Three-in-One World Model: Energy-Based Consistency, Prediction, and Counterfactual Inference for Marketing Intervention
Junichiro Niimi

TL;DR
This paper introduces a unified three-in-one world model architecture using a Deep Boltzmann Machine to improve marketing decision-making by capturing consumer heterogeneity, enabling outcome prediction, and facilitating counterfactual inference.
Contribution
It proposes a novel DBM-based framework that supports energy-based consistency, prediction, and counterfactual inference within a single model for marketing applications.
Findings
Adapters match strong MLP baseline on visit- and purchase-AUC.
Adapters recover heterogeneous treatment effects better than baseline meta-learners.
Free-energy clamps penalize implausible counterfactual purchase trajectories.
Abstract
Marketing decisions reflect the interaction of latent consumer heterogeneity, time-varying internal states, and explicit interventions, a structure that current prediction- and language-oriented models do not capture in a unified manner. We propose a Three-in-One world-model architecture in which a Deep Boltzmann Machine (DBM) learns a frozen belief representation from demographics, time, and lagged actions and outcomes, with lightweight task-specific adapters attached on top. The same belief supports three tasks within a single framework: (i) energy-based consistency evaluation through the DBM's free energy, (ii) outcome prediction through adapters, and (iii) counterfactual inference by holding the belief fixed and varying only the action input given to the adapter. Using a controlled simulation in which the latent price sensitivity, promotion responsiveness, and base preference of…
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