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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,051 papers · 148 categories

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48 results for social policies

Heterogeneous SVO leads to diverse policies in sequential social dilemmas.

problem Understanding how diverse social value orientations affect behavior in sequential social dilemmas.
method Extending prior reinforcement learning studies, we instantiated heterogeneous SVO in a sequential social dilemma setting and measured task-specific diversity metrics.
result Heterogeneous SVO leads to meaningfully diverse policies across various incentive structures.

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.

Divestment from fossil fuels can accelerate climate policy, study finds.

problem Achieving Paris climate agreement requires reducing fossil fuel reserves.
method Stochastic agent-based model of financial market and investors' beliefs.
result Small share of socially responsible investors can initiate decarbonization.

Efficiently models agent dependencies in large social networks.

problem Challenges in incorporating inter-agent dependencies in social reinforcement learning models.
method Clusters users based on payoff and contribution, combines cluster-level policies with personalized agent-level policies, and uses dynamic clustering.
result Our approach learns more accurate policy estimates and converges more quickly than existing methods.

Reduces variance in noisy social outcomes to improve policy evaluation and optimization.

problem Improving access to opportunity through personalized treatment decisions.
method Data-driven dimensionality-reduction using reduced rank regression to denoise multiple outcomes.
result Improves estimation error in policy evaluation and optimization, including on real-world data.

Social Security and other public policies can be viewed as a series of cash in and outflows that depend on parameters such as the age distribution of the population and the retirement age. Given forecasts of these parameters, policies can be designed to be financially stable, i.e., to terminate with a zero balance. If …

2012-01-30abs ↗pdf ↗

The paper optimizes pension policies with guarantees and sustainability constraints.

problem Designing optimal pension policies with guarantees and sustainability constraints.
method Dynamic utility model, stochastic domain, overlapping generations, time-consistent decision criterion.
result Optimal investment/pension policy computed for a general framework.

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.

Develops c-GNF for personalized social science policy analysis.

problem Challenges in estimating causal effects and counterfactual inference in social sciences.
method causal-Graphical Normalizing Flow (c-GNF) method.
result c-GNF performs well in estimating causal effects and counterfactual inference.

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 algorithm improves group fairness in social classification problems by exploiting performativity.

problem Inequities in social classification problems due to performativity.
method Develops algorithmic fairness practices that leverage performativity to achieve stronger group fairness guarantees.
result Achieves stronger group fairness guarantees compared to non-performative settings.

Study shows Lula's Zero Hunger program reduced income inequality in Brazil.

problem Income inequality in Brazil during Lula's administration.
method Breakpoint regression analysis using detailed descriptive statistics.
result The Zero Hunger program substantially reduced income inequality and provided income security for the poor.

We consider a collaborative online learning paradigm, wherein a group of agents connected through a social network are engaged in playing a stochastic multi-armed bandit game. Each time an agent takes an action, the corresponding reward is instantaneously observed by the agent, as well as its neighbours in the social n…

2016-02-29abs ↗pdf ↗

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 ↗

AI-driven tax policies improve economic equality and productivity.

problem Lack of appropriate economic data and limited opportunity to experiment.
method Two-level deep reinforcement learning approach to learn dynamic tax policies from observational data.
result AI-driven tax policies improve the trade-off between equality and productivity by 16%.

This work shifts focus from prediction to intervention in social systems.

problem The limitations of focusing solely on prediction in automated decision systems.
method Shift from prediction-focused paradigm to intervention-oriented approach.
result A new perspective unifies statistical frameworks and tools for ADS design, implementation, and evaluation.

Two computational models analyze political topics in social media tweets.

problem Measuring political attention in social media is labor-intensive and restrictive.
method Two computational models: supervised classifier and unsupervised topic model.
result Models provide different benefits: supervised classifier reduces labor, unsupervised model uncovers political and non-political uses.

Spatial machine learning improves poverty targeting in Indonesia.

problem Conventional PMT methods have high exclusion and inclusion errors due to spatial dependencies and regional heterogeneity.
method Integrates spatial contiguity matrices into SML models to identify and compare poverty clusters.
result SML reduces exclusion errors from 28% to 20% compared to standard machine learning models.

