Karen Cornish-Adebiyi v. Caesars Entertainment Inc

PRECEDENTIAL

UNITED STATES COURT OF APPEALS
FOR THE THIRD CIRCUIT
_____________

No. 24-3006
_____________

KAREN CORNISH-ADEBIYI; LUIS SANTIAGO;
MONICA BLAIR-SMITH, individually and on behalf of all
others similarly situated,

Appellants

v.

CAESARS ENTERTAINMENT, INC.; BOARDWALK
REGENCY LLC, d/b/a Caesars Atlantic City Hotel &
Casino; HARRAHS ATLANTIC CITY OPERATING
COMPANY, LLC, d/b/a Harrahs Resort Atlantic City Hotel
& Casino; TROPICANA ATLANTIC CITY
CORPORATION, d/b/a Tropicana Casino and Resort
Atlantic City; MGM RESORTS INTERNATIONAL;
MARINA DISTRICT DEVELOPMENT COMPANY, LLC,
d/b/a Borgata Hotel Casino & Spa; HARD ROCK
INTERNATIONAL INC.; SEMINOLE HARD ROCK
SUPPORT SERVICES, LLC; BOARDWALK 1000, LLC,
d/b/a Hard Rock Hotel & Casino Atlantic City; CENDYN
GROUP, LLC
_____________

2

On Appeal from the United States District Court
for the District of New Jersey
(D.C. No. 1:23-cv-02536)
District Judge: Honorable Karen M. Williams
_______________

Argued: September 17, 2025

Before: RESTREPO, McKEE, and SMITH, Circuit
Judges.

(Opinion filed: July 29, 2026)
_______________

Christopher J. Cormier
Burns Charest
2445 M Street NW
Suite 740
Washington, DC 20037

Joseph J. DePalma
Catherine B. Derenze
Lite DePalma Greenberg & Afanador
570 Broad Street
Suite 1201
Newark, NJ 07102

Joseph Z. Fields [ARGUED]
Susman Godfrey
One Manhattan West
395 9th Avenue, 50th Floor
New York, NY 10001

3

Shawn Raymond
Susman Godfrey
1000 Louisiana Street
Suite 5100
Houston, TX 77002

Mindee J. Reuben
Hausfeld
325 Chestnut Street
Suite 900
Philadelphia, PA 19106
Counsel for Appellants

Jason S. Rathod
Migliaccio & Rathod
412 H Street NE
Washington, DC 20002
Counsel for Amicus Appellant Open Markets
Institute

Joshua P. Davis
Matthew I. Summers [ARGUED]
Berger Montague
505 Montgomery Street
San Francisco, CA 94111

David O. Fisher
American Antitrust Institute
1025 Connecticut Avenue NW
Washington, DC 20036
Counsel for Amicus Appellant American
Antitrust Institute

4

Sam Auld
Boris Bershteyn [ARGUED]
Michael H. Menitove
Andrew Muscato
Kenneth Schwartz
Tansy Woan
Skadden Arps Slate Meagher & Flom
One Manhattan West
New York, NY 10001
Counsel for Appellees Caesars Entertainment,
Inc., Boardwalk Regency, LLC, DBA Caesars
Atlantic City Hotel & Casino, Harrah’s Atlantic
City Operating Company, LLC, DBA Harrah’s
Resort Atlantic City Hotel & Casino, and
Tropicana Atlantic City Corp., DBA Tropicana
Casino and Resort, Atlantic City

Bethany W. Kristovich
Munger Tolles & Olson
350 S Grand Avenue
50
th
Floor
Los Angeles, CA 90071

Justin P. Raphael
Munger Tolles & Olson
560 Mission Street
27
th
Floor
San Francisco, CA 94105
Counsel for Appellees MGM Resorts
International, Marina District Development
Company, LLC, DBA Borgata Hotel Casino &
Spa

5

Jennifer L. Del Medico
Laura W. Sawyer
Jones Day
250 Vesey Street
Floor 31
New York, NY 10281

David C. Kiernan
Matthew Silveria
Jones Day
555 California Street
26
th
Floor
San Francisco, CA 94104
Counsel for Appellees Hard Rock International,
Inc., Seminole Hard Rock Support Services,
LLC

Craig Carpenito
David S. Lesser
King & Spalding
1290 Avenue of the Americas
New York, NY 10104
Counsel for Appellee Boardwalk 1000, LLC,
DBA Hard Rock Hotel & Casino Atlantic City

Melissa Arbus Sherry
Christopher Brown
Lawrence E. Buterman
Graham B. Haviland
Anna M. Rathbun
Latham & Watkins
555 11
th
Street NW
Suite 1000

6

Washington, DC 20004

Sadik Huseny
Brendan A. McShane
Latham & Watkins
505 Montgomery Street
Suite 2000
San Francisco, CA 94111
Counsel for Appellee Cendyn Group, LLC

Thomas J. Sullivan, Jr.
Shook Hardy & Bacon
2001 Market Street
Two Commerce Square
Philadelphia, PA 19103
Counsel for Amicus Appellee International
Center for Law & Economics

_______________

OPINION OF THE COURT
_______________

McKEE, Circuit Judge.
Plaintiffs, casino-hotel guests, appeal the District
Court’s dismissal of their Complaint alleging that Defendants,
select casino-hotels in Atlantic City and their algorithmic
software provider, Cendyn Group, LLC, have conspired to fix
prices of hotel rooms in violation of Section 1 of the Sherman
Antitrust Act. For the reasons that follow, we will reverse the
District Court’s dismissal of the Complaint and remand for
further proceedings consistent with this opinion.

7

I.
Section 1 of the Sherman Antitrust Act is deceptively
simple. It states: “Every contract, combination . . ., or
conspiracy, in restraint of trade or commerce . . ., is declared
to be illegal.”
1
But every contract imposes some restraint on
trade; indeed, that is the very point of entering into a
contractual relationship. Accordingly, very early on, the
Supreme Court interpreted Section 1 to apply only to
“unreasonable” restraints on trade.
2

Few would doubt that today’s business world is
unrecognizable from the world in 1890 when Congress enacted
the Sherman Act. Today, software programs automated with
artificial intelligence (“AI”) can help businesses optimize
operations, respond rapidly to demand fluctuations, reduce
transaction costs, and enhance market transparency and
efficiency in ways that were unimaginable in 1890.
3

It is therefore not surprising that researchers have
cautioned that in today’s business environment, AI programs
that provide “dynamic pricing” algorithms can also facilitate

1
15 U.S.C. § 1.
2
See Standard Oil Co. of New Jersey v. United States, 221
U.S. 1, 87 (1911).
3
See generally Darrell M. West & John R. Allen, How
Artificial Intelligence is Transforming the World, B
ROOKINGS
(Apr. 24, 2018), https://www.brookings.edu/articles/how-
artificial-intelligence-is-transforming-the-world/
[https://perma.cc/UQ5Z-VQKW].

8

anticompetitive behavior.
4
Historically, successful collusion
may have been hindered by gaps in communication and the
cost of ensuring compliance. Today, these algorithms have the
capacity to bridge any such gaps.
5
This makes it possible for
firms to collude in ways that were inconceivable in 1890.
Collusion among competitors in today’s world need not be
characterized by handshakes (or a wink and a nod) in smoke-
filled rooms or by tell-tale telephone conversations. Instead,
AI can enable participants in the marketplace to coordinate
pricing and thereby collude in ways that reduce competition at
the expense of consumers who bear the brunt of higher prices
and reduced output or supply. This is what the Sherman Act is
intended to prevent.

