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Found 34 papers, 10 papers with code
Competitive Retrieval: Going Beyond the Single Query
Previous work on the competitive retrieval setting focused on a single-query setting: document authors manipulate their documents so as to improve their future ranking for a given query.
Prediction-sharing During Training and Inference
Second, we narrow our focus to two natural settings within this framework: (i) a setting in which the accuracy of each firm's prediction model is common knowledge, but the correlation between the respective models is unknown; and (ii) a setting in which two hypotheses exist regarding the optimal predictor, and one of the firms has a structural advantage in deducing it.
Rationality Report Cards: Assessing the Economic Rationality of Large Language Models
We begin by surveying the economic literature on rational decision making, taxonomizing a large set of fine-grained "elements" that an agent should exhibit, along with dependencies between them.
Can Large Language Models Replace Economic Choice Prediction Labs?
Economic choice prediction is an essential challenging task, often constrained by the difficulties in acquiring human choice data.
Robust Price Discrimination
We consider a model of third-degree price discrimination, in which the seller has a valuation for the product which is unknown to the market designer, who aims to maximize the buyers' surplus by revealing information regarding the buyer's valuation to the seller.
Resilient Information Aggregation
The payoff of the senders and of the receiver depend on both the state of the world and the action selected by the receiver.
The Search for Stability: Learning Dynamics of Strategic Publishers with Initial Documents
We study a game-theoretic information retrieval model in which strategic publishers aim to maximize their chances of being ranked first by the search engine while maintaining the integrity of their original documents.
Human Choice Prediction in Language-based Persuasion Games: Simulation-based Off-Policy Evaluation
Recent advances in Large Language Models (LLMs) have spurred interest in designing LLM-based agents for tasks that involve interaction with human and artificial agents.
Optimal Mechanism Design for Agents with DSL Strategies: The Case of Sybil Attacks in Combinatorial Auctions
We consider two refined notions: (i) a term we call DSL (distinguishable safety level), and is based on the notion of ``discrimin'', which uses a pairwise comparison of actions while removing trivial equivalencies.
Budget-Constrained Reinforcement of Ranked Objects
Commercial entries, such as hotels, are ranked according to score by a search engine or recommendation system, and the score of each can be improved upon by making a targeted investment, e. g., advertising.
Long-term Data Sharing under Exclusivity Attacks
We conclude that the choice of protocol, as well as the number of Sybil identities an attacker may control, is material to vulnerability.
Driving the Herd: Search Engines as Content Influencers
We present a first study of the ability of search engines to drive pre-defined, targeted, content effects in the corpus using simple techniques.
Designing an Automatic Agent for Repeated Language based Persuasion Games
Persuasion games are fundamental in economics and AI research and serve as the basis for important applications.
Predicting Decisions in Language Based Persuasion Games
Our results demonstrate that given a prefix of the interaction sequence, our models can predict the future decisions of the decision-maker, particularly when a sequential modeling approach and hand-crafted textual features are applied.
Content Provider Dynamics and Coordination in Recommendation Ecosystems
Recommendation Systems like YouTube are vibrant ecosystems with two types of users: Content consumers (those who watch videos) and content providers (those who create videos).
PMI-Masking: Principled masking of correlated spans
Specifically, we show experimentally that PMI-Masking reaches the performance of prior masking approaches in half the training time, and consistently improves performance at the end of training.
Representative Committees of Peers
We show that a k-sortition (a random committee of k voters with the majority vote within the committee) leads to an outcome within the factor 1+O(1/k) of the optimal social cost for any number of voters n, any number of issues $m$, and any preference profile.
Learning under Invariable Bayesian Safety
A recent body of work addresses safety constraints in explore-and-exploit systems.
Studying Ranking-Incentivized Web Dynamics
The ranking incentives of many authors of Web pages play an important role in the Web dynamics.
Ranking-Incentivized Quality Preserving Content Modification
The Web is a canonical example of a competitive retrieval setting where many documents' authors consistently modify their documents to promote them in rankings.
Predicting Strategic Behavior from Free Text
In ablation analysis, we demonstrate the importance of our modeling choices---the representation of the text with the commonsensical personality attributes and our classifier---to the predictive power of our model.
Privacy, Altruism, and Experience: Estimating the Perceived Value of Internet Data for Medical Uses
Here we describe experiments where methods from Mechanism Design were used to elicit a truthful valuation from users for their Internet data and for services to screen people for medical conditions.
Protecting the Protected Group: Circumventing Harmful Fairness
In this family, we characterize conditions under which the fairness constraint helps the disadvantaged group.
Fiduciary Bandits
Recommendation systems often face exploration-exploitation tradeoffs: the system can only learn about the desirability of new options by recommending them to some user.
Regression Equilibrium
Despite their centrality in the competition between online companies who offer prediction-based products, the \textit{strategic} use of prediction algorithms remains unexplored.
Predicting human decisions with behavioral theories and machine learning
Here, we introduce BEAST Gradient Boosting (BEAST-GB), a novel hybrid model that synergizes behavioral theories, specifically the model BEAST, with machine learning techniques.
Convergence of Learning Dynamics in Information Retrieval Games
We consider a game-theoretic model of information retrieval with strategic authors.
Competing Prediction Algorithms
Despite their centrality in the competition between online companies who offer prediction-based products, the strategic use of prediction algorithms remains unexplored.
Computer Science and Game Theory
Characterizing Efficient Referrals in Social Networks
Users of social networks often focus on specific areas of that network, leading to the well-known "filter bubble" effect.
Social and Information Networks
Best Response Regression
In this work, we initiate the study of strategic predictions in machine learning.
Group Recommendations: Axioms, Impossibilities, and Random Walks
We introduce an axiomatic approach to group recommendations, in line of previous work on the axiomatic treatment of trust-based recommendation systems, ranking systems, and other foundational work on the axiomatic approach to internet mechanisms in social choice settings.
An Axiomatic Approach to Routing
Information delivery in a network of agents is a key issue for large, complex systems that need to do so in a predictable, efficient manner.
A Reinforcement Learning System to Encourage Physical Activity in Diabetes Patients
Messages were personalized through a Reinforcement Learning (RL) algorithm which optimized messages to improve each participant's compliance with the activity regimen.
Chasing Ghosts: Competing with Stateful Policies
We consider sequential decision making in a setting where regret is measured with respect to a set of stateful reference policies, and feedback is limited to observing the rewards of the actions performed (the so called "bandit" setting).
