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…
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38 results for “principal-agent”
The main purpose of this paper is to formalize the modelling process, analysis and mathematical definition of corruption when entering into a contract between principal agent and producers. The formulation of the problem and the definition of concepts for the general case are considered. For definiteness, all calculati…
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.
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.
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.
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.
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.
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 regret bound for exploratory learning agent in i.i.d. reward setup.
Analysis of the Risk-Sharing Principal-Agent problem through the Reverse-H{ö}lder inequalityq-fin.RM
In this paper we provide an alternative framework to tackle the first-best Principal-Agent problem under CARA utilities. This framework leads to both a proof of existence and uniqueness of the solution to the Risk-Sharing problem under very general assumptions on the underlying contract space. Our analysis relies on an…
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.
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.
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…
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.
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.
Learning to Incentivize Information Acquisition: Proper Scoring Rules Meet Principal-Agent Modelcs.LG
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 -regret after iterations, independent of the number of states.
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.
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 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 , highlighting the importance of 'golden questions' for monitoring annotators.
We consider the problem of designing a derivatives exchange aiming at addressing clients needs in terms of listed options and providing suitable liquidity. We proceed into two steps. First we use a quantization method to select the options that should be displayed by the exchange. Then, using a principal-agent approach…
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.
We consider an exchange who wishes to set suitable make-take fees to attract liquidity on its platform. Using a principal-agent approach, we are able to describe in quasi-explicit form the optimal contract to propose to a market maker. This contract depends essentially on the market maker inventory trajectory and on th…
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.
We consider a continuous time Principal-Agent model on a finite time horizon, where we look for the existence of an optimal contract both parties agreed on. Contrary to the main stream, where the principal is modelled as risk-neutral, we assume that both the principal and the agent have exponential utility, and are ris…
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.
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 …
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 t…
Following the recent literature on make take fees policies, we consider an exchange wishing to set a suitable contract with several market makers in order to improve trading quality on its platform. To do so, we use a principal-agent approach, where the agents (the market makers) optimise their quotes in a Nash equilib…
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.
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.
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.
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.
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.
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.
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.