Third Circuit Revives Algorithmic Price-Fixing Claims in Casino-Hotel Case in Precedential Opinion

Key Takeaways

• The Third Circuit reversed the dismissal of an alleged price-fixing conspiracy utilizing common pricing software in the casino-hotel industry.
• The decision differs from holdings from other courts in a number of aspects regarding pricing software, including timing, pricing recommendations, "plus factors" and how algorithms actually function, among others.
• The Third Circuit's ruling creates a likely Circuit split in a growing body of antitrust law addressing whether algorithmic pricing software can facilitate collusion.

On July 29, 2026, the Third Circuit reversed the dismissal of price-fixing conspiracy claims alleging that several Atlantic City casino-hotels used a common software product to price hotel rooms. The decision is a significant development - and creates a potential circuit split - in the growing body of antitrust case law addressing whether algorithmic pricing software can facilitate collusion among competitors in violation of the Sherman Act.

The case, Cornish-Adebiyi v. Caesars Entertainment, Inc. et al., came to the Third Circuit on appeal from the District of New Jersey[1] after the District Court dismissed a putative class-action complaint alleging that Cendyn Group LLC's (Cendyn) Rainmaker hotel room pricing software served as a vehicle for casino-hotel operators to fix the price of hotel rooms. The plaintiffs alleged that nine casino-hotel defendants and Cendyn conspired to fix the price of hotel rooms. Specifically, the complaint alleged that Cendyn's pricing algorithms use casino-hotels' data to recommend "optimal" room prices to other casino-hotels, creating a "coordinating mechanism"[2] or "hub" for a price-fixing conspiracy. The complaint also alleged that the participating casino-hotels "understood" that they each would use Cendyn's pricing algorithms to allegedly fix the prices of hotel rooms. These allegations follow an increasingly common pattern in algorithmic price-fixing claims across industries,[3] including claims addressing casino-hotels in a different locale - Las Vegas.[4]

The Third Circuit's decision creates several potential rifts in the way courts apply the antitrust laws to algorithmic pricing issues. In reversing the District Court's dismissal of the complaint, the Third Circuit rejected several arguments that defendants have successfully used in similar hub-and-spoke cases.

Timing

The court found that staggered adoption of the pricing software, even over 14 years in this case,[5] does not necessarily defeat an inference of conspiracy.[6] Allegations of continuous use and adherence to the software's recommendations during the alleged conspiracy period were sufficient to survive dismissal.[7]

This differs from other courts' timing analyses, which have found that while parallel or temporally close adoption of a common software may suggest collusion, disparate determinations to adopt a software platform over time do not.

Rejection of Recommendations

The court emphasized that even where users can reject pricing recommendations, antitrust claims can still survive dismissal where there are alleged "practical difficulties" in deviating from the recommended rates, such as requiring "override permissions."[8] The court further held that even where users canreject recommendations, if plaintiffs plausibly allege that the users overwhelmingly follow them and understand that their competitors do the same, antitrust concerns may persist.[9] As the court put it, an "alleged 90 percent compliance rate certainly supports an inference of an agreement between casino-hotel defendants to price rooms consistent with Cendyn's suggestions."[10]

In other cases, compliance rates, overrides and default settings relating to price recommendations have been explored in depth in discovery. Defendants have relied on differences in software usage from one subscriber to the next as a basis to undermine inferences of collusion. But here, the court suggests an alleged high compliance rate alone can support an inference of collective action.

Plus Factors

The court further concluded that the plaintiffs adequately alleged requisite "plus factors." In addition to a motive and opportunity to conspire, the court emphasized as evidence of parallel conduct the allegations that the casino-hotels experienced synchronized increases in room rates despite declining occupancy levels.[11] Specifically, the court found that it was "quite significant[]" that "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."[12] The court also found the allegations that certain Cendyn and Rainmaker executives' discussion of its customers avoiding a "race to the bottom" or "price wars" via the use of the software permitted a plausible inference that the defendants fixed the room rates.[13]

While plaintiffs in other cases have made similar references to certain marketing and/or testimonial materials, most courts have found this puffery alone cannot establish plus factors. Rather, plaintiffs need to show some concerted agreement among subscribers to coordinate either usage, strategy or information sharing outside the software to optimize its performance. The Third Circuit's holding ostensibly lowers these pleading hurdles by finding that high adherence to pricing recommendations, even without a clear agreement along an alleged hub-and-spoke rim, may be sufficient.

