Graph-embedded reinforcement learning for dynamic pricing and advertising under network effects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Firms increasingly rely on both price discounts and advertising campaigns to shape product diffusion in socially connected markets, yet existing models rarely treat these levers jointly or account for network heterogeneity. This study develops an integrated, network-aware framework for dynamic pricing and advertising control. A stochastic compartmental model of the consumer decision-making model (CDM) is formulated on a social graph, with transition intensities modulated by price, advertising spend, and peer influence. A deterministic mean-field approximation yields closed-form expressions for a trade-free equilibrium (TFE) and a reproduction number threshold that delineates when adoption dies out versus persists. Building on this analytical core, the paper introduces twin delayed deep deterministic policy gradient with encoded state (TD3ES), a reinforcement learning (RL) controller that couples an actor-critic architecture with a graph-convolutional autoencoder, thereby compressing high-dimensional network states into a tractable latent representation. A custom GPU-accelerated simulator facilitates large-scale training. Numerical experiments on Erdős-Rényi and heavy-tailed exponential networks show that twin delayed deep deterministic policy gradient with encoded state (TD3ES) swiftly converges to profit-maximizing joint policies and, on heterogeneous graphs, outperforms a TD3 baseline that lacks network-structural information. Error analysis reveals that the autoencoder naturally prioritizes high-degree hubs in dominant CDM compartments, explaining its superior performance. Managerially, the results demonstrate that ignoring topology can forfeit substantial revenue and that adaptive, network-aware coordination of price and advertising is both feasible and valuable. The framework thus unites rigorous diffusion theory with scalable learning, offering a practical tool for data-driven marketing in connected consumer ecosystems. • Stochastic diffusion model links price, ads, and peer influence on networks. • Mean-field analysis yields a reproduction threshold and trade-free equilibrium stability. • Introduce TD3ES: RL with GCN autoencoder for joint pricing-advertising control. • GPU simulator enables scalable training on large-scale heterogeneous graphs. • TD3ES lifts profit on heavy-tailed networks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it