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4387130173 · Jun 202019922001200920172026
48 results for Social Welfare Maximization

The paper tackles adaptive policy selection to maximize social welfare, achieving optimal regret bounds.

problem Maximizing social welfare through adaptive policy selection, considering both private utility and public revenue.
method The approach involves learning response functions through experimentation, deriving lower and upper bounds for regret, and using algorithms like Exp3.
result The algorithm achieves optimal regret bounds, showing that welfare maximization is harder than multi-armed bandit problems.

Our work extends Coase's theorem to settings with uncertainty, showing how to maximize social welfare through property rights and learning.

problem Theoretical models of externality often assume perfect knowledge, limiting practical solutions.
method We extend Coase's theorem to a two-player bandit setting with uncertainty, designing a learning policy to maximize social welfare.
result We show that property rights and learning can recover Coase's theorem in settings with uncertainty.

Current methodologies in machine learning analyze the effects of various statistical parity notions of fairness primarily in light of their impacts on predictive accuracy and vendor utility loss. In this paper, we propose a new framework for interpreting the effects of fairness criteria by converting the constrained lo…

2018-07-03abs ↗pdf ↗

Now that machine learning algorithms lie at the center of many resource allocation pipelines, computer scientists have been unwittingly cast as partial social planners. Given this state of affairs, important questions follow. What is the relationship between fairness as defined by computer scientists and notions of soc…

2019-05-01abs ↗pdf ↗

Study incentive efficiency in monopoly insurance markets with hidden information.

problem Maximizing social welfare in a monopoly insurance market with hidden agent types.
method Maximizes social welfare function subject to incentive compatibility and individual rationality constraints.
result Optimal menus of contracts depend on the level of social welfare weight and agent risk attitudes.

This paper studies the problem of optimally allocating treatments in the presence of spillover effects, using information from a (quasi-)experiment. I introduce a method that maximizes the sample analog of average social welfare when spillovers occur. I construct semi-parametric welfare estimators with known and unknow…

2019-06-24abs ↗pdf ↗

The paper explores fair machine learning policies for balancing competing objectives in noisy data.

problem Balancing competing objectives in noisy data.
method Analyzes a class of policies that trace an empirical Pareto frontier based on learned scores.
result Characterizes optimal strategies and bounds Pareto errors due to score inaccuracies.

This paper tackles no-regret learning for fair multi-agent social welfare optimization.

problem Maximizing social welfare in a fair manner for multiple agents.
method Developed algorithms for stochastic and adversarial multi-agent settings, proving regret bounds and tightness.
result Achieved no-regret learning for fair multi-agent social welfare optimization in various settings.

Optimizes long-term social welfare in recommender systems by matching users to providers.

problem Realistic recommender systems dynamics affect all agents, not just users.
method Formulated as an optimal constrained matching problem, solved using dynamical system equilibrium selection.
result Ensures maximal social welfare with diverse viable providers, improving over myopic matching.

Adopting a zonal structure of electricity market requires specification of zones' borders. In this paper we use social welfare as the measure to assess quality of various zonal divisions. The social welfare is calculated by Market Coupling algorithm. The analyzed divisions are found by the usage of extended Locational …

2014-05-05abs ↗pdf ↗

Framework for online resource allocation using social welfare functions.

problem Optimal allocation of resources over time steps in a population.
method Confidence sequence framework for SWF-based online learning and inference, valid for any monotonic, concave, and Lipschitz-continuous SWF.
result Achieves near-optimal regret of ildeO(n+nkT) ilde{O}(n+\sqrt{nkT}) for SWF-agnostic algorithm SWF-UCB.

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.

Improved model accuracy can reduce overall user accuracy in competitive markets.

problem The impact of model competition on overall user accuracy.
method Defined a model of competition for classification tasks and used data representations to study the effect of scale.
result Improving data representation quality can decrease overall predictive accuracy across users (social welfare) in a competitive market.

Study compares two market clearing methods for European power markets.

problem Optimizing market clearing for European power markets considering cost and social welfare.
method Introduces Cost Minimization and Social Welfare Maximization models, and four algorithms to solve the CM model.
result Cost Minimization reduces market power and decreases total procurement cost.

This paper identifies and analyzes biases in risk-adjusted index weighting methods, affecting social welfare and market fairness.

problem Biases in risk-adjusted index weighting methods lead to tracking errors and fraud in indices and ETFs.
method Characterizes and analyzes the biases and adverse effects of risk-adjusted index weighting methods.
result These biases reduce social welfare and can enable harmful arbitrage activities.

Study bridges welfare maximization and CATE estimation in policy learning.

problem Tackles the gap between empirical welfare maximization and conditional average treatment effect estimation in policy learning.
method Shows equivalence between EWM and least squares over reparameterized policy class, proposes regularization method.
result Both approaches are interchangeable under common conditions and share theoretical guarantees.

Study tackles RLHF with diverse human feedback, showing limitations and proposing a meta-learning approach.

problem Traditional RLHF fails to balance diverse human preferences.
method Integrates meta-learning and multiple social welfare functions to optimize diverse preferences.
result Establishes sample complexity bounds for optimizing diverse social welfare functions.

Study allocates resources to strategic agents while balancing cost and incentives.

problem Dynamic allocation of reusable resources to strategic agents with private valuations under long-term cost constraints.
method Incentive-aware framework combining epoch-based lazy updates and randomized exploration rounds.
result Achieves ildeO(T) ilde{\mathcal{O}}(\sqrt{T}) social welfare regret, satisfies all cost constraints, and ensures incentive alignment.

