5D Library › The Economics of Attention · 中文
Chapter 5. Two-Sided Markets and the Commission Game
A market with two kinds of customer
A shopping platform, a short-video app and a creator agency all serve two groups that need each other. Brands want creators and audiences; creators want brands and audiences; each side is worth more to the other the larger it is. Economists call such businesses two-sided platforms, and their pricing follows rules that look strange from the outside: one side is often charged little or nothing, or even paid, while the other carries the margin.
Evidence
Jean-Charles Rochet and Jean Tirole set out the general theory of two-sided markets, showing that what matters is not only the total price a platform charges but how it is split between the two sides, and that the split depends on how much each side values the other (Rochet and Tirole 2003, 2006). Mark Armstrong derived a simple pricing rule for platforms with cross-group externalities (Armstrong 2006), and Bernard Caillaud and Bruno Jullien analysed the “chicken and egg” problem of getting both sides on board (Caillaud and Jullien 2003). Tirole received the 2014 Nobel Memorial Prize in Economic Sciences “for his analysis of market power and regulation”; work on platforms is part of that body of research, not the whole of it (The Royal Swedish Academy of Sciences 2014).
Armstrong’s rule
Suppose brands gain from each additional creator on the platform, creators gain from each additional brand, and the number joining each side rises with the net benefit offered to it. A profit-maximising platform sets the price to each side at
Each side’s price is cut by the value that side brings to the other. If creators bring a lot of value to brands, the platform should charge creators less, perhaps less than nothing, and recover the money from brands.
Calculation
A platform with linear participation on both sides. Creators gain a little from each extra brand (); the value brands place on creators varies. The prices are found by searching a grid, then checked against Armstrong’s rule.
# A monopoly platform with two sides (after Armstrong 2006). Brands value each creator by aB,
# creators value
# each brand by aC. Participation is linear in the net benefit: nB = 1 + aB*nC - pB, nC = 1 +
# aC*nB - pC.
# The platform picks the two prices to maximise pB*nB + pC*nC (costs set to zero).
def sides(pB, pC, aB, aC):
# solve the two participation equations together
det = 1 - aB * aC
nB = ((1 - pB) + aB * (1 - pC)) / det
nC = ((1 - pC) + aC * (1 - pB)) / det
return max(nB, 0), max(nC, 0)
def best_prices(aB, aC):
best = None
for i in range(-150, 301):
for j in range(-150, 301):
pB, pC = i / 100, j / 100
nB, nC = sides(pB, pC, aB, aC)
profit = pB * nB + pC * nC
if best is None or profit > best[0]:
best = (profit, pB, pC, nB, nC)
return best
# creators gain a little from each extra brand
aC = 0.2
print("brands' value of creators price to brands price to creators brands creators")
for aB in (0.2, 0.6, 1.0, 1.4):
profit, pB, pC, nB, nC = best_prices(aB, aC)
print(f"{aB:26.1f} {pB:17.2f} {pC:19.2f} {nB:8.2f} {nC:10.2f}")
print("check: each price = own participation - value it gives the other side (Armstrong's rule)")
profit, pB, pC, nB, nC = best_prices(1.4, aC)
print(f" brands: {nB - aC * nC:.2f} creators: {nC - 1.4 * nB:.2f}")
# brands' value of creators price to brands price to creators brands creators
# 0.2 0.50 0.50 0.62 0.62
# 0.6 0.67 0.33 0.83 0.84
# 1.0 1.00 0.00 1.25 1.25
# 1.4 2.00 -1.00 2.50 2.50
# check: each price = own participation - value it gives the other side (Armstrong's rule)
# brands: 2.00 creators: -1.00
When brands value creators highly, the platform charges creators a negative price: it pays them. Creator funds, bonuses for live hours, free tools and traffic support are all forms of a negative price to the side whose presence makes the other side pay.