This appeal asks us to consider whether Plaintiffs have
plausibly alleged that Defendants have colluded to fix the
prices of casino-hotel guest rooms in violation of Section 1 of

4
We discuss algorithms and dynamic pricing in more detail
infra Part II.
5
See, e.g., Zach Y. Brown & Alexander MacKay,
Competition in Pricing Algorithm, 15 A
M. ECON. J.:
MICROECONOMICS 109, 115 (2023); Kevin T. White &
Tammy W. Cowart, Behind the Cloaking Device: Is There an
Anti-Competitive Agreement Lurking under the Use of
Common Pricing Algorithms by Multifamily Landlords?, 63
W
ASHBURN L. J. 287, 292 (2024). But see Jeanine Miklós-
Thal & Catherine Tucker, Collusion by Algorithm: Does
Better Demand Prediction Facilitate Coordination Between
Sellers?, 65 M
GMT SCI. 1552, 1553 (2019) (finding that better
demand forecasting by algorithms “increases each firm’s
temptation to undercut price in periods when consumers are
predicted to be willing to pay high prices”).

9

the Sherman Act.
6
According to Plaintiffs’ Consolidated
Amended Complaint (“CAC”), the casino-hotels send their
“current, non-public room pricing and occupancy data” to
Cendyn’s Rainmaker software, an AI-powered dynamic
pricing program, which then processes each casino-hotel’s
non-public data, “along with” similar data provided by their
competitors, and thereby generates suggested room rates for
each of the participating casino-hotels.
7
Rainmaker, according
to Plaintiffs, then functions as a coordinating mechanism,
using the collective data to generate anticompetitive prices
across participating casino-hotels. Plaintiffs allege that the
resulting anticompetitive rates are automatically uploaded into
each casino-hotel’s room-selling platform, causing consumers
to pay anticompetitively high prices for guest rooms. This
practice is allegedly a “stark change from how [casino-hotels]
independently priced rooms for years” at “significantly
reduced room rates” in order to draw guests to their casinos,
since “gambling has always been the main revenue driver and
profit center for casino-hotels.”
8
According to the allegations
in the CAC, Cendyn’s software allows casino-hotel
Defendants to maintain the resulting inflated room rates
without fear that potential hotel guests will be induced to book
rooms with one of their competitors because the colluding
casino-hotels have collectively agreed to charge the

6
Casino-hotels differ from standard hotels in that they are
engaged in providing short-term lodging in hotel facilities
with a casino and other entertainment facilities on the
premises. See Gaming & Gambling, L
IBR. OF CONG. RSCH.
GUIDES, https://guides.loc.gov/tourism-and-travel/gambling
[https://perma.cc/F5UT-C6E9].
7
App. 211-12 (CAC ¶ 6.)
8
App. 308 (CAC ¶¶ 311-12).

10

anticompetitively high rates recommended by Cendyn’s
Rainmaker software. According to the CAC, Cendyn notes that
its clients, including Defendant casino-hotels, do in fact charge
the room rate recommended by Rainmaker 90 percent of the
time. According to the CAC, the Defendants can thereby
charge inflated rates for their hotel rooms without fear that their
prices will be undercut by a competitor in the Atlantic City
market.

The District Court dismissed the CAC because it
concluded that Plaintiffs did not sufficiently plead the
existence of a hub-and-spoke price-fixing scheme. The court
reasoned that Plaintiffs’ allegations failed to show a “rim,” i.e.,
an agreement between the casino-hotel defendants. We
disagree. We hold that the well-pleaded allegations in the CAC
are sufficient to support a finding that casino-hotel Defendants
have conspired to fix prices through Cendyn’s software.
Accordingly, we will reverse the District Court’s order
dismissing the Complaint.

II.

As we have already noted, advancements in technology
have resulted in software capabilities dubbed “artificial
intelligence” or “AI”—program- and machine-based systems
comprised of multiple algorithms.
9
An algorithm is a set of

9
Leah Friedman, Nancye Blair Black, Erin Walker & Jeremy
Roschelle, Safe AI In Education Needs You, C
OMMC’NS OF
THE
ACM: BLOG@CACM (Nov. 8, 2021),
https://web.archive.org/web/20211113002115/https://cacm.ac
m.org/blogs/blog-cacm/256657-safe-ai-in-education-needs-
you/fulltext; see also White & Cowart, supra note 5, at 292.

11

rules programmed to produce a defined output based on
specific inputs.
10
It sometimes functions as a group of step-by-
step instructions dictating how to process incoming data.
11

Software programs using AI process data through a series of
algorithms in order to make a prediction, solve a problem,
interpret conditions, or actuate something, such as activating
autopilot control of a system.
12

One feature of highly advanced AI programs is machine
learning—a process by which an AI program uses the large
amounts of data available to it to continuously learn and
improve on previous outputs.
13
“The function of a machine
learning system can be descriptive, meaning that the system
uses the data to explain what happened; predictive, meaning
the system uses the data to predict what will happen; or

10
See Brown & MacKay, supra note 5, at 115.
11
Chris Lewis, The Need for a Legal Framework to Regulate
the Use of Artificial Intelligence, 47 U.
DAYTON L. REV. 285,
289 (2022).
12
Id. (citing Stephen F. DeAngelis, Artificial Intelligence:
How Algorithms Make Systems Smart, W
IRED,
https://web.archive.org/web/20160210095249/https://www.wi
red.com/insights/2014/09/artificial-intelligence-algorithms-2/
(last visited May 17, 2022)).
13
See Sara Brown, Machine Learning, Explained, MIT
SLOAN SCH. MGMT, https://mitsloan.mit.edu/ideas-made-to-
matter/machine-learning-explained [https://perma.cc/5TMG-
R47W]; see also OECD,
ALGORITHMS AND COLLUSION:
COMPETITION POLICY IN THE DIGITAL AGE 9 (2017),
www.oecd.org/competition/algorithms-collusion-
competition-policy-in-the-digital-age.htm
[https://perma.cc/BL4U-LNAP].

12

prescriptive, meaning the system will use the data to make
suggestions about what action to take.”
14
The more
voluminous the data inputs, the more refined the program’s
outputs will be.
15

Dynamic pricing algorithms are prescriptive AI systems
that automate pricing for sellers in marketplaces.
16
These
algorithmic programs are typically trained on data specific to a
vendor or market to suggest (and in some cases implement)
prices.
17
They learn through an iterative process of “trial and
error and through finding patterns from a great volume and
variety of data.”
18
They may use data “related to past, present,
and future supply and demand conditions,” including data on
competitors’ public prices, to allow vendors to adjust prices
frequently at a lower transaction cost.
19
Although dynamic
pricing algorithms vary in settings, parameters, function, and
sophistication, they share the common purpose of
“optimiz[ing] business processes, [thereby] allowing
businesses to gain a competitive advantage by . . . setting

14
Thomas W. Malone, Daniela Rus, & Robert Laubacher,
Artificial Intelligence and the Future of Work, M
ASS. INST.
TECH. WORK OF THE FUTURE 6 (2020), 2020-Research-Brief-
Malone-Rus-Laubacher2.pdf [https://perma.cc/L8SA-8R3G].
15
See id.
16
Giacomo Calzolari & Philip Hanspach, Pricing Algorithms
Out of the Box: A Study of the Repricing Industry, 21 J.

COMPETITION L. & ECON. 163, 163 (2025).
17
See id.; see also Joseph E. Harrington, Jr., The Challenges
of Third Party Pricing Algorithms for Competition Law, 26
T
HEORETICAL INQUIRIES IN LAW 123, 126 (2025).
18
OECD, supra note 13, at 16.
19
See Brown & MacKay, supra note 5, at 115.

