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data science

2,518 papers

#climate science Open access Sep 2026

Reconstructing the Dark Forest as a Bayesian Game: Exact Conditions for Concealment, Pre-emption and Credible Contact

The Dark Forest hypothesis treats cosmic silence as the strategic consequence of uncertainty, catastrophic vulnerability and incentives for pre-emptive attack. Its central conclusion is usually stated intuitively rather than derived from an explicit incomplete-information game. This paper develops the Bayesian Dark For...

Kwan Hong TAN · 0 citations
#climate science Open access Sep 2026

Reconstructing the Dark Forest as a Bayesian Game: Exact Conditions for Concealment, Pre-emption and Credible Contact

The Dark Forest hypothesis treats cosmic silence as the strategic consequence of uncertainty, catastrophic vulnerability and incentives for pre-emptive attack. Its central conclusion is usually stated intuitively rather than derived from an explicit incomplete-information game. This paper develops the Bayesian Dark For...

Kwan Hong TAN · 0 citations
#human-computer interacti... Open access Sep 2026

The Conjunctive Misfortune Problem: Behavioural Tail Risk, Outcome Luck, and Punishment Cascades

A serious behavioural failure may be uncommon within a person’s history yet become the principal basis for judgments of character and future risk. This conceptual article develops the conjunctive misfortune problem: adverse conditions, culpable action, stochastic harm, categorical institutional responses, and persisten...

Kwan Hong TAN · 0 citations
#human-computer interacti... Open access Sep 2026

The Conjunctive Misfortune Problem: Behavioural Tail Risk, Outcome Luck, and Punishment Cascades

A serious behavioural failure may be uncommon within a person’s history yet become the principal basis for judgments of character and future risk. This conceptual article develops the conjunctive misfortune problem: adverse conditions, culpable action, stochastic harm, categorical institutional responses, and persisten...

Kwan Hong TAN · 0 citations
#data science Open access Sep 2026

The Status-Punishment Paradox: Status-Amplified Schadenfreude, Moralized Reputational Aggression, and the Digital Afterlife of Punishment

Why can prior achievement become a liability after wrongdoing? This conceptual article integrates research on tall-poppy effects, social comparison, schadenfreude, moral outrage, public shaming, news values, and criminal-record stigma into a theory of the Status-Punishment Paradox. Status ordinarily increases social va...

Kwan Hong TAN · 0 citations
#data science Open access Sep 2026

The Status-Punishment Paradox: Status-Amplified Schadenfreude, Moralized Reputational Aggression, and the Digital Afterlife of Punishment

Why can prior achievement become a liability after wrongdoing? This conceptual article integrates research on tall-poppy effects, social comparison, schadenfreude, moral outrage, public shaming, news values, and criminal-record stigma into a theory of the Status-Punishment Paradox. Status ordinarily increases social va...

Kwan Hong TAN · 0 citations
#natural language process... Open access Sep 2026

The Liminal Ontology: A New Framework for Consciousness, Identity, and the So-Called Supernatural

This thesis introduces a novel theoretical framework, the Liminal Ontology, to address the enduring philosophical and scientific challenges posed by consciousness, identity, and anomalous experiences. Moving beyond the traditional dualism of mind and matter and the limitations of materialism, the Liminal Ontology posit...

Kwan Hong TAN · 0 citations
#natural language process... Open access Sep 2026

The Liminal Ontology: A New Framework for Consciousness, Identity, and the So-Called Supernatural

This thesis introduces a novel theoretical framework, the Liminal Ontology, to address the enduring philosophical and scientific challenges posed by consciousness, identity, and anomalous experiences. Moving beyond the traditional dualism of mind and matter and the limitations of materialism, the Liminal Ontology posit...

Kwan Hong TAN · 0 citations
#machine learning Open access Aug 2026

Are Our Current Rational Decision-Making Models Truly Rational? A Critical Analysis and a New Neurobiological Framework

This paper critically examines the foundational assumptions of rational decision-making models and finds them to be systematically and comprehensively flawed. Through a rigorous analysis of empirical evidence from behavioral economics and neuroscience, we demonstrate that traditional models, such as Expected Utility Th...

Kwan Hong TAN · 0 citations
#machine learning Open access Aug 2026

Are Our Current Rational Decision-Making Models Truly Rational? A Critical Analysis and a New Neurobiological Framework

This paper critically examines the foundational assumptions of rational decision-making models and finds them to be systematically and comprehensively flawed. Through a rigorous analysis of empirical evidence from behavioral economics and neuroscience, we demonstrate that traditional models, such as Expected Utility Th...

Kwan Hong TAN · 0 citations
#computer vision Open access Aug 2026

Are Predictive Models Epistemically Superior to Causal Ones?

This thesis investigates the complex and often contentious question of whether predictive models are epistemically superior to causal models. It challenges the simplistic dichotomy that often frames this debate, arguing that the epistemic superiority of a model is not an intrinsic property but is contingent upon the sp...

Kwan Hong TAN · 0 citations
#computer vision Open access Aug 2026

Are Predictive Models Epistemically Superior to Causal Ones?

This thesis investigates the complex and often contentious question of whether predictive models are epistemically superior to causal models. It challenges the simplistic dichotomy that often frames this debate, arguing that the epistemic superiority of a model is not an intrinsic property but is contingent upon the sp...

Kwan Hong TAN · 0 citations

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Microsoft Research Blog Oct 6, 2026

What AI gets wrong and what failure teaches us

Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity.  The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.

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