Deviation-based learning improves recommender systems by abstaining from recommending choices users might follow.

problem Recommender systems learn from user choices but can stall if users blindly follow recommendations.
method The recommender learns user knowledge by observing choices, abstaining from recommending a choice when multiple alternatives produce similar payoffs.
result Learning rate and social welfare improve when the recommender abstains from recommending certain choices.

Modeling European spot power markets with game theory for Nash equilibria.

problem Optimizing electricity markets with risk-averse players and constraints.
method Game-theoretic framework with Jacobi and Gauss-Seidel schemes for approximate Nash equilibria.
result Innovative risk aversion model reduces price dimensionality and ensures boundedness.

The paper tackles fair policy targeting by optimizing allocation rules to minimize unfairness.

problem Discrimination in individualized treatments of social welfare programs.
method Formulated as a mixed-integer linear program, solved using off-the-shelf algorithms, derived regret bounds and small sample guarantees.
result Designs fair and efficient treatment allocation rules within the Pareto frontier.

New framework tackles submodular welfare with multi-agent combinatorial bandits.

problem Maximizing total welfare among agents with shared constraints and submodular utilities under bandit feedback.
method Proposes an explore-then-commit strategy with randomized assignments for multi-agent combinatorial bandits.
result Achieves ildeO(T2/3) ilde{\mathcal{O}}(T^{2/3}) regret, first for partition-based submodular welfare problem under bandit feedback.

Investors suffer welfare loss despite having better information.

problem Welfare loss among investors with absolute information advantages.
method Examined financial markets with heterogenous investors and objective measures of welfare.
result Investors incur welfare loss even with better information, revealing a double loss phenomenon.

The so called "globalization" process (i.e. the inexorable integration of markets, currencies, nation-states, technologies and the intensification of consciousness of the world as a whole) has a behavior exactly equivalent to a system that is tending to a maximum entropy state. This globalization process obeys a collec…

2007-10-05abs ↗pdf ↗

Consequential decision-making typically incentivizes individuals to behave strategically, tailoring their behavior to the specifics of the decision rule. A long line of work has therefore sought to counteract strategic behavior by designing more conservative decision boundaries in an effort to increase robustness to th…

2018-08-25abs ↗pdf ↗

New analysis shows rational actors will deploy AGI despite negative social value due to catastrophic risk.

problem Rational actors will deploy AGI despite negative social value due to shared catastrophic risk.
method Continuous-time preemption game with shared catastrophic externalities, showing suicide region and welfare distortion.
result The suicide region widens as catastrophic risk grows, and two mechanisms can close it.

Ranking models are typically designed to provide rankings that optimize some measure of immediate utility to the users. As a result, they have been unable to anticipate an increasing number of undesirable long-term consequences of their proposed rankings, from fueling the spread of misinformation and increasing polariz…

2019-05-13abs ↗pdf ↗

The paper develops methods to estimate optimal treatment sequences under policy constraints.

problem Estimating the best sequence of treatments over multiple stages for individuals.
method Empirical welfare maximization approach, solving treatment assignment sequentially or simultaneously.
result Established convergence rates and upper bounds for estimation methods.

We introduce a quantitative approach to comparative statics that allows to bound the maximum effect of an exogenous parameter change on a system's equilibrium. The motivation for this approach is a well known paradox in multimarket Cournot competition, where a positive price shock on a monopoly market may actually redu…

2013-07-22abs ↗pdf ↗

A new method reduces preference distortion in LLM alignment.

problem Vulnerability of traditional LLM alignment methods to human preference heterogeneity.
method Sign Estimator: A simple, provably consistent, and efficient estimator using binary classification loss.
result Substantially reduces preference distortion over a panel of simulated personas.

Dynamic pricing improves DeFi lending efficiency by reducing regret to logarithmic levels.

problem Static pricing mechanisms in DeFi lending protocols lead to suboptimal welfare and revenue.
method Online learning model for static and dynamic pricing models in DeFi lending.
result Adaptive supply models achieve logarithmic regret, outperforming static models.

Fairness in algorithmic decision-making processes is attracting increasing concern. When an algorithm is applied to human-related decision-making an estimator solely optimizing its predictive power can learn biases on the existing data, which motivates us the notion of fairness in machine learning. while several differ…

2018-06-13abs ↗pdf ↗

New study finds targeting based on treatment effects outperforms risk-based targeting in social interventions.

problem Lack of accurate treatment effect estimates for machine learning-based targeting in social domains.
method Empirical assessment of targeting strategies using data from 5 real-world RCTs in various domains.
result Treatment effect-based targeting outperforms risk-based targeting, even with biased estimates.

Motivated by cognitive radios, stochastic multi-player multi-armed bandits gained a lot of interest recently. In this class of problems, several players simultaneously pull arms and encounter a collision - with 0 reward - if some of them pull the same arm at the same time. While the cooperative case where players maxim…

2020-02-04abs ↗pdf ↗

Georgia's pension reform affects individual welfare.

problem Optimizing pension systems to ensure old age welfare and reduce budgetary pressure.
method Historical analysis, comparative international experience, and assessment of investment potential.
result Challenges and potential improvements of the mandatory funded pension system.

This paper introduces metrics for welfare analysis in dynamic models. We develop estimation and inference for these parameters even in the presence of a high-dimensional state space. Examples of welfare metrics include average welfare, average marginal welfare effects, and welfare decompositions into direct and indirec…

2019-08-24abs ↗pdf ↗

Decision support systems (e.g., for ecological conservation) and autonomous systems (e.g., adaptive controllers in smart cities) start to be deployed in real applications. Although their operations often impact many users or stakeholders, no fairness consideration is generally taken into account in their design, which …

2019-07-24abs ↗pdf ↗