The commission chain
In creator commerce there is often more than one intermediary. A platform keeps a commission on each sale; an agency or multi-channel network (MCN) keeps a share of what the creator earns; the creator keeps the rest and decides how hard to work. When each intermediary sets its own cut to maximise its own income, the result is a classic problem first described for manufacturers and retailers: double marginalisation (Spengler 1950). Each cut reduces the creator’s incentive, so the total pie shrinks, and every party, including the intermediaries, can end up with less than if the cuts had been set together.
Calculation
GMV rises with the creator’s effort, with diminishing returns; effort is costly. The platform sets its commission first; an MCN then sets its own cut. In the last two rows, the MCN raises the creator’s productivity by 40 per cent through training and brand deals.
# Commission chain: the platform keeps c of GMV, the MCN keeps m of what is left, the creator
# keeps the rest.
# GMV = A * e**b rises with the creator's effort e (b < 1: diminishing returns); effort costs e.
# The creator picks e to maximise (1-c)(1-m) * A * e**b - e. An MCN may also raise A (training,
# brand deals).
b = 0.5
def outcome(c, m, A):
keep = (1 - c) * (1 - m)
e = (b * keep * A) ** (1 / (1 - b)) # creator's best effort
gmv = A * e ** b
return gmv, c * gmv, (1 - c) * m * gmv, keep * gmv - e
def best(f): # grid search on a share from 0 to 0.99
return max((i / 100 for i in range(100)), key=f)
c = best(lambda c: outcome(c, 0, 10)[1]) # the platform's own best commission
print(f"platform's best commission: {c:.2f} (theory: 1 - b = {1 - b:.2f})")
print("creator works... MCN cut GMV platform MCN creator (net)")
cases = (("alone", 10.0, 0.0), ("with an MCN that adds nothing", 10.0, None),
("with an MCN that lifts A by 40%", 14.0, None), ("same MCN, cut capped at 20%", 14.0, 0.20))
for name, A, m in cases:
if m is None:
m = best(lambda m: outcome(c, m, A)[2]) # the MCN sets its own best cut
g, p, n, k = outcome(c, m, A)
print(f"{name:36s} {m:7.2f} {g:6.1f} {p:9.1f} {n:6.1f} {k:9.1f}")
# platform's best commission: 0.50 (theory: 1 - b = 0.50)
# creator works... MCN cut GMV platform MCN creator (net)
# alone 0.00 25.0 12.5 0.0 6.2
# with an MCN that adds nothing 0.50 12.5 6.2 3.1 1.6
# with an MCN that lifts A by 40% 0.50 24.5 12.2 6.1 3.1
# same MCN, cut capped at 20% 0.20 39.2 19.6 3.9 7.8The table carries three lessons for agencies. An intermediary that adds nothing and takes its profit-maximising cut halves the business and leaves the creator with a quarter of what they earned alone. An agency that genuinely raises productivity can create value for everyone, but only if its cut leaves the creator enough incentive: with a 40 per cent productivity gain and a 50 per cent cut, the creator is still worse off than alone; with the same gain and a 20 per cent cut, the creator, the platform and total sales are all better off, and the agency earns less than its narrow optimum but more than nothing. And the platform gains from moderate agency cuts, which helps explain why platforms often set rules or caps on how intermediaries are paid.
Hypothesis · not established
Not established. Long-term creator–agency relationships are more stable, and total sales higher, when the agency’s share is set below its short-run profit-maximising level and tied to measured productivity gains. The hypothesis predicts lower creator churn and higher GMV growth for agencies with capped, performance-linked shares than for agencies with high flat shares, after accounting for creator size and category. It would be refuted if churn and growth did not differ, or favoured high flat shares.
Disclosure
The author’s company, Asia Influencer X, works as an intermediary between brands, creators and platforms of the kind modelled in this chapter. The model’s conclusion, that an intermediary should take less than it could, is not a description of that company’s terms, which this book does not discuss.
References
Fan, Y. (2026). The Economics of Attention: Simon's Scarcity Law, Power-Law Networks and Conversion Funnels in Creator Commerce [注意力的经济学] (Version 1.0.0). 5D Library. https://doi.org/10.5281/zenodo.23689302 · Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0).