Long bet will lose: demystifying seemingly fair gambling via two-armed Futurity bandit
Zengjing Chen, Huaijin Liang, Wei Wang, Xiaodong Yan

TL;DR
This paper reveals that long-term gambling, even when seemingly fair, is inherently biased in favor of casinos due to the properties of two-armed Futurity bandits, exposing casino unfairness and consumer manipulation.
Contribution
It provides a theoretical and experimental analysis demonstrating that two-armed Futurity slot machines are fundamentally unfair, with casinos having a win rate above 50%, challenging assumptions of fairness.
Findings
Casino win rate exceeds 50% in Futurity bandits
Long bets inevitably lead to losses for gamblers
Futurity bandit analysis exposes casino unfairness
Abstract
No matter how much some gamblers occasionally win, as long as they continue to gamble, sooner or later they will lose more to the casino, which is the so-called long bet will lose. Our results demonstrate the counter-intuitive phenomenon, that gamblers involved in long bets will lose but casinos always advertise their unprofitable circumstances. Here we expose the law of inevitability behind long bet will loss by theoretically and experimentally demystifying the profitable mystery behind casinos under two-armed antique Mills Futurity slot machine. The main results straightforwardly elucidate that all casino projects are seemingly a fair gamble but essentially unfair, i.e., the casino's win rate is greater than 50%. We anticipate our assay to be a starting point for studying the fairness of more sophisticated multi-armed Futurity bandits based on the mathematical tool. In application, a…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsArtificial Intelligence in Games · Advanced Bandit Algorithms Research · Sports Analytics and Performance
