Domain TWIN

Place models and practice exercises

Start here · Questions · Progress · People decide

Use a digital model of a place to understand evidence, compare choices and practise demanding situations.

Question	What we are working out	AI-generated potential answers and research	Work state
TWIN-01	What dated local digital twin—a computer representation of a person, place or system—will support useful kiosk navigation and outage planning?	AI-generated option: Begin with a dated, inspectable representation of the places and systems needed for kiosk service: access, facilities, routes and dependencies. Add personal twins only through each person's choices. Keep measurements, inferred features, gaps, permissions and corrections visible in every edition. AI-generated future concept: Explore a layered XR view through which residents can examine a route, source date and proposed improvement, then submit a correction for review. Extend detail where a practical task demonstrates the value of richer modelling. Research examples: Our Changing City: Wellington's public climate-adaptation digital installation · NSW Spatial Services: mapping and the Emergency Management Digital Twin	open
TWIN-02	Which demanding exercises will show how the network serves people through combined, prolonged failures?	AI-generated option: Build a 2–3-day practical exercise with people and equipment, walking through interruptions to power, communications, ferries, supplies and available staff. Begin with storms, cyclones and fire. Digital twins can simulate any chosen length of disruption; record simulated time separately from actual computer runtime. Clearly identify practice events and record simulation, bench measurements and field demonstrations as distinct evidence. AI-generated future concept: Explore a shared scenario where people and Auras practise handovers and compare changing resource choices. Keep advanced-agent performance and simulated robot tasks as measured exercise assumptions, with readable participation available outside XR. Research examples: Common Alerting Protocol Version 1.2 · NSW Spatial Services: mapping and the Emergency Management Digital Twin	open
TWIN-03	What checks will show when a computer model is useful for a real decision and where its limits remain?	AI-generated option: Give every model a purpose, input edition, assumptions, uncertainty and stated decision use. Compare it with an understandable baseline on cases held back for checking. Publish errors and useful limits alongside successful results, then seek relevant professional assessment before applying it to consequential decisions. AI-generated future concept: Explore separate Auras or advanced AI/SI agents proposing counterexamples and alternative assumptions. Keep their findings traceable, then evaluate against held-out evidence; agreement between agents would remain a research result awaiting external checking. Research examples: NIST AI Risk Management Framework 1.0: core functions · OGC SensorThings API Standard	open
TWIN-04	How will digital exercises show the connections between information, transport, supplies, power, equipment and available people?	AI-generated option: Represent the dependencies behind each promised task, including qualified people, access, fuel, charging, cooling, freight and spares. Replay a failure through those connections and show the service retained. Connect measured asset observations to the scenario while preserving uncertain availability and human responsibility. AI-generated future concept: Explore a supervised robot inspection or delivery role inside the exercise, including its charger, links, maintainer and safe recovery. Compare it with a person-led alternative using measured performance when available and stated assumptions beforehand. Research examples: Robots to the rescue in hazardous environments · NSW Spatial Services: mapping and the Emergency Management Digital Twin	open
TWIN-05	How will people clearly recognise current operations, older saved information, historical playback and practice exercises wherever they encounter them?	AI-generated option: Carry purpose and time labels inside the content itself: current confirmed operation, saved information, historical playback or exercise. Use separate release destinations and permissions for practice. Repeat the label in screenshots, audio, exports and XR so context survives copying or a disconnected device. AI-generated future concept: Explore a scene header and source panel that remain visible when a twin is shared or replayed. A personal Aura could explain what changed between editions while preserving the difference between observed conditions and a scenario. Research examples: Common Alerting Protocol Version 1.2 · Web Content Accessibility Guidelines 2.2	open
TWIN-06	How will drills and actual outages turn people's observations into reviewed improvements to the service and its learning material?	AI-generated option: After each drill or outage, connect people's observations with the actual event record, expected service and differences. Name a reviewer for each proposed improvement. Preserve the original evidence, approve a specific change where needed, update the lesson or design and check the result again. AI-generated future concept: Explore an assistant preparing a timeline from authorised logs and participant notes, marking uncertain ordering and conflicting accounts. People would correct the account before it informs a proposal, training record or public summary. Research examples: ODK Collect: managing draft, ready-to-send and sent forms · NIST AI Risk Management Framework 1.0: core functions	open