13

optimal prices that effectively respond to market
circumstances.”
20
Indeed, these algorithms have become a
common feature of many markets, including “[r]ide-sharing
apps, airlines, and [major online retailers]” and thus shape the
marketplace for millions of Americans every day.
21

It is important to note that “[t]here is nothing inherently
wrong [or anticompetitive] with using [algorithms] to engage
more effectively in commercial activity, regardless of whether
that activity is participation in the financial markets or the
selling of goods and services.”
22
Nevertheless, the potential for
abuse is obvious. It is therefore not surprising that many
leading economists and legal scholars have raised concerns
about the potential drawbacks of dynamic pricing algorithms
on consumers and market competition, such as increased prices
and reduced output.
23
Some suggest that these programs may
be able to “facilitate price-fixing and ... collusion” and thus
must be “treated with skepticism” to ensure that they do not

20
See White & Cowart, supra note 5, at 292 (citing OECD,
supra note 13, at 11).
21
Alexander MacKay & Samuel N. Weinstein, Dynamic
Pricing Algorithms, Consumer Harm, and Regulatory
Response, 100 WASH. U. L. REV. 111, 113 (2022).
22
Maureen K. Ohlhausen, Should We Fear the Things That
Go Beep in the Night? Some Initial Thoughts on the
Intersection of Antitrust Laws and Algorithmic Pricing, F
ED.
TRADE COMM’N 3 (May 23, 2017),
https://www.ftc.gov/system/files/documents/public_statement
s/1220893/ohlhausen_-_concurrences_5-23-17.pdf
[https://perma.cc/Z2N8-5GWF].
23
See, e.g., MacKay & Weinstein, supra note 21, at 129.

14

cross the line established by the Sherman Act.
24
Here,
Plaintiffs allege that the casino-hotels’ use of Cendyn’s
dynamic pricing algorithm crossed the line, from
procompetitive efficiency to anticompetitive collusion, and
thereby unreasonably interfered with market competition.

III.

A. Section 1 of the Sherman Act

Section 1 of the Sherman Act provides: “Every contract,
combination in the form of trust or otherwise, or conspiracy, in
restraint of trade or commerce among the several States, or
with foreign nations, is declared to be illegal.”
25
As this court
has emphasized, “Section 1 only prohibits contracts,
combinations, or conspiracies that unreasonably restrain
trade.”
26
We have interpreted the terms “contract, combination,
. . . or conspiracy” collectively to require some form of
agreement or “concerted action.”
27
In other words, a plaintiff

24
Brief for Antitrust Law & Economic Professors as Amicus
Curiae Supporting Appellants, at 4 (citing MacKay &
Weinstein, supra note 21, at 129).
25
15 U.S.C. § 1.
26
Toledo Mack Sales & Serv., Inc. v. Mack Trucks, Inc., 530
F.3d 204, 218 (3d Cir. 2008) (quoting In re Flat Glass
Antitrust Litig., 385 F.3d 350, 356 (3d Cir. 2004)).
27
In re Ins. Brokerage Antitrust Litig., 618 F.3d 300, 315 (3d
Cir. 2010) (quoting In re Baby Food Antitrust Litig., 166 F.3d
112, 117 (3d Cir. 1999)); see also Burtch v. Milberg Factors,
Inc., 662 F.3d 212, 221 (3d Cir. 2011) (“Section 1 claims
always require the existence of an agreement.” (citation
modified)).

15

alleging a violation of Section 1 must plausibly show that
defendants had a “unity of purpose,” a “common design and
understanding,” a “meeting of minds,” or a “conscious
commitment to a common scheme.”
28
A plaintiff may support
allegations of a collusive agreement either with direct evidence
or circumstantial evidence. Because direct evidence, “the
proverbial ‘smoking gun,’” is usually unavailable to those
outside the alleged conspiracy, “plaintiffs have been permitted
to rely solely on circumstantial evidence (and the reasonable
inferences that may be drawn therefrom) to prove a
conspiracy.”
29
Indeed, they must be permitted to do so if the
law is to reach more sophisticated or surreptitious agreements.

Secondly, a plaintiff must show that this agreement or
concerted action “imposed an unreasonable restraint on
trade.”
30
Most restraints of trade are analyzed under the “rule
of reason,” which requires comprehensively looking at the
market in which the defendant operates and then weighing the
alleged anticompetitive effects against the proffered
procompetitive benefits.
31
However, a few categories of

28
In re Flat Glass Antitrust Litig., 385 F.3d at 357 (quoting
Monsanto Co. v. Spray–Rite Serv. Corp., 465 U.S. 752, 764
(1984)) (citation modified).
29
In re Processed Egg Prods. Antitrust Litig., 962 F.3d 719,
726 (3d Cir. 2020); InterVest, Inc. v. Bloomberg, L.P., 340
F.3d 144, 159 (3d Cir. 2003).
30
In re Ins. Brokerage Antitrust Litig., 618 F.3d at 315
(citation modified).
31
See InterVest, 340 F.3d at 159; see also Leegin Creative
Leather Prods., Inc. v. PSKS, Inc., 551 U.S. 877, 886 (2007)
(“In its design and function the rule [of reason] distinguishes
between restraints with anticompetitive effect that are harmful

16

restraints are said to be so manifestly anticompetitive that they
are “conclusively presumed to unreasonably restrain
competition.”
32
These agreements are deemed as a matter of
law per se illegal, because they almost always tend to restrict
competition and decrease output.
33
“Paradigmatic examples
[of per se illegal restraints] are ‘horizontal agreements among
competitors to fix prices or to divide markets.’”
34
Horizontal
agreements between competitors form a key part of what has
been dubbed a “hub-and-spoke” conspiracy, a form of
conspiracy analogized to the shape of a wheel.
35
Such a
conspiracy involves a hub at the center, usually an agent,
supplier, or purchaser with whom all the competitors, the
spokes, have a relationship.
36
Establishing a per se violation
with the hub-and-spoke model requires showing that the
spokes are connected to each other via a horizontal
agreement.
37
This horizontal agreement connecting the
competitors is the “rim” of the wheel.
38

to the consumer and restraints stimulating competition that
are in the consumer’s best interest.”).
32
In re Flat Glass Antitrust Litig., 385 F.3d at 356 (citation
modified).
33
See In re Ins. Brokerage Antitrust Litig., 618 F.3d at 316.
34
Id. (quoting Leegin, 551 U.S. at 886).
35
See Howard Hess Dental Lab’ys. Inc. v. Dentsply Int’l,
Inc., 602 F.3d 237, 255 (3d Cir. 2010).
36
See id.
37
See In re Ins. Brokerage Antitrust Litig., 618 F.3d at 327
(quoting Total Benefits Planning Agency, Inc. v. Anthem Blue
Cross & Blue Shield, 552 F.3d 430, 436 (6th Cir. 2008)).
38
Dentsply Int’l, 602 F.3d at 255.

17

To state a claim of horizontal agreement under the
Sherman Act with circumstantial evidence, plaintiffs must
plead “something more than merely parallel [conduct]” among
the defendants.
39
This is because parallel conduct may occur
without any coordination and result merely from each
competitor independently working to gain a competitive
advantage or maximize its market position.
40
Accordingly,
plaintiffs alleging an illegal conspiracy to restrain trade based
on circumstantial evidence must also allege that certain “plus
factors” exist beyond the mere fact of parallel conduct.
41
We
have identified at least three types of “plus factors” that “tend
to demonstrate the existence of an agreement: ‘(1) evidence
that the defendant had a motive to enter into a price fixing
conspiracy; (2) evidence that the defendant acted contrary to
its interests; and (3) evidence implying a traditional
conspiracy.’”
42
These are not exhaustive, as many antitrust

39
In re Ins. Brokerage Antitrust Litig., 618 F.3d at 322
(quoting Bell Atl. Corp. v. Twombly, 550 U.S. 544, 560
(2007)).
40
See id. at 321–22.
41
In re Chocolate Confectionary Antitrust Litig., 801 F.3d
383, 398 (3d Cir. 2015); see also In re Ins. Brokerage
Antitrust Litig., 618 F.3d at 321 (collecting cases
demonstrating the principle that “evidence of parallel conduct
by alleged co-conspirators is not sufficient to show an
agreement”); In re Flat Glass Antitrust Litig., 385 F.3d at 360
n.11.
42
Burtch, 662 F.3d at 227 (quoting In re Ins. Brokerage
Antitrust Litig., 618 F.3d at 321–22). In this context, conduct
contrary to the defendant’s interests means “conduct that
would be irrational assuming that the defendant operated in a

18

claims arise in unique contexts and market conditions. This is
particularly true given the evolving complexity and
sophistication of the technology that has become integral to
today’s marketplace.

