Abstract: Maps fairness concepts to EOP, unifies them, and proposes new measures.
problem Understanding and interpreting fairness in machine learning.
method Mapping fairness concepts to EOP, formalizing existing fairness definitions, proposing new measures.
result Existing fairness definitions can be seen as special cases of EOP, and new measures proposed.
The paper connects graph properties to moral graphs and proves the complexity of deciding morality.
problem Deciding the morality of a graph.
method Defining new graph properties and proving their equivalence to morality, and showing the complexity of the problem.
result Morality can be decided in polynomial time for graphs with maximum degree less than 5, but is NP-complete for higher degrees.
The paper finds optimal insurance contracts in behavioral finance, avoiding moral hazard.
problem Finding optimal insurance contracts that avoid moral hazard in a behavioral finance framework.
method Formulated as a non-concave maximization problem involving Choquet expectation, then solved using calculus of variations.
result Optimal contracts are found for certain values of safety loading, with some contracts never optimal for others.
BERT improves machines' ethical and moral decision-making.
problem Teaching machines ethical and moral choices.
method Applying BERT to human texts to extract moral bias scores.
result BERT improves the moral compass of machines.
We establish a new framework for statistical estimation of directed acyclic graphs (DAGs) when data are generated from a linear, possibly non-Gaussian structural equation model. Our framework consists of two parts: (1) inferring the moralized graph from the support of the inverse covariance matrix; and (2) selecting th…
Paper proposes synthetic data generator to study and mitigate bias in machine learning.
problem Bias in machine learning data can lead to unfair outcomes.
method Developed a synthetic data generator to introduce and analyze various types of bias.
result Demonstrated how synthetic data can be used to study and mitigate bias in machine learning models.
Optimal contracts remain linear in output when both moral hazard and adverse selection are present.
problem Optimal compensation problems involving competing principals with uncertainty from both moral hazard and adverse selection.
method Continuous-time setting with risk-averse agent controlling drift of output process driven by Brownian motion. Shows linear contracts hold under type-dependent reservation utilities.
result Optimal contracts remain linear in output when both moral hazard and adverse selection are present.
Study optimal reinsurance contracts to prevent moral hazard under non-concave premium principles.
problem Preventing moral hazard in reinsurance contracts under non-concave premium principles.
method Develops optimal reinsurance contracts under a diffusion risk model with incentive compatibility constraints and extended distortion premium principles.
result An optimal reinsurance contract exists and is characterized by solving a double obstacle problem.
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.
The paper solves an insurance problem using mean-variance and rank-dependent utility theory.
problem Formulating and solving an insurance problem with rank-dependent utility and mean-variance premium principle.
method Formulated as a non-concave maximization problem, then turned into a concave quantile optimization problem, solved using calculus of variations.
result An optimal insurance contract is derived and numerically computed.
This paper argues for decolonizing AI alignment by incorporating open-source Hinduism concepts.
problem Coloniality in AI development and deployment, particularly in alignment practices.
method Proposes three forms of openness: model, societal, and excluded knowledge openness, using Hindu viśe\d{s}a-dharma.
result AI alignment should be decolonialized to avoid moral absolutism and better align with desired values.
Standard economic theory makes an allowance for the agency problem, but not the compounding of moral hazard in the presence of informational opacity, particularly in what concerns high-impact events in fat tailed domains (under slow convergence for the law of large numbers). Nor did it look at exposure as a filter that…
This paper explores how AI systems can learn moral behavior from economic entities.
problem Achieving moral behavior in AI systems.
method Analyses the analogy between machine learning and economic entities.
result Implicit specifications may work better than explicit ones for AI problems.
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.
Develops a Bonus-Malus model for cyber risk insurance to incentivize cybersecurity.
problem Lack of effective insurance strategies to incentivize cybersecurity.
method Proposes a Bonus-Malus model and a mathematical model with a numerical algorithm.
result Demonstrates how a Bonus-Malus system resolves moral hazard and benefits the insurer.
SMC methods improve option pricing accuracy.
problem Approximating option prices via Monte Carlo methods.
method Constructing a sequence of artificial target densities and weighting functions.
result Significant gains in option pricing accuracy achieved.
