Cycle 1

23 findings. Ordered by priority: invariants, calibration, degeneracy, plausibility, saturation, sensitivity, framework.

F-cycle-001-01: No backcast can be scored: there are no reconstructed historical starting states.

Layer: harness. Class: data_gap. Source: rules

Evidence: checks.parquet backcast: nation_states holds year 2026 only; backcast_comparison returns no rows.

Hypothesis: The calibration loop needs starting states for the five calibration nations and the two hold-outs before 2026.

Proposed change: Reconstruct nation_states rows for KOR 1990, EST 1995, ARE 2005, SGP 1990 and IRL 1995 (calibration) and CHL and POL (hold-out) from the GINC Research, Patents, Trade and Infrastructure indices, with provenance = reconstructed and a confidence grade; add scripted historical shock schedules and an E4 batch.

Scope: harness

Expected effect: The backcast check reports an error by nation and domain.

Verification: Run E4; accept if the backcast check returns an error for all seven nations.

Risk: Reconstructed data is itself uncertain: every backcast report must carry the caveat. Never tune to CHL or POL.

F-cycle-001-02: Greedy marginal is the best policy on every objective for 3 of the five E3 nations.

Layer: harness. Class: degenerate_strategy. Source: rules

Evidence: checks.parquet greedy_dominates: BRA: greedy is best on 4 of 4 objectives; EST: greedy is best on 4 of 4 objectives; KEN: greedy is best on 4 of 4 objectives [baseline_comparison].

Hypothesis: The other baselines are deliberately naive, so greedy's lead says little about whether the model rewards sequencing. Its value model is the only one that sees the graph's long-run spillovers.

Proposed change: Add a sequencing-aware comparison policy to the E3 batch (greedy with a two-round lookahead on the same value model) so that the bar a solver must beat is tested against a policy that can exploit lags.

Scope: harness

Expected effect: A comparison that can tell whether order matters: lookahead beats greedy where lags matter.

Verification: Run E3 with the lookahead policy; accept if it differs from greedy on at least one objective for at least two nations.

Risk: Adds run time; a lookahead policy multiplies policy cost by the branching factor.

F-cycle-001-03: The same move is repeated in most rounds in 3 experiments.

Layer: harness. Class: degenerate_strategy. Source: rules

Evidence: checks.parquet same_action: e3-kor-mirror-peer 95% of rounds; e3-ken-mirror-peer 65% of rounds; e3-ken-greedy-marginal-10y 80% of rounds [decision_sequence].

Hypothesis: The mirror-peer policy measures its gaps against current scores and ignores levels still in delivery, so it keeps funding a gap it has already paid to close; near its horizon greedy can only value fast (bought) moves, so it repeats them.

Proposed change: Count levels still in delivery when mirror_peer measures a gap and when greedy measures the room left below 20 (pipeline/sim/policies).

Scope: harness

Expected effect: same_action passes for the mirror-peer experiments.

Verification: Rerun e3-*-mirror-peer; accept if same_action passes for all five nations.

Risk: Changes the baselines' behaviour, so cycle-on-cycle comparison of those experiments breaks once.

F-cycle-001-04: Groups move more than 3 points in a year without any shock.

Layer: parameter. Class: implausible_dynamics. Source: rules

Evidence: 7 experiments: e3-kor-random move_without_shock 3.84; e3-gbr-mirror-peer move_without_shock 3.33; e3-kor-greedy-marginal move_without_shock 3.20; e3-gbr-greedy-marginal move_without_shock 3.20; e3-gbr-random move_without_shock 3.18 (checks.parquet) [decision_sequence]. Of the 135 group-years that moved more than 3 points without a shock, investment arriving averaged 3.11 points and propagation -0.000: the moves are investment, not the graph.

Hypothesis: A move is a share of income, and income scales with the cost of holding the whole position, so the largest chunk buys several levels of a weak group in one year.

Proposed change: Lower chunk_large from 0.05 to 0.03 in data/sim/seed/params_v0_1.yaml (model).

