arXiv subjects

Moran Koren

Publications and source records attributed to Moran Koren.

15 recordsLinked to original sources

arXiv2026

New Complexity Results for Fair Repetitive Scheduling

We revisit the problem of finding fair solutions to repetitive scheduling problems with a single machine. In this problem, we are given a set of $n$ clients and a planning horizon consisting of $q$ periods (days). Each day, every client submits a single job that must be processed by the machine. The objective is to construct a set of $q$ schedules, one for each day, such that the quality of service (QoS) received by each client meets a predefined threshold. The QoS measure may be any standard scheduling criterion, such as the total waiting time or total completion time of a client's jobs over the entire planning horizon. This problem has been studied in the literature, with previous works providing complexity classifications and approximation algorithms for various QoS measures. Nevertheless, several important questions remain open. In this paper, we resolve three of these questions and identify several additional directions for future research.

cs.DS↗

arXiv2026

Underwriting the Agent Economy: The Blueprint for an AI Insurance Stack

From maritime trade to commercial nuclear power, insurance has been the enabler of major economic and technological developments by pricing risk, limiting downside, and spreading best practices. The emerging AI agent economy, projected to handle trillions of dollars in transactions by 2030, looks to be the next such development. Yet insurers' exposure to AI agent risk currently sits largely unpriced across existing insurance lines; between this silent coverage and growing exclusions, coverage is not fit for purpose. Furthermore, insurability is trending the wrong way: AI agent capabilities appear to be outpacing reliability, leading to rising incident severity; concentration among a few foundation model providers threatens correlated losses; and traditional actuarial modeling will struggle to keep pace with a technology evolving as rapidly as frontier AI. This report argues that affirmative AI coverage with limits in the billions is achievable by 2030, but only with industry-wide coordination. Drawing on successful historical precedents such as Underwriters Laboratories, the Closed Claims Project, and others, we lay out an eight-component AI insurance stack spanning incident data collection, catastrophe modeling, standards, contract design, risk selection, pricing, monitoring, and claims management. Building out this infrastructure is what will enable insurers to cover and manage AI agent risk sustainably and at scale. Finally, we discuss coverage for catastrophic risk from frontier AI ("AI CAT"), including CBRN, critical infrastructure collapse, and loss of control scenarios. Addressing these tail risks will require purpose-built instruments, potentially including a frontier model developer mutual, catastrophe bonds, bespoke liability regimes, and government backstops.

cs.CY↗

arXiv2026

Theorist Toolbox: Tools for Agent Based LLM-assisted economic theory Research

Empirical economists often start their projects with a toolbox. Shared packages, replication archives, and circulated guides shorten the time between and idea and a rough initial draft. Theorists, on the other-hand, largely start from a blank page. By 2026, large language models can a produce and check nontrivial mathematics. The can also hallucinate and write wrong claims very convincingly. The current bottleneck on machine-assisted theory is no longer production but trust: a model will claim to prove a false theorem as readily as a true one. Building on recent attempts in mathematics, I present 3 methods for doing economic theory with a language model. These methods differ on how the work is verified: a single disciplined pass, an adversarial prover-verifier pair (Claude Opus~4.8 proposing, OpenAI Codex refuting), and a structured multi-agent project with a reviewer gate (inspired by the Google co-mathematician architecture). I demonstrate these protocols on one open worked example: designing a Groves/Pigouvian incentive mechanism for the Gans--Kominers eigengrade model of grade inflation. None of the three runs produced a strict direct-revelation VCG/Clarke mechanism (as requested, perhaps due to the non-existence of such mechanism). Three phenomena recur. First, convergent discovery: two runs derive the same effective-resistance externality kernel on opposite margins. Second, adversarial verification is load-bearing: the pair caught three of its own false claims and the gate rejected a sub-goal. Third, polish is not rigor: the most finished-looking output was the least verified. The methodological takeaway is that external verification, not model capability, is the design variable.

econ.TH↗

arXiv2025

Strategic Behavior in Crowdfunding: Insights from a Large-Scale Online Experiment

This study examines strategic behavior in crowdfunding using a large-scale online experiment. Building on the model of Arieli et. al 2023, we test predictions about risk aversion (i.e., opting out despite seeing a positive private signal) and mutual insurance (i.e., opting in despite seeing a negative private signal) in a static, single-shot crowdfunding game, focusing on informational incentives rather than dynamic effects. Our results validate key theoretical predictions: crowdfunding mechanisms induce distinct strategic behaviors compared to voting, where participants are more likely to follow private signals (odds ratio: 0.139, $p < 0.001$). Additionally, the study demonstrates that higher signal accuracy (85\% vs. 55\%) decreases risk aversion (odds ratio: 0.414, $p = 0.024$) but increases reliance on mutual insurance (odds ratio: 2.532, $p = 0.026$). However, contrary to theory, increasing the required participation threshold (50\% to 80\%) amplifies risk aversion (odds ratio: 3.251, $p = 0.005$), which, pending further investigation, may indicate cognitive constraints. Furthermore, we show that while mutual insurance supports participation, it may hinder information aggregation, particularly as signal accuracy increases. These findings advance crowdfunding theory by confirming the impact of informational incentives and identifying behavioral deviations that challenge standard models, offering insights for platform design and mechanism refinement.

econ.TH↗

arXiv2025

Can (A)I Change Your Mind?

