Paper offers a new method to solve risk-sharing problems in Principal-Agent models.
problem Risk-sharing in Principal-Agent models with CARA utilities.
method Optimal decomposition of expected utility using Reverse-H{ö}lder inequality.
result Proof of existence and uniqueness of the solution under general assumptions.
A model for collaborative learning with principal-agent interaction.
problem Optimizing parameter estimates in a collaborative learning setting.
method Decision-theoretic model with aggregation coefficients and Langevin dynamics.
result Advantages in stability and generalization due to cooperative behavior.
Paper models corruption in contract negotiations between agents and producers.
problem Formalizing corruption in contract negotiations between agents and producers.
method Mathematical model and economic analysis for three producers, one agent, and one intermediary.
result Optimal non-corruption schemes of financial resources distribution are proposed.
Study a continuous-time PA problem with private effort and consumption decisions.
problem Continuous-time Principal-Agent problem with private information.
method Proposes a new sufficient condition for solving the agent's problem directly.
result Directly yields a solution to the agent's problem without verification.
Study proposes new framework for Board-CEO relationship.
problem Recent corporate failures highlight Board's role in governance effectiveness.
method Analyzes Board-CEO relationship and role reversal in Principal-Agent Theory.
result Role reversal in governance may lead to organizational collapse.
We analyze conditional optimization problems arising in discrete time Principal-Agent problems of delegated portfolio optimization with linear contracts. Applying tools from Conditional Analysis we show that some results known in the literature for very specific instances of the problem carry over to translation invari…
Optimal contracts are found for agents with quadratic effort costs.
problem Finding optimal contracts in principal-agent problems with quadratic effort costs.
method Modeling the problem using Hamilton-Jacobi-Bellman (HJB) equations and proving the existence of classical solutions.
result Existence of optimal contracts for agents with quadratic effort costs is proven.
A note on learning with agents having global perspectives and a principal optimizing their performance.
problem Learning with dynamic-optimizing principal-agent setting, where agents have global views and the principal optimizes performance.
method Empirical-likelihood estimator under conditional moment restrictions model, considering agents' out-of-sample and private dataset performances.
result A coherent mathematical argument for the learning process in this framework.
A study on how a principal can incentivize an agent to make better decisions in a repeated game.
problem Optimizing a principal's utility in a misaligned principal-agent bandit game.
method Developed nearly optimal learning algorithms for the principal's regret in multi-armed and linear contextual settings.
result The principal can iteratively learn an incentive policy to maximize her total utility.
Study of repeated principal-agent bandit game with self-interested and exploratory learning agents.
problem Interaction between principal and agent in unknown environments with learning and exploration behaviors.
method Developed algorithms for self-interested and exploratory learning agents with bandit feedback, achieving regret bounds.
result Achieved O ~ ( T 2 / 3 ) \widetilde{O}(T^{2/3}) O ( T 2/3 ) regret bound for exploratory learning agent in i.i.d. reward setup. Optimal contract found for risk averse agent and principal with unknown quality.
problem Finding an optimal contract for a risk averse agent and principal with unknown quality.
method Continuous time Principal-Agent model with exponential utility, moral hazard, and filtering of quality.
result Explicit solution to the optimal contract problem.
We use a principal-agent model to analyze the structure of a book-driven dealer market when the dealer faces competition from a crossing network or dark pool. The agents are privately informed about their types (e.g. their portfolios), which is something that the dealer must take into account when engaging his counterp…
Deep learning solves complex PA mean field games with market-clearing conditions.
problem Optimizing Principal-Agent interactions in renewable energy markets with market-clearing conditions.
method Actor-critic approach, deep backward stochastic differential equations (BSDE), neural net approximation.
result Efficacy of the deep learning algorithm in solving complex PA mean field games.
Algorithm learns optimal contracts for unaware principals.
problem Learning optimal contracts when principal is unaware of agent's utility and action space.
method Sequential contract offers with observed outcomes, using algorithm to approximate optimal contract.
result Algorithm learns optimal contract within epsilon of optimal net profit with bounded samples.
Exchange sets optimal make-take fees to attract liquidity.
problem Setting suitable make-take fees to attract liquidity on an exchange.
method Principal-agent approach to describe optimal contract and quotes.
result Optimal contracting leads to higher quality liquidity and lower trading costs.
Study uses machine learning to estimate effective policies in settings with hidden individual actions.
problem Estimating effective policies in settings with hidden individual actions.
method Instrumental Regression and Generalized Method of Moments (GMM) estimator.
result Demonstrates how to estimate a good contract in principal-agent problems.
