[Paper] Polarization by Design: How Elites Could Shape Mass Preferences as AI Reduces Persuasion Costs

Source: arXiv - 2512.04047v1

Overview

Nadav Kunievsky’s paper explores how AI‑driven persuasion tools could turn the shaping of public opinion into a deliberate, strategic lever for political elites. By modeling the economics of “designing” preference distributions, the work shows that cheaper, more precise persuasion can systematically push societies toward polarization—or, under certain power‑sharing arrangements, toward more stable but still engineered opinion clusters.

Key Contributions

• Dynamic persuasion‑cost model: Introduces a formal framework where elites decide how much to reshape the distribution of policy preferences, balancing persuasion costs against the need for a majority.
• Polarization pull: Demonstrates that, with a single dominant elite, optimal persuasion strategies tend to increase opinion polarization as AI lowers the cost of targeted messaging.
• Semi‑lock dynamics: Shows that when two opposing elites alternate in power, advanced persuasion can create “semi‑lock” zones—cohesive opinion blocks that are hard for the rival elite to overturn.
• Technology‑driven strategic shift: Argues that AI‑enabled persuasion transforms polarization from a passive social byproduct into an active governance tool.
• Policy relevance: Provides a theoretical lens for assessing democratic stability in the age of low‑cost, high‑precision influence operations.

Methodology

The author builds a continuous‑time game between political elites and the mass electorate:

1. Preference distribution – Society’s policy preferences are represented as a probability density over a one‑dimensional ideological axis.
2. Elite decision – An elite chooses a “reshaping function” that nudges the distribution toward a target shape, incurring a cost that depends on the magnitude of change and the technology’s persuasion efficiency.
3. Majority rule constraint – The elite must ensure that the resulting distribution yields a majority for its preferred policy; otherwise the intervention fails.
4. Dynamic evolution – The model iterates over periods, allowing the elite to adjust its intervention as the distribution evolves.
5. Two‑elite extension – A second elite with opposite policy goals alternates power, leading to a game of “lock‑in” versus “unlock‑in” strategies.

The mathematics relies on convex optimization and differential equations, but the intuition is straightforward: cheaper, more accurate persuasion makes it economically viable to push public opinion farther from the center, because the cost of doing so falls faster than the political benefit of securing a majority.

Results & Findings

• Single elite scenario: The optimal persuasion policy is to increase the variance of the preference distribution, creating a bimodal (polarized) shape that still guarantees a majority for the elite’s preferred policy. As persuasion costs decline (e.g., via AI‑generated micro‑targeted content), the “polarization pull” intensifies.
• Dual‑elite scenario: When power alternates, each elite faces a trade‑off between polarizing (making the opponent’s job harder) and consolidating (creating a semi‑lock where its own supporters are tightly clustered). Advanced persuasion can tip the balance either way:
  • Heightened polarization if each elite seeks to maximize the opponent’s reshaping cost.
  • Reduced polarization if elites prefer to lock in a moderate, cohesive block that is resistant to future attacks.
• Technology trajectory: The model predicts a non‑linear relationship between persuasion efficiency and polarization outcomes—small improvements may have modest effects, but once a “critical mass” of low‑cost AI tools is reached, the system can rapidly shift to either extreme polarization or entrenched semi‑locks.

Practical Implications

• Political campaigning: Campaign managers can view AI‑generated content (deepfakes, hyper‑personalized ads) not just as a way to amplify a message, but as a budget‑allocation problem—spending to reshape the electorate’s distribution rather than merely to persuade existing voters.
• Regulatory focus: Policymakers should monitor cost curves of persuasion technologies. Regulations that raise the marginal cost of micro‑targeted persuasion (e.g., transparency mandates, data‑access limits) could blunt the polarization pull.
• Platform design: Social media services can embed friction mechanisms (rate limits, audit trails) that increase the effective cost of large‑scale preference reshaping, thereby reducing the incentive for elites to engineer extreme opinion splits.
• Risk assessment for NGOs & watchdogs: The semi‑lock concept suggests that opposition groups may need to invest in counter‑persuasion capabilities to avoid being locked out of the public discourse.
• Strategic forecasting: Companies building AI‑driven marketing or political tech tools can use the model to anticipate market demand spikes when political cycles align with low persuasion costs (e.g., election years).

Limitations & Future Work

• One‑dimensional ideology: Real‑world preferences span multiple issues; extending the model to higher‑dimensional spaces could reveal richer dynamics.
• Homogeneous elite assumption: The analysis treats elites as monolithic decision‑makers; incorporating intra‑elite competition (e.g., party factions) may alter optimal strategies.
• Empirical validation: The paper is theoretical; future work should calibrate the model with data on ad spend, AI tool adoption, and observed polarization trends.
• Ethical considerations: While the model highlights strategic incentives, it does not address normative questions about the desirability of engineered opinion landscapes—an avenue for interdisciplinary research.

Authors

• Nadav Kunievsky

Paper Information

• arXiv ID: 2512.04047v1
• Categories: econ.GN, cs.AI, cs.CY
• Published: December 3, 2025
• PDF: Download PDF