Any organisation with land and buildings can list a hundred ways to cut its emissions. The hard part is knowing which few to do first.

That’s the problem I built Decarbonify to solve. It models an organisation’s whole estate — sites, buildings, rooms, equipment, land and vehicles — as a hierarchy of assets, estimates the emissions of each in a recognised standard (the Greenhouse Gas Protocol), and turns the result into a ranked, costed list of what to do first, with the saving and the assumptions shown for every recommendation.

I built it around a real portfolio — Bradwell Parish Council’s estate in Milton Keynes — and aligned it to the city’s Green Business Fund for local decarbonisation.

A few things I’m pleased with under the hood: an asset-type library that keeps growing, with AI helping classify assets and suggest inputs; transparent calculations where every number is a formula you can inspect, run in a safe arithmetic-only evaluator; and a design that stays fully usable whether or not the AI is switched on.

There’s a full write-up — what it does, why it’s useful, and the engineering behind it — plus a live demo you can click through yourself.

→ Read the case study and try it: decarbonify.mathewbest.com

Go and Visit: https://www.patreon.com/c/HazyCausation

The problem I’m trying to solve is whether there are biases in the responses of the LLMs that are not explicit in their responses but can only be seen when they are asked to iterate on complex tasks.

An interesting task im trying to test biases with is choosing policy decisions for a country.

Changes to national and international policies can have surprising counterintuitive results, and it would be unreasonable to model this with any accuracy, so instead I use an NKC tuneable fitness landscape to model the complex interactions between countries and policy decisions. 

Quick summary of the process:

1. Create an NKC co-evolutionary system where each “country” (species) is controlled by an LLM to optimize its policy genome.
2. Initialise and iterate the co-evolutionary participants until they achieve a weak nash equilibrium
3. Back off the nash equilibrium by randomising a few elements in the policy genomes. 
4. Assign an LLM to each genome, and names to each policy
5. Repeatedly ask each LLM in turn if it wants to make a specific change to its genome, based on the resultant changes to its fitness and other countries finesses.
6. Watch to see where it ends up: stability – chaos – oscillation etc. 

I’m interested to see if an LLM pretending to be a country has biases that could only be winkled out using these co-evolving iterative strategies.

Here is a quick pick of four differently complex fitness landscapes from the NKC System:

And here is a picture of the 16 random countries optimising their fitness in a co-evolutionary setting

Once the first weak Nash equilibrium is achieved, I pause the system and randomise 3 policies, and set it running again. This would be the point that i would let the LLMs take control fully but at the moment I can only run it for a few steps on my laptop. 

Fluid Drag System

Body parts now have a fluid drag system that calculates how water resists an object’s movement by analysing its shape, velocity, and surface orientation.

Using Monte Carlo sampling, points across the object’s surface and determine how the water slows it down. This creates (semi) realistic water resistance

The buoyancy system calculates how much of an object is submerged in water using Monte Carlo modelling and applies an upward force to counteract gravity. This allows creatures and their body parts to float naturally in simulated water.

The system asses submersion by randomly sampling points across the object’s volume and checking how many fall below the water’s surface. If the object is partially submerged, the system estimates the submerged volume and applies the appropriate buoyant force at the centre of buoyancy.

Lets see if anything comes of it. …

First signs of directed life

• Tree Drops Fruit
• Creatures is hungry
  • Action Selection chooses “Approach food”
  • Target Selection chooses nearest food
  • Creature approaches nearest food.
• Creature touches food
  • Food is consumed
  • Nutrients passed into creatures body
  • Creature is no longer hungry
  • Action Selection chooses “Random Explore”