## Concept explanation In this simulation, **plague transmission** depends on how easily infection passes from person to person within towns, how strongly **trade connectivity** links settlements through merchant routes, and how much **population density** packs people together. You can see how denser towns and busier trade networks help disease move faster across medieval Europe, while rising **mortality** shrinks the labour force, pushes up wage pressure, and weakens commercial exchange. ## What you see You are looking at a map-like network of medieval towns on the left, where route thickness reflects trade activity and red movement along routes suggests infection travelling between settlements. Below it, the line chart tracks infected people and cumulative deaths over time. On the right, the top readouts summarise mortality and the estimated timing of peak spread, while the economic panel shows how the outbreak changes labour supply, wage pressure, and trade decline as the simulation unfolds. ## Try it yourself - **Press Play** to let the outbreak run forward and watch how infection spreads from town to town. - **Increase the plague transmission rate** and notice how quickly local outbreaks intensify inside each settlement. - **Raise trade route activity** to see routes carry infection farther and make the regional wave peak sooner. - **Move population density upward** and compare how crowded towns produce faster spread and larger death totals. - **Pause the run**, **change one slider at a time**, and **press Rerun** to test historian-style counterfactuals. - **Compare the line chart and economic bars together** to see how higher mortality lowers labour supply while wage pressure and trade disruption rise. - **Try a low-trade, low-density scenario** and then **switch to a high-trade, high-density scenario** to contrast slower local containment with rapid network-wide contagion. ## Concept explanation A complex water-management society depends on **monsoon rainfall**, **infrastructure reliability**, and **population pressure** staying in balance. In this model, Angkor’s reservoirs and canals help store wet-season water and spread it across rice fields, but when rainfall becomes weaker or more erratic, and when a growing population demands more than the system can deliver, the network becomes less dependable. That leads to falling **food output**, rising **unrest**, and eventually **urban abandonment**, showing how environmental stress can trigger a cascading social collapse rather than a single sudden failure. ## What you see You’re looking at a stylised Angkor landscape at the top, with reservoirs, canals, rice fields, and city blocks that visually respond to conditions at the selected year. Below it is a 200-year timeline that you can scrub through, and four linked charts tracing **water storage**, **food output**, **unrest level**, and **population remaining in the city**. The control panel on the right changes the overall rainfall regime and starting population pressure, so you can compare a resilient system with one pushed beyond capacity. ## Try it yourself - **Drag the timeline marker** across the two centuries and watch how reservoir colour, field productivity, unrest, and city blocks change together. - **Press Play / Pause** to animate the long-term sequence and follow the collapse as a chain of connected failures. - **Lower the monsoon rainfall level** and notice how water storage drops first, then food output weakens, then unrest rises. - **Raise the population size** and see how higher demand makes the same water system less reliable even before rainfall becomes severe. - **Combine low rainfall with high population** to create the strongest pressure test, then watch the city population curve fall as abandonment accelerates. - **Reset time** and compare different settings from the same starting point to see which combinations remain stable and which cross into collapse. ## Concept explanation The **Columbian Exchange** linked ecosystems that had been separated for thousands of years, but its demographic effects were very uneven. In **Europe** and **Asia**, newly adopted American crops like potatoes and maize increased calories per acre, raising **carrying capacity** and supporting population growth over time. In the **Americas**, however, Old World pathogens spread through populations with little prior exposure, so **disease mortality** often arrived faster and hit harder than crop benefits could offset. The key idea is that different **diffusion speeds** for crops and diseases produced opposite outcomes across connected regions. ## What you see You can compare the exchange in two ways at once. The map shows major flows: crop transfers move eastward from the Americas, while disease flows move back into the Americas from Europe and Asia. The top chart tracks rising population levels in Europe and Asia as crop spread accelerates, and the lower chart shows the Americas falling into deeper decline as disease transmission speeds up. When you hover or click a region, the summary card updates with local estimates for food gain, mortality, and net population change at the current time point. ## Try it yourself - **Drag the `crop spread speed` slider upward** and watch Europe and Asia bend upward earlier as added food raises carrying capacity faster. - **Drag the `disease spread speed` slider upward** and notice how the Americas decline sooner and more sharply. - **Keep crop speed low but disease speed high** to see the strongest contrast between Old World growth and New World collapse. - **Reverse that balance** by raising crop speed and lowering disease speed, then compare how much more gradual the divergence becomes. - **Hover over Europe, Asia, and the Americas** to inspect each region’s changing `Food gain`, `Mortality`, and `Net change` values. - **Click a region** to hold its numbers in place while you continue adjusting the sliders. - **Press `Reset time`** to replay the sequence from `1492` and compare when each effect begins to dominate.