Content Promotion as a Strategic Game: How to Design Agentic Publishers for the Evolving Search Ecosystem in the GenAI Era?

With the rise of LLMs, publishers now operate in a dual world where traditional search and chat-like systems coexist. We propose a unified, game-theoretic view of this environment and highlight different tools, such as Multi-Agent Reinforcement Learning, that support the development of competitive content-optimization agents.

TL;DR

LLMs are transforming search from static ranked lists of results into interactive, chat-like experiences. LLMs also reshape the way publishers (i.e., content creators) generate content: they increasingly use LLMs and agentic tools to optimize rankings in traditional search and to improve their content visibility in the novel chat-like systems. Importantly, publishers engage in an iterative process: they observe how their content performs — whether it is highly ranked in the traditional search or visible in chat-like answers — and then strategically modify or regenerate their content in response to this feedback. As a result, the ecosystem becomes a strategic game with incomplete information, evolving feedback, and repeated interactions. In this strategic ecosystem, the publishers act as players striving to promote their content, competing for rankings and visibility under mechanisms that are only partially known to them. This blog outlines why game theory offers a natural framework to model these settings and to design agents acting on behalf of publishers. We highlight several frameworks such as Game theory and Multi-agent Reinforcement learning and show how they contribute to model and design effective agents. Our aim is to provide principled lens for content promotion in the LLM era.

LLMs Reshape Both Sides of the Search Ecosystem

Large language models have fundamentally reshaped the search experience . While users once typed short keyword queries and scanned a ranked list of links, modern systems now offer chat-like interfaces that feel more like interacting with an assistant than browsing a directory. These systems provide conversational question-answering (QA) capabilities by generating responses synthesized from multiple sources. Search is no longer about “links” — it is about responses. In the service of providing answers to questions, paradigms such as Retrieval Augmented Generate (RAG) have been developed. Similar paradigms were suggested for integrate commercial content in the answer . The new chat-like setting introduces new challenges, such as providing users with pointers to sources of information, leading to techniques known as source attribution . Interestingly, effective retrieval has become a necessity in this new setting: augmenting an LLM with the ability to retrieve relevant information during answer generation or to retrieve the most relevant source to attribute to related pieces of information.

But the transformation is not limited to the user side of the ecosystem.

LLMs are also reshaping the publisher side. In the traditional Web era, publishers focused on keyword and hyperlink optimization, and content structure to influence ranking . Today, publishers increasingly rely on LLMs:

The paradigm of helping publishers in improving their content visibility in generative engine responses is often called Generative Engine Optimization (GEO) .

Publisher Incentives Across Coexisting Ecosystems

The search ecosystem is not transitioning overnight — it is evolving. Today, publishers operate in a hybrid world where traditional search engines and LLM-driven chat interfaces coexist. Users still issue keyword queries and click ranked results, yet they also increasingly engage with conversational systems that generate responses. As a result, publishers must navigate two overlapping incentive structures at once.

Publishers’ Incentives in the Traditional Setting

In the traditional setting, publisher goals are well-defined and widely understood. Visibility depends on ranking, which in turn hinges on factors, some of which are familiar:

Metrics such as impressions and CTR (Click Through Rate) offer interpretable objectives.

Publishers’ Incentives in the Chat-Like Settings

By contrast, the objectives in the chat-like settings are far less defined. Publishers know that visibility is tied to whether the chat-like setting:

But unlike in traditional search, where there is a clear notion of a ranked results page, LLM-based systems provide no public ranking, and often little transparency about how sources are selected for retrieval or attribution.

The publisher’s utility is therefore inherently vague: Is success defined by:

The field has not yet converged on a clear success criterion, and the objective itself is still ill-defined, making strategic optimization an open problem.

Leveraging LLMs for Content Optimization

Publishers — or agents acting on behalf of them — can leverage LLMs as powerful content optimization tools (See Figures 1 and 2). The publishers are incentivized to have effective automatic generation to promote their content. However, any such agentic systems should be carefully designed:

Content optimization in a traditional search setting
Figure 1: Content optimization in a traditional search setting.
Content optimization in a chat-like setting
Figure 2: Content optimization in a chat-like setting.

