Abstract
We continue to study the endogenous threshold selection game. Two firms have interchangeable products with qualities that are drawn i.i.d. from a common prior. A principal wants to minimize the probability of selecting the product of lower quality by offering a set of threshold tests. The threshold test is that given a threshold, the product can be estimated whether its quality exceeds the threshold. Two firms pick the threshold endogenously to maximize the probability of being chosen by the principal.
We consider the scenario where the principal restricts threshold tests to be two-interval. We further characterize properties of Bayes-Nash equilibrium between two firms. Numerically, the minimum probability of incorrect selection is 0.22096 where threshold tests are supported on \([0,0.19]\cup [0.33,0.79]\). Moreover, our exploration aids in offering valuable insights into signaling games with one receiver and two senders.
Supported by the National Natural Science Foundation of China (Grant No. 62172422).
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Notes
- 1.
For any quality distribution, its quantile is distributed uniformly in [0, 1]. Once we get the results of quantile functions, we can derive the results of the original functions. Thus, it allows us to concentrate on the uniform distribution.
- 2.
We have \(\mathbb {E}[X|X<\theta ]\le \mathbb {E}[X]\le \mathbb {E}[X|X\ge \theta ]\) It indicates that the expected quality of the product passing a test will not be lower than the failed one.
- 3.
For the reason why we do not consider \(a\le b\le c\le d\), if \(a=b\) or \(c=d\), then a point appears in the restricted set. The Bayes-Nash equilibrium may not be unique and symmetric. Because we know that every pair supported on S forms an equilibrium when \(S=\{1-\frac{\sqrt{2}}{2}, \frac{\sqrt{2}}{2}\}\). And if \(b=c\), the case is equal to one interval and is well studied.
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Ren, Z., Liu, Y. (2024). Endogenous Threshold Selection with Two-Interval Restricted Tests. In: Chen, X., Li, B. (eds) Theory and Applications of Models of Computation. TAMC 2024. Lecture Notes in Computer Science, vol 14637. Springer, Singapore. https://doi.org/10.1007/978-981-97-2340-9_7
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DOI: https://doi.org/10.1007/978-981-97-2340-9_7
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