“The main threat of horizontal agreements is that they
can enable participants collectively to reduce the output of
goods in some market, thus causing higher prices, inefficient
substitutions, and the resultant losses in consumer or labor
welfare.”
43
In a competitive market, the presence of many
firms competing for market can make such agreements
impractical.
44
And “[f]irms that seek to [coordinate prices]
through the conscious parallelism of oligopoly must rely on
uncertain and ambiguous signals to achieve concerted
action.”
45
However, AI-enabled algorithms are capable of
solving these problems while simultaneously evading
traditional detection methods.
46
AI software can facilitate
collusion by enabling competitors to coordinate prices and
share information without ever communicating with each
other. And real-time price monitoring enables cartels to more
effectively police each other’s pricing behavior and adjust

competitive market.” See In re Flat Glass Antitrust Litig., 385
F.3d at 360–61.
43
Phillip E. Areeda & Herbert Hovenkamp, Antitrust Law:
An Analysis of Antitrust Principles and Their Application ¶
1902, Wolters Kluwer (updated Sept. 2025).
44
See In re Chocolate Confectionary Antitrust Litig., 801
F.3d at 397.
45
Brooke Grp. Ltd. v. Brown & Williamson Tobacco Corp.,
509 U.S. 209, 227 (1993).
46
See generally Brief of American Antitrust Institute as
Amicus Curiae Supporting Appellants, at 12–15.

19

accordingly. Our review of Plaintiffs’ Section 1 claims must
proceed against this background.

B. Standard of Review

Whether Plaintiffs have stated a claim upon which relief
can be granted is a question of law. Accordingly, “[w]e
exercise plenary review of the District Court’s order[] granting
[D]efendants’ motion to dismiss under Federal Rule of Civil
Procedure 12(b)(6).”
47
To state a claim, “a complaint must
contain factual allegations that, taken as a whole, render the
plaintiff’s entitlement to relief plausible.”
48
The plausibility
standard at the pleading stage is not a “probability
requirement.”
49
It simply requires the reviewing court to
determine whether a complaint which pleads a Section 1 claim
includes sufficient factual allegations “to raise a reasonable
expectation that discovery will reveal evidence of [an] illegal
agreement.”
50
In reviewing the complaint, we take the well-
pleaded allegations to be true, and we construe them in the light
most favorable to plaintiffs.
51
Twombly recognized that
although antitrust cases can be complex and expensive, we

47
In re Ins. Brokerage Antitrust Litig., 618 F.3d at 314.
48
W. Penn Allegheny Health Sys., Inc. v. UPMC, 627 F.3d
85, 98 (3d Cir. 2010).
49
Id. (quoting Phillips v. Cnty. of Allegheny, 515 F.3d 224,
234 (3d Cir. 2008)); Twombly, 550 U.S. at 556.
50
Twombly, 550 U.S. at 556; see also W. Penn Allegheny
Health Sys., Inc., 627 F.3d at 98 (citing Phillips, 515 F.3d at
234).
51
See W. Penn Allegheny Health Sys., Inc., 627 F.3d at 91.

20

must not require heightened fact pleading.
52
Rather, we review
the well-pleaded allegations “in light of common economic
experience.”
53
This is a “context-specific task” that requires us
to draw on our judicial experience and common sense in
assessing the totality of the circumstances.
54

C. The Consolidated Amended Complaint (CAC)
55

This putative class action is a consolidation of three
similar cases with identical price-fixing claims filed before the
District Court. Plaintiffs are consumers who directly rented

52
See Twombly, 550 U.S. at 558, 570. Following the Supreme
Court’s decisions on fact pleading in Twombly and Ashcroft v.
Iqbal, 556 U.S. 662, 663 (2009), we repeatedly affirmed that
notice pleading still applies in antitrust cases. See, e.g., W.
Penn Allegheny Health System, Inc., 627 F.3d at 98 (noting that
Rule 8’s pleading standard applies with the same level of rigor
in all civil actions); In re Ins. Brokerage Antitrust Litig., 618
F.3d at 320 (noting that Twombly is “an essential guide to the
application of [the notice pleading] standard in the antitrust
context”).
53
In re Ins. Brokerage Antitrust Litig., 618 F.3d at 325 (quoting
Twombly, 550 U.S. at 565).
54
Iqbal, 556 U.S. at 679; see also In re Flat Glass Antitrust
Litig., 385 F.3d at 369 (“A court must look to the evidence as
a whole and consider any single piece of evidence in the
context of other evidence.”).
55
The facts herein are taken from the CAC, accepted as true,
and viewed in the light most favorable to the Plaintiff-
Appellants. See Doe v. Princeton Univ., 30 F.4th 335, 340 (3d
Cir. 2022).

21

guest rooms from the Defendant casino-hotels, allegedly at
anticompetitively high prices, during a period which began no
later than June 28, 2018, to the present (the “class period”).
They allege that, although the casino-hotels started using
Rainmaker products
56
at various times preceding the class
period (as early as 2004 and as recently as 2018), all of them
were using Rainmaker’s prescriptive algorithm for their
pricing decisions by the start of the class period.
57
Rainmaker
allegedly acts as an exchange hub and a shared pricing agent
for the casino-hotels. Plaintiffs allege that the “products
comprising the Rainmaker pricing algorithm platform—
GuestREV, REVCaster, and GroupREV—enable casino-hotel
clients to achieve higher rates and profits on guest rooms” by
forecasting market demand for individual and large group
bookings and recommending to each client the optimal price

56
In 2019, Cendyn acquired Rainmaker, which includes a
suite of dynamic pricing software programs. See App. 238–
39.
57
At oral argument, Plaintiffs stated that the dynamic pricing
algorithm program at the center of their Complaint was
actually “not available to [Casino-Hotel defendants] until at
least 2009 or 2010.” Oral Argument Tr. at 47. This newly-
raised fact, however, does not appear in either the CAC or the
district court record, and thus, we do not consider it in our
analysis. See In re Cap. Cities/ABC, Inc.'s Application for
Access to Sealed Transcripts, 913 F.2d 89, 96 (3d Cir. 1990)
(“This Court has said on numerous occasions that it cannot
consider material on appeal that is outside of the district court
record.”).

22

rate to charge.
58
According to Plaintiffs, the process works like
this:
Rainmaker’s software is installed directly into
casino-hotels’ on-site room pricing and
occupancy data systems, such that each casino-
hotel client provides its current, non-public
room pricing and occupancy data to the
Rainmaker platform on a continuous basis. In
turn, the algorithm continuously processes and
analyzes this non-public, real-time information,
along with the same type of non-public, real-
time data the client’s participating competitors
also submit to the platform, and other relevant
supply and demand-related data. The algorithm
utilizes this continuous flow of real-time data to
obtain a clear and complete picture of market
supply and demand and competitive dynamics
at any given time. The algorithm ultimately uses
this information to generate “optimal” room
rates, updated multiple times per day, for each
client to charge guests.
59

Since the software is allegedly “integrated directly into
a casino-hotel’s property management system,” the pricing
recommendations generated by the algorithm are “directly and

58
App. 212 (CAC ¶ 7). However, Plaintiffs concede in their
Opening Brief that REVCaster, a “price-comparison tool”
described in the CAC as driven by its clients to monitor rate
parity and solve for competitive rate shopping by guests, see
App. 248-50 (CAC ¶¶ 159, 165), was discontinued in January
2019. Appellants’ Opening Br. at 18 n.5.
59
App. 212 (CAC ¶ 6).