Scope: parameter

Expected effect: move_without_shock at or below 3 points in the E3 experiments that fail it.

Verification: Rerun e3-gbr-greedy-marginal, e3-gbr-mirror-peer, e3-gbr-random; accept if the share of failing experiments falls by half.

Risk: Slower growth for every nation and policy; less of the headroom is spent, so carried credits rise.

F-cycle-001-05: Domains gain more than two tiers within five years, including under the hold policy, which makes no moves.

Layer: methodology. Class: methodology_question. Source: rules

Evidence: 22 experiments: e3-bra-greedy-marginal 6 tiers; e3-kor-greedy-marginal-30y 6 tiers; e3-kor-greedy-marginal-10y 6 tiers; e3-kor-greedy-marginal 6 tiers; 3 of them hold experiments (checks.parquet) [tier_transitions].

Hypothesis: Below Tier 1 the Pareto layers of a domain are close together (a tolerance of a quarter of a point across 197 nations), so a small move crosses several layers. The heuristic's two tiers may suit coarse tiers, not Pareto layers.

Proposed change: Decide how the model should read tier movement: against the Index's published tier bands, with a wider tolerance, or not at all.

Scope: methodology

Expected effect: A tier measure that moves only when capability moves materially.

Verification: Rerun the cycle batch with the chosen measure; accept if tier_jump passes for the hold experiments.

Risk: Changing the tiering here would make results incomparable with the Index's own tiers.

F-cycle-001-06: The tier rank picks the same best policy as the capability index for 5 of 5 nations, and ties policies for 3.

Layer: methodology. Class: methodology_question. Source: rules

Evidence: batch_summary: median tier rank and median index by nation and policy, 20-year E3 experiments [objective_distributions]. Tier rank has whole-number values over 197 nations and frozen rivals, so it is coarse at the top and saturates once a nation leads.

Hypothesis: As an objective, the lexicographic tier rank mostly repeats the index where they agree and adds noise where they tie; its value is in describing position (which domains lead), not in choosing between policies.

Proposed change: Keep tiers as a reported outcome; use the capability index (the mean of domains of groups) and score_sum as the objectives policies optimise and are ranked by; review whether the tier rank should be dropped from the objective set.

Scope: methodology

Expected effect: Policy rankings stop depending on ties in the tier rank.

Verification: Rerun E3; compare policy rankings by index and by tier rank.

Risk: Dropping tier rank from the objectives loses the brief's lexicographic reward, which the search layer was designed around.

F-cycle-001-07: Groups are driven to 20 and held there in many runs, across nations and policies.

Layer: parameter. Class: saturation. Source: rules

Evidence: 8 experiments above 25 per cent of runs: e3-gbr-greedy-marginal 96%; e3-kor-greedy-marginal 95%; e3-kor-greedy-marginal-10y 92%; e3-kor-mirror-peer 76%; e3-gbr-random 65%. Groups most often driven to 20: Technical & Vocational Skills (321), Advanced Education (223), Foundational Education (215), Digital & AI Competence (209), Lifelong Learning & Reskilling (174) [trajectory_by_domain].

Hypothesis: Costs above the frontier (17) do not steepen enough to stop a well-funded nation buying the last levels.

Proposed change: Raise frontier_premium from 0.6 to 0.9 (model).

Scope: parameter

Expected effect: The share of runs ending with a group newly at 20 falls below 25 per cent in the greedy E3 experiments.

Verification: Rerun the E3 greedy experiments; accept if saturation_high passes in at least half of those that failed.

Risk: Frontier nations gain less; the frontier parameter is already the most sensitive.

F-cycle-001-08: Groups of low-rated nations are driven to 0 by shocks and stay there.

Layer: mechanic. Class: saturation. Source: rules

Evidence: e3-ken-hold: 31% of runs end with a group at 0 that did not start there (median 0 groups); e3-ken-mirror-peer: 26% of runs end with a group at 0 that did not start there (median 0 groups) (checks.parquet) [shock_damage_by_group].

