k: a[k] for k in ("Ystar", "B", "V", "Vsel", "sigma")}} + row["VFAC"] = vfac(s) + row["VFAC01"] = vfac(s, with_attention=False) + rows.append(row) + if i < steps: + s = {k: min(1.0, max(0.0, s[k] + dt * d[k])) for k in STOCKS} + return rows + + +def floor_violations(rows: list[dict]) -> dict: + """Share of recorded periods in which each constitutional floor is violated.""" + out = {} + for k, (op, lim) in FLOORS.items(): + bad = [r for r in rows if (r[k] < lim if op == ">=" else r[k] > lim)] + out[k] = len(bad) / len(rows) + return out diff --git a/simulations/gadm02/results/attention_oligopoly.csv b/simulations/gadm02/results/attention_oligopoly.csv new file mode 100644 index 0000000..777a3d4 --- /dev/null +++ b/simulations/gadm02/results/attention_oligopoly.csv @@ -0,0 +1,102 @@ +t,H,X,Kp,Ko,F,G,N,Q,P,E,S,A,Ystar,B,V,Vsel,sigma,VFAC,VFAC01 +0.0,0.5,0.4,0.5,0.05,0.4,0.5,0.5,0.5,0.4,0.5,0.5,0.5,0.37123,0.69904,0.33894,0.16502,0.48685,0.49774,0.49746 +1.0,0.51277,0.3945,0.49428,0.07575,0.40624,0.50341,0.50075,0.50171,0.42751,0.51384,0.51146,0.52545,0.38178,0.69236,0.34122,0.16683,0.48893,0.50194,0.49908 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+99.0,0.64561,0.72252,0.73856,0.78138,0.61688,0.63923,0.76044,0.5248,0.30722,0.47514,0.68082,0.77738,0.6499,0.70412,0.94437,0.5625,0.59563,0.65007,0.63569 +100.0,0.64561,0.72252,0.73856,0.78138,0.61688,0.63923,0.76044,0.5248,0.30722,0.47514,0.68082,0.77738,0.6499,0.70412,0.94437,0.5625,0.59563,0.65007,0.63569 diff --git a/simulations/gadm02/results/sensitivity.md b/simulations/gadm02/results/sensitivity.md new file mode 100644 index 0000000..46c78dc --- /dev/null +++ b/simulations/gadm02/results/sensitivity.md @@ -0,0 +1,50 @@ +# GADM-0.2 sensitivity (one-at-a-time, ±20%) + +Generated by `simulations/gadm02/sensitivity.py`. ΔVFAC is the change in +final VFAC (t = 100) when one parameter moves down or up by 20 %, all others +fixed. Levers in [0, 1] are capped at 1. Zero-valued parameters are tested at +0.05. One-at-a-time analysis misses interactions. + +## Baseline (current trends) (VFAC = 0.461) + +| Rank | Parameter | ΔVFAC at −20 % | ΔVFAC at +20 % | +|---|---|---|---| +| 1 | `dO` | -0.0202 | +0.0172 | +| 2 | `ell` | +0.0112 | -0.0104 | +| 3 | `p7` | -0.0109 | +0.0092 | +| 4 | `wL0` | -0.0093 | +0.0083 | +| 5 | `IX` | -0.0082 | +0.0068 | +| 6 | `n1` | -0.0073 | +0.0058 | +| 7 | `p2` | +0.0064 | -0.0061 | +| 8 | `disc` | +0.0063 | -0.0061 | +| 9 | `g1` | -0.0063 | +0.0053 | +| 10 | `p5` | -0.0063 | +0.0059 | +| 11 | `dX` | +0.0058 | -0.0053 | +| 12 | `arec` | -0.0054 | +0.0040 | +| 13 | `aX` | -0.0053 | +0.0051 | +| 14 | `p3` | +0.0053 | -0.0050 | +| 15 | `p4` | +0.0053 | -0.0050 | + +Reuse coefficient `rho0` ranks 20 of 80. + +## Generative regime (VFAC = 0.650) + +| Rank | Parameter | ΔVFAC at −20 % | ΔVFAC at +20 % | +|---|---|---|---| +| 1 | `m` | -0.0151 | +0.0159 | +| 2 | `L` | -0.0100 | +0.0102 | +| 3 | `ereg` | -0.0088 | +0.0066 | +| 4 | `expl` | -0.0067 | +0.0065 | +| 5 | `v0` | +0.0064 | -0.0025 | +| 6 | `dshare` | -0.0050 | +0.0050 | +| 7 | `e1` | +0.0045 | -0.0042 | +| 8 | `thr` | +0.0045 | -0.0042 | +| 9 | `arec` | -0.0045 | +0.0032 | +| 10 | `aX` | -0.0043 | +0.0034 | +| 11 | `lin_open` | -0.0043 | +0.0041 | +| 12 | `p7` | -0.0043 | +0.0038 | +| 13 | `aext` | +0.0038 | -0.0036 | +| 14 | `x_att` | +0.0038 | -0.0036 | +| 15 | `auto` | +0.0038 | -0.0010 | + +Reuse coefficient `rho0` ranks 31 of 80. diff --git a/simulations/gadm02/results/summary.md b/simulations/gadm02/results/summary.md new file mode 100644 index 0000000..be7c0e7 --- /dev/null +++ b/simulations/gadm02/results/summary.md @@ -0,0 +1,18 @@ +# GADM-0.2 scenario results + +Generated by `simulations/gadm02/run_scenarios.py`. Values at t = 100 unless noted. +Stocks are normalized to [0, 1]. VFAC is the geometric mean with attention (A); +VFAC01 omits A, as in GADM-0.1. Floor violations are the share of recorded +periods outside G ≥ 0.2, E ≥ 0.2, P ≤ 0.7 and A ≥ 0.2. + +| Scenario | VFAC t25 | VFAC | VFAC01 | H | X | Kp | Ko | F | G | N | Q | P | E | S | A | Ystar | B | min G | max P | floor violations | +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +| Baseline (current trends) | 0.504 | 0.461 | 0.442 | 0.53 | 0.27 | 0.36 | 0.47 | 0.48 | 0.44 | 0.34 | 0.44 | 0.52 | 0.65 | 0.50 | 0.65 | 0.53 | 0.59 | 0.44 | 0.52 | none | +| Automated capitalism | 0.370 | 0.317 | 0.294 | 0.41 | 0.18 | 0.13 | 0.53 | 0.42 | 0.28 | 0.12 | 0.38 | 0.73 | 0.72 | 0.31 | 0.58 | 0.50 | 0.33 | 0.28 | 0.73 | P 69% | +| Dividend abundance | 0.473 | 0.445 | 0.427 | 0.51 | 0.26 | 0.38 | 0.64 | 0.51 | 0.45 | 0.31 | 0.42 | 0.54 | 0.59 | 0.51 | 0.62 | 0.60 | 0.69 | 0.45 | 0.54 | none | +| Universal venture access | 0.524 | 0.487 | 0.471 | 0.53 | 0.30 | 0.43 | 0.50 | 0.76 | 0.57 | 0.38 | 0.44 | 0.52 | 0.62 | 0.55 | 0.64 | 0.56 | 0.58 | 0.50 | 0.53 | none | +| Commons-rich economy | 0.568 | 0.544 | 0.535 | 0.55 | 0.66 | 0.51 | 0.55 | 0.49 | 0.47 | 0.52 | 0.49 | 0.48 | 0.59 | 0.51 | 0.62 | 0.60 | 0.57 | 0.47 | 0.48 | none | +| Attention oligopoly | 0.415 | 0.372 | 0.355 | 0.49 | 0.21 | 0.21 | 0.39 | 0.46 | 0.38 | 0.17 | 0.40 | 0.69 | 0.72 | 0.41 | 0.55 | 0.44 | 0.61 | 0.38 | 0.69 | none | +| Generative regime | 0.654 | 0.650 | 0.636 | 0.65 | 0.72 | 0.74 | 0.78 | 0.62 | 0.64 | 0.76 | 0.52 | 0.31 | 0.48 | 0.68 | 0.78 | 0.65 | 0.70 | 0.50 | 0.40 | none | +| Generative regime, concentrated lineages | 0.543 | 0.530 | 0.504 | 0.53 | 0.47 | 0.58 | 0.73 | 0.62 | 0.46 | 0.45 | 0.58 | 0.50 | 0.52 | 0.54 | 0.79 | 0.64 | 0.71 | 0.46 | 0.50 | none | +| Generative regime, attention extraction | 0.582 | 0.575 | 0.594 | 0.60 | 0.64 | 0.64 | 0.74 | 0.62 | 0.60 | 0.68 | 0.54 | 0.41 | 0.51 | 0.60 | 0.45 | 0.64 | 0.71 | 0.50 | 0.41 | none | diff --git a/simulations/gadm02/results/universal_venture_access.csv 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b/simulations/gadm02/run_scenarios.py new file mode 100644 index 0000000..9cbc5e4 --- /dev/null +++ b/simulations/gadm02/run_scenarios.py @@ -0,0 +1,51 @@ +"""Run all scenarios and write CSV trajectories plus a Markdown summary. + +Usage: python3 simulations/gadm02/run_scenarios.py +""" + +from __future__ import annotations + +import csv +from pathlib import Path + +import gadm02 as g +from scenarios import load_all + +OUT = Path(__file__).resolve().parent / "results" +COLS = ["H", "X", "Kp", "Ko", "F", "G", "N", "Q", "P", "E", "S", "A", "Ystar", "B"] + + +def main() -> None: + OUT.mkdir(exist_ok=True) + lines = [ + f"# {g.MODEL_VERSION} scenario