23

automatically uploaded into the casino-hotel’s system.”
60
It is
undisputed among the parties that the casino-hotels retain final
pricing authority and may choose to change the algorithm’s
suggested prices. However, Plaintiffs allege that Cendyn
constrains the casino-hotels’ ability to do so by requiring a
special override to be used only in times of “need and extreme
circumstances” and by scoring each casino-hotel on how often
it overrides the algorithm’s price and forecast
recommendation.
61
The Defendant casino-hotels are alleged to
accept Cendyn’s recommendations 90 percent of the time,
resulting in the alleged anticompetitive guest room rates in
Atlantic City during the class period.

Cendyn allegedly “conveyed [to the casino-hotel
Defendants] that uniform adoption would enable their access,
under the auspices of a single shared algorithm, to one
another’s real-time, non-public pricing and occupancy data,
[and] that the Rainmaker platform[] . . . would generate
significantly higher prices for each [casino-hotel Defendant]
than if each one did so independently without use of that
platform.”
62

The CAC also purports to describe the structural
features of the Atlantic City casino-hotels market and the
financial difficulties faced by casino-hotels in this market in
the years preceding the class period.

Plaintiffs posit that
Defendants had the motive to conspire to fix prices and that
Rainmaker provided the opportunity to do so. They allege that
“[s]tarting in mid-2018 . . . [the] Casino-Hotel Defendants . . .

60
App. 241 (CAC ¶ 137).
61
App. 241–42 (CAC ¶¶ 138–39).
62
App. 269 (CAC ¶ 220).

24

collectively had market power and incentive to recoup years of
losses.”
63
The CAC includes aggregate statistics from Atlantic
City casino-hotel market reports suggesting that during the
class period, casino-hotels’ guest room rates and
corresponding revenue significantly increased in parallel as
room occupancy levels decreased.
64
For example, between
2017 and 2019—before the COVID-19 pandemic temporarily
shut down hotels in 2020—casino-hotel Defendants’ collective
room occupancy allegedly began to trend downward with an 8
percent decrease in occupancy, while their room revenue began
to trend upward with about a 22 percent increase in room
revenue.
65
And in 2022, the casino-hotels are alleged to have
collectively rented 5 percent fewer rooms but charged 25
percent more for those rooms as compared to data from 2019.
66

Accordingly, Plaintiffs allege that this parallel conduct during
the class period, and other “plus” factors, like the purported
exchange of non-public information through Cendyn’s
algorithm, constitute circumstantial evidence of a horizontal
price-fixing agreement.

D. Defendants’ 12(b)(6) Motion and District
Court’s Order Granting Motion

63
App. 270 (CAC ¶ 222).
64
See App. 285 (CAC ¶ 242–45) (citing data from the New
Jersey Division of Gaming Enforcement and the New Jersey
Casino Control Commission showing each casino-hotel
Defendant’s occupancy rate and average daily room rate
between 2015 and 2022).
65
App. 285–86 (CAC ¶ 243).
66
App. 285 (CAC ¶ 242).

25

Defendants moved to dismiss the Complaint for failure
to state a claim under Federal Rule of Civil Procedure 12(b)(6).
The District Court granted the motion to dismiss, reasoning
that the CAC contained several factual deficiencies and did not
establish a plausible price-fixing agreement among the casino-
hotel Defendants.
67
The District Court understood Plaintiffs’
allegations of collusion to be premised merely on the casino-
hotel Defendants’ “knowing” and “purposeful” use of the
Rainmaker products.
68
It found that the CAC was substantially
similar to a complaint from an action in the District of Nevada
which was dismissed because its allegations of a horizontal
agreement did not “cross[] the line from conceivable to
plausible.”
69
Among many factors the Court determined to be
fatally insufficient, it noted that the parallel conduct pled was
not parallel because the casino-hotel Defendants signed up to
use Rainmaker over a fourteen-year period.
70
It also found that
Plaintiffs did not adequately show how Cendyn used the data
it received from the casino-hotels—namely that the CAC did
not allege that the information was “pooled or otherwise
commingled” or somehow improperly exchanged —and that
the casino-hotel Defendants continued to retain and exercise
pricing authority.
71
Accordingly, the District Court concluded

67
Cornish-Adebiyi v. Caesars Ent., Inc., No. 1:23-CV-02536-
KMW-EAP, 2024 WL 4356188, at *4–5 (D.N.J. Sept. 30,
2024).
68
Id. at *5.
69
See Gibson v. Cendyn Grp., LLC, No. 2:23-CV-00140,
2024 WL 2060260, at *3 (D. Nev. May 8, 2024), aff'd, 148
F.4th 1069 (9th Cir. 2025), cert. denied, No. 25-1109, 2026
WL 1052046 (U.S. Apr. 20, 2026).
70
Cornish-Adebiyi, 2024 WL 4356188, at *5.
71
Id.

26

that these factors militated against finding an agreement among
the Defendants such that the conspiracy Plaintiffs allege fatally
“lacks a rim.”
72
We disagree with the District Court.

The CAC contains many allegations that, taken together
as true, lead to a plausible inference that Defendants have
agreed to fix their room rates through Cendyn’s dynamic
pricing algorithm and thereby inflate room rates and avoid
competing. Moreover, and quite significantly, Plaintiffs allege
that even when it would have been in the casino-hotels’
economic interests to reduce room rates to increase occupancy
so as to capture more guests and thereby generate more casino
revenue when occupancy was declining, the casino-hotels
overwhelmingly adhered to Cendyn’s price recommendation.
Even more significantly, the CAC alleges that Cendyn’s
former Vice President of Data Science and Analytics (who also
served as Rainmaker’s Vice President of Revenue Analytics)
encouraged casino-hotels to “avoid the infamous ‘race to the
bottom’ when competition inevitably becomes fierce within a
market.”
73
Cendyn is also alleged to have “led discussions
involving industry executives and managers, including
personnel from [c]asino-[h]otel Defendants, on the best
practices for maximizing room revenue and profitability while
avoiding price wars, including through use of the Rainmaker
platform.”
74
But of course, that is just one way of indicating
that Rainmaker could help the casino-hotel Defendants avoid
having to lower prices to compete with one another. Thus, the

72
Id. at *7.
73
App. 252 (CAC ¶ 173) (quoting comment from May 2020
hospitality industry publication).
74
App. 323 (CAC ¶ 360) (emphasis added).

27

District Court erred in failing to appreciate or recognize the
nature of the conspiracy alleged by Plaintiffs.

IV.
A. Circumstantial Evidence of Defendants’
Horizontal Agreement

Plaintiffs’ Complaint relies primarily on circumstantial
evidence to state a claim of collusion. Accordingly, Plaintiffs
must sufficiently allege parallel conduct and plus factors.
75

1. Parallel Conduct
Plaintiffs’ two theories of parallel conduct include
allegations of Defendant casino-hotels’ (1) contemporaneous
use of Cendyn’s software and (2) synchronous price and output
movement during the class period. First, Plaintiffs allege that
at some point between 2004 and 2018, each of the casino-hotel
Defendants adopted Rainmaker and “knowing[ly] and
purposeful[ly]” used it to set prices.
76
In doing so, the casino-
hotel Defendants allegedly gave Rainmaker not only
information that was generally available to the public but also
proprietary information knowing that competitors would
benefit from it. Cendyn’s dynamic pricing algorithm therefore
functioned as a “shared pricing agent” for defendants and
“generate[d] recommended room rates for each of them using
non-public pricing and occupancy data shared by each casino-
hotel Defendant with the platform in real-time.”
77
A key

75
In re Ins. Brokerage, 618 F.3d at 323.
76
App. 210 (CAC ¶ 1).
77
App. 212–13 (CAC ¶ 9).

28

allegation is that during the relevant class period, all of the
casino-hotel Defendants were delegating their pricing
decisions to Cendyn by accepting Rainmaker’s pricing
recommendations 90% of the time. This alleged parallel
behavior among casino-hotel Defendants after 2017
purportedly replaced a “historically independent room pricing
system” with an “interdependent, collusive one.”
78
The
anticompetitive effects are alleged to be consistently higher
room rates despite declining occupancy, and this resulting
anticompetitive upward pressure on the price of hotel rooms
during the class period is clearly alleged in the CAC. Cendyn
purportedly relied on the resulting higher room rates to show
how its software product kept revenue high despite the
downward pressure that would otherwise have resulted in
lowering room rates.