Study optimizes scoring rules for incentivizing agent's information gathering in online settings.
problem Optimizing incentives for agents to acquire information in online settings.
method Designing a sample-efficient algorithm that tailors the UCB algorithm to the strategic agent's model.
result Achieves sublinear T 2 / 3 T^{2/3} T 2/3 -regret after T T T iterations, independent of the number of states. Study shows fiduciary duty reduces municipal bond yields by 9% after SEC rule.
problem Effect of fiduciary duty on municipal bond yields and fees.
method Difference-in-differences analysis using hand-collected data.
result Bond yields decrease by 9% after SEC rule, but smaller issuers see increased borrowing costs.
Optimal reinsurance contracts designed for a continuum of risk types.
problem Designing optimal reinsurance contracts with a continuum of risk types.
method Principal-agent model, VaR at risk tolerance level, change of variables, univariate approach.
result Optimal reinsurance contracts are in stop-loss form, classifying agents into high and low risk groups.
Study of repeated games with unobserved agent rewards using MAB framework.
problem Designing policies for principals in repeated principal-agent games with unobservable agent rewards.
method Developed a policy achieving low regret (square-root regret up to a log factor) for perfect-knowledge agents.
result Constructed an estimator for agent's expected reward and designed a policy achieving low regret.
Algorithm learns optimal coordination for strategic agents in uncertain settings.
problem Optimizing rewards for strategic agents with private types and actions.
method Combines delaying mechanism, reward angle estimation, and LinUCB algorithm.
result Near optimal regret bound of O ~ ( T ) \tilde{O}(\sqrt{T}) O ~ ( T ) for learning optimal policy. The paper proposes incentivizing human annotators with 'golden questions' to improve data quality.
problem Ensuring high-quality human annotations for training large language models.
method A principal-agent model is used to incentivize annotators with bonuses based on the maximum likelihood estimators (MLE) of their annotations. Hypothesis testing is applied to monitor the annotators' performance.
result The hypothesis testing rate for the principal-agent model is of Θ ( 1 / n log n ) Θ(1/\sqrt{n \log n}) Θ ( 1/ n log n ) , highlighting the importance of 'golden questions' for monitoring annotators. Paper revisits optimal incentives in continuous-time problems with new contract types.
problem Optimal incentives in continuous-time principal-agent problems with drift and volatility control.
method Introduces a more general class of contracts parametrized by a function ψ, providing two natural specifications.
result The optimality result of previous methods relies on an assumption that may not hold in general.
Optimal penalties for RECs balance environmental and revenue impacts.
problem Optimizing penalties for RECs to balance environmental and revenue impacts.
method Mean field games and extended McKean-Vlasov control problems.
result Optimal penalty function is linear in agents' state.
In this paper, we take up the analysis of a principal/agent model with moral hazard introduced in [17], with optimal contracting between competitive investors and an impatient bank monitoring a pool of long-term loans subject to Markovian contagion. We provide here a comprehensive mathematical formulation of the model …
Designs a derivatives exchange to meet client needs and provide liquidity.
problem Addressing clients needs in listed options and providing suitable liquidity.
method Quantization method for selecting options and principal-agent approach for designing fees contract.
result Incentives provided to market maker to offer small spreads, attracting transactions.
The independence of Central Banks is shown to be a myth.
problem The independence of Central Banks is questioned as a flawed concept.
method Analyzes the arguments for Central Bank independence and demonstrates their logical flaws.
result The independence of Central Banks is found to be a chimera.
In this paper, we consider a problem of contract theory in which several Principals hire a common Agent and we study the model in the continuous time setting. We show that optimal contracts should satisfy some equilibrium conditions and we reduce the optimisation problem of the Principals to a system of coupled Hamilto…
Study optimal contracts for pandemic risk, offering fixed shares and prevention mechanisms.
problem Optimal delegation contracts in the face of pandemic shutdown risk.
method Dynamic principal-agent model with exogenous early termination risk.
result Explicit characterization of optimal wage and action for prevention mechanisms.
The paper develops an economic foundation for multi-agent learning in markets.
problem Learning dynamics in markets with strategic externalities.
method A two-phase incentive mechanism that estimates and uses implementable transfers to steer long-run dynamics.
result The mechanism achieves sublinear social-welfare regret and asymptotically optimal welfare under mild rationality and exploration conditions.
Optimal linear contracts are possible even with memory in Gaussian settings.
problem Can optimal dynamic contracts be linear when agents control memory processes?
method Developed a methodology for non-Markovian and non-semimartingale settings, showed linear contracts are optimal for one-dimensional models.
result Linear contracts are optimal for one-dimensional models with memory, and for radial effort cost functions in higher dimensions.
Optimizes fees for multiple market makers to enhance trading quality.
problem Improving trading quality on an exchange platform with multiple market makers.
method Principal-agent approach with Nash equilibrium for market makers optimizing quotes.
result Optimal make take fees contract and spread policies for market makers derived.