Content Promotion as a Game

We argue that game theory provides a major and essential framework for analyzing the resulting competitive dynamics and for designing agents that assist publishers to strategically promote their content. More precisely, repeated games with incomplete information , induced by the system (search or chat-like system). Actions correspond to content generation (or modification). Strategies are history dependent content selection/generation algorithms and strategy profiles capture joint activity of agents which are active in the ecosystem. The utility function of an agent is the accumulated payoff along time, taking into account aspects such as ranking (for traditional search), visibility (for the chat-like systems) and faithfulness to the original content before any modification. As the selection/generation mechanism is only partially known, the setting is of incomplete information. Since the publisher may keep improving the content once she receives feedback from the system, we argue that the game should be treated as a repeated game. Figures 3 and 4 present schematic illustrations of the resulting games for search systems and chat-like systems, respectively.

Schematic illustration of a traditional search game
Figure 3: Schematic illustration of a traditional search game.
Schematic illustration of a chat-like game
Figure 4: Schematic illustration of a chat-like game.

The viewpoint just presented is aligned with a growing body of research that treats content promotion as a strategic process. During recent years, several works have applied game-theoretic approaches to model competitive search , diversity-based ranking , to study the convergence of long term behavior to equilibrium in retrieval settings and to design equilibrium in ranking games using corpus enrichment . explored strategic document modifications in response to ranking feedback, revealing rich and sometimes counterintuitive dynamics. Yao et al.’s work provides deep, game-theoretic insight into how creators behave strategically in recommender systems and offers actionable designs for systems to align creator incentives with user welfare . In the context of strategic retrieval systems, presented the first formal game-theoretic analysis of a sponsored QA system where advertisers bid to have their sponsored content fused in the generated answer of question-answering systems. showed how this new ad-integration model introduces strategic bidding behavior and derived equilibrium properties and welfare implications in this setting.

However, the technological transformation of the last few years — particularly the rise of LLM-based chat interfaces — calls for re-examination and adaptation of the assumptions in classical game theoretic models. Traditional work often assumes fixed ranking functions and explicit publishers’ incentives. In contrast, chat-like systems generate personalized and dynamic answers ; the objective of publishers becomes much harder to define. These shifts motivate the development of new models that account for generative selection mechanisms, opaque feedback, and high adaptivity — all of which strengthen the case for game-theoretic reasoning and for new solution concepts.

Agent Design Aspects for Traditional Search and Chat-like Systems

We delve into three important aspects that should be considered when designing agents that assist publishers promote their content.

The first ingredient that can (and will) be naturally used, is to exploit the significant power of LLMs, e.g., use clever prompt engineering to promote content and maintain faithfulness in such games.

A second ingredient one should be aware of is incomplete information about the selection or generation mechanism applied by the system. Using, for example, information about the decision made by the mechanism (e.g., past responses) can help to train and fine tune agents.

The third ingredient is the need to be aware of competitors’ behavior. With limited access to annotated data — and only partial observability of the decisions made by these systems — this calls for carefully designed simulation tools. here again LLMs can be used as effective generative tools.

Heretofore, we have described content promotion as a publishers game, shaped by evolving search and chat-based ecosystems and by the increasing role of generative AI. We now discuss different frameworks that can be employed to model and design effective agents in such settings.

Frameworks

Multi-Agent Reinforcement Learning (MARL)

This framework models each player as an autonomous learning agent that optimizes its policy through repeated interaction with the environment and with other agents. MARL enables us to study how strategic behaviors emerge end-to-end from experience, without imposing strong assumptions about how these systems (i.e., search or chat-like systems) operate internally . It also allows us to analyze coordination, competition, and non- stationarity — core properties of competitive games under a principled RL formulation. In our setting, the policy of the agent is the LLM itself, and reinforcement learning is the mechanism that aligns this LLM to the objectives of the game, given whatever information the agent can observe; different RL alignment algorithms may be adopted; e.g., . If we had access to a full log of games — for example, historical queries and the ranking of pages in traditional search, or user questions and the resulting responses in chat-like systems — then we could directly formulate a supervised or RL-based learning problem. With such logs, the agent could be trained to optimize performance with respect to well-defined metrics (e.g., rank position, click-through, user engagement, rejection rates, etc.). Beyond the standard trade-offs encountered in reinforcement learning, our settings introduce unique design tensions that must be addressed:

(a) Short-term wins vs. long-term information leakage

An agent may be able to win an individual round of a game by aggressively optimizing its content. However, doing so may reveal its strategy to competing agents, enabling them to quickly imitate or counter it. Since the objective is to maximize cumulative reward across many rounds, the agent must balance immediate gains with strategic opacity — sometimes deliberately avoiding overly distinctive or extreme actions to prevent being copied .

