A Multi-Agent Perspective on Modern Information Retrieval

The rise of large language models (LLMs) has introduced a new era in information retrieval (IR), where queries and documents that were once assumed to be generated exclusively by humans can now also be created by automated agents. These agents can formulate queries, generate documents, and perform ranking. This shift challenges some long-standing IR paradigms and calls for a reassessment of both theoretical frameworks and practical methodologies. We advocate for a multi-agent perspective to better capture the complex interactions between query agents, document agents, and ranker agents. Through empirical exploration of various multi-agent retrieval settings, we reveal the significant impact of these interactions on system performance. Our findings underscore the need to revisit classical IR paradigms and develop new frameworks for more effective modeling and evaluation of modern retrieval systems.

Prompt-Based Document Modifications In Ranking Competitions

We study prompting-based approaches with Large Language Models (LLMs) for modifying documents so as to promote their ranking in a competitive search setting. Our methods are inspired by prior work on leveraging LLMs as rankers. We evaluate our approach by deploying it as a bot in previous ranking competitions and in competitions we organized. Our findings demonstrate that our approach effectively improves document ranking while preserving high levels of faithfulness to the original content and maintaining overall document quality.

Search results diversification in competitive search

In Web retrieval, there are many cases of competition between authors of Web documents: their incentive is to have their documents highly ranked for queries of interest. As such, the Web is a prominent example of a competitive search setting. Past work on competitive search focused on ranking functions based solely on relevance estimation. We study ranking functions that integrate a results-diversification aspect. We show that the competitive search setting with diversity-based ranking has an equilibrium. Furthermore, we theoretically and empirically show that the phenomenon of authors mimicking content in documents highly ranked in the past, which was demonstrated in previous work, is mitigated when search results diversification is applied.

GLEE: A Unified Framework and Benchmark for Language-based Economic Environments

Large Language Models (LLMs) show significant potential in economic and strategic interactions, where communication via natural language is often prevalent. This raises key questions: Do LLMs behave rationally? Can they mimic human behavior? Do they tend to reach an efficient and fair outcome? What is the role of natural language in the strategic interaction? How do characteristics of the economic environment influence these dynamics? These questions become crucial concerning the economic and societal implications of integrating LLM-based agents into real-world data-driven systems, such as online retail platforms and recommender systems. While the ML community has been exploring the potential of LLMs in such multi-agent setups, varying assumptions, design choices and evaluation criteria across studies make it difficult to draw robust and meaningful conclusions. To address this, we introduce a benchmark for standardizing research on two-player, sequential, language-based games. Inspired by the economic literature, we define three base families of games with consistent parameterization, degrees of freedom and economic measures to evaluate agents' performance (self-gain), as well as the game outcome (efficiency and fairness). We develop an open-source framework for interaction simulation and analysis, and utilize it to collect a dataset of LLM vs. LLM interactions across numerous game configurations and an additional dataset of human vs. LLM interactions. Through extensive experimentation, we demonstrate how our framework and dataset can be used to: (i) compare the behavior of LLM-based agents to human players in various economic contexts; (ii) evaluate agents in both individual and collective performance measures; and (iii) quantify the effect of the economic characteristics of the environments on the behavior of agents.

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. We study a competitive setting where authors opt to improve their document's ranking for multiple queries. We use game theoretic analysis to prove that equilibrium does not necessarily exist. We then empirically show that it is more difficult for authors to improve their documents' rankings for multiple queries with a neural ranker than with a state-of-the-art feature-based ranker. We also present an effective approach for predicting the document most highly ranked in the next induced ranking.

Prediction-sharing During Training and Inference

Two firms are engaged in a competitive prediction task. Each firm has two sources of data -- labeled historical data and unlabeled inference-time data -- and uses the former to derive a prediction model, and the latter to make predictions on new instances. We study data-sharing contracts between the firms. The novelty of our study is to introduce and highlight the differences between contracts that share prediction models only, contracts to share inference-time predictions only, and contracts to share both. Our analysis proceeds on three levels. First, we develop a general Bayesian framework that facilitates our study. 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. Within these two settings we study optimal contract choice. More specifically, we find the individually rational and Pareto-optimal contracts for some notable cases, and describe specific settings where each of the different sharing contracts emerge as optimal. Finally, in the third level of our analysis we demonstrate the applicability of our concepts in a synthetic simulation using real loan data.

Rationality Report Cards: Assessing the Economic Rationality of Large Language Models

There is increasing interest in using LLMs as decision-making "agents." Doing so includes many degrees of freedom: which model should be used; how should it be prompted; should it be asked to introspect, conduct chain-of-thought reasoning, etc? Settling these questions -- and more broadly, determining whether an LLM agent is reliable enough to be trusted -- requires a methodology for assessing such an agent's economic rationality. In this paper, we provide one. 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. We then propose a benchmark distribution that quantitatively scores an LLMs performance on these elements and, combined with a user-provided rubric, produces a "rationality report card." Finally, we describe the results of a large-scale empirical experiment with 14 different LLMs, characterizing the both current state of the art and the impact of different model sizes on models' ability to exhibit rational behavior.

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. Indeed, experimental economics studies had focused mostly on simple choice settings. The AI community has recently contributed to that effort in two ways: considering whether LLMs can substitute for humans in the above-mentioned simple choice prediction settings, and the study through ML lens of more elaborated but still rigorous experimental economics settings, employing incomplete information, repetitive play, and natural language communication, notably language-based persuasion games. This leaves us with a major inspiration: can LLMs be used to fully simulate the economic environment and generate data for efficient human choice prediction, substituting for the elaborated economic lab studies? We pioneer the study of this subject, demonstrating its feasibility. In particular, we show that a model trained solely on LLM-generated data can effectively predict human behavior in a language-based persuasion game, and can even outperform models trained on actual human data.

The Search for Stability: Learning Dynamics of Strategic Publishers with Initial Documents

We study a game-theoretic model of information retrieval, 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. We show that the commonly used PRP ranking scheme results in an unstable environment where games often fail to reach pure Nash equilibrium. We propose the Relative Ranking Principle (RRP) as an alternative ranking principle, and introduce two ranking functions that are instances of the RRP. We provide both theoretical and empirical evidence that these methods lead to a stable search ecosystem, by providing positive results on the learning dynamics convergence. We also define the publishers' and users' welfare, and demonstrate a possible publisher-user trade-off, which highlights the complexity of determining which ranking function should be selected by the search engine designer.

Human Choice Prediction in Language-based Non-Cooperative Games: Simulation-based Off-Policy Evaluation

Persuasion games have been fundamental in economics and AI research, and have significant practical applications. Recent works in this area have started to incorporate natural language, moving beyond the traditional stylized message setting. However, previous research has focused on on-policy prediction, where the train and test data have the same distribution, which is not representative of real-life scenarios. In this paper, we tackle the challenging problem of off-policy evaluation (OPE) in language-based persuasion games. To address the inherent difficulty of human data collection in this setup, we propose a novel approach which combines real and simulated human-bot interaction data. Our simulated data is created by an exogenous model assuming decision makers (DMs) start with a mixture of random and decision-theoretic based behaviors and improve over time. We present a deep learning training algorithm that effectively integrates real interaction and simulated data, substantially improving over models that train only with interaction data. Our results demonstrate the potential of real interaction and simulation mixtures as a cost-effective and scalable solution for OPE in language-based persuasion games.\footnote{Our code and the large dataset we collected and generated are submitted as supplementary material and will be made publicly available upon acceptance.