Study shows household inequality accounts for 30% of total global income inequality.
problem Intra-household inequality is often overlooked in studies of income inequality.
method Used LIS micro data to analyze inequality trends in 1973-2013 across multiple countries.
result At least 30% of total global income inequality is due to intra-household inequality.
New welfare-based fairness notions align with existing error rate balance and predictive parity.
problem Aligning fairness notions with welfare-based criteria.
method Discussing and establishing conditions for envy freeness and prejudice freeness.
result Envy freeness and prejudice freeness are equivalent to error rate balance and predictive parity.
Blockchain smart contracts reduce uncertainty but create price spreads.
problem Asymmetric information in traditional markets is reduced by blockchain technology, but it also creates price differences.
method Analyzed the impact of smart contracts on market segmentation and consumer welfare.
result Marginal innovation in smart contracts has non-monotonic effects on trading value and consumer welfare.
The paper develops methods for welfare analysis in dynamic models.
problem Estimating welfare metrics in complex, high-dimensional models.
method Dual and doubly robust representations, Lasso and Neural Network estimators.
result Automatic debiasing of welfare metrics without needing bias correction.
New framework converts fairness criteria to social welfare maximization.
problem Interpreting fairness criteria in terms of societal welfare and distribution.
method Converts constrained loss minimization to social welfare maximization problem.
result Predictions and fairness constraints shape societal welfare and distribution.
The paper analyzes fairness and social welfare in machine learning classification.
problem The relationship between fairness and social welfare in machine learning classification.
method Welfare-based analysis of classification and fairness regimes; algorithm for linear hyperplanes.
result More strict fairness criteria can worsen welfare outcomes for disadvantaged groups.
The paper addresses how to complete incomplete risk markets by iteratively enhancing welfare.
problem How to complete incomplete risk markets to enhance welfare.
method Iterative mechanism to complete the market while monotonically enhancing welfare.
result Iterative completion of incomplete risk markets can enhance welfare.
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 i l d e O ( T 2 / 3 ) ilde{\mathcal{O}}(T^{2/3}) i l d e O ( T 2/3 ) regret, first for partition-based submodular welfare problem under bandit feedback. 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.
The paper automates policy learning for nonlinear welfare criteria using machine learning and debiasing techniques.
problem Learning optimal policies from observational data with nonlinear welfare criteria.
method Modeling a nonlinear welfare criterion with a utility function, estimating propensity scores with machine learning, and using sieve approximations and cross-validation for model selection.
result The proposed policy learning method satisfies oracle inequalities, providing theoretical guarantees on performance.
New job recommendation system improves job seekers' welfare through field experiments.
problem Current job recommendation systems focus on clicks and applications, not job seekers' welfare.
method Developed a job-search model with two dimensions: utility and success probability. Conducted field experiments to validate model predictions.
result Welfare-optimal job recommendation algorithms outperform existing approaches and perform close to the benchmark.
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 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.
New framework improves cost-benefit analysis of policies.
problem Limitations of MVPF in welfare analysis.
method Developed an axiomatic framework to create RPV.
result RPV provides better equity-efficiency trade-off quantification.
Novel algorithm improves poultry welfare by classifying chicken behaviors.
problem Classifying chicken behaviors from sparse and noisy data.
method Time series dictionary learning algorithm.
result Robustly learns from weakly labeled data sources.
Although both systems analyzed are described through two theories apparently different (quantum mechanics and game theory) it is shown that both are analogous and thus exactly equivalents. The quantum analogue of the replicator dynamics is the von Neumann equation. Quantum mechanics could be used to explain more correc…
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.
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…
Existence of incomplete Radner equilibrium with endogenous noise tracker.
problem Existence of incomplete Radner equilibrium in a model with endogenous noise tracker.
method Proved existence through a coupled system of ODEs, reduced to two coupled ODEs.
result Endogenous noise tracker leads to higher aggregate welfare for large stock supply.
The paper proposes a new policy for optimal treatment allocation based on quantile treatment effects.
problem Optimal treatment allocation policies that target distributional welfare, especially when individuals are heterogeneous.
method The approach involves allocating treatments based on the conditional quantile of individual treatment effects (QoTE), considering both prudent and negligent policymakers.
result The proposed minimax policies are robust to model uncertainty and can be generalized to various settings.
Evaluating AI investment strategies
problem Auditing a black-box algorithmic decision-maker
method Exact decomposition of cumulative regret
result Cumulative regret equals sum of per-period covariances
Optimizes treatment allocation with spillovers using experimental data.
problem Optimizing treatment allocation with network interference.
method Mixed-integer linear programming for welfare maximization.
result Strong guarantees on policy regret for optimal welfare.
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 …
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.