Study optimizes rebate design in auction markets to enhance efficiency.
problem Designing optimal rebate policies in auction markets to improve efficiency.
method Formulated as a principal-agent problem, solved using Hamilton-Jacobi-Bellman equations and Deep BSDE method.
result Optimal transaction fees and rebates narrow the price spread, improving market efficiency.
We consider a contracting problem in which a principal hires an agent to manage a risky project. When the agent chooses volatility components of the output process and the principal observes the output continuously, the principal can compute the quadratic variation of the output, but not the individual components. This…
Model shows incentives in shared order book can lead to free-rider problem.
problem Incentives in shared order books can lead to free-rider problem.
method Developed a Principal-Agent model with CARA utility functions.
result Equilibrium analysis shows incentives can lead to reduced competition.
AI task delegation faces incentive collapse with unbounded payments as AI accuracy rises.
problem Incentive collapse in AI-assisted task delegation schemes.
method General impossibility result and sentinel-auditing payment mechanism.
result Sentinel-auditing mechanism enforces positive human effort at finite cost, independent of AI accuracy.
A study on portfolio delegation with random default times, addressing complex uncertainties.
problem Optimal portfolio delegation with uncertain investment horizon due to random default.
method Developed a theoretical framework using BSDEs and control theory, and deep learning for high-dimensional problems.
result Solutions to integro-partial Hamilton-Jacobi-Bellman equations for both scenarios of default time.
This paper addresses reward estimation and incentive design for agents with hidden rewards.
problem Estimating and incentivizing agents with unknown rewards in a learning setting.
method Repeated adverse selection game with a self-interested learning agent and a learning principal. Introduces an estimator for consistent reward estimation and a data-driven incentive policy.
result Finite-sample consistency of the estimator and a rigorous regret bound for the principal.
Paper develops streaming algorithms to estimate classifier accuracy on unlabeled data.
problem Estimating classifier accuracy on unlabeled data with noisy decisions.
method Two algebraic evaluators: majority voting and a novel method to handle correlated classifiers.
result The novel method can be as accurate as 1% when handling small amounts of correlation.
Study develops time-continuous models and probabilistic descriptions for agent-based economic market models.
problem Formulating and describing agent-based economic market models in a time-continuous and probabilistic manner.
method Derived time-continuous formulations, discussed impact of time-scaling, proved stability, presented probabilistic descriptions using kinetic theory.
result Time-continuous formulations and probabilistic descriptions for agent-based economic market models.
Hybrid model combines interpretable and black-box models for better transparency and performance.
problem Balancing interpretability and predictive performance in machine learning models.
method Proposes a Hybrid Predictive Model (HPM) integrating an interpretable model with a black-box model, using principled objective functions and customized training algorithms.
result Hybrid models achieve an efficient trade-off between transparency and predictive performance.
The paper introduces BCART models for aggregate claim amount, improving frequency-severity and joint modeling.
problem Modeling aggregate claim amount with frequency-severity and joint dependencies.
method Developed three types of BCART models: frequency-severity, sequential, and joint models. Used various distributions for claim severity data.
result Weibull distribution outperforms gamma and lognormal for right-skewed, heavy-tailed claim severity data.
Boosts generative models by combining multiple meta-models.
problem Challenges in creating a single generative model that accurately represents complex data.
method Cascades multiple meta-models (like RBM and VAE) to create a stronger generative model.
result Derives a decomposable variational lower bound for training and evaluating the boosted model.
The paper uses model-based trees to create interpretable surrogate models for complex machine learning models.
problem Interpreting complex machine learning models.
method Using model-based trees to partition feature space and create interpretable models.
result Model-based trees generate optimal surrogate models that balance interpretability and performance.
Study on limits of community detection in various network models.
problem Limits of community detection in network models.
method Analysis of several network models including Stochastic Block Model, Exponential Random Graph Model, Latent Space Model, Directed Preferential Attachment Model, and Directed Small-world Model.
result Information-theoretic limits for recovery of node labels in network models.
Gauge Flow Models use a learnable Gauge Field in Generative Flow Models.
problem Improving generative model performance.
method Integrates a learnable Gauge Field into Flow ODEs.
result Gauge Flow Models outperform traditional Flow Models in Flow Matching experiments.
The study examines how model predictions hold up under model extensions.
problem Model predictions may not be robust under model extensions, limiting their applicability.
method The study uses causal ordering to assess robustness of qualitative model predictions and characterizes model extensions that preserve predictions.
result Conditions and techniques are provided to assess robustness of model predictions under model extensions.
MALC combines interpretable linear models with black-box models for better predictions and transparency.
problem Combining interpretability with black-box models for better predictions.
method Formulates MALC as a convex optimization problem and uses accelerated proximal gradient method for training.
result MALC provides an efficient frontier balancing prediction accuracy and transparency.