Hypothesis: Shock damage is in points regardless of the group's score, so a group rated near 1 loses everything to one shock; nothing rebuilds it unless the policy chooses it.

Proposed change: Scale a shock's points on each group by min(1, score / 10) in model.event_scale, so damage is proportional for weak groups.

Scope: structural

Expected effect: saturation_low passes for KEN.

Verification: Rerun e3-ken-*; accept if saturation_low passes and the shock tests of the scenario library are unchanged for nations above 10.

Risk: Departs from the scenario load test, which is in points; needs approval.

F-cycle-001-09: 23 of the model's scalar parameters move no objective by more than 0.5 per cent at +-20 per cent.

Layer: harness. Class: dead_parameter. Source: rules

Evidence: checks.parquet dead_parameter: maintenance_share, upstream_lambda, depth_gain, depth_erosion, depth0_intercept, depth0_slope, partner_vulnerability, propagation_floor, carry_decay, headroom_base, headroom_slope, chunk_small, chunk_medium, chunk_large, shock_magnitude_low, shock_magnitude_high, upside_magnitude_low, upside_magnitude_high, upside_probability_scale, absorption_max, credit_effect_high, credit_effect_medium, credit_effect_low (sensitivity.parquet; one nation, greedy, 12 seeds).

Hypothesis: The sweep runs one nation under one policy, which exercises few mechanics: shock magnitudes matter little to a rich nation, chunk sizes change how lumpy spending is but not how much, and depth acts only through vulnerability. Most are untested rather than dead.

Proposed change: Run the sensitivity sweep across three nations (KOR, EST, KEN), two policies (hold, greedy) and a stress scenario before removing any parameter; remove only those still inert.

Scope: harness

Expected effect: A list of parameters inert across the wider sweep, each then removed or justified.

Verification: The next scheduled sensitivity batch.

Risk: Removing a parameter is a model change and bumps the minor version.

F-cycle-001-10: frontier dominates the model's sensitivity.

Layer: parameter. Class: sensitivity. Source: rules

Evidence: sensitivity.parquet: +-20 per cent moves the score sum by -213.4 / +42.3 (max 100.0 per cent of an objective).

Hypothesis: A structural judgement with no evidence behind it carries the results.

Proposed change: Put frontier to the expert network for an elicited range, and report results at both ends.

Scope: parameter

Expected effect: A range with provenance 'expert' instead of a structural point value.

Verification: Rerun E3 at both ends of the elicited range.

Risk: None to the model; delays the next change to this parameter.

F-cycle-001-11: Monetary & Currency Capability cannot be given a defensible cost, decay or vulnerability.

Layer: framework. Class: framework_question. Source: rules

Evidence: cost_params and decay_params, defensible = false: Monetary credibility and the international use of a currency follow from history and the size of the economy; neither a cost per level nor a decay rate is defensible. Left at the class values.

Hypothesis: The group may be mis-specified for a single-nation investment model.

Proposed change: None here: a question for the framework review.

Scope: framework

Expected effect: A decision from the framework review.

Verification: Not applicable.

Risk: Tuning it here would hide a framework question.

F-cycle-001-12: Economic Leverage & Dependence cannot be given a defensible cost, decay or vulnerability.

Layer: framework. Class: framework_question. Source: rules

Evidence: cost_params and decay_params, defensible = false: Leverage is relational; a nation's level depends on others' dependence on it, which phase one freezes. No cost or vulnerability is defensible for a single-nation model.

Hypothesis: The group may be mis-specified for a single-nation investment model.

Proposed change: None here: a question for the framework review.

Scope: framework

Expected effect: A decision from the framework review.

Verification: Not applicable.

Risk: Tuning it here would hide a framework question.

F-cycle-001-13: Nuclear Deterrence cannot be given a defensible cost, decay or vulnerability.

Layer: framework. Class: framework_question. Source: rules

Evidence: cost_params and decay_params, defensible = false: Deterrence is close to binary and governed by treaty; a level scale, a yearly cost per level and a decay rate do not describe it. Kept expensive and unbuyable so the solver cannot treat it as a cheap source of points.