results", + "", + "Generated by `simulations/gadm02/run_scenarios.py`. Values at t = 100 unless noted.", + "Stocks are normalized to [0, 1]. VFAC is the geometric mean with attention (A);", + "VFAC01 omits A, as in GADM-0.1. Floor violations are the share of recorded", + "periods outside G ≥ 0.2, E ≥ 0.2, P ≤ 0.7 and A ≥ 0.2.", + "", + "| Scenario | VFAC t25 | VFAC | VFAC01 | " + " | ".join(COLS) + " | min G | max P | floor violations |", + "|---|" + "---|" * (len(COLS) + 6), + ] + for name, sc in load_all().items(): + rows = g.simulate(sc["params"], sc["init"]) + with open(OUT / f"{name}.csv", "w", newline="") as fh: + w = csv.DictWriter(fh, fieldnames=list(rows[0].keys())) + w.writeheader() + for r in rows: + w.writerow({k: (round(v, 5) if isinstance(v, float) else v) for k, v in r.items()}) + last, t25 = rows[-1], next(r for r in rows if r["t"] >= 25) + viol = g.floor_violations(rows) + vtxt = ", ".join(f"{k} {v:.0%}" for k, v in viol.items() if v > 0) or "none" + lines.append( + f"| {sc['title']} | {t25['VFAC']:.3f} | {last['VFAC']:.3f} | {last['VFAC01']:.3f} | " + + " | ".join(f"{last[c]:.2f}" for c in COLS) + + f" | {min(r['G'] for r in rows):.2f} | {max(r['P'] for r in rows):.2f} | {vtxt} |" + ) + (OUT / "summary.md").write_text("\n".join(lines) + "\n") + print("\n".join(lines)) + + +if __name__ == "__main__": + main() diff --git a/simulations/gadm02/scenarios.py b/simulations/gadm02/scenarios.py new file mode 100644 index 0000000..ee69667 --- /dev/null +++ b/simulations/gadm02/scenarios.py @@ -0,0 +1,29 @@ +"""Load scenario definitions from scenarios/*.json (with single-level `extends`).""" + +from __future__ import annotations + +import json +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[2] +SCENARIO_DIR = ROOT / "scenarios" + +ORDER = [ + "baseline", "automated_capitalism", "dividend_abundance", "universal_venture_access", + "commons_rich", "attention_oligopoly", "generative_regime", + "generative_concentrated_lineages", "generative_attention_extraction", +] + + +def load_all() -> dict[str, dict]: + raw = {p.stem: json.loads(p.read_text()) for p in sorted(SCENARIO_DIR.glob("*.json"))} + out = {} + for name, sc in raw.items(): + params, init = dict(sc.get("params", {})), dict(sc.get("init", {})) + if "extends" in sc: + base = raw[sc["extends"]] + params = {**base.get("params", {}), **params} + init = {**base.get("init", {}), **init} + out[name] = {**sc, "params": params, "init": init} + ordered = [n for n in ORDER if n in out] + sorted(set(out) - set(ORDER)) + return {n: out[n] for n in ordered} diff --git a/simulations/gadm02/sensitivity.py b/simulations/gadm02/sensitivity.py new file mode 100644 index 0000000..c233178 --- /dev/null +++ b/simulations/gadm02/sensitivity.py @@ -0,0 +1,60 @@ +"""One-at-a-time sensitivity of final VFAC to every parameter (±20 %). + +Usage: python3 simulations/gadm02/sensitivity.py +""" + +from __future__ import annotations + +from pathlib import Path + +import gadm02 as g +from scenarios import load_all + +OUT = Path(__file__).resolve().parent / "results" +LEVERS = {"m", "L", "lin_open", "std", "disc", "expl", "x_att", "auto", "dshare", "cp", "od"} +DELTA = 0.20 +TOP = 15 + + +def final_vfac(params: dict, init: dict) -> float: + return