Batch mcts-m2b. Model 0.2.0, graph 0.2.0, parameters 0.1.1. 20 simulations, seeds 1 to 20; probes on 5. Every figure is a distribution over simulations of a structural model with seed parameter values; it shows what the model implies under these settings, not what will happen. Other nations are frozen at their 2026 ratings. No reconstructed historical data is used in this experiment.
Headline
Brazil played 20 rounds from 2026 under the tree search (mcts) method, with stochastic events, 20 times. Its capability index moved from 11.69 to a median of 12.82 (+1.13), with 90 per cent of simulations between 11.88 and 13.53 [experiment_objectives].
Starting position
In 2026 the nation ranks 30 on the index against the frozen field, holds 0 of nine domains at Tier 1 and has a tier rank of 33. Its base income is 435 credits a year, calibrated so that its 2026 position is affordable with headroom for investment.
What was decided
A simulation spent a median of 1,382 credits on actions. The five groups that took most were Fiscal Capacity & Public Finance (partner, 130 credits, 9.4 per cent); Digital & AI Competence (build, 77 credits, 5.6 per cent); Semiconductors & Advanced Computing (buy, 75 credits, 5.4 per cent); Artificial Intelligence (buy, 72 credits, 5.2 per cent); Alliances & Security Cooperation (partner, 71 credits, 5.2 per cent) [experiment_decisions_by_group]. By mode: buy 38 per cent, partner 37 per cent, build 25 per cent [experiment_decisions_by_group]. Concentration of spending (HHI) had a median of 0.048, and the most repeated action appeared in 50 per cent of rounds.
The leading domain by period: rounds 1 to 5, Human Capital; rounds 6 to 10, Human Capital; rounds 11 to 15, Trade & Investment; rounds 16 to 20, Critical Technology [experiment_spend_by_period].
The method's chosen action was within 5 per cent of its best alternative in 100 per cent of actions and ahead by more than 25 per cent in 0 per cent [experiment_decision_margins].
What the world did
A median simulation saw 2 shocks and 1 upside events. The most damaging per firing were S09 (3 firings in 3 simulations, −30.4 points each); S08 (12 firings in 11 simulations, −25.5 points each); S07 (2 firings in 2 simulations, −23.6 points each) [experiment_shocks]. Upside events fired 18 times across all simulations [experiment_shocks].
The groups that lost most to shocks, per simulation: Fiscal Capacity & Public Finance −1.41; Information Environment & Strategic Communication −1.22; Digital & Services Trade −1.18; Labour & Employment −1.07 [experiment_shock_damage_by_group].
The edges that carried most change through the graph, per simulation: Semiconductors & Advanced Computing to Artificial Intelligence (+0.83 points); Fiscal Capacity & Public Finance to Social Infrastructure (+0.66 points); Fiscal Capacity & Public Finance to Force Generation & Mobilisation (+0.51 points) [experiment_propagation_by_edge].
Where it ended
At 2046 the median rank is 23 (5th to 95th percentile 21 to 30), the median count of Tier 1 domains 1 (0 to 1) and the median tier rank 19; the resilient score, the mean score sum over simulations, is 920 [experiment_objectives].
Tier 1 probability changed most in: human capital (0 to 75 per cent of simulations) [experiment_tiers_by_round].
Why
Without shocks the index ends +0.442 higher on average, so the shocks cost about 0.442 index points [experiment_probe_attribution].
Holding instead of following this method changes the index by −1.724: that is the method's worth against doing nothing [experiment_probe_attribution].
Removing any single one of the five largest actions changes the index by between −0.016 and −0.011, for example 2046 buy Connectivity, Cyber & Trust Technologies (15 credits) [experiment_probe_attribution]. The largest actions by credits come late, when income is highest, and are rarely the most valuable.
Removing the largest shock of a simulation raises the index by +0.332 on average [experiment_probe_attribution].
The replay probe reproduces the simulations exactly (index change 0.000000), so the attributions are clean [experiment_probe_attribution].
Surprises
tier_jump (warn): largest gain in one domain within five years: 6 tiers (20 simulations above 2); tiers are reported, not an objective.
The simulation furthest from the median (seed 8) ends −1.41 from it, with 5 shocks and 0 upside events [experiment_outlier_simulations].
Comparison
This is the baseline (hold) experiment, or there is none for this nation in the batch.
No backcast comparison: this experiment does not start from a historical position [experiment_against_history].
The search
Monte Carlo tree search 0.1.0 with candidate generators 0.1.0 (marginal return, graph leverage, goal gap; top 4 groups each), 500 iterations a decision, UCT c = 1.4 scaled to the reward spread, widening alpha = 0.5, greedy playouts to the horizon, objective index; playouts drew their futures from the none (certainty-equivalent playouts: the planner ignores shocks) scenario.
The committed decision most often chosen, round by round: round 1 partner Fiscal Capacity & Public Finance (95 per cent of simulations, visit share 24 per cent); round 2 partner Fiscal Capacity & Public Finance (70 per cent of simulations, visit share 25 per cent); round 3 partner Fiscal Capacity & Public Finance (90 per cent of simulations, visit share 40 per cent); round 4 partner Fiscal Capacity & Public Finance (90 per cent of simulations, visit share 44 per cent); round 5 partner Fiscal Capacity & Public Finance (85 per cent of simulations, visit share 45 per cent) [mcts_decision_sequence]. The search hesitated (top two root candidates within 10 per cent) in 31 per cent of decisions, considered a mean of 33 candidates, grew trees of a mean 436 nodes to depth 5.0, and took 0.9 seconds a decision [mcts_decision_sequence].
Where the committed actions came from, by proposing generator (committed simulation-rounds): marginal return 12.6; graph leverage 9.7; graph leverage | marginal return + graph leverage 3.3; greedy 3.1; marginal return + graph leverage | graph leverage 0.4 [mcts_root_candidates].
Against the greedy experiment with the same seeds (m2b-bra-greedy), the search's index at the horizon is +0.065 on average (10th to 90th percentile +0.047 to +0.082), better in 100 per cent of seeds; on the index +0.065, the score sum +4.0 and Tier 1 domains −0.05 [mcts_regret].
Across seeds the same root decision won in a mean 59 per cent of simulations (first five rounds 86 per cent) [mcts_stability].
The principal variation was followed 100 per cent at depth 1, 51 per cent at depth 2, 33 per cent at depth 3, 26 per cent at depth 4 [mcts_pv_vs_path].