Hypothesis: The group may be mis-specified for a single-nation investment model.

Proposed change: None here: a question for the framework review.

Scope: framework

Expected effect: A decision from the framework review.

Verification: Not applicable.

Risk: Tuning it here would hide a framework question.

F-cycle-001-14: Early Childhood Development cannot be given a defensible cost, decay or vulnerability.

Layer: framework. Class: framework_question. Source: rules

Evidence: cost_params and decay_params, defensible = false: Early childhood investment pays out in adult skills ten to fifteen years later; a six-year delivery and a decay rate on the group's own score do not represent that lag. The graph's edges carry it instead.

Hypothesis: The group may be mis-specified for a single-nation investment model.

Proposed change: None here: a question for the framework review.

Scope: framework

Expected effect: A decision from the framework review.

Verification: Not applicable.

Risk: Tuning it here would hide a framework question.

F-cycle-001-15: Diaspora & Global Communities cannot be given a defensible cost, decay or vulnerability.

Layer: framework. Class: framework_question. Source: rules

Evidence: cost_params and decay_params, defensible = false: A diaspora is a demographic stock formed over generations; a budget does not build it, so no cost per level is defensible. Kept costly so that it is not a cheap target.

Hypothesis: The group may be mis-specified for a single-nation investment model.

Proposed change: None here: a question for the framework review.

Scope: framework

Expected effect: A decision from the framework review.

Verification: Not applicable.

Risk: Tuning it here would hide a framework question.

F-cycle-001-AI01: One-year moves above 3 points occur in groups with no shock landing, but the check does not distinguish new moves from delivery of earlier multi-year build/buy/partner decisions.

Layer: mechanic. Class: implausible_dynamics. Source: ai:deepseek-v4-pro

Evidence: move_without_shock warnings for KOR random 3.84, GBR mirror 3.33, KOR greedy 3.20 [checks].

Hypothesis: Stacked delivery from prior years, not only current chunk_large, contributes to the largest one-year changes.

Proposed change: Add a diagnostic that decomposes each non-shock one-year move into new move, prior-year delivery, partner delivery and propagation before changing chunk sizes.

Scope: structural

Expected effect: Would identify the true source of large moves and avoid over-tuning chunk_large.

Verification: Re-run the warning after decomposition; if new moves dominate, then reduce chunk_large.

Risk: Delays a parameter fix, but prevents an ineffective change.

F-cycle-001-AI02: Many high-income active runs end with groups at 20 that did not start there.

Layer: parameter. Class: saturation. Source: ai:deepseek-v4-pro

Evidence: e3-gbr-greedy-marginal 96 per cent of runs, median 8 groups; e3-kor-greedy-marginal 95 per cent, median 8 groups [checks].

Hypothesis: The current frontier_premium of 0.60 does not make the top of the scale expensive enough under high income and greedy spending.

Proposed change: Raise frontier_premium in steps, beginning with 0.9, while holding frontier fixed and monitoring distributional effects.

Scope: parameter

Expected effect: Would reduce the share of runs with saturated groups and increase differentiation among high performers.

Verification: Re-run the batch; expect saturation_high share to fall substantially, but not eliminate all gains.

Risk: May over-penalise high scorers and reduce realistic high-end progress.

F-cycle-001-AI03: Weak groups in Kenya can be driven to zero and remain there, especially under hold and mirror-peer.

Layer: mechanic. Class: saturation. Source: ai:deepseek-v4-pro

Evidence: e3-ken-hold saturation_low 31 per cent of runs; e3-ken-mirror-peer 26 per cent [checks].

Hypothesis: Shock damage is proportional to exposure but not to the group's starting level, so low-score groups can be eliminated by a single large shock.

Proposed change: Apply a score-sensitive shock scaling such as min(1, score/10) or a floor that leaves weak groups at a small positive level, rather than reducing global shock magnitudes.

