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Steering the Herd: A Framework for LLM-based Control of Social Learning

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.5Download Report PDF
Social learningLLMsoptimal controlinformation designdynamic programming
Abstract:

Algorithms increasingly serve as information mediators -- from social media feeds and targeted advertising to the increasing ubiquity of LLMs. This engenders a joint process where agents combine private, algorithmically-mediated signals with observational learning from peers to arrive at decisions. To study such settings, we introduce a model of controlled sequential social learning in which an information-mediating planner (e.g., an LLM) controls the information structure of agents while they also learn from the decisions of earlier agents. The planner may seek to improve social welfare (an altruistic planner) or to induce a specific action the planner prefers (a biased planner). Our framework presents a new optimization problem for social learning that combines dynamic programming with decentralized action choices and Bayesian belief updates. In this setting, we prove the convexity of the value function and characterize the optimal policies of altruistic and biased planners, which attain desired tradeoffs between the costs they incur and the payoffs they earn from induced agent choices. The characterization reveals that the optimal planner operates in different modes depending on the range of belief values. The modes include investing the maximum allowed resource, not investing any resource, or the investment increasing or decreasing with increase in the belief. Notably, for some ranges of belief the biased planner even intentionally obfuscates the agents' signals. Even under stringent transparency constraints—information parity with individuals, no lying or cherry‑picking, and full observability—we show that information mediation can substantially shift social welfare in either direction. We complement our theory with simulations in which LLMs act as both planner and agents. Notably, the LLM-based planner in our simulations exhibits emergent strategic behavior in steering public opinion that broadly mirrors the trends predicted, though key deviations suggest the influence of non-Bayesian reasoning—consistent with the cognitive patterns of both human users and LLMs trained on human-like data. Together, we establish our framework as a tractable basis for studying the impact and regulation of LLM information mediators that corresponds to real behavior.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper introduces a framework for controlled sequential social learning where an information-mediating planner (e.g., an LLM) shapes agent information structures while agents also learn from predecessors' decisions. It resides in the 'Bayesian Social Learning and Belief Dynamics' leaf, which contains only three papers total. This leaf focuses on Bayesian inference frameworks for sequential decision-making with observational learning. The sparse population suggests this specific intersection—combining planner-mediated control with Bayesian social learning—is relatively underexplored compared to broader reinforcement learning or multi-agent branches in the taxonomy.

The taxonomy reveals neighboring research directions that contextualize this work. The sibling leaf 'Controlled and Incentivized Sequential Learning' (three papers) addresses planner-mediated settings but may differ in formalism or incentive structures. The parent branch 'Sequential Social Learning Theory and Mechanisms' also includes 'Adaptive and Doubly Adaptive Learning Mechanisms' (one paper) and 'Social Learning in Applied Decision Contexts' (two papers), indicating that while foundational social learning theory is established, the controlled variant with dynamic programming and Bayesian updates occupies a niche position. Nearby branches like 'Multi-Step Reinforcement Learning' and 'Multi-Agent Collaborative Learning' address sequential optimization and coordination but typically without the planner-agent-observational learning triad.

Among 27 candidates examined across three contributions, no refutable prior work was identified. For the novel theoretical framework (10 candidates examined, 0 refutable), the rigorous policy characterization (7 candidates, 0 refutable), and the LLM-based empirical validation (10 candidates, 0 refutable), the search found no overlapping claims. This suggests that within the limited semantic neighborhood explored, the combination of controlled information mediation, Bayesian belief updates, and dynamic programming for social learning appears distinctive. However, the modest search scale (27 papers) and the sparse taxonomy leaf (3 papers) mean the analysis captures a focused slice rather than exhaustive coverage.