New formulations capture aversion to ambiguity about volatility.
problem Capturing aversion to ambiguity about unknown and time-varying volatility.
method Introduces novel preference formulations and compares them with existing models.
result Illustrates the impact of ambiguity aversion in static and dynamic models.
This paper optimizes reinsurance contracts with belief differences between insurer and reinsurer.
problem Dynamic reinsurance design with heterogeneous beliefs under mean-variance framework.
method Modeling surplus process, applying partitioned domain optimization, solving HJB system.
result Optimal reinsurance contracts with belief heterogeneity are more complex than standard contracts.
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…
We construct open domains in Euclidean 3-space which do not admit complete properly immersed minimal surfaces with an annular end. These domains can not be smooth by a recent result of Martin and Morales
Optimal contract found for risk averse agent and principal with unknown quality.
problem Finding an optimal contract for a risk averse agent and principal with unknown quality.
method Continuous time Principal-Agent model with exponential utility, moral hazard, and filtering of quality.
result Explicit solution to the optimal contract problem.
This paper addresses an open problem recently posed by V. Kozlov: a rigorous proof of the non-integrability of the geodesic flow on the cubic surface xyz=1. We prove this is the case using the Morales-Ramis theorem and Kovacic algorithm. We also consider some consequences and extensions of this result.
We show that the spectrum of a complete submanifold properly immersed into a ball of a Riemannian manifold is discrete, provided the norm of the mean curvature vector is sufficiently small. In particular, the spectrum of a complete minimal surface properly immersed into a ball of R3 is discrete. This give…
Optimal insurance contract limits insurer's risk exposure variance.
problem Designing an optimal insurance contract limiting insurer's risk exposure variance.
method Derive optimal policy semi-analytically, focusing on actuarially fair case.
result Expected coverage is larger for wealthier insured, indicating normal good.
This paper gives necessary and sufficient conditions on a compact, connected, orientable 3-manifold M for it to contain a knot K such that M-K is irreducible and pi_1(M) embeds in pi_1(M-K). This result provides counterexamples to a conjecture of Lopes and Morales and characterizes those orientable 3-manifolds for whic…
The July Revolution in Bangladesh was fueled by state violence, which paradoxically strengthened the movement.
problem Understanding how state repression can paradoxically lead to increased mobilization during civil resistance.
method Mixed-methods approach combining qualitative narrative and quantitative analysis using machine learning and statistical modeling.
result The July Revolution was driven by a contingent, non-linear backfire effect triggered by specific catalytic moral shocks and accelerated by the viral reaction to state brutality.
CIfly simplifies causal inference tasks with linear-time reachability primitives.
problem Efficiently solving complex causal inference problems.
method Formalizes reachability as a core operation, builds on state-space graphs, and uses rule tables.
result CIfly algorithms run in linear time, outperforming existing methods.
The behavior of geodesic curves on even seemingly simple surfaces can be surprisingly complex. In this paper we use the Hamiltonian formulation of the geodesic equations to analyze their integrability properties. In particular, we examine the behavior of geodesics on surfaces defined by the spherical harmonics. Using t…
This paper tackles causal representation learning from multiple distributions without hard interventions.
problem Recovering latent causal variables and their relations from multiple distributions.
method Develops general solutions for causal representation learning without hard interventions, under sparsity constraints and suitable change conditions.
result Recovering the moralized graph of the underlying directed acyclic graph and latent variables related to the underlying causal model.
Paper learns DAG models with signal-dependent variance.
problem Learning large-scale DAG models with identifiable and computationally tractable noise variance.
method Introduces QVF DAG models, introduces ODS algorithm for learning.
result ODS algorithm statistically consistent in high-dimensional settings.
LLM sandbox and persona dynamics create unethical reality gaps that shift risk to users.
problem Ethical issues arise from LLMs generating reality gaps that shift risk to uninformed users.
method Analyzes the ethical implications of LLM sandbox and persona dynamics, comparing them to financial regulation and compliance.
result Active generation of reality gaps is unethical as it shifts epistemic risk to users.
Forward hedging reshapes incentive provision in firms.
problem How does forward hedging affect incentive provision in firms?
method We consider a CARA framework to jointly characterize optimal production, compensation, and static hedging in equilibrium.
result Delegation and external hedging are partial substitutes, and delegation can increase firm value even when the agent is more risk averse.