(b) On-policy vs. off-policy training

A crucial design question is whether the agent should continue learning during the game (on-policy) or rely solely on offline training before the game begins (off-policy).

(c) Constructing the loss function

A core challenge is designing a loss function that balances ranking promotion or content visibility with faithfulness to the original content. On one hand, the agent should modify content to improve performance; on the other hand, excessive deviation risks harming credibility, violating constraints, or triggering penalties. The loss function must encode this balance explicitly — penalizing harmful drift while incentivizing strategic, beneficial content modifications.

Synthetic Dataset Generation

Sometimes real-world data presents substantial limitations: it may be inaccessible, noisy, incomplete, or simply ill-suited for the specific questions we wish to study. For this reason, we propose constructing synthetic datasets with controlled statistical properties. Synthetic data enables us to systematically probe targeted hypotheses — for example, how diversity constraints, noise structure, or shifts in the query or question distribution influence agent behavior and system-level outcomes. Moreover, synthetic datasets provide repeatability and experimental control that are often impossible with logs of production systems. They allow for clean ablation studies, controlled perturbations, and the isolation of causal factors, all of which are essential for understanding and improving agents operating in competitive ranking and chat-like ecosystems. Importantly LLMs now make it feasible to generate high-fidelity synthetic datasets at scale, enabling richer and more flexible experimental environments than were previously possible.

Simulation Environment

A simulation environment that models the rules of the game, the ranking or generation function, and the interaction protocol between agents is essential in the discussed strategic ecosystems . Such simulation can be used for generating strategic synthetic training datasets and for evaluating agents under diverse conditions. Together with the increasing proliferation over the Web of generated AI content , we suggest using simulators of the discussed competitions. Such simulators support rapid iteration and controlled experimentation across a wide range of configurations — rankers, prompts, datasets, reward structures, and hyperparameters. By isolating specific components of the system, the simulation environment helps uncover causal relationships, identify sensitivities to design choices, and systematically analyze how agents behave under different competitive scenarios.

Self-Play

Self-play is a method for iteratively training agents to be improved by competing (via simulation) against evolving versions of themselves . This approach is particularly useful in settings where optimal strategies are not known a priori and where the competitive landscape shifts over time. Self-play helps uncover equilibrium behaviors, stability properties, and potential failure modes of learning algorithms in adversarial or co-adaptive settings.

Meta Games and Learning Equilibrium

Agent design itself introduces an additional strategic layer: designers may choose between different LLM architectures, training procedures, datasets, prompting strategies, or RL frameworks. In this broader perspective, agents are not only competing within a single ranking or generation game, but the designers are competing in a meta-game over agent designs . Long-run success therefore depends on performing well not only within the fixed game, but also across the distribution of possible design choices made by other participants. In such meta-games, no single deterministic design may be universally optimal.

Instead, probabilistic ensembles of designs — mixtures over algorithms, prompting strategies, or training regimes — may provide better robustness and theoretical guarantees. These ensemble-based strategies reduce exploitability, adapt better to heterogeneous opponents, and align with classical results in game theory showing that mixed strategies often outperform pure ones in competitive environments.

Within meta-games over agent designs, a natural solution concept is learning equilibrium . Rather than focusing on equilibria of the underlying content-generation game — where actions correspond to the content produced — learning equilibrium concerns the design algorithms themselves being in equilibrium. That is, each designer’s strategy for constructing agents is optimal given the strategies of other designers, forming a stable configuration at the meta-level.

This distinction is subtle but important: learning equilibrium ensures that agent designs collectively converge to a stable distribution, even as the content-generation game continues to evolve. Such a concept becomes particularly relevant in scenarios where a platform or cloud provider offers content-generation services to multiple competing parties. In this setting, providing agent designs that satisfy learning equilibrium naturally mitigates exploitability, promotes fairness, and guarantees robust performance across heterogeneous competitors.

