A Black Swan Hypothesis: The Role of Human Irrationality in AI Safety
Hyunin Lee, Chanwoo Park, David Abel, Ming Jin
TL;DR
This paper redefines black swan events as high-risk, rare occurrences caused by human misperception, even in unchanging environments, emphasizing the importance of understanding human biases for AI safety.
Contribution
It introduces a new classification of black swan events, especially spatial black swans, and formalizes their definition to aid in developing algorithms that mitigate human perception errors.
Findings
Categorized black swan events, focusing on spatial black swans.
Mathematically formalized the definition of black swan events.
Proposed a framework for algorithm development to correct human perception.
Abstract
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.
Peer Reviews
Decision·ICLR 2025 Poster
- The paper presents a novel hypothesis that black swan events can occur in static environments due to human misperception, which is a significant departure from the traditional view. This new perspective could open up fresh avenues for research in risk management and machine learning. - The theoretical framework is well-developed, with rigorous mathematical formalizations and proofs. The use of Markov Decision Processes (MDPs) to model human perception and misperception is particularly robust.
- The paper lacks empirical validation of the proposed hypothesis. While the theoretical framework is strong, it would benefit from experimental results or real-world case studies demonstrating the occurrence of S-BLACK SWAN events. - The mathematical formalizations, while rigorous, are quite complex and may be difficult for practitioners to apply directly. Simplifying some of the models or providing more intuitive explanations could enhance accessibility. - The paper primarily focuses on financ
- This paper presents a new view point on how to look at Black Swan events. Specifically, it points to the case of stationary MDPs where agents have distorted perspective on reward signals and visitation probabilities which are likely to be overlooked by researchers. - The mathematical rigor is strong - the authors have done a great job at defining s-Black Swan using the existing concepts of MDP and its special cases. It gives a good framework for future researchers to build upon while trying
- While the paper is very rigorous, the details might be very hard to follow for non-specialists. Some of the aspects are not very intuitive. - This paper lacks a practical application demonstration - it'll be great if the authors can describe how a practitioner can use the definitions that the paper provides for a practical applications. - Building upon the previous point - it'll be useful for us to understand how frequent are such MDPs where the users have a distorted view of the reward si
* The main arguments of the paper are well-structured and well-communicated. The flow of the paper is helpful to the reader in communicating both the preliminary materials as well as leading to the mathematical formulation of the S-Black-Swan definition. For a theory paper, which could have the tendency to overcomplicate results, it feels like the authors have made significant effort to make the paper readable and therefore potentially meaningful to those who might use it as a future reference.
* One weakness of the paper is in its ability to build a strong link between the results of the paper and how it might affect the wider machine learning community. This weakness can be broken down into a combination of the following: * The related works section is left to the end and reads a bit like a list of works at the intersection of expected utility theory and reinforcement learning. The reader gets to the end of the section and is then told that this literature does not cover black swan
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Taxonomy
TopicsMulti-Agent Systems and Negotiation