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.
Study uses Perelman and Ricci flow methods to analyze economic inequality.
problem Impact of socio-economic challenges and technological progress on economic inequality.
method Perelman model and Ricci flow methods.
result Technological innovations and social protection programs reduce inequality.
Study optimizes CT and microinsurance for efficient social protection in low-income countries.
problem Efficient targeting of cash transfers to reduce social protection costs in low-income countries.
method Modelled household capital dynamics using piecewise-deterministic Markov process, derived HJB equation for optimal injection, used dynamic programming.
result Optimal level of capital injection above poverty threshold for cost-effective social protection.
The rise in online social networking has brought about a revolution in social relations. However, its effects on offline interactions and its implications for collective well-being are still not clear and are under-investigated. We study the ecology of online and offline interaction in an evolutionary game framework wh…
New framework for fair ranking with noisy protected attributes.
problem Errors in socially-salient attributes undermine fairness guarantees.
method Modeling perturbations in protected attributes and incorporating probabilistic information.
result Framework provides provable guarantees on fairness and utility.
AI framework for automated policy-making connects with econometrics and social choice.
problem Improving government and economic policy-making through AI.
method Social Environment Design framework integrating Reinforcement Learning, EconCS, and Computational Social Choice.
result Promotes ethical and responsible decision making by solving open problems.
Subsidized insurance reduces poverty by providing social benefits and lowering government costs.
problem Reducing poverty through effective social protection mechanisms.
method Modeling household capital dynamics under four insurance frameworks (uninsured, insured, insured with subsidies, insured with flexible premiums) to assess poverty reduction and governmental costs.
result Subsidized insurance schemes provide maximum social benefits while reducing governmental costs, effectively reducing poverty.
Investors target specific regions of payoff distributions for portfolio optimization.
problem Optimizing portfolio performance across different return distribution regions.
method Developed a dynamic portfolio-choice framework targeting downside or upside quantiles.
result Policies focused on downside regions provide stronger left-tail protection and higher Sharpe ratios.
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.
Protects proprietary policies from imitation learning by training adversarial policy ensembles.
problem Protecting policies from external observers cloning them.
method Introduces a reinforcement learning framework that trains an ensemble of near-optimal policies, making demonstrations useless for external observers.
result Demonstrates the existence of 'non-clonable' ensembles and provides a solution to the optimization problem.
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.
Algorithm improves learning by integrating diverse agents' behaviors.
problem Lack of social learning in reinforcement learning algorithms.
method Free energy approach for social bandit learning.
result Algorithm converges to optimal policy and enhances learning.
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.
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 …
To achieve the ambitious aims of the Paris climate agreement, the majority of fossil-fuel reserves needs to remain underground. As current national government commitments to mitigate greenhouse gas emissions are insufficient by far, actors such as institutional and private investors and the social movement on divestmen…
The dynamics of protection processes has been a fundamental challenge in systemic risk analysis. The conceptual principle and methodological techniques behind the mechanisms involved [in such dynamics] have been harder to grasp than researchers understood them to be. In this paper, we show how to construct a large vari…
In this paper, the agent-based modeling is employed to model the effect of intellectual property policy at the speed of technological advancement. Every agent has inborn preferences towards investing their capital into independent technological development, innovation appropriation, and production. The relative cost of…
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.
A new framework for causal inference in networked settings.
problem Causal inference under network interference.
method Characterize agent network configuration and use it to estimate treatment effects.
result Finite-sample bounds and asymptotically valid tests for policy irrelevance.
Controversies around race and machine learning have sparked debate among computer scientists over how to design machine learning systems that guarantee fairness. These debates rarely engage with how racial identity is embedded in our social experience, making for sociological and psychological complexity. This complexi…
APGE protects graph node representations from inference attacks.
problem Privacy leakage in graph embedding methods.
method Adversarial training framework with disentangling and purging mechanisms.
result APGE preserves structural and utility attributes while concealing private information.
Introduces privilege scores to measure and interpret protected attribute-related privilege in machine learning models.
problem Lack of explicit formulation of non-neutrality in fairness-aware machine learning methods.
method Privilege scores (PS) and privilege score contributions (PSCs) to measure and interpret protected attribute-related privilege.
result Demonstrates the broad applicability of PS and PSCs in gender and racial privilege in mortgage and college admissions applications.
