City Simulation: one month, two cities

Playable at cookwala.ai/sim/city (code: [sim/city/](../sim/city)). A deterministic, agent-based model of two cities of about 5,000 people each:

• Farms and producers: vegetable farms, orchards, grain farms, cattle and dairy, poultry, fisheries.
• Distribution: a wholesale hub and supply trucks, plus a supermarket and a corner shop in each of three neighbourhoods (mid, high and low income).
• Homes: 1,500 per city, with and without Cookwala robot cooks.
• Transport and waste: delivery vans, shopping car trips, garbage trucks, and rush-hour traffic (BPR congestion model).
• Energy: gas, electric and induction stoves; robot electricity; vehicle fuel; and store and wholesale refrigeration.

Each home's daily appetite varies (weekends, guests, eating out). The same people, appetites and randomness run in both cities. What differs is how many homes plan with robot cooks, and whether the city's stores, wholesale, farms and logistics use the protocol:

• demand signals;
• consolidated delivery slots;
• surplus rescue to a community kitchen;
• reusable or bulk packaging.

A 7-day warm-up runs before the measured month.

Calibration (traditional homes and supply chain) #

Robot-home behaviour is deliberately conservative:

• they buy a 5% buffer;
• cook 4% extra;
• waste 30% of leftovers;
• spoilage watch saves only 60% of items about to expire;
• 12% of days are unplanned eating-out, the same as traditional homes.

Inedible parts (peels, bones, shells) are identical for every kitchen. Every assumption is listed on the page.

Results (seed 11, 30 days) #

(The headless runner node sim/run.mjs regenerates this table into sim/out/city-results.json; CI runs it on every push.)

What it shows #

1. Homes: in the same neighbourhood, robot-cook homes waste about 45% less food per person (edible waste about 70% less). The causes are planned buying, exact portions, leftovers repurposed, use-first and freezer planning. At 40% adoption that's −15% city-wide household waste.
2. The ecosystem matters more than the robot. With the protocol, food lost before homes falls about 22%, garbage about 16%, food-related traffic about 15%, and emissions about 25%. Stockouts fall too, while about 3,000 meals' worth of end-of-life food reaches the low-income neighbourhood instead of the bin.
3. Robots without the protocol can make things worse upstream. Robot homes ordering in batches every few days look like erratic demand to stores that can't see their plans (a bullwhip effect). Losses before homes rose to 41 t vs 37 t. The protocol's demand signals turn home planning into upstream savings.
4. The protocol without plans is weak. With demand signals but only 5% planning homes, upstream gains are small. Most of the benefit came from surplus rescue. Plans, made by robots or by guided humans, are what the protocol runs on.
5. Traffic: about 35% fewer car shopping trips and about 25% faster deliveries in consolidated slots. But rush-hour speed barely moves (+1%), because food trips are a small share of city traffic. Food logistics alone won't fix congestion.
6. Energy and natural gas:
   • In the kitchen: robot homes need about 14% less stove energy per person (energy-saver methods: lids, pressure cooking, residual heat, batching). That means less natural gas in gas-stove homes. But each robot adds about 0.35 kWh of electricity a day, which offsets most of that saving.
   • Across the chain: the Cookwala city uses ~3% less natural gas and ~3% less total energy (cooking, robots, transport fuel, store and wholesale refrigeration). Most of the saving comes from 7% less vehicle fuel and 7% less refrigeration energy (smaller chilled inventories), not from the stove.
   • Without the protocol: robots raise total energy (+2%). Their electricity is added with no supply-chain savings to offset it.
   • Emissions: they fall more than energy (−20%), because most of the CO2e saved is in food that was never wasted.
7. Money and time: about $5 per person per month less food spend (robot hardware not included), and about 10 fewer hours of cooking and shopping per home per month.

Limitations and next steps #

• Illustrative model, not a forecast. Behavioural parameters are assumptions, adjustable in the code and listed on the page.
• Excludes restaurants and food service (UNEP's 36 kg/person/year), robot costs, energy prices, home fridge/freezer energy, farm and food-processing energy, and the energy embedded in producing food that is later wasted (only its CO2e is counted).
• Energy values are planning estimates: 0.47 kWh of useful heat per kg cooked, and stove efficiency of 38% (gas), 72% (electric) and 85% (induction). Stove mix 55/35/10 in both cities.
• One wholesale hub per city; no cross-city trade; no seasonality or price elasticity.
• Next steps:
  • multi-seed runs with confidence intervals;
  • guided-human planning homes (no robot) as a third home type;
  • food-service kitchens;
  • farm planting decisions over a season;
  • a relief-program layer feeding the low-income community at scale.

Protocol on/off per city #

Each city card has a Cookwala protocol: On | Off · robots alone switch. The Robots alone vs robots + the Cookwala protocol panel reruns each city at the same robot share. It does this twice: once with demand signals, consolidated delivery, surplus rescue, bulk packaging and energy-saver modes, and once without them. Nilebridge, default preset:

• food lost before homes: −32%;
• garbage: −15%;
• CO2e: −14%;
• delivery vehicle-km: −25%.

Robots alone order through their own vendors' apps, so stores cannot see their plans and upstream losses grow (the bullwhip effect).