How do I simulate a public good?

Open this page and use the live Network Reliability tool below — set your inputs and the simulation runs instantly in your browser using real numerics. No install, no account needed.

Yes. The simulation runs free in your browser. A one-time unlock or a Pro plan adds advanced parameters, saved presets, data import, and clean exports.

Can I use my own numbers?

Absolutely — every input is adjustable, and with data import you can drive a public good from your own measurements.

Use case · powered by Network Reliability

Network Reliability for a public good

Simulate a public good live in your browser. This runs the real Network Reliability solver — adjust the inputs, watch it respond instantly, and export the result. No install, no account.

For:Students Researchers Educators Engineers Hobbyists & Makers K-12 Students First Responders

Max Flow

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Maximum FlowLive

Edmonds-Karp network flow

Controls

Presets

Capacity S→A

Capacity B→D

The max-flow problem asks how much can be pushed from a source to a sink through a network of capacity-limited edges. Edmonds-Karp repeatedly finds an augmenting path and saturates it until none remain. By the max-flow min-cut theorem, the answer equals the capacity of the cheapest set of edges that, if cut, disconnects source from sink — the bottleneck.

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Data Inspector

Maximum flow18

Nodes6

Bottleneckmin cut

Governing equation

∣ f ∣ = v \sum f (s, v) = 18 = cut min c (S, T)

Reading this result: Both feed edges are generous, so the bottleneck has moved to the fixed interior capacities — adding more feed capacity no longer raises the max flow (min-cut theorem).

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

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Centrality

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Network CentralityLive

who matters most

Controls

Centrality measures which nodes are most important in a network. Degree counts direct connections; betweenness counts how often a node lies on shortest paths (a bridge or broker); closeness measures how near a node is to all others. Switching metrics changes who matters — a key insight for social networks, infrastructure, and epidemiology.

▶ Run in Python

Data Inspector

Metricbetweenness

Most centralD

Nodes7

Governing equation

C_{B} (x) = s \neq = t \neq = x \sum \frac{σ _{s t} ( x )}{σ _{s t}}

Reading this result: By betweenness, node D is the key broker: it sits on the most shortest paths, so removing it would fracture the network even if it has few direct links.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

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About this simulation

The full Network Reliability tool models a public good with the same numerics engineers and scientists use — running entirely client-side. Change any parameter and the result updates in real time, so you can build intuition, check a design, or teach the concept without spreadsheets or installs.

More you can do with Network Reliability

Network Reliability for a supply-demand market Network Reliability for a monopoly Network Reliability for an auction Network Reliability for a tax policy Network Reliability for a trade tariff Network Reliability for an inflation model Network Reliability for a labor market Network Reliability for a game-theory standoff

Other ways to simulate a public good

Supply & Demand for a public good Price Elasticity for a public good Monopoly Pricing for a public good Cobb-Douglas for a public good Lorenz & Gini for a public good Laffer Curve for a public good

Frequently asked questions

How do I simulate a public good?: Open this page and use the live Network Reliability tool below — set your inputs and the simulation runs instantly in your browser using real numerics. No install, no account needed.
Is it free?: Yes. The simulation runs free in your browser. A one-time unlock or a Pro plan adds advanced parameters, saved presets, data import, and clean exports.
Can I use my own numbers?: Absolutely — every input is adjustable, and with data import you can drive a public good from your own measurements.