Scope: structural

Expected effect: Would reduce pathological zero saturation while preserving relative vulnerability across groups.

Verification: Re-run Kenya experiments; saturation_low should fall without eliminating shock losses altogether.

Risk: May understate the collapse risk of very weak groups.

F-cycle-001-AI04: Greedy marginal is the best policy on every objective for three of five nations, and the same move is repeated in most rounds in several experiments.

Layer: harness. Class: degenerate_strategy. Source: ai:deepseek-v4-pro

Evidence: Research notes: greedy best for 3 of 5; same_action warnings for e3-kor-mirror-peer median 95 per cent, e3-ken-mirror-peer 65 per cent, e3-ken-greedy-marginal-10y 80 per cent [checks].

Hypothesis: The policy set lacks a comparator that accounts for multi-year delivery lags, so greedy marginal wins too easily.

Proposed change: Add a sequencing-aware policy such as greedy with a two-round lookahead on the same value model, and count levels already in delivery when measuring gaps or remaining room.

Scope: harness

Expected effect: Would provide a stronger benchmark and reduce repeated identical moves.

Verification: Compare policies across all five nations; if the lookahead beats greedy on index or resilient score, replace the default.

Risk: More complex policy may be harder to interpret and verify.

F-cycle-001-AI05: No backcast comparison exists, so the model cannot be tested against observed historical capability changes.

Layer: harness. Class: data_gap. Source: ai:deepseek-v4-pro

Evidence: Both experiment reports end with 'No backcast comparison' [TWO_EXPERIMENT_REPORTS].

Hypothesis: Reconstructed historical states and event schedules would allow a meaningful out-of-sample test of the model's dynamics.

Proposed change: Construct reconstructed nation_states rows for KOR 1990, EST 1995, ARE 2005, SGP 1990 and IRL 1995, plus CHL and POL as hold-outs, with provenance and confidence grades, and add scripted historical shock schedules.

Scope: harness

Expected effect: Would enable backcast scoring and reveal whether the model's decay, shock and propagation dynamics reproduce history.

Verification: Run an E4 batch and compare simulated backcasts with observed historical scores.

Risk: Reconstructed data quality may be low and could mislead calibration if treated as observed.

F-cycle-001-AI06: The tier rank chooses the same best policy as the capability index for all five nations and ties policies for three, while tier_jump warnings show tier movement is noisy.

Layer: methodology. Class: methodology_question. Source: ai:deepseek-v4-pro

Evidence: F-cycle-001-06: tier rank same best policy for 5 of 5 and ties 3; tier_jump warnings all experiments, including hold [checks].

Hypothesis: The lexicographic Pareto tier rank adds little decision value over the capability index and score_sum, but may still be a useful reported diagnostic.

Proposed change: Use capability index and score_sum as the objectives policies optimise and are ranked by; retain tier rank and Tier 1 count as reported outcomes, not primary objectives.

Scope: methodology

Expected effect: Would reduce noise in policy selection while preserving the non-linear top-end information for descriptive analysis.

Verification: Confirm policy ranking remains stable and that dropping tier rank from objectives does not change the recommended best policy.

Risk: Could lose a signal that matters for top-ranked nations if the capability index is too coarse.

F-cycle-001-AI07: Many scalar parameters show zero sensitivity in the single Korea greedy sweep, but this may not generalise across nations, policies or scenarios.

Layer: parameter. Class: dead_parameter. Source: ai:deepseek-v4-pro

Evidence: Sensitivity table: 23 parameters with max_rel_change 0.0000 or below 0.001 [sensitivity]; F-cycle-001-09 already flags this.

Hypothesis: Parameters inert under one baseline may become active under low-income or stress conditions.

Proposed change: Run the sensitivity sweep across KOR, EST and KEN, under hold and greedy, plus a stress scenario, before removing any parameter.

Scope: parameter

Expected effect: Would identify genuinely dead parameters and avoid deleting context-dependent ones.

Verification: Compare sensitivity ranks across conditions; remove only parameters inert in all.

