arXiv subjects

Boaz Taitler

Publications and source records attributed to Boaz Taitler.

8 recordsLinked to original sources

arXiv2026

Content Creation with Spillovers: An Incentive Design Approach

The rise of AI amplifies the economic phenomenon of \emph{positive spillovers}: when creators contribute content that can be reused and adapted by LLMs, one creator's effort may improve the content quality of others through recombination. While such spillovers can improve content quality, they also reshape incentives, as creators may reduce effort when they can benefit from others' contributions. We introduce the \emph{Content Creation with Spillovers} (CCS) model, a game between a platform and strategic creators. In this game, each creator chooses an effort level, and qualities are jointly determined by all creators' efforts. The platform contracts on qualities while seeking to maximize social welfare. We show that standard contest mechanisms can be unstable, and propose a parametrized family of \emph{Provisional Allocation} mechanisms that guarantee a Pareto-dominant equilibrium. Although optimizing welfare within this family is hard to approximate in general, we develop approximation algorithms whose guarantees hold either deterministically across structured spillover classes or with high probability under random instances.

cs.AI↗

arXiv2025

Collaborating with GenAI: Incentives and Replacements

The rise of Generative AI (GenAI) is reshaping how workers contribute to shared projects. While workers can use GenAI to boost productivity or reduce effort, managers may use it to replace some workers entirely. We present a theoretical framework to analyze how GenAI affects collaboration in such settings. In our model, the manager selects a team to work on a shared task, with GenAI substituting for unselected workers. Each worker selects how much effort to exert, and incurs a cost that increases with the level of effort. We show that GenAI can lead workers to exert no effort, even if GenAI is almost ineffective. We further show that the manager's optimization problem is NP-complete, and provide an efficient algorithm for the special class of (almost-) linear instances. Our analysis shows that even workers with low individual value may play a critical role in sustaining overall output, and excluding such workers can trigger a cascade. Finally, we conduct extensive simulations to illustrate our theoretical findings.

cs.GT↗

arXiv2025

Selective Response Strategies for GenAI

The rise of Generative AI (GenAI) has significantly impacted human-based forums like Stack Overflow, which are essential for generating high-quality data. This creates a negative feedback loop, hindering the development of GenAI systems, which rely on such data to provide accurate responses. In this paper, we provide a possible remedy: A novel strategy we call selective response. Selective response implies that GenAI could strategically provide inaccurate (or conservative) responses to queries involving emerging topics and novel technologies, thereby driving users to use human-based forums like Stack Overflow. We show that selective response can potentially have a compounding effect on the data generation process, increasing both GenAI's revenue and user welfare in the long term. From an algorithmic perspective, we propose an approximately optimal approach to maximize GenAI's revenue under social welfare constraints. From a regulatory perspective, we derive sufficient and necessary conditions for selective response to improve welfare improvements.

cs.AI↗

arXiv2025

Data Sharing with a Generative AI Competitor

As GenAI platforms grow, their dependence on content from competing providers, combined with access to alternative data sources, creates new challenges for data-sharing decisions. In this paper, we provide a model of data sharing between a content creation firm and a GenAI platform that can also acquire content from third-party experts. The interaction is modeled as a Stackelberg game: the firm first decides how much of its proprietary dataset to share with GenAI, and GenAI subsequently determines how much additional data to acquire from external experts. Their utilities depend on user traffic, monetary transfers, and the cost of acquiring additional data from external experts. We characterize the unique subgame perfect equilibrium of the game and uncover a surprising phenomenon: The firm may be willing to pay GenAI to share the firm's own data, leading to a costly data-sharing equilibrium. We further characterize the set of Pareto improving data prices, and show that such improvements occur only when the firm pays to share data. Finally, we study how the price can be set to optimize different design objectives, such as promoting firm data sharing, expert data acquisition, or a balance of both. Our results shed light on the economic forces shaping data-sharing partnerships in the age of GenAI, and provide guidance for platforms, regulators and policymakers seeking to design effective data exchange mechanisms.

