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Stackelberg competition

About: Stackelberg competition is a research topic. Over the lifetime, 6611 publications have been published within this topic receiving 109213 citations.


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Journal ArticleDOI
TL;DR: In this article, a joint optimisation model of pricing strategies, quality levels, effort decisions, and return policies was proposed by considering the reference price effect in a three-level supply chain under different channel power structures.
Abstract: Over the last few decades, the closed loop supply chain (CLSC) has been examined because of concerns over the environment and social liability. In this paper, we propose a joint optimisation model of pricing strategies, quality levels, effort decisions, and return policies by considering the reference price effect in a three-level supply chain under different channel power structures. To investigate the impact of different scenarios on optimal decisions and performance of a CLSC, we address five different channel power structures: centralised, vertical Nash, manufacturer Stackelberg, retailer Stackelberg, and third party Stackelberg. We present a numerical example to demonstrate the theoretical results of the developed model, and we also compare the optimal decisions to determine the best channel power structures considered. Then, to examine the impact of the key parameters on the model's behaviour, we conduct a sensitivity analysis on the main parameters, and finally, we provide a conclusion. [Received 5 October 2016; Revised 9 March 2017; Accepted 24 March 2017]

66 citations

Journal ArticleDOI
TL;DR: In this article, a set of Stackelberg game models considering different pricing strategies, profit coordination modes and information pattens are constructed and analyzed, in which two competitive retailers purchase a type of green product from a manufacturer who commits to green investment.

66 citations

Proceedings ArticleDOI
10 Apr 2011
TL;DR: This paper proposes a pricing-based spectrum trading mechanism that enables SUs to contend for channel usage by random access, in a distributed manner, which naturally mitigates the complexity and time overhead associated with centralized scheduling.
Abstract: Market-based mechanisms offer promising approaches for spectrum access in cognitive radio networks. In this paper, we focus on two market models, one with a monopoly primary user (PU) market and the other with a multiple PU market, where each PU sells its temporarily unused spectrum to secondary users (SUs). We propose a pricing-based spectrum trading mechanism that enables SUs to contend for channel usage by random access, in a distributed manner, which naturally mitigates the complexity and time overhead associated with centralized scheduling. For the monopoly PU market model, we first consider SUs contending via slotted Aloha. The revenue maximization problems here are nonconvex. We first characterize the Pareto optimal region, and then obtain a Pareto optimal solution that maximizes the SUs' throughput subject to the SUs' budget constraints. To mitigate the spectrum underutilization due to the “price of contention,” we revisit the problem where SUs contend via CSMA, and show that spectrum utilization is enhanced, resulting in higher revenue. When the PU's unused spectrum is a control parameter, we study further the tradeoff between the PU's utility and its revenue. For the multiple PU market model, we cast the competition among PUs as a three-stage Stackelberg game, where each SU selects a PU's channel to maximize its throughput. We characterize the Nash equilibria, in terms of access prices and the spectrum offered to SUs. Our findings reveal that the number of equilibria exhibits a phase transition phenomenon, in the sense that when the number of PUs is greater than a threshold, there exist infinitely many equilibria; otherwise, there exists a unique Nash equilibrium, where the access prices and spectrum opportunities are determined by the budgets/elasticity of SUs and the utility level of PUs.

66 citations

Journal ArticleDOI
01 Jul 2002
TL;DR: The team-optimal state feedback Stackelberg strategy (with no delays) of an important class of discrete-time two- person nonzero-sum dynamic games characterized by linear state dynamics and quadratic cost functionals is obtained.