Function of the Algorithm

The Third Circuit also rejected the District Court's implication that the plaintiffs needed to plead the precise mechanics of how Cendyn's proprietary algorithm functioned.[14] Requiring such specificity in a complaint, the court explained, would improperly force plaintiffs to explain the operation of proprietary software that could only be found through discovery.[15] Thus, at the pleading stage, allegations that competitors supplied nonpublic information to a common platform and received pricing recommendations informed by that information were sufficient.

In contrast, other courts have required some measure of allegations about the function of the algorithm. As a matter of adequate pleading, courts have required plaintiffs to allege how they understand the software works to facilitate collusion and in turn impact prices or output.

The Third Circuit's treatment of algorithmic pricing departs notably from that of several other preceding courts' decisions in similar cases. The Third Circuit proposes to effectively eliminate the requirements that plaintiffs allege (1) how a pricing algorithm works, and (2) horizontal parallel conduct that amounts to more than knowledge of a competitor's use of the same software products. The court's opinion suggests something closer to a strict liability standard for antitrust claims based on algorithmic pricing, similar to that articulated in the proposed Preventing Algorithmic Collusion Act.[16]

Although the Third Circuit stopped short of finding that the use of algorithmic pricing tools is inherently unlawful,[17] it endorsed the proposition espoused by opponents of algorithmic pricing tools that if a man named "Bob" could not lawfully collect confidential pricing information from competitors and tell them how to price, replacing the man with an algorithm does not necessarily change the antitrust analysis.[18] The court did, however, draw a distinction between independent use of pricing software and use of a common software that effectively functions as a coordinating mechanism by collecting nonpublic competitor information and generating recommendations based on that pooled data.[19] The latter allegations, the Third Circuit concluded, were sufficient to support an inference that the casino-hotel defendants agreed to fix room prices through Cendyn's price recommendations software.

Key Takeaways for Companies

For businesses using revenue-management and algorithmic-pricing software, the Third Circuit's decision potentially strengthens plaintiffs' and regulators' ability to pursue algorithmic-collusion theories and underscores the importance of evaluating:

• The nature of information provided to and utilized by third-party pricing platforms
• What other entities are using the same third-party pricing platforms
• Whether competitors' nonpublic information influences pricing outputs
• Whether the user's ability to override the software's recommendations is constrained in any way
• How frequently algorithmic recommendations are adopted in practice
• Whether increases in price are sustained even in the face of decreased demand
• Antitrust compliance measures governing the use of AI and pricing tools

***

BakerHostetler's Antitrust team has extensive experience advising clients on antitrust risks associated with data sharing arrangements and litigating alleged price fixing via alleged algorithmic collusion, information sharing and benchmarking. The Antitrust team includes former Department of Justice Antitrust Division leaders and state attorney general personnel who have experience investigating alleged collusive conduct via data sharing. Please feel free to contact any of our experienced professionals if you have questions about this alert.

[1] No. 1:23-CV-02536-KMW-EAP, 2024 WL 4356188 (D.N.J. Sept. 30, 2024).

[2] Cornish-Adebiyi v. Caesars Ent., Inc., No. 24-3006, at *9, --- F.4th --- (3d Cir. July 29, 2026).

[3] E.g., Portillo v. CoStar Grp. Inc., No. 2:24-cv-00229 (W.D. Wash.); In re Manufactured Home Lot Rents Antitrust Litig., No. 23-cv-06715 (N.D. Ill.); Segal v. Amadeus IT Grp., S.A., No. 24-CV-1783 (N.D. Ill.); See In re. RealPage, Inc. Rental Software Antitrust Litig., No. 3:23-md-03071 (M.D. Tenn.); Duffy v. Yardi Sys., No. 2:23-cv-01391-RSL (W.D. Wash.); See Mendez v. Optimal Blue, LLC, No. 3:25-cv-01140 (M.D. Tenn.); United States v. Agristats, Inc., No. 0:23-cv-03009 (D. Minn.).

[4] Gibson v. Cendyn Grp., LLC, 2024 WL 2060260 (D. Nev. May 8, 2024).

[5] Cornish-Adebiyi, No. 24-3006, at *21.

[6] Id. at **30-31.

[7] Id.

[8] Id. at *38.

[9] Id. at **10, 23 (relying on allegations that defendants adhered to the software's recommendations approximately 90 percent of the time).

[10] Id. at *38.

[11]Id. at **24, 28-29, 32.

[12] Id. at *26.

[13] Id.

[14] Id. at **37-38.

[15] Id.

[16] See https://www.congress.gov/bill/119th-congress/senate-bill/232.

[17] Cornish-Adebiyi, No. 24-3006, at **13, 40-41.

[18] Id. at *42.

[19] Id. at *41.