Given the limited search scope and the sparse taxonomy leaf, the work appears to occupy a relatively novel position at the intersection of algorithmic control, Bayesian social learning, and sequential decision-making. The absence of refutable candidates among 27 examined papers, combined with the small sibling set, suggests the specific formulation is not heavily populated in the immediate literature. Nonetheless, the analysis does not rule out related work in adjacent subfields (e.g., mechanism design, information design) that may not have surfaced in the top-K semantic matches.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
27
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Algorithmic control of sequential social learning dynamics. This field examines how agents learn from one another over time and how external mechanisms can steer collective belief formation or decision-making. The taxonomy organizes research into five main branches. Sequential Social Learning Theory and Mechanisms explores foundational models of belief updating and information cascades, including Bayesian frameworks (e.g., Bayesian Social Learning[2]) and agent-based opinion dynamics (Agent-based Opinion Dynamics[22]). Multi-Step Reinforcement Learning and Sequential Decision-Making addresses how agents optimize policies through iterative feedback, with works like Unifying Multi-step RL[6] and Elastic Step DDPG[50] tackling credit assignment and temporal horizons. Multi-Agent and Collaborative Learning Systems investigates coordination and knowledge sharing among multiple learners, as seen in Collaborative PV Forecasting[9] and federated approaches (Robust Federated Aggregation[24]). Multi-Step Learning in Specialized Application Domains applies sequential reasoning to tasks ranging from arithmetic (Arithmetic Reasoning Tasks[37]) to medical decisions (Lung Transplant Decision[18]). Finally, Incremental and Adaptive Learning Paradigms focuses on systems that evolve their representations or strategies over time, including classic methods (Incremental Conceptual Clustering[39]) and modern adaptive schemes (Doubly Adaptive Learning[17]). A particularly active line of work centers on how external interventions or platform designs can influence collective outcomes in social learning settings. Steering the Herd[0] sits squarely within the Bayesian Social Learning and Belief Dynamics cluster, examining algorithmic strategies to guide sequential belief updates among interacting agents. Its emphasis on control mechanisms distinguishes it from purely descriptive models like Bayesian Social Learning[2], which characterizes equilibrium behavior without intervention, and from Agent-based Opinion Dynamics[22], which simulates emergent patterns but does not optimize steering policies. Meanwhile, related efforts such as Controlled Social Learning[29] and Sequential Audience Conversions[5] explore similar themes of shaping information flow, though they may differ in the formalism or application context. Across these branches, key open questions include the trade-offs between centralized control and decentralized adaptation, the robustness of steering strategies under model misspecification, and the ethical implications of algorithmically mediated social learning.

Claimed Contributions

Novel theoretical framework for controlled sequential social learning

The authors develop a new model combining dynamic programming with decentralized agent choices and Bayesian belief updates, where an information-mediating planner controls signal precision while agents engage in social learning from predecessors' actions.

10 retrieved papers
Rigorous characterization of optimal planner policies

The authors prove the convexity of the value function for altruistic planners and derive optimal policies for both altruistic and biased planners, revealing different operational modes depending on belief ranges, including cases where biased planners intentionally obfuscate signals.

7 retrieved papers
Empirical validation using LLMs as planner and agents

The authors implement LLM-based simulations showing that planners accounting for social learning substantially impact welfare, that LLM planners exhibit emergent strategic behavior mirroring theoretical predictions despite non-Bayesian agents, and that the framework corresponds to real behavior patterns.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Novel theoretical framework for controlled sequential social learning

The authors develop a new model combining dynamic programming with decentralized agent choices and Bayesian belief updates, where an information-mediating planner controls signal precision while agents engage in social learning from predecessors' actions.

Contribution

Rigorous characterization of optimal planner policies

The authors prove the convexity of the value function for altruistic planners and derive optimal policies for both altruistic and biased planners, revealing different operational modes depending on belief ranges, including cases where biased planners intentionally obfuscate signals.

Contribution

Empirical validation using LLMs as planner and agents

The authors implement LLM-based simulations showing that planners accounting for social learning substantially impact welfare, that LLM planners exhibit emergent strategic behavior mirroring theoretical predictions despite non-Bayesian agents, and that the framework corresponds to real behavior patterns.

Table of Contents