Title
BONUS! Maximizing Surprise
Abstract
ABSTRACT Multi-round competitions often double or triple the points awarded in the final round, calling it a bonus, to maximize spectators’ excitement. In a two-player competition with n rounds, we aim to derive the optimal bonus size to maximize the audience’s overall expected surprise (as defined in [7]). We model the audience’s prior belief over the two players’ ability levels as a beta distribution. Using a novel analysis that clarifies and simplifies the computation, we find that the optimal bonus depends greatly upon the prior belief and obtain solutions of various forms for both the case of a finite number of rounds and the asymptotic case. In an interesting special case, we show that the optimal bonus approximately and asymptotically equals to the “expected lead”, the number of points the weaker player will need to come back in expectation. Moreover, we observe that priors with a higher skewness lead to a higher optimal bonus size, and in the symmetric case, priors with a higher uncertainty also lead to a higher optimal bonus size. This matches our intuition since a highly asymmetric prior leads to a high “expected lead”, and a highly uncertain symmetric prior often leads to a lopsided game, which again benefits from a larger bonus.
Year
DOI
Venue
2022
10.1145/3485447.3512049
International World Wide Web Conference
Keywords
DocType
Citations 
information design, entertainment utility, surprise and suspense
Conference
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Zhihuan Huang100.68
Yuqing Kong2144.31
Tracy Xiao Liu300.68
Grant Schoenebeck450939.48
Shengwei Xu532.44