Preliminary Results

We now turn to discuss several papers that illustrate components (or the absence thereof) of the frameworks discussed above.

Prompt-based Agent

  introduced a set of prompt-based agents designed to modify a human-authored document so that the updated version is ranked higher in the next round of the ranking. Their prompting strategy explicitly constrains the agent to produce revisions that remain faithful — at least to some extent — to the original document. introduced prompt- engineering techniques that, in an offline evaluation, outperformed human-authored documents without requiring any learning. Their evaluation was conducted using data from a ranking competition previously organized among human document authors.

RLRF-based Agent

  proposed a ranker-aligned agent, trained via a reinforcement- learning-from-ranker-feedback (RLRF) framework. They constructed a synthetic dataset generated through a simulated ranking competition and used it to train the agent with the DPO algorithm . Notably, the resulting agent learned to adapt both to the ranking mechanism and to the behavior of competing agents. Their simulation-based evaluation demonstrates robustness with respect to various environmental conditions, including the number of competitors and the choice of ranking function. The simulation assumed that the competing agents are nonoptimized agents; this setup can be viewed as a variant of self-play, where agents iteratively interact under stable, nonlearning strategies.

LEMSS

  also introduced LEMSS, a large-scale simulation framework for ranking competitions among LLM-based agents. Their results show that LLM-driven agents exhibit behavioral patterns like those of humans in iterative ranking games. LEMSS provides fine-grained control over nearly all aspects of the competition — such as the number of competitors, the ranking mechanism, and the reward structure — and enables comprehensive analysis of the induced repeated game. The simulation models a traditional ranking competition. To design agents capable of promoting content in a chat-like interaction setting, a simulation of such a mechanism is called for.

Attacking RAG-based systems

Recent work highlights the growing vulnerability of retrieval-augmented generation (RAG) systems to targeted manipulation. PoisonedRAG shows that inserting only a few malicious documents into a RAG system’s knowledge base can steer model outputs with high success, revealing a potent “knowledge-corruption” attack surface. In contrast, RIPRAG use reinforcement learning to craft effective poisoned documents without access to the system’s internals. suggests to cooperatively optimize content for generative search engines by learning their preference rules. However, all these papers overlook the competitive nature of real-world environments, where multiple strategic content creators adaptively improve their content — potentially using agents — in a repeated game. This missing game-theoretic perspective limits their ability to capture the dynamics and equilibria that emerge when many actors simultaneously attempt to influence RAG- based systems.

Tools

Designing agents for ranking and content promotion competition benefits from a growing ecosystem of LLM-oriented development tools. Copilot Studio, Vertex AI, and LangChain provide the infrastructure needed to orchestrate complex LLM workflows, connect models to external data, and manage iterative agent-environment interactions. Copilot Studio enables rapid prototyping of agent behaviors within controlled interfaces, while Vertex AI offers scalable model hosting, fine-tuning, and evaluation pipelines suited for experimentation at scale. LangChain adds modular abstractions — such as memory, tools, retrieval, and multi- step reasoning — that make it easier to construct agents capable of interacting with dynamic environments. In addition, there are many python packages that may help to design and simulate RL-based agents such as .

Conclusion

In this blog post, we discussed how LLMs are reshaping the search ecosystem — both in traditional search and in emerging chat-like question-answering systems. Beyond transforming how users search, LLMs also change how publishers operate, enabling them to leverage LLMs to strategically optimize and promote iteratively their content.

We discussed several frameworks for modelling and designing such agents, including game- theoretic models and reinforcement learning approaches. These frameworks help account for multiple layers of uncertainty: the opaque ranking model or response-generation mechanisms of retrieval systems, the dynamic nature of evolving user queries/questions, and the competitive behavior of other strategic publishers or human content creators.

To build robust and adaptable agents, we emphasized that simulation plays a crucial role. Simulations enable the creation of strategic synthetic datasets and provide controlled environments for evaluating agent behavior, stress-testing policy robustness, and studying long-term dynamics in repeated competitive scenarios.

Overall, as the ecosystem continues to evolve, combining insights from game theory, reinforcement learning, and simulation offers a principled path toward designing agents capable of succeeding in both current and future search paradigms.