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Persuasive Prediction via Decision Calibration

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
CalibrationDecision CalibrationPersuasionInformation Design
Abstract:

Bayesian persuasion, a central model in information design, studies how a sender, who privately observes a state drawn from a prior distribution, strategically sends a signal to influence a receiver's action. A key assumption is that both sender and receiver share the precise knowledge of the prior. Although this prior can be estimated from past data, such assumptions break down in high-dimensional or infinite state spaces, where learning an accurate prior may require a prohibitive amount of data. In this paper, we study a learning-based variant of persuasion, which we term persuasive prediction. This setting mirrors Bayesian persuasion with large state spaces, but crucially does not assume a common prior: the sender observes covariates XX, learns to predict a payoff-relevant outcome YY from past data, and releases a prediction to influence a population of receivers. To model rational receiver behavior without a common prior, we adopt a learnable proxy: decision calibration, which requires the prediction to be unbiased conditioned on the receiver's best response to the prediction. This condition guarantees that myopically responding to the prediction yields no swap regret. Assuming the receivers best respond to decision-calibrated predictors, we design a provably efficient algorithm that learns a decision-calibrated predictor within a randomized predictor class that optimizes the sender's utility. In the commonly studied single-receiver case, our method matches the utility of a Bayesian sender who has full knowledge of the underlying prior distribution. Finally, we extend our algorithmic result to a setting where receivers respond stochastically to predictions and the sender may randomize over an infinite predictor class.

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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 persuasive prediction framework that extends Bayesian persuasion to high-dimensional state spaces without requiring a common prior between sender and receiver. It occupies the sole position in the 'Bayesian Persuasion and Decision Calibration' leaf, which sits within the broader 'Strategic Information Disclosure and Persuasion' branch. This leaf contains only the original paper itself, indicating a relatively sparse research direction within a taxonomy of seventeen papers across ten leaf nodes. The work addresses a gap where classical persuasion models assume shared priors, which becomes impractical when states are learned from data rather than known a priori.

The taxonomy reveals neighboring work in platform information design, multi-agent disclosure, and crowdfunding strategies, all of which study sender-receiver interactions but under different structural assumptions. The closest conceptual neighbors appear in 'Decision-Making Under Strategic Agent Behavior,' particularly work on strategic response to predictions and performative debiasing. However, the exclude notes clarify boundaries: platform-mediated disclosure focuses on marketplace profit maximization, while the original paper centers on learning-based persuasion with decision calibration as a behavioral model. The taxonomy structure suggests the paper bridges classical information design with modern machine learning concerns, occupying a distinct niche between strategic disclosure and predictive modeling.

Among twenty-two candidates examined across three contributions, none were identified as clearly refuting the paper's claims. The persuasive prediction framework examined eight candidates with zero refutations; decision calibration as a behavioral model examined ten candidates with zero refutations; and the oracle-efficient algorithm examined four candidates with zero refutations. This suggests that within the limited search scope—top-K semantic matches plus citation expansion—the specific combination of learning-based persuasion, decision calibration without common priors, and algorithmic guarantees appears relatively unexplored. The framework and calibration contributions show broader examination (eight and ten candidates respectively) compared to the algorithmic component (four candidates), possibly reflecting more established literatures in persuasion theory and calibration.

Based on the limited search of twenty-two candidates, the work appears to occupy a novel position combining classical persuasion theory with modern learning-theoretic tools. The taxonomy's sparse population in this specific leaf, coupled with zero refutations across all contributions, suggests the paper addresses a gap not extensively covered by prior work within the examined scope. However, this assessment is constrained by the search methodology and does not constitute an exhaustive review of all potentially relevant literature in information design, calibration, or strategic prediction.

Taxonomy

Core-task Taxonomy Papers
17
3
Claimed Contributions
22
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: learning optimal decision-calibrated predictors for strategic information disclosure. The field encompasses how information providers design signals or predictions to influence downstream decision-makers, balancing transparency with strategic objectives. The taxonomy organizes this landscape into four main branches. Strategic Information Disclosure and Persuasion examines how senders craft signals to steer receiver beliefs and actions, often drawing on Bayesian persuasion frameworks and decision calibration techniques such as those in Decision Calibration[0]. Decision-Making Under Strategic Agent Behavior focuses on settings where agents respond strategically to predictions or policies, including performative prediction and gaming dynamics seen in works like Strategic Behavior[1] and Performative Debias[7]. Human-AI Collaboration and Forecast-Based Decision Design explores how forecasts and AI-generated insights shape human choices in collaborative environments, with applications ranging from workforce allocation (Workforce Allocation[3]) to dashboard design (Forecasting Dashboards[13]). Information Acquisition and Adversarial Information Trade-offs addresses scenarios where information gathering itself is costly or contested, including adversarial settings like Pursuit Evasion[10] and constrained communication channels (Communication Constraints[15]). A particularly active line of work centers on calibration and persuasion under strategic constraints, where the challenge is to design signals that are both informative and aligned with sender objectives. Decision Calibration[0] sits squarely within this cluster, emphasizing how to learn predictors that induce desired downstream decisions while maintaining calibration guarantees. This contrasts with approaches in the strategic behavior branch, such as Performative Debias[7], which focus more on mitigating feedback loops when agents adapt to predictions. Meanwhile, applied studies like Credit Ratings[8] and Platform Reputation[6] illustrate domain-specific instantiations of these principles in finance and online markets. A key open question across these branches is how to balance the informativeness of disclosed signals with the sender's strategic goals, especially when receivers are sophisticated or when communication is constrained, as explored in Communication Constraints[15] and Sufficient Statistic[4].

Claimed Contributions

Persuasive prediction framework without common prior assumption

The authors introduce a persuasive prediction framework that extends Bayesian persuasion to high-dimensional or infinite state spaces where learning an accurate prior from finite data is infeasible. Unlike classical Bayesian persuasion, this framework does not require sender and receiver to share precise knowledge of the prior distribution.

8 retrieved papers
Decision calibration as behavioral model for rational receivers

The authors propose using decision calibration as a behavioral model that ensures receivers have no swap regret when myopically best responding to predictions. This provides a tractable alternative to modeling receiver rationality when a common prior is unavailable.

10 retrieved papers
Oracle-efficient algorithm for learning optimal decision-calibrated predictors

The authors develop an oracle-efficient minimax-based algorithm (PerDecCal) that learns a near-optimal decision-calibrated predictor from finite samples. The algorithm requires polynomial sample complexity in the number of receiver actions and outcome dimension, independent of the feature space size, and matches the Bayesian benchmark utility in the single-receiver case.

4 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Persuasive prediction framework without common prior assumption

The authors introduce a persuasive prediction framework that extends Bayesian persuasion to high-dimensional or infinite state spaces where learning an accurate prior from finite data is infeasible. Unlike classical Bayesian persuasion, this framework does not require sender and receiver to share precise knowledge of the prior distribution.

Contribution

Decision calibration as behavioral model for rational receivers

The authors propose using decision calibration as a behavioral model that ensures receivers have no swap regret when myopically best responding to predictions. This provides a tractable alternative to modeling receiver rationality when a common prior is unavailable.

Contribution

Oracle-efficient algorithm for learning optimal decision-calibrated predictors

The authors develop an oracle-efficient minimax-based algorithm (PerDecCal) that learns a near-optimal decision-calibrated predictor from finite samples. The algorithm requires polynomial sample complexity in the number of receiver actions and outcome dimension, independent of the feature space size, and matches the Bayesian benchmark utility in the single-receiver case.

Table of Contents

Persuasive Prediction via Decision Calibration | Novelty Validation