The increasing integration of large language models (LLMs) based conversational agents into everyday life raises critical cognitive and social questions about their potential to influence human opinions. Although previous studies have shown that LLM-based agents can generate persuasive content, these typically involve controlled English-language settings. Addressing this, our preregistered study explored LLMs' persuasive capabilities in more ecological, unconstrained scenarios, examining both static (written paragraphs) and dynamic (conversations via Telegram) interaction types. Conducted entirely in Hebrew with 200 participants, the study assessed the persuasive effects of both LLM and human interlocutors on controversial civil policy topics. Results indicated that participants adopted LLM and human perspectives similarly, with significant opinion changes evident across all conditions, regardless of interlocutor type or interaction mode. Confidence levels increased significantly in most scenarios. These findings demonstrate LLM-based agents' robust persuasive capabilities across diverse sources and settings, highlighting their potential impact on shaping public opinions.

cs.CL↗

arXiv2024

Classification Under Strategic Self-Selection

When users stand to gain from certain predictions, they are prone to act strategically to obtain favorable predictive outcomes. Whereas most works on strategic classification consider user actions that manifest as feature modifications, we study a novel setting in which users decide -- in response to the learned classifier -- whether to at all participate (or not). For learning approaches of increasing strategic awareness, we study the effects of self-selection on learning, and the implications of learning on the composition of the self-selected population. We then propose a differentiable framework for learning under self-selective behavior, which can be optimized effectively. We conclude with experiments on real data and simulated behavior that both complement our analysis and demonstrate the utility of our approach.

cs.LG↗

arXiv2024

Counterbalancing Learning and Strategic Incentives in Allocation Markets

This paper considers the problem of offering a scarce object with a common unobserved quality to strategic agents in a priority queue. Each agent has a private signal over the quality of the object and observes the decisions made by other agents. We first show that, under the widely-used first-come-first-served sequential offering mechanism, herding behavior emerges: initial rejections create an information cascade resulting in inefficient waste. To address this issue, we then introduce a class of batching mechanisms. Agents in each batch report whether they would be willing to accept or reject the object based on their private signals and prior information. If the majority opts to accept, the object is randomly allocated within that batch. We prove that suitable batching mechanisms are incentive-compatible and improve efficiency. A key property of the mechanism is the gradual increase of the batch size after each failed allocation; the size is chosen so that it elicits as much information as possible without distorting the incentives of agents to report truthfully. Additionally, from a healthcare policy perspective, our results can shed light on the large wastage in organ allocation. In particular, wastage that arises due to herding may be reduced by applying adaptive simultaneous offering mechanisms.

cs.GT↗

arXiv2023

The Gatekeeper Effect: The Implications of Pre-Screening, Self-selection, and Bias for Hiring Processes

We study the problem of screening in decision-making processes under uncertainty, focusing on the impact of adding an additional screening stage, commonly known as a 'gatekeeper.' While our primary analysis is rooted in the context of job market hiring, the principles and findings are broadly applicable to areas such as educational admissions, healthcare patient selection, and financial loan approvals. The gatekeeper's role is to assess applicants' suitability before significant investments are made. Our study reveals that while gatekeepers are designed to streamline the selection process by filtering out less likely candidates, they can sometimes inadvertently affect the candidates' own decision-making process. We explore the conditions under which the introduction of a gatekeeper can enhance or impede the efficiency of these processes. Additionally, we consider how adjusting gatekeeping strategies might impact the accuracy of selection decisions. Our research also extends to scenarios where gatekeeping is influenced by historical biases, particularly in competitive settings like hiring. We discover that candidates confronted with a statistically biased gatekeeping process are more likely to withdraw from applying, thereby perpetuating the previously mentioned historical biases. The study suggests that measures such as affirmative action can be effective in addressing these biases. While centered on hiring, the insights and methodologies from our study have significant implications for a wide range of fields where screening and gatekeeping are integral.

econ.TH↗

arXiv2023

The Multi-BMBY Mechanism: Proportionality-Preserving and Strategyproof Ownership Restructuring in Private Companies

