A Hypothesis on Black Swan in Unchanging Environments
Black swan events are statistically rare occurrences that carry extremely high risks. A typical view of defining black swan events is heavily assumed to originate from an unpredictable time-varying environments; however, the community lacks a comprehensive definition of black swan events. To this end, this paper challenges that the standard view is incomplete and claims that high-risk, statistically rare events can also occur in unchanging environments due to human misperception of their value and likelihood, which we call as spatial black swan event. We first carefully categorize black swan events, focusing on spatial black swan events, and mathematically formalize the definition of black swan events. We hope these definitions can pave the way for the development of algorithms to prevent such events by rationally correcting human perception.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Black Swan: Abductive and Defeasible Video Reasoning in Unpredictable Events
The commonsense reasoning capabilities of vision-language models (VLMs), especially in abductive reasoning and defeasible reasoning, remain poorly understood. Most benchmarks focus on typical visual scenarios, making it …
Training Cross-Lingual embeddings for Setswana and Sepedi
African languages still lag in the advances of Natural Language Processing techniques, one reason being the lack of representative data, having a technique that can transfer information between languages can help mitigat…
Cross-Lingual TransferSemantic SimilaritySemantic Textual SimilarityWord EmbeddingsThe EarlyBird Gets the WORM: Heuristically Accelerating EarlyBird Convergence
The Lottery Ticket hypothesis proposes that ideal, sparse subnetworks, called lottery tickets, exist in untrained dense neural networks. The Early Bird hypothesis proposes an efficient algorithm to find these winning lot…
Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection
Despite most existing anomaly detection studies assume the availability of normal training samples only, a few labeled anomaly examples are often available in many real-world applications, such as defect samples identifi…
Anomaly DetectionSupervised Anomaly DetectionSupervised Defect DetectionHypothesis Driven Coordinate Ascent for Reinforcement Learning
This work develops a novel black box optimization technique for learning robust policies for stochastic environments. Through combining coordinate ascent with hypothesis testing, Hypothesis Driven Coordinate Ascent (HDCA…
MuJoCoOpenAI Gymreinforcement-learningReinforcement Learning+1