Risk: More computational cost, but affordable for 23 candidate parameters.

F-cycle-001-AI08: Several capability groups lack a defensible cost, decay or vulnerability as investment targets.

Layer: framework. Class: framework_question. Source: ai:deepseek-v4-pro

Evidence: F-cycle-001-11 to -15 list Monetary & Currency Capability, Economic Leverage & Dependence, Nuclear Deterrence, Early Childhood Development, Diaspora & Global Communities [RECOMMENDATIONS_ALREADY_RAISED].

Hypothesis: These groups should be treated as scenario-driven or exogenous rather than directly investable until the framework review defines credible cost and decay structures.

Proposed change: Hold a framework review to classify these groups as non-scorable or scenario-only for the next model version.

Scope: framework

Expected effect: Would reduce implausible investment decisions and improve the remaining groups' calibration.

Verification: Framework review output and subsequent batch with revised group treatment.

Risk: May alter the model's group set and require regression against the 2026 index.

What the batch taught (research notes, not defects)

Policy ranking by median index at 20 years is not the same for every nation: BRA: greedy_marginal > mirror_peer > random > hold; EST: greedy_marginal > mirror_peer > random > hold; GBR: greedy_marginal > random > mirror_peer > hold; KEN: greedy_marginal > mirror_peer > random > hold; KOR: greedy_marginal > mirror_peer > random > hold [baseline_comparison].

What greedy is worth over holding, in median index points at 20 years: KEN +0.98, GBR +2.21, KOR +2.27, EST +2.36, BRA +2.38. Low-rated nations gain least, because income is calibrated to the cost of their position [objective_distributions].

Removing the shock library raises the median index by 0.205 to 1.509 points at 20 years across E3 experiments: the cost of the scenario library's risks, as the model prices them [probe_attribution].

Greedy by horizon (median index): KEN: 10y 7.25, 20y 7.38, 30y 7.52; KOR: 10y 17.41, 20y 17.98, 30y 18.08. The 10-year greedy stops investing in slow builds near its horizon, so horizons recommend different strategies [objective_distributions].

Across the batch, the graph's busiest edges are Semiconductors & Advanced Computing to Artificial Intelligence (+0.51 points a run); Semiconductors & Advanced Computing to Economic Leverage & Dependence (+0.19 points a run); Economic Output & Productivity to Fiscal Capacity & Public Finance (+0.18 points a run) [top_edges].

Batch cycle-001 shows a model in which the hold policy decays the capability index for all five e3 nations over 20 years, with median changes from -0.31 for Great Britain (16.454 to 16.142) to -1.10 for Estonia (11.487 to 10.589) [batch_summary]. Active policies reverse some of this: greedy_marginal is best in median index for all five, but the gain over holding is uneven, from +0.98 points for Kenya to +2.38 for Brazil [research_notes]. The e0-null run for Korea falls to 6.214 after 30 years, consistent with no income by design. Shocks are a large drag: removing the shock library raises median index by 0.205 to 1.509 points across e3 experiments, with Kenya hold benefiting by 1.473 and Korea hold by 1.008 [batch_summary, probe_attribution]. Saturation is common in high-income active runs: 95-96 per cent of Korea and Great Britain greedy runs end with at least one group at 20 that did not start there, median 8 groups [checks]. Low-rated Kenya can be driven to zero groups: 31 per cent of hold runs and 26 per cent of mirror-peer runs end with a group at 0 that did not start there [checks]. The graph's busiest edges are Semiconductors & Advanced Computing to Artificial Intelligence and to Economic Leverage & Dependence, implying technology investments spread widely [research_notes]. The sensitivity sweep shows the frontier parameter dominates, with a relative change of 1.000 at +-20 per cent, while many scalars show zero movement in these probes [sensitivity]. Policy ranking is stable for the best policy but horizon changes choices: 10-year greedy stops investing in slow builds near its horizon [research_notes]. Tier measures are noisy: tier_jump warns in all experiments, including hold, with up to six tiers in five years [checks].