g.simulate(params, init)[-1]["VFAC"] + + +def analyse(sc: dict) -> list[tuple[str, float, float, float]]: + base_p = {**g.PARAMS, **sc["params"]} + v0 = final_vfac(base_p, sc["init"]) + res = [] + for k, val in base_p.items(): + lo, hi = val * (1 - DELTA), val * (1 + DELTA) + if k in LEVERS: + hi = min(hi, 1.0) + if val == 0: + lo, hi = 0.0, 0.05 + d_lo = final_vfac({**base_p, k: lo}, sc["init"]) - v0 + d_hi = final_vfac({**base_p, k: hi}, sc["init"]) - v0 + res.append((k, d_lo, d_hi, max(abs(d_lo), abs(d_hi)))) + res.sort(key=lambda r: -r[3]) + return v0, res + + +def main() -> None: + OUT.mkdir(exist_ok=True) + scs = load_all() + lines = [f"# {g.MODEL_VERSION} sensitivity (one-at-a-time, ±{DELTA:.0%})", "", + "Generated by `simulations/gadm02/sensitivity.py`. ΔVFAC is the change in", + "final VFAC (t = 100) when one parameter moves down or up by 20 %, all others", + "fixed. Levers in [0, 1] are capped at 1. Zero-valued parameters are tested at", + "0.05. One-at-a-time analysis misses interactions.", ""] + for name in ("baseline", "generative_regime"): + v0, res = analyse(scs[name]) + rank = {r[0]: i + 1 for i, r in enumerate(res)} + lines += [f"## {scs[name]['title']} (VFAC = {v0:.3f})", "", + "| Rank | Parameter | ΔVFAC at −20 % | ΔVFAC at +20 % |", "|---|---|---|---|"] + lines += [f"| {i + 1} | `{k}` | {lo:+.4f} | {hi:+.4f} |" for i, (k, lo, hi, _) in enumerate(res[:TOP])] + lines += ["", f"Reuse coefficient `rho0` ranks {rank['rho0']} of {len(res)}.", ""] + (OUT / "sensitivity.md").write_text("\n".join(lines)) + print("\n".join(lines)) + + +if __name__ == "__main__": + main() diff --git a/simulations/gadm02/test_gadm02.py b/simulations/gadm02/test_gadm02.py new file mode 100644 index 0000000..b32863c --- /dev/null +++ b/simulations/gadm02/test_gadm02.py @@ -0,0 +1,50 @@ +"""Sanity tests for the GADM-0.2 toy simulation. + +Usage: python3 -m unittest discover -s simulations/gadm02 +""" + +import unittest + +import gadm02 as g +from scenarios import load_all + + +class TestGADM02(unittest.TestCase): + def test_stocks_stay_bounded_in_all_scenarios(self): + for name, sc in load_all().items(): + for row in g.simulate(sc["params"], sc["init"]): + for k in g.STOCKS: + self.assertGreaterEqual(row[k], 0.0, (name, k)) + self.assertLessEqual(row[k], 1.0, (name, k)) + + def test_deterministic(self): + self.assertEqual(g.simulate(), g.simulate()) + + def test_unknown_parameter_rejected(self): + with self.assertRaises(KeyError): + g.simulate({"not_a_param": 1.0}) + + def test_vfac_is_geometric_mean(self): + s = {k: 0.5 for k in g.STOCKS} + self.assertAlmostEqual(g.vfac(s), 0.5) + s["E"] = 0.0 + self.assertEqual(g.vfac(s), 0.0) # full collapse in one factor collapses VFAC + s["E"] = 0.01 + # equal exponents damp near-collapse: (0.01/0.5)^(1/9) ≈ 0.65 of the level, + # which is why constitutional floors are checked separately from VFAC + self.assertAlmostEqual(g.vfac(s), 0.5 * (0.02 ** (1 / 9)), places=6) + + def test_scenarios_only_use_known_parameters(self): + for name, sc in load_all().items(): + self.assertFalse(set(sc["params"]) - set(g.PARAMS), name) + + def test_floor_violations(self): + rows = [{"G": 0.1, "E": 0.5, "P": 0.8, "A": 0.5}, {"G": 0.5, "E": 0.5, "P": 0.5, "A": 0.5}] + v = g.floor_violations(rows) + self.assertEqual(v["G"], 