Paper proposes a method to encrypt faces while maintaining visual similarity.
problem Protecting personal data from unauthorized face recognition.
method Targeted identity-protection iterative method (TIP-IM) to generate adversarial identity masks.
result TIP-IM provides 95%+ protection success rate against face recognition models.
Locally private reinforcement learning protects individual environments from reverse engineering.
problem Protecting private information in distributed reinforcement learning environments.
method Locally differentially private algorithms that protect local agents' models from adversarial reverse engineering.
result Demonstrated that the proposed algorithm performs well under local differential privacy (LDP).
Offline RL algorithms protect privacy while learning from sensitive data.
problem Learning sensitive data in financial, legal, and healthcare applications.
method Differential privacy guarantees for offline RL algorithms.
result Provable prevention of privacy risks with strong learning bounds.
A commonly expressed concern about the rise of the peer-to-peer rental market Airbnb is that hosts---those renting out their properties---impose costs on their unwitting neighbors. I consider the question of whether apartment building owners will, in a competitive rental market, set a building-specific Airbnb hosting p…
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 model for fair clustering ensures balanced representation of protected attributes.
problem Ensuring fair representation in clustering for protected attributes.
method Model-based formulation of fair clustering, balancing protected attributes across clusters.
result Demonstrates improved fairness in clustering through a new model.
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.
Machine learning algorithms for prediction are increasingly being used in critical decisions affecting human lives. Various fairness formalizations, with no firm consensus yet, are employed to prevent such algorithms from systematically discriminating against people based on certain attributes protected by law. The aim…
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…
Algorithm improves wildlife protection patrols.
problem Balancing exploration and exploitation in patrolling vast protected areas.
method Formulated as a stochastic multi-armed bandit problem, leveraging smoothness and decomposability.
result Algorithm LIZARD improves performance on real-world poaching data.
We study the stochastic multi-armed bandit (MAB) problem in the presence of side-observations across actions that occur as a result of an underlying network structure. In our model, a bipartite graph captures the relationship between actions and a common set of unknowns such that choosing an action reveals observations…
Develops algorithm to reduce real-world inequality.
problem Reduces inequality in real-world disparities.
method Impact remediation framework using social science insights and constrained optimization.
result Optimal intervention policies discovered to improve equity.
Privacy attacks reveal hidden information in network embeddings after node removal.
problem Privacy concerns in network embeddings after node deletion.
method Analyzed network embeddings and developed an attack to recover removed node information.
result Significant information about removed node's neighbors can be retrieved from remaining embeddings.
The paper examines how risk reduction and insurance choices interact under convex premium principles.
problem Interaction between self-protection and insurance demand under convex premium principles.
method Investigates optimal prevention efforts and insurance shares using distortion risk measures.
result Self-protection and insurance are complementary, but ex ante moral hazard can turn this into a substitution effect.
New approach to counterfactual reasoning avoids demographic interventions.
problem Limitations of traditional counterfactual reasoning in AI systems.
method Backtracking counterfactual approach instead of interventional.
result Allows addressing social concerns without demographic interventions.
2020 Census uses more noise to protect privacy than needed, improving data accuracy.
problem Ensuring privacy in census data while maintaining accuracy for policy decisions.
method Applied f-differential privacy to track and reduce noise across geographical levels. result The 2020 Census provides stronger privacy protections than its nominal guarantees suggest.
Study shows how price protection affects online learning algorithms for dynamic pricing.
problem Impact of price protection guarantee on online learning algorithms for dynamic pricing.
method Characterized the effect of price protection period length on optimal regret, proposed LEAP algorithm.
result Optimal regret is Θ(√T + min{M, T^2/3}) with LEAP matching lower bounds up to logarithmic factors.
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…
Novel trading strategy for generalized lattice markets ensures positive profits.
problem Trading in markets with serially correlated returns and asset correlation.
method Multi-double linear policies in a generalized lattice market model.
result Proposed policies ensure positive expected profits in a lattice market.
Paper shows how to hide individuals in graphs to fool community detection models.
problem Adversarial attack on community detection models by hiding individuals.
method Iterative learning framework that updates a graph generator and a community detection model.
result Adversarial graphs generated by the method can fool multiple community detection models.
Researchers develop methods to protect reinforcement learning and control systems from policy poisoning attacks.
problem Attacks on reinforcement learning and control systems that manipulate learned policies.
method Unified framework for solving policy poisoning attacks, demonstrating global optimality and feasibility.
result Policy poisoning attacks are feasible and can be defended with a convex optimization approach.