Efficiently selects seed nodes to maximize content influence in unknown social networks.

problem Maximizing content spread in social networks with unknown network model.
method Formulated as an infinite-horizon discounted MDP, uses model-based reinforcement learning to select seed users adaptively.
result Established a regret bound of O~(T)\widetilde O(\sqrt{T}) for the algorithm.

Investigates optimal pension policies in PAYG systems with forward utility and ageing population.

problem Optimal investment and pension policies in PAYG systems with sustainability and adequacy constraints.
method Non-zero volatility forward CRRA utilities, closed-form optimal policies, detailed numerical analysis.
result Characterization of optimal policies and detailed impact analysis under various scenarios.

DQ4FairIM uses RL to maximize influence while ensuring fairness across all groups.

problem Fairness in influence maximization in social networks.
method Fairness-aware deep RL method using Structure2Vec network embedding.
result DQ4FairIM achieves higher fairness than fairness-agnostic and fairness-aware baselines.

Iran's FiT policy boosts REs but risks financial crisis and plant failure.

problem Financial challenges in maintaining renewable energy growth after policy targets.
method System dynamics model considering budget status, social tolerance, and investor trust.
result Adjusting tax on electricity consumption based on budget status prevents financial crises and maintains stable growth.

Flash crash on Ethereum shows social coordination can destabilize blockchain systems.

problem Interconnected socio-technical systems can be fragile due to social coordination.
method Analyzed the 2017 Ethereum flash-crash to illustrate the fragility of blockchain systems.
result Emergent centralization can lead to catastrophic cascades in interconnected systems.

Analyzes COVID-19 data to predict mortality, forecast spread, and optimize resource allocation.

problem Challenges in patient triage, treatment, and care management during the pandemic.
method Integrated four-step approach combining descriptive, predictive, and prescriptive analytics.
result Optimized resource allocation and informed policy decisions.

Policy shifts between Trump and Biden impact ESG investments, creating volatility.

problem Dramatic policy shifts between Trump and Biden administrations affect ESG investments.
method Analyzes contrasting policies of Trump and Biden administrations and their impacts on ESG investments.
result Policy changes significantly influence ESG investments, leading to volatility and portfolio reassessment.

S-DIDML integrates structural DID with ML for causal inference in high-dimensional data.

problem Causal inference in high-dimensional observational panel data with confounding variables.
method Structural identification with high-dimensional estimation, Neyman orthogonality, cross-fitting, causal forests, semi-parametric models.
result Precision in identifying policy-sensitive groups and optimizing resource allocation.

Why do nations produce scientific research? This is a fundamental problem in the field of social studies of science. The paper confronts this question here by showing vital determinants of science to explain the sources of social power and wealth creation by nations. Firstly, this study suggests a new general definitio…

2018-06-13abs ↗pdf ↗

This paper considers stochastic bandits with side observations, a model that accounts for both the exploration/exploitation dilemma and relationships between arms. In this setting, after pulling an arm i, the decision maker also observes the rewards for some other actions related to i. We will see that this model is su…

2012-10-16abs ↗pdf ↗

The paper explores how fairness policies can lead to social equality in decision-making.

problem Fairness in decision-making systems and its long-term effects on the population.
method A model that combines selection process and dynamics of group qualifications, focusing on demographic parity.
result Unconstrained fairness policies may not lead to equality, and imposing demographic parity can worsen qualifications in some cases.

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.

This study models FOMC policy decisions using debate-based LLMs.

problem Accurately predicting central bank policy decisions, especially FOMC's, is challenging.
method A novel framework that simulates FOMC's collective decision-making process through iterative rounds of LLMs interacting as agents.
result The debate-based approach significantly outperforms standard LLMs in prediction accuracy.

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.

We look at a collection of conjectures with the unifying message that smaller social systems, tend to be less complex and can be aligned better, towards fulfilling their intended objectives. We touch upon a framework, referred to as the four pronged approach that can aid the analysis of social systems. The four prongs …

2016-03-19abs ↗pdf ↗