Plaintiffs also point to the alleged synchrony of “pricing
and occupancy rate movement [among the casino-hotels]
during the class period,” for which the CAC includes numerous
data points in support.
79
Relying on data from the New Jersey
Division of Gaming Enforcement, plaintiffs include graphs
indicating that after 2017, the year preceding the class period,
there is sudden upward trend in casino-hotel Defendants’
average daily room rates.
80
The graphs indicate that by 2019,

78
App. 214 (CAC ¶ 13).
79
Appellants’ Opening Br. at 43; see also App. 287–91 (CAC
¶¶ 245–249).
80
The exception was Hard Rock Atlantic City, which was
newly opened in 2018, and so there is no pre-2018 data for
comparison. There is also no discernible trend in Hard Rock’s
prices during the class period, and its occupancy rates mostly
trended upward during this time. See App. 288 (CAC ¶ 245).

29

room rates had increased by an additional $15 to $60.
81

Meanwhile, occupancy rates for all the casino-hotel
Defendants (except for Hard Rock) “meaningfully decreased”
during this time.
82
The graphs purport to show that this
downward trend in room occupancy began even before the
outlier year of 2020 when the COVID-19 pandemic and
subsequent lockdown forced the temporary closure of many
businesses.
83
According to Plaintiffs, “economic principles
should have compelled at least some Casino-Hotel Defendants
to drop room rates in order to compete,” especially in the
casino-hotel business where greater occupancy is likely to
result in greater overall revenue from the additional guests who
would patronize the casino and other entertainment facilities.
84

However, according to the CAC, casino-hotel defendants did
not reduce room pricing.

The alleged contemporaneous use of Cendyn’s software
and the synchrony of the casino-hotels’ alleged upward pricing
and downward room occupancy—a large departure from their
traditional practice and under economic circumstances that
should typically lead to competitive price-cutting—are
sufficient to show conscious parallel conduct.
85

81
Room rates increased by an additional $14.77 at Bally’s
Atlantic City and by an additional $60.46 at Tropicana Ocean
City. See App. 288 (CAC ¶ 245).
82
App. 291 (CAC ¶ 249).
83
See App. 289-90 (CAC ¶ 247).
84
App. 291 (CAC ¶ 250).
85
We need not decide whether they are independently
sufficient because they are, at least, sufficient in combination.

30

Defendants ask us to find otherwise, partly because of
allegations that the casino-hotels adopted the software
programs at various points within a fourteen-year period. They
also point to the fact that the alleged period of collusion here is
“over 50 times longer than the three-month gap in Burtch [v.
Milberg Factors, Inc., 662 F.3d 212, 221 (3d Cir. 2011)],”
which we held to be insufficiently parallel.
86
In Burtch, we
found that business decisions made by defendant-financiers at
various points between March 13, 2002, and September 15,
2003, to allegedly end their business relationship with the
plaintiff was not sufficiently parallel to sustain a claim of
conspiracy.
87
The conduct in question was the financiers’
decisions regarding the plaintiff’s requests for a credit line. The
Burtch complaint alleged that, during the relevant period, some
defendants declined those credit requests altogether, others
decreased the existing credit line, and others even increased
credit to the plaintiff.
88
Thus, each of the defendants there took
very different approaches toward the plaintiff at various points
during the relevant time period.
89

Burtch does not support Defendants’ argument, because
here, the alleged relevant conduct is (1) the casino-hotels’
“continu[ous] deployment” of Cendyn’s pricing agent and
continuous delegation of pricing to the software, and (2) the
subsequent, sudden synchronous movement of prices and
output (i.e. hotel room occupancy).
90
The CAC does not allege
that the conspiracy began with the adoption of Cendyn’s

86
Appellees Resp. Br. at 27 (citing Burtch, 662 F.3d at 228).
87
See Burtch, 662 F.3d at 228.
88
Id.
89
See id.
90
Appellants’ Opening Br. at 42.

31

software, but instead relies upon the continuous parallel
conduct of the casino-hotels during the relevant period of the
alleged conspiracy.
91
Nonetheless, because parallel conduct
alone cannot sustain a claim of horizontal price-fixing, we also
consider Plaintiffs’ allegations of additional “plus” factors.
92

2. “Plus” Factors
Plaintiffs allege several other “plus” factors which
further support an inference of collusion. In the CAC, Plaintiffs
allege (1) that Defendants had motive to conspire because of

91
Moreover, it is important to note that because AI-driven
tools can adapt and adjust based on new data, collusive
conduct may occur at later points in time when other
competitors begin to share their non-public commercial data
with the software. Thus, the fact that the casino-hotel
Defendants adopted the software at various times may well be
irrelevant, or relevant only to the weight of the evidence of a
conspiracy, not to its existence. The advancements in
artificial intelligence over the past fourteen years further
support the inference that the opportune time and capability
for collusion could have arisen later in time irrespective of
when the casino-hotel Defendants adopted the software. See
Brief for American Antitrust Institute as Amicus Curiae
Supporting Appellants at 19.
92
See Twombly, 550 U.S. at 554; see also In re Baby Food
Antitrust Litig., 166 F.3d at 122 (“Because the evidence of
conscious parallelism is circumstantial in nature, courts are
concerned that they do not punish unilateral, independent
conduct of competitors . . . They therefore require that
evidence of a defendant’s parallel pricing be supplemented
with plus factors.”) (citation modified).

32

“an extended period of financial hardship in the years
[preceding] the class period;” (2) that the casino-hotel
Defendants’ adoption of Cendyn’s price recommendations
when they could have competed more aggressively on price
was against their economic interests; and (3) non-economic
evidence showing exchange of non-public commercial
information, opportunities to collude, and sudden changes in
longstanding business practices.
93

Plaintiffs allege that Defendants’ incentive to conspire
stems from the financial hardship the casino-hotel market
experienced due to high debt levels and depressed cash flows
for many years following the 2008 recession. The CAC
suggests that the structural features of the Atlantic City casino-
hotel market—high barriers to entry, lack of reasonable
substitutes, and the high market concentration—are conducive
to collusion and contributed to the casino-hotels’ motive.

The CAC also alleges that the failure of at least some
Defendants to undercut each other’s prices during the class
period was against their individual economic self-interest in
the absence of collusion.