0.5) + self.assertEqual(v["P"], 0.5) + self.assertEqual(v["E"], 0.0) + + +if __name__ == "__main__": + unittest.main() diff --git a/workplans/ASSEMBLY-WP-0004-gadm-0.2-modeling.md b/workplans/ASSEMBLY-WP-0004-gadm-0.2-modeling.md index 9377d1d..cd18fbb 100644 --- a/workplans/ASSEMBLY-WP-0004-gadm-0.2-modeling.md +++ b/workplans/ASSEMBLY-WP-0004-gadm-0.2-modeling.md @@ -4,7 +4,7 @@ type: workplan title: "GADM-0.2: causal structure, first simulation and scenarios" domain: communication repo: assembly-economics -status: proposed +status: finished flavor: implementation depends_on: [ASSEMBLY-WP-0002, ASSEMBLY-WP-0003] owner: claude-code @@ -26,7 +26,7 @@ extensions. The outputs are hypothesis generators, not predictions ```task id: ASSEMBLY-WP-0004-T01 -status: todo +status: done priority: high state_hub_task_id: "a238f7ca-247a-5e9c-8d83-fb377dd087bf" ``` @@ -39,7 +39,7 @@ or text diagram) and identify the reinforcing and balancing loops. ```task id: ASSEMBLY-WP-0004-T02 -status: todo +status: done priority: high state_hub_task_id: "086b54e0-4bc1-5a7b-943a-a120a86eb9f1" ``` @@ -51,7 +51,7 @@ confidence and source section, and flag links whose sign is contested. ```task id: ASSEMBLY-WP-0004-T03 -status: todo +status: done priority: high state_hub_task_id: "6dacf4f6-aed9-5264-850e-8cc512d9203f" ``` @@ -66,7 +66,7 @@ the model. ```task id: ASSEMBLY-WP-0004-T04 -status: todo +status: done priority: high state_hub_task_id: "6d145fe9-098e-50ea-a7d2-e7009779dec4" ``` @@ -79,7 +79,7 @@ runs. The simulation must name the model version it implements. ```task id: ASSEMBLY-WP-0004-T05 -status: todo +status: done priority: medium state_hub_task_id: "7ad27691-37f6-5423-9cc0-782af817104e" ``` @@ -93,7 +93,7 @@ oligopoly, and the generative regime. Store scenario definitions in ```task id: ASSEMBLY-WP-0004-T06 -status: todo +status: done priority: medium state_hub_task_id: "477a9e29-353b-5def-973b-f9db03e8826a" ``` @@ -106,7 +106,7 @@ coefficient ρ that GADM-0.1 flags as critical. ```task id: ASSEMBLY-WP-0004-T07 -status: todo +status: done priority: medium state_hub_task_id: "46f4213e-135f-5d90-86a8-1f4159b852cc" ``` @@ -120,7 +120,7 @@ Goodhart risks. Include the attention-headroom risk from the mapping note ```task id: ASSEMBLY-WP-0004-T08 -status: todo +status: done priority: low state_hub_task_id: "89087ab4-054b-5828-881b-a54c00fe4696" ``` @@ -133,7 +133,7 @@ control) as GADM-0.3 candidates. ```task id: ASSEMBLY-WP-0004-T09 -status: todo +status: done priority: low state_hub_task_id: "d7334ae6-e358-56ed-acb1-7d4b2ae9b3fd" ``` @@ -141,3 +141,27 @@ state_hub_task_id: "d7334ae6-e358-56ed-acb1-7d4b2ae9b3fd" INTENT next step 10. Compare the framework against economics, systems theory, complexity science, institutional economics, political economy, development theory, and Assembly Theory. Store the comparison in `references/`. + +## Outcome + +- T01/T02: `models/GADM-CLD.md` has the Mermaid causal-loop diagram, 14 + loops, and a 49-link signed table with confidence ratings. +- T03: `models/GADM-0.2.md` § 1. Adopted: K split, attention headroom A, + organism selection channel O, membrane and lineage levers, participation + claims. Deferred: compute as a stock, and cohorts. +- T04: `simulations/gadm02/` holds