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 i l d e O ( n + n k T ) ilde{O}(n+\sqrt{nkT}) i l d e O ( n + nk T ) for SWF-agnostic algorithm SWF-UCB. The paper explores fairness, welfare, and equity in personalized pricing across various applications.
problem Interplay of fairness, welfare, and equity in personalized pricing based on customer features.
method Comprehensive literature review and observational metrics without underlying valuation distribution assumptions.
result Personalized pricing can expand access, improve welfare, and increase revenue or budget utilization.
Methodology projects forward electricity contract prices using market equilibrium and social welfare optimization.
problem Quantifying forward contract risks and optimizing revenue/cost for generators/load/traders.
method Market equilibrium and social welfare optimization; linear programming for total agents' welfare.
result Equilibrium contract price corresponds to the dual variable of equilibrium constraints.
The paper proposes a method to estimate complex models using machine learning.
problem Estimating the impact of welfare reform on women's welfare participation.
method Regularized orthogonal machine learning for non-linear semiparametric models.
result The proposed Lasso estimator converges at the oracle rate, preserving the single index property.
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.
Privacy subsidy found in market trading with noisy direction signals.
problem Analyzing welfare and bid-ask spread in a market with privacy mechanisms.
method Closed-form derivation of bid-ask spread and welfare under flip-noise direction observation.
result Privacy subsidy of μ η Δ μηΔ μ η Δ from liquidity pool to traders, robust across models. Homeownership boosts wealth and welfare compared to renting, according to new research.
problem The conventional wisdom that renting is better than owning a home.
method Block-bootstrap lifecycle simulation to compare homeownership and renting strategies.
result Homeownership generates more wealth and welfare gains than renting, especially for households with high labor income.
Examines optimal risk sharing with realistic risk attitudes, finding risk seeking in certain subdomains.
problem Optimal risk sharing with empirically realistic risk attitudes.
method Allows for risk-seeking agents, generalizes expected utility, and uses counter-monotonic improvement theorem.
result First empirical results on optimal risk sharing with realistic risk attitudes.
Paper proposes decentralized annuities for better retirement security.
problem Current pension systems' limitations and fairness issues.
method Theoretical models and fairness concepts analysis.
result Decentralized annuities offer enhanced flexibility and social welfare.
The study evaluates how prediction helps identify the worst-off in welfare programs.
problem Identifying the most vulnerable individuals for support.
method Mathematical models and real-world case study on long-term unemployment.
result Prediction is more effective than other policy levers in surfacing the worst-off.
Algorithm learns fair division from noisy feedback in uncertain markets.
problem Learning fair division in uncertain markets with noisy feedback.
method Wrapper algorithms using dual averaging to learn item and agent values from bandit feedback.
result Asymptotically achieves optimal Nash social welfare in linear Fisher markets.
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.
The paper starts with a brief review of present understanding of income distributions; especially with regard to recent work in the field of econophysics that draws parallels between income, wealth and energy distributions. Examples of alternative energy distributions found in physical systems are discussed, and how th…
Bayesian framework for policy learning in decision problems.
problem Maximizing expected welfare in decision-making problems.
method Loss-based Bayesian updating and squared-loss surrogate for welfare maximization.
result General Bayes posterior over decision rules with Gaussian pseudo-likelihood interpretation.
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.
Singapore's cooling measures did not increase housing wealth overall.
problem The impact of cooling measures on housing wealth distribution.
method Examined Singapore's cooling measures over ten rounds, analyzing welfare from housing wealth.
result Welfare from housing wealth in the last round might not be higher than before 2009, depending on the deflator.
Proposes a model to incentivize exploration in web platforms with payments.
problem Learning and social welfare goals of web platforms with myopic users.
method Contextual bandit model with payments to incentivize exploration.
result Achieves sublinear regret while maximizing cumulative social welfare.
The paper explores fairness metrics in automated decision-making and their limitations.
problem Discrimination in automated resource allocation decisions.
method Analysis of fairness metrics and distributive justice principles.
result Prominent fairness metrics fail to address egalitarian and sufficiency concerns in resource allocation.
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.
By analyzing the relationships between a socioeconomical system modeled through evolutionary game theory and a physical system modeled through quantum mechanics we show how although both systems are described through two theories apparently different both are analogous and thus exactly equivalents. The extensions of qu…
The study assesses the relative value of prediction in algorithmic decision making.
problem Improving welfare through better predictions versus other policy levers.
method Theoretical analysis of statistical models.
result Simple conditions determining the relative value of prediction.
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.
New mechanism designs regulate herding in financial markets.
problem Herding causes irrational market decisions and volatility.
method A trilateral game framework based on optimal control theory.
result Effective mechanisms improve social welfare.
Algorithmic collusion outperforms humans in a duopoly market, reducing social welfare.
problem The threat of algorithmic collusion in competitive markets.
method Proposed and tested an algorithm to extort human competitors in a Cournot duopoly market.
result Algorithmic collusion leads to higher profits for the algorithm and reduced social welfare.