In Lifewatch Services Inc. v. Highmark, we noted that
when the market in question is alleged to be an oligopoly, that
is, a highly concentrated market comprised of few dominant
firms with fungible products, the first two categories of “plus”
factors, motive to conspire and actions against self-interest,
may simply be attributed to market transparency and not

93
App. 304-310.

33

necessarily to collusive conduct.
94
This is because it is easy for
competitors to monitor and be influenced by each other’s
behavior in oligopolistic markets.
95
Sellers in such markets
might be hesitant to undercut each other’s prices as competitors
can easily notice and reciprocate by reducing their own
prices.
96
Thus, we said in Valspar Corporation v. E.I. Du Pont

94
902 F.3d 323, 333 (3d Cir. 2018) (quoting Valspar Corp. v.
E.I. Du Pont De Nemours & Co., 873 F.3d 185, 193 (3d Cir.
2017)) (noting that in oligopolistic markets, the first two plus
factors tend to reflect conscious parallelism and “largely
restate [the] phenomenon of interdependence” among the few
firms in the market); In re Flat Glass Antitrust Litig., 385
F.3d at 360. This is not to say that oligopolies do not benefit
from concerted action; the same issues of uncertainty that
plague other markets affect oligopolies. See Brooke Grp. Ltd.
v. Brown & Williamson Tobacco Corp., 509 U.S. at 227–28
(explaining that even in a disciplined oligopoly, “signals are
subject to misinterpretation and are a blunt and imprecise
means of ensuring smooth cooperation”).
95
See In re Flat Glass Antitrust Litig., 385 F.3d at 359.
96
Id. (“[F]irms in a concentrated market may maintain their
prices at supracompetitive levels, or even raise them to those
levels, without engaging in any overt concerted action.”); see
also In re Text Messaging Antitrust Litig., 782 F.3d 867, 874-
75 (7th Cir. 2015); Blomkest Fertilizer, Inc. v. Potash Corp.
of Saskatchewan, 203 F.3d 1028, 1041–42 (8th Cir. 2000)
(Gibson, J., dissenting) (“The other oligopolists know that if
they keep their prices low, the brave price leader will simply
cut his prices and the battle will resume. On the other hand, if
they raise their prices in turn, all sellers will receive higher
prices and end up with more money in their pockets. The
loser will be the consumer, who benefits from competition,

34

De Nemours and Company, that evidence that “the market was
primed for anticompetitive interdependence” may do nothing
more than describe the market characteristics in some cases.
97

However, “certain types of ‘actions against self[-]interest’ may
do more than restate economic interdependence” even in
alleged oligopolistic markets.
98
And here, more is alleged.

In this unique context of casino-hotels, the allegation
that the casino-hotel Defendants agreed not to compete on
room rates is significant. As we have discussed, if hotel rooms
allegedly draw more guests into the casino facilities, casino-
hotels “have even more incentive than [standard] hotels to fill
their hotels to capacity” as they may obtain “more income from
the rental of their rooms . . . [and a] greater amount of revenue
from their casinos.”
99
Taken as true, economic principles state
that a casino-hotel whose room occupancy is steadily
decreasing over the years would lower room rates in order to
compete for more hotel guests who will then be available to
venture into the casino. Moreover, common sense suggests as
much.
100
But, this might spur competition and create

not peaceful coexistence between suppliers.” (citation
omitted)). See generally Phillip E. Areeda & Herbert
Hovenkamp, Antitrust Law: An Analysis of Antitrust
Principles and Their Application ¶ 404, Wolters Kluwer
(updated Sept. 2025) (discussing oligopolies).
97
873 F.3d 185, 197 (3d Cir. 2017).
98
In re Flat Glass Antitrust Litig., 385 F.3d at 361 n.12.
99
App. 307 (CAC ¶ 308).
100
Cf. N.J. CASINO CONTROL COMM’N., 2019 ANNUAL
REPORT OF THE NEW JERSEY CASINO CONTROL COMMISSION
47 (2017),
https://www.nj.gov/casinos/about/reports/pdf/2017_ccc_annu

35

downward price pressure thus resulting in the very “race to the
bottom” that Cendyn’s former executive allegedly cautioned
hotels to avoid.
101
Accordingly, the alleged scheme would only
work if casino-hotels could maintain higher room prices
knowing that other casino-hotels would not reduce their rates
to compete for the pool of potential hotel guests. This certainly
supports an inference of collusion; indeed, we can think of no
other explanation.

Plaintiffs also allege evidence of a traditional
conspiracy, which we have described as “non-economic
evidence that there was an actual, manifest agreement not to
compete” such as “proof that the defendants got together and
exchanged assurances of common action or otherwise adopted
a common plan even though no meetings, conversations, or

al_report.pdf [https://perma.cc/252E-M6BB] (showing that
casino revenue contributed about 72% of total revenue while
rooms make up only 11%) with N.J.
CASINO CONTROL
COMM’N., 2019 ANNUAL REPORT OF THE NEW JERSEY
CASINO CONTROL COMMISSION 53 (2019),
https://www.nj.gov/casinos/about/reports/pdf/2019_ccc_annu
al_report.pdf [https://perma.cc/6SLZ-CX3C] (showing that
casino revenue contributed to about 55% of the industry total
revenue, while rooms contributed only about 19%) and N.J.

CASINO CONTROL COMM’N., 2022 ANNUAL REPORT OF THE
NEW JERSEY CASINO CONTROL COMMISSION 59 (2022),
https://www.nj.gov/casinos/about/reports/pdf/2022_ccc_annu
al_report.pdf [https://perma.cc/R7U5-JSRA] (showing about
the same).
101
See App. 252 (CAC ¶ 173).

36

exchanged documents are shown.”
102
Plaintiffs allege, among
other things, de facto data exchanges through the software,
opportunities for the Defendants to conspire at various industry
events, knowledge of each other’s relationship with Cendyn
through publications and events, and the sudden change in the
casino-hotels’ business practices.

The CAC alleges that the casino-hotel Defendants
mutually used the Cendyn pricing software as a means to
exchange non-public proprietary information and that these
information exchanges directly impacted their pricing
decisions. Of course, information exchanges among
competitors are not per se illegal. They are, however, “a
facilitating practice that can help support an inference of a
price-fixing agreement.”
103

The District Court held that “Plaintiffs’ ‘failure to
plausibly allege the exchange of confidential information from
one of the spokes to the other through the hub’s algorithm is
[a] fatal defect . . . [and it] compels the conclusion that there is

102
In re Ins. Brokerage Antitrust Litig., 618 F.3d at 322
(citation modified).
103
Todd v. Exxon Corp., 275 F.3d 191, 198 (2d Cir. 2001);
see also United States v. U.S. Gypsum Co., 438 U.S. 422, 441
n.16 (1978) (“The exchange of price data and other
information among competitors [is not a per se violation]; . . .
A number of factors including most prominently the structure
of the industry involved and the nature of the information
exchanged are generally considered in divining the
procompetitive or anticompetitive effects of this type of
interseller communication.”).

37

no rim [in the alleged hub-and-spoke conspiracy].’”
104
But,
there are numerous paragraphs in the CAC that plausibly make
such allegations in the context of AI-driven dynamic pricing.

105
The CAC alleges that each casino-hotel knew and
“understood” that “the recommended room rates they were
receiving from [Cendyn’s software] were based on real-time,
non-public pricing and occupancy data [that] they and their co-
defendants all were providing to the platform.”
106
It is further
alleged that they “understood that their co-defendants also
knew that the recommended room rates they each were
receiving from [Cendyn’s software] were based on real-time,
non-public pricing and occupancy data [that] they and their co-
defendants were all providing to the platform.”
107
The CAC
adequately alleges that the casino-hotels understood that each
of them was committed to a common plan of setting room rates
based on the recommended rates received from Cendyn’s
software, the hub for their collective data, while also “knowing
that their competitors would not lower their room rates to take
market share.”
108

The District Court required plaintiffs to plead with more
specificity how the algorithm functions to facilitate the
exchange of information.
109
This is tantamount to expecting
Plaintiffs to explain how Cendyn’s proprietary software works

104
Cornish-Adebiyi, 2024 WL 4356188, at *7 (quoting
Gibson, 2024 WL 2060260, at *4).
105
See, e.g., App. 212, 241, 264, 269, 270-73 (CAC ¶¶ 6,
136, 205, 220, and 224-28).
106
App. 271-72 (CAC ¶ 226).
107
Id.
108
App. 215 (CAC ¶ 17).
109
See Cornish-Adebiyi, 2024 WL 4356188, at *4.

