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“New Keynesian models are based on microfoundations”, Figure 2 of the 2026 prize announcement. © Johan Jarnestad/The Royal Swedish Academy of Sciences.

On 12 October the 2026 prize in economic sciences went to Robert E. Hall of Stanford and Michael Woodford of Columbia “for microeconomic foundations of macroeconomic stabilisation”. Their work underpins the models central banks use to decide when to raise or cut interest rates. The Royal Swedish Academy of Sciences describes the approach in words that sound very familiar around this project: the models are built “from the bottom up. Economic outcomes are the result of millions of decisions made at the microlevel by households and businesses.”

The BAM model at the heart of BAM Engine comes from a book called Macroeconomics from the Bottom-up. So it is worth asking what the two traditions share and where they part ways.

BAM Engine 0.10.2 improves how memory scales with population, keeping the footprint low even at large agent counts.

BAM Engine 0.10.0 measures itself against the wider agent-based modelling ecosystem. A new benchmark suite runs the identical baseline BAM model on five frameworks, and a round of performance work makes BAM Engine the fastest of the group at scale. There are no breaking changes to the public API.

BAM Engine 0.9.2 is a maintenance release that restores compatibility with NumPy 2.5, alongside validation and tooling fixes accumulated since 0.9.1.

BAM Engine 0.9.0 redesigns the results collection API, making simulation data easier to access and explore. A follow-up patch (0.9.1) adds a log_level convenience parameter.

BAM Engine 0.8.0 extends the calibration toolkit with four new composable tools, recalibrates all validation targets from extracted book figure data, and redesigns buffer-stock validation around aggregate improvement over Growth+.

BAM Engine 0.7.0 replaces the batch-sequential goods market with a pure sequential implementation, eliminating inventory collisions and delivering 6.5% faster simulations.

BAM Engine 0.6.0 consolidates two parallel market-matching implementations into a single vectorized path, delivering ~30% faster simulations.

We are excited to announce BAM Engine 0.5.1 and the launch of bamengine.org.