cs.GT↗

arXiv2025

Magneto-optics of a charge-tunable quantum dot: Observation of a negative diamagnetic shift

We present magneto-optical studies of a self-assembled semiconductor quantum dot in neutral and positively charged states. The diamagnetic shifts and Zeeman splitting of many well-identified optical transitions are precisely measured. Remarkably, a pronounced negative diamagnetic shift is observed for spectral lines resulting from a doubly positively charged excitonic complex. We use the Hartree - Fock approximation for describing the direct Coulomb and exchange interactions between the quantum dot confined carriers in various configurations. A simple harmonic potential model, which we extend to capture the influence of an externally applied magnetic field in Faraday configuration, is then used to quantitatively account for all the measured diamagnetic shifts. We show that the negative shift is due to the change in the hole-hole exchange interaction energy induced by the magnetic field. Using this model and the measured shifts we extract the dielectric constant of the quantum dot material and get a decent estimate of the quantum dot dimensions. Further, the measured Zeeman splitting of the various spectral lines are also explained by a simple model using algebraic sums and differences of the $g$-factors of the confined charge carriers in their respective first and second discrete energy levels. Finally, the obtained values of the electronic $g$-factor and that of the dielectric constant are independently used to determine the effective composition (x) of the ternary In$_{x}$Ga$_{1-x}$As quantum dot. Both agree to within the experimental uncertainties.

cond-mat.mes-hall↗

arXiv2024

Braess's Paradox of Generative AI

ChatGPT has established Generative AI (GenAI) as a significant technological advancement. However, GenAI's intricate relationship with competing platforms and its downstream impact on users remains under-explored. This paper initiates the study of GenAI's long-term social impact resulting from the weakening network effect of human-based platforms like Stack Overflow. First, we study GenAI's revenue-maximization optimization problem. We develop an approximately optimal solution and show that the optimal solution has a non-cyclic structure. Then, we analyze the social impact, showing that GenAI could be socially harmful. Specifically, we present an analog to Braess's paradox in which all users would be better off without GenAI. Finally, we develop necessary and sufficient conditions for a regulator with incomplete information to ensure that GenAI is socially beneficial.

cs.GT↗

arXiv2024

Continuous and deterministic all-photonic cluster state of indistinguishable photons

Cluster states are key resources for measurement-based quantum information processing. Photonic cluster and graph states, in particular, play indispensable roles in quantum network and quantum metrology. We demonstrate a semiconductor quantum dot based device in which the confined hole spin acts as a needle in a quantum knitting machine producing continuously and deterministically at sub-Gigahertz repetition rate single indistinguishable photons which are all polarization entangled to each other and to the spin in a one dimensional cluster state. By projecting two nonadjacent photons onto circular polarization bases we disentangle the spin from the photons emitted in between, thus continuously and deterministically preparing all-photonic cluster states for the first time. We use polarization tomography on four sequentially detected photons to demonstrate and to directly quantify the robustness of the cluster's entanglement and the determinism in its photon generation.

quant-ph↗

arXiv2023

Principal-Agent Reward Shaping in MDPs

Principal-agent problems arise when one party acts on behalf of another, leading to conflicts of interest. The economic literature has extensively studied principal-agent problems, and recent work has extended this to more complex scenarios such as Markov Decision Processes (MDPs). In this paper, we further explore this line of research by investigating how reward shaping under budget constraints can improve the principal's utility. We study a two-player Stackelberg game where the principal and the agent have different reward functions, and the agent chooses an MDP policy for both players. The principal offers an additional reward to the agent, and the agent picks their policy selfishly to maximize their reward, which is the sum of the original and the offered reward. Our results establish the NP-hardness of the problem and offer polynomial approximation algorithms for two classes of instances: Stochastic trees and deterministic decision processes with a finite horizon.

cs.AI↗