the standard-library simulation and 6 + unit tests. +- T05: nine scenarios in `scenarios/`: the six from GADM-0.1 plus a baseline + and two organism-axis variants. Results are in + `simulations/gadm02/results/summary.md`; readings in `GADM-0.2.md` § 4. +- T06: one-at-a-time sensitivity in `results/sensitivity.md`. Under baseline + governance, organism learning lowers VFAC. In the generative regime, the + membrane-control and lineage-diffusion levers dominate. ρ alone is not a top + driver; reuse works as a bundle. +- T07: `metrics/MetricsCatalog.md`. +- T08: `models/GADM-0.3-Candidates.md`. +- T09: `references/LiteratureComparison.md` (bibliographic details still + unverified). + +Residual: ASSEMBLY-WP-0005 (GADM-0.3, proposed) carries the deferred items, +global sensitivity analysis, and literature verification. diff --git a/workplans/ASSEMBLY-WP-0005-gadm-0.3.md b/workplans/ASSEMBLY-WP-0005-gadm-0.3.md new file mode 100644 index 0000000..c964b7b --- /dev/null +++ b/workplans/ASSEMBLY-WP-0005-gadm-0.3.md @@ -0,0 +1,89 @@ +--- +id: ASSEMBLY-WP-0005 +type: workplan +title: "GADM-0.3: global sensitivity, power channels, cohorts" +domain: communication +repo: assembly-economics +status: proposed +flavor: residual +depends_on: [ASSEMBLY-WP-0004] +owner: claude-code +topic_slug: coulomb-social +created: "2026-10-12" +updated: "2026-10-12" +--- + +# GADM-0.3: global sensitivity, power channels, cohorts + +Residual of ASSEMBLY-WP-0004. Follows the suggested order in +`models/GADM-0.3-Candidates.md` § 4. Not to be started until it is promoted +to `ready`. + +## Fix time unit and add global sensitivity analysis + +```task +id: ASSEMBLY-WP-0005-T01 +status: todo +priority: high +``` + +Set one period = one year. Add Morris screening, then Sobol indices for the +top parameters, to `simulations/`. Re-check the GADM-0.2 readings, especially +the "reuse as a bundle" interaction. + +## Verify the literature comparison + +```task +id: ASSEMBLY-WP-0005-T02 +status: todo +priority: high +``` + +Verify every bibliographic detail in `references/LiteratureComparison.md`, +and search for prior macro models that treat AI systems as learning, +inheritable capital (§ 7, claim 2). + +## Power channels as sub-stocks + +```task +id: ASSEMBLY-WP-0005-T03 +status: todo +priority: medium +``` + +Implement `GADM-0.3-Candidates.md` § 2.2: market, infrastructure, discovery, +membrane, lineage and political channels, aggregated as 1 − ∏(1 − P_k). Test +hypotheses P1 and P2. + +## Separate wealth from power + +```task +id: ASSEMBLY-WP-0005-T04 +status: todo +priority: medium +``` + +Implement `GADM-0.3-Candidates.md` § 2.1: a wealth-concentration stock W_c, +with a check-dependent conversion into P. Test hypothesis P3. + +## Strengthen attention dynamics and the full organism grid + +```task +id: ASSEMBLY-WP-0005-T05 +status: todo +priority: medium +``` + +Re-examine `aext` against time-use evidence, then run the full 2 × 2 × 2 +organism scenario grid (lineage × membrane × attention regime). + +## Life-cycle cohorts + +```task +id: ASSEMBLY-WP-0005-T06 +status: todo +priority: low +``` + +Implement `GADM-0.3-Candidates.md` § 1: four cohorts, intergenerational +transfer, and care demand on attention. Test hypotheses L1–L3.