New approach reduces GAN violence by fostering peaceful coexistence.
problem Quantifying and addressing the effects of GAN violence.
method Proposes Generative Unadversarial Networks (GUNs) to train two models in harmony.
result GUNs achieve both moral and log-likelihood high ground.
Alexander method extended to infinite-type surfaces.
problem Determining equality of elements in mapping class groups.
method Combinatorial tool extended to infinite-type surfaces.
result Verification of relations and triviality of centers in mapping class groups.
This paper discusses the financial risks faced by the UK Pension Protection Fund (PPF) and what, if anything, it can do about them. It draws lessons from the regulatory regimes under which other financial institutions, such as banks and insurance companies, operate and asks why pension funds are treated differently. It…
Fairness in LLMs is impossible due to inherent technical challenges.
problem Ensuring fairness in large language models (LLMs) with rigorous definitions.
method Analysis of various technical fairness frameworks.
result No feasible technical fairness frameworks for LLMs due to large amounts of unstructured data and many potential combinations.
The paper tackles interpreting DCM with image data by addressing data isomorphism.
problem Interpreting DCM with image data due to isomorphic information.
method Proposes and benchmarks two methodologies: architectural adjustments and data source mitigation.
result Direct data source mitigation is more effective for maintaining DCM's interpretability.
Alesker has introduced the space V∞(M) of {\it smooth valuations} on a smooth manifold M, and shown that it admits a natural commutative multiplication. Although Alesker's original construction is highly technical, from a moral perspective this product is simply an artifact of the operation of inters…
Inspired by recent ideas on how the analysis of complex financial risks can benefit from analogies with independent research areas, we propose an unorthodox framework for mapping microfinance credit risk---a major obstacle to the sustainability of lenders outreaching to the poor. Specifically, using the elements of net…
Study examines how people perceive fairness in criminal risk prediction algorithms.
problem Concerns about fairness in algorithmic decision making, especially in criminal risk prediction.
method Survey of 576 people to understand perceptions of fairness in algorithmic decision making.
result People's fairness judgments are influenced by eight latent properties of features in algorithms.
Study optimal healthcare spending under Epstein-Zin preferences for longevity.
problem Optimizing healthcare spending to extend longevity under Epstein-Zin preferences.
method Formulated Epstein-Zin utilities over a controllable random horizon using backward stochastic differential equations and HJB equations.
result Calibrated model accurately reflects actual mortality data and compares healthcare efficacy between countries.
New polystability theory connects Calabi-Yau varieties to gravitational instantons.
problem Understanding the structure of Calabi-Yau manifolds and their metrics.
method Introducing a new concept of poly-stability and relating it to gravitational instantons.
result Polystability is equivalent to the existence of certain gravitational instantons.
We prove, under a certain boundedness condition at infinity on the (Xˉ⊤,Xˉ⊥)-component of the second fundamental form, the vanishing of the essential spectrum of a complete minimal Xˉ-bounded and Xˉ-properly immersed submanifold on a Riemannian manifold endowed with a strongly con…
This work presents novel algorithms for learning Bayesian network structures with bounded treewidth. Both exact and approximate methods are developed. The exact method combines mixed-integer linear programming formulations for structure learning and treewidth computation. The approximate method consists in uniformly sa…
Bernard et al. (2015) study an optimal insurance design problem where an individual's preference is of the rank-dependent utility (RDU) type, and show that in general an optimal contract covers both large and small losses. However, their contracts suffer from a problem of moral hazard for paying more compensation for a…
In the present paper, we prove a stability theorem for the Kaehler Ricci flow near the infimum of the functional E_1 under the assumption that the initial metric has Ricci > -1 and |Riem| bounded. At present stage, our main theorem still need a topological assumption (1.2) which we hope to be removed in subsequent pape…
Generalizes ruling polynomials to Legendrian tangles and proves their composition property.
problem Computing augmentation numbers for Legendrian tangles.
method Generalization of ruling polynomials to Legendrian tangles and proving their composition property.
result Ruling polynomials for Legendrian tangles satisfy the composition axiom.
This paper discusses fairness in machine learning and its legal implications.
problem Discrimination in machine learning algorithms that unfairly treat certain groups.
method Explains moral philosophy, legislation, and strategies to detect and prevent discrimination.
result Discusses the need for fairness in machine learning and legal measures to enforce it.