38

without affording the discovery required to do that. Moreover,
at this stage of litigation, such level of detail into the software’s
operations is neither required nor appropriate.
110
Thus, at this
stage, Plaintiffs have sufficiently alleged that a material
exchange is likely occurring and facilitating collusion.

Defendants also argue that because each casino-hotel
retained final pricing authority and could override Cendyn’s
price recommendation, Plaintiffs have not sufficiently shown
that the casino-hotels enforced their agreement by delegating
their respective pricing decisions to Cendyn. However,
“[p]rices are fixed when they are agreed upon,” irrespective of
whether conspirators always adhere to them.
111
Although an
antitrust plaintiff’s allegations may well fail if the alleged
colluders routinely varied from recommended prices, that is
not what is alleged here. The alleged 90 percent compliance
rate certainly supports an inference of an agreement between
casino-hotel defendants to price rooms consistent with
Cendyn’s suggestions. This is especially true given the alleged
practical difficulties of deviating from Cendyn’s rates, such as
requiring “override permissions” accessible to select staff at
each casino-hotel.
112
Moreover, as we have explained,
dynamic pricing algorithms tend to rely on the collective input

110
Sherman Act jurisprudence “eschew[s] . . . formalistic
distinctions in favor of a functional consideration of how the
parties involved in the alleged anticompetitive conduct
actually operate.” Am. Needle, Inc. v. Nat’l Football League,
560 U.S. 183, 191 (2010).
111
United States v. Masonite Corp., 316 U.S. 265, 276
(1942) (citing United States v. Socony-Vacuum Oil Co., 310
U.S. 150, 222 (1940)).
112
App. 241–42 (CAC ¶ 138–39).

39

of data to draw patterns and make suggestions. This, coupled
with the well-pleaded allegations in the Complaint, further
supports an inference that information exchanges have resulted
in collusive pricing and diminished competition.

To be clear, we do not make any assumptions or
conclusions about how Cendyn’s dynamic pricing software
works. We only conclude that, taking Plaintiffs’ allegations as
true, the software is, in effect, facilitating collusive conduct by
receiving from each client non-public commercial information,
and in return, giving each client the benefit of their
competitors’ non-public data in formulating a price
recommendation which Defendants purportedly agreed to
comply with.
113
Such exchanges involving current pricing and

113
See In re Domestic Airline Travel Antitrust Litig., 691 F.
Supp. 3d 175, 208 (D.D.C. 2023) (quoting In re Domestic
Drywall Antitrust Litigation, 163 F. Supp. 3d 175, 241 n.49
(E.D. Pa. 2016)) (“A ‘facilitating practice’ is ‘an activity that
makes it easier for parties to coordinate pricing or their
behavior in an anticompetitive way [and] increases the
likelihood of a consequence offensive to antitrust policy.”
(alteration in original)).
Whether Cendyn’s software operates as a hub, a facilitating
practice, an intermediary, or a conduit, to the extent it is
plausibly alleged to be facilitating a horizontal agreement
among some of its clientele, it crosses the line into
impermissible conduct per our Sherman Act jurisprudence.
See also Socony-Vacuum Oil Co., 310 U.S. at 223 (“[M]arket
manipulation in its various manifestations is implicitly an
artificial stimulus applied to (or at times a brake on) market
prices, a force which distorts those prices, a factor which

40

occupancy data have great potential for anticompetitive
collusion.
114
While there are independent business reasons to
use dynamic pricing software, there are rarely legitimate
business justifications for affording competitors the benefit of
commercially sensitive proprietary information under the
circumstances alleged here.

V.

In conclusion, we find that the District Court gave
inadequate consideration to the allegations in the CAC in
context with the complexity and novelty of dynamic pricing
algorithms. Nevertheless, the point made by amicus curiae
International Center for Law & Economics (ICLE) cautioning
us to avoid criminalizing industry-wide use of the same
algorithmic software is well taken. They ask, “[i]f multiple gas
stations use Excel spreadsheets with the same pricing formulas,
is that an antitrust violation? If retailers use the same market
research firm’s pricing surveys, have they joined a hub-and-

prevents the determination of those prices by free competition
alone.”).
114
See Antitrust L. & Econ. Prof. Amicus Br. at 18 (quoting
United States v. U.S. Gypsum Co., 438 U.S. 422, 443 (1978))
(cautioning that such exchanges have the “greatest potential
for generating anticompetitive effects”); see also William E.
Kovacic, et al., Plus Factors and Agreement in Antitrust Law,
110 M
ICH. L. REV. 393, 424 (2011) (identifying information
conveyances among competitors as a “super plus factor”));
Valspar Corp., 873 F.3d at 197 n.8 (noting that unilateral
exchanges of confidential price information is “one example
of an action against self-interest that may not simply be a
result of interdependence”).

41

spoke conspiracy? If manufacturers rely on the same
forecasting software to set production levels, are they
unlawfully coordinating output?”
115
But these questions
oversimplify the issues and ignore many of the specific
allegations in the CAC that are unique to this litigation. The
allegations here imply more than using identical spreadsheets
or pricing formulas without colluding on the prices that will be
charged as a result.

The CAC involves many more factors that go beyond
merely using the same independently-operated software to set
production levels. For example, it alleges an exchange of non-
public commercial information from the spokes that is
consolidated in the hub and then shared back to the spokes in
the form of price recommendations which automatically
determine room rates under circumstances where each spoke is
confident that the resulting rate will not be undercut by the
competing spokes. Far from ICLE’s proffered examples of
software programs used separately and independently to help
businesses compete against one another, Rainmaker is alleged
to operate as a single decision-maker or hub, coordinating
pricing for a majority of the market.

Some researchers also caution that existing antitrust
jurisprudence may not be fully equipped to tackle future
developments in technology and the new issues that may
subsequently arise.
116
We do not disagree. As we noted at the

115
Brief of International Center for Law & Economics as
Amicus Curiae Supporting Appellees at 17.
116
See, e.g., Ai Deng, Algorithmic Collusion and Algorithmic
Compliance: Risks and Opportunities, 27 G
LOB. ANTITRUST
INST. REP. ON DIGIT. ECON. 964, 970 (2020) (“The antitrust

42

outset, Section 1 of the Sherman Act was enacted well over a
century ago in 1890. Nevertheless, the goal of our antitrust
jurisprudence remains the same: to ensure the continued
existence of “independent centers of decision-making.”
117

As former acting chair of the U.S. Federal Trade
Commission, Maureen K. Ohlhausen, inquired:
“Is it ok for a guy named Bob to collect
confidential price strategy information from all
the participants in a market, and then tell
everybody how they should price? If it isn’t ok
for a guy named Bob to do it, then it probably
isn’t ok for an algorithm to do it either.”
118

If Cendyn’s algorithm is in effect collecting non-public
commercial information from defendants and utilizing the
collective pot of data to “suggest” prices to each, under the
circumstances alleged here, plaintiffs have surely raised a
plausible inference of collusion under Section 1 of the Sherman
Act even though the alleged hub is named “Rainmaker” rather

community is largely playing catch-up on the technical
aspects of AI and machine learning.”); Michal S. Gal & Niva
Elkin-Koren, Algorithmic Consumers, 30 H
ARV. J. L. &
TECH. 309, 347 (2017) (cautioning that the functions of
algorithms may evade antitrust scrutiny).
117
Copperweld v. Indep. Tube Corp., 467 U.S. 752, 769
(1984) (citation modified).
118
Ohlhausen, supra note 22, at 10.

43

than “Bob.”
119
Accordingly, we will reverse and remand for
further proceedings in accordance with this Opinion.
120

119
Of course, at the next stage of litigation, plaintiffs will face
a higher burden to sustain their claims by further developing
the facts of the case.
120
Because we will reverse, we do not reach the issue of
whether the District Court abused its discretion in denying
Plaintiffs leave to amend their pleadings.