In privately held startups, restructuring ownership is challenging due to diverse and uncertain valuations among owners. Traditional approaches, including the BMBY mechanism for equal partnerships, fail to address the complexities of multi-owner settings and don't elicit true valuations. We propose a novel mechanism that extends the BMBY rationale to accommodate these complex scenarios. Our mechanism ensures truthful valuation elicitation while offering several advantages: it is easy to implement, budget balanced, resistant to collusion, individually rational, and allocates shares to those who value them most. Crucially, it preserves proportionality among remaining owners, maintaining existing power dynamics. The mechanism allows for adaptive control of the eventual number of owners, addressing unique startup needs such as incentivizing employee ownership. This paper contributes to the field of ownership restructuring by providing a practical, theoretically-grounded solution for the complex dynamics of startup recapitalization, potentially improving decision-making processes and stakeholder relationships in these pivotal business transitions.

econ.TH↗

arXiv2022

Learning Approximately Optimal Contracts

In principal-agent models, a principal offers a contract to an agent to perform a certain task. The agent exerts a level of effort that maximizes her utility. The principal is oblivious to the agent's chosen level of effort, and conditions her wage only on possible outcomes. In this work, we consider a model in which the principal is unaware of the agent's utility and action space: she sequentially offers contracts to identical agents, and observes the resulting outcomes. We present an algorithm for learning the optimal contract under mild assumptions. We bound the number of samples needed for the principal to obtain a contract that is within $\eps$ of her optimal net profit for every $\eps>0$. Our results are robust even when considering risk-averse agents. Furthermore, we show that when there are only two possible outcomes or the agent is risk-neutral, the algorithm's outcome approximates the optimal contract described in the classical theory.

cs.GT↗

arXiv2021

Sequential Fundraising and Mutual Insurance

Seed fundraising for ventures often takes place by sequentially approaching potential contributors, who make observable decisions. The fundraising succeeds when a target number of investments is reached. Though resembling classic information cascades models, its behavior is radically different, exhibiting surprising complexities. Assuming a common distribution for contributors' levels of information, we show that participants rely on {\em mutual insurance}, i.e., invest despite unfavorable information, trusting future player strategies to protect them from loss. {\em Delegation} occurs when contributors invest unconditionally, empowering the decision to future players. Often, all early contributors delegate, in effect empowering the last few contributors to decide the outcome. Similar dynamics hold in sequential voting, as in voting in committees.

cs.GT↗

arXiv2020

A Practical Approach to Social Learning

Models of social learning feature either binary signals or abstract signal structures often deprived of micro-foundations. Both models are limited when analyzing interim results or performing empirical analysis. We present a method of generating signal structures which are richer than the binary model, yet are tractable enough to perform simulations and empirical analysis. We demonstrate the method's usability by revisiting two classical papers: (1) we discuss the economic significance of unbounded signals Smith and Sorensen (2000); (2) we use experimental data from Anderson and Holt (1997) to perform econometric analysis. Additionally, we provide a necessary and sufficient condition for the occurrence of action cascades.

econ.TH↗

arXiv2019

The Implications of Pricing on Social Learning

We study the implications of endogenous pricing for learning and welfare in the classic herding model . When prices are determined exogenously, it is known that learning occurs if and only if signals are unbounded. By contrast, we show that learning can occur when signals are bounded as long as non-conformism among consumers is scarce. More formally, learning happens if and only if signals exhibit the vanishing likelihood property introduced bellow. We discuss the implications of our results for potential market failure in the context of Schumpeterian growth with uncertainty over the value of innovations.

econ.TH↗

arXiv2018

The One-Shot Crowdfunding Game

The recent success of crowd-funding for supporting new and innovative products has been overwhelming with over 34 Billion Dollars raised in 2015. In many crowd-funding platforms, firms set a campaign goal and contributions are collected only if this goal is reached. At the time of the campaign, consumers are often uncertain as to the ex-post value of the product, the business model viability, or the seller's reliability. Consumer who commit to a contribution therefore gambles. This gamble is effected by the campaign's threshold. Contributions to campaigns with higher thresholds are collected only if a greater number of agents find the offering acceptable. Therefore, high threshold serves as a social insurance and thus in high-threshold campaigns, potential contributors feel more at ease with contributing. We introduce the crowdunding game and explore the contributor's dilemma in the context of experience goods. We discuss equilibrium existence and related social welfare, information aggregation and revenue implications.

cs.GT↗

arXiv2017

The Crowdfunding Game

The recent success of crowdfunding for supporting new and innovative products has been overwhelming with over 34 Billion Dollars raised in 2015. In many crowdfunding platforms, firms set a campaign threshold and contributions are collected only if this threshold is reached. During the campaign, consumers are uncertain as to the ex-post value of the product, the business model viability, and the seller's reliability. Consumer who commit to a contribution therefore gambles. This gamble is effected by the campaign's threshold. Contributions to campaigns with higher thresholds are collected only if a greater number of agents find the offering acceptable. Therefore, high threshold serves as a social insurance and thus in high-threshold campaigns, potential contributors feel more at ease with contributing. We introduce the crowdfunding game and explore the contributor's dilemma in the context of experience goods. We discuss equilibrium existence and related social welfare, information aggregation and revenue implications.

cs.GT↗