Marketplace Simulator

Simulate a simple two-sided marketplace with linear demand, linear supply, and a platform take rate.

In this demo, buyers pay a transaction price, sellers receive a net payout after fees, and the platform keeps the wedge. Shift demand, seller costs, or the fee rate to see how equilibrium, revenue, and welfare move.

Inputs

Set baseline demand and supply, then introduce shocks or a larger fee wedge.

Demand intercept

Buyer willingness to pay at zero quantity.

Demand slope

How quickly buyer willingness to pay falls as quantity grows.

Supply intercept

Seller reservation payout at zero quantity.

Supply slope

How much higher seller payout must be to unlock more supply.

Platform take rate

Percent of the transaction price retained by the platform.

Demand shock

Percent lift or drop in buyer willingness to pay, for example from ranking or promotion changes.

Seller cost shock

Absolute upward or downward shift in seller cost, payout requirement, or logistics burden.

Market outcome

The chart is drawn in buyer-price space. Sellers receive the net payout after the fee wedge.

Equilibrium quantity 65.0

Buyer price $68.0

Seller payout $55.8

Fee wedge $12.2

Gross merchandise value $4,420

Platform revenue $796

Consumer surplus $1,690

Seller surplus $1,162

Total surplus $3,648

Deadweight loss vs zero fee $55

Reading: buyers pay $68.0, sellers keep $55.8, and the fee wedge is $12.2 per transaction.

Benchmark: without a fee, quantity would be 74.1. The current wedge reduces volume by 12.2% and creates deadweight loss of $55.

Scenario: Demand is at baseline. Seller costs are at baseline.

What this demo is good for

Building intuition for how a take rate creates a wedge between buyer price and seller payout
Testing whether a demand lift or seller cost shock expands or shrinks trade
Comparing platform revenue against quantity loss and deadweight loss
Explaining marketplace tradeoffs to product, pricing, or economics teams

This is a static partial-equilibrium toy model. It does not include matching frictions, capacity constraints, inventory dynamics, quality selection, multi-homing, or strategic responses over time.

How the simulator works

The simulator uses linear inverse demand and supply:

$P_{b} (Q) = a (1 + δ) - b Q$

$P_{s} (Q) = c + κ + d Q$

where:

$P_{b} (Q)$ is the buyer-facing transaction price
$P_{s} (Q)$ is the seller payout required to supply quantity $Q$
$a$ and $b$ control demand level and steepness
$c$ and $d$ control supply level and steepness
$δ$ is the demand shock
$κ$ is the seller cost shock

If the platform keeps a take rate $τ$ , sellers receive:

$P_{s} = (1 - τ) P_{b}$

So equilibrium solves:

$(1 - τ) [a (1 + δ) - b Q^{*}] = c + κ + d Q^{*}$

which gives:

$Q^{*} = \frac{(1 - τ) a (1 + δ) - (c + κ)}{d + (1 - τ) b}$

Once $Q^{*}$ is known, the simulator computes the buyer price, seller payout, GMV, platform revenue, and surplus measures. It also compares the outcome with a zero-fee benchmark to estimate deadweight loss.