In this article we consider a noncooperative n-person optimal stopping game of Showcase Showdown, in which each player observes the sum of independent and identically distributed random variables uniformly distributed in [0, 1]. Players can decide to stop the draw in each moment. The objective of a player is to get the maximal number of scores that does is not exceeded level 1. If the scores of all players exceed 1, then the winner is the player whose score is closest to 1. We derive the equilibrium in this game on the basis of the dynamic programming approach.
AbstractThis paper explores a multi-player game of optimal stopping over a finite time horizon. A player wins by retaining a higher value than her competitors do, from a series of independent draws. In our game, a cutoff strategy is optimal, we derive its form, and we show that there is a unique Bayesian Nash Equilibrium in symmetric cutoff strategies. We establish results concerning the cutoff value in its limit and expose techniques, in particular, use of the Budan-Fourier Theorem, that may be useful in other similar problems.
We study the value and the optimal strategies for a two-player zero-sum optimal stopping game with incomplete and asymmetric information. In our Bayesian setup, the drift of the underlying diffusion process is unknown to one player (incomplete information feature), but known to the other one (asymmetric information feature). We formulate the problem and reduce it to a fully Markovian setup where the uninformed player optimises over stopping times and the informed one uses randomised stopping times in order to hide their informational advantage. Then we provide a general verification result that allows us to find the value of the game and players’ optimal strategies by solving suitable quasi-variational inequalities with some nonstandard constraints. Finally, we study an example with linear payoffs, in which an explicit solution of the corresponding quasi-variational inequalities can be obtained.
Background:
Number of resource allocation and bidding schemes had been enormously
arrived for on demand supply scheme of cloud services. But accessing and presenting the Cloud
services depending on the reputation would not produce fair result in cloud computing. Since the
cloud users not only looking for the efficient services but in major they look towards the cost. So
here there is a way of introducing the bidding option system that includes efficient user centric behavior
analysis model to render the cloud services and resource allocation with low cost.
Objective:
The allocation of resources is not flexible and dynamic for the users in the recent days.
This gave me the key idea and generated as a problem statement for my proposed work.
Methods:
An online auction framework that ensures multi bidding mechanism which utilizes user
centric behavioral analysis to produce the efficient and reliable usage of cloud resources according
to the user choice.
Results:
we implement Efficient Resource Allocation using Multi Bidding Model with User Centric
Behavior Analysis. Thus the algorithm is implemented and system is designed in such a way to
provide better allocation of cloud resources which ensures bidding and user behavior.
Conclusion:
Thus the algorithm Efficient Resource Allocation using Multi Bidding Model with
User Centric Behavior Analysis is implemented & system is designed in such a way to provide better
allocation of cloud resources which ensures bidding, user behavior. The user bid data is trained
accordingly such that to produce efficient resource utilization. Further the work can be taken towards
data analytics and prediction of user behavior while allocating the cloud resources.
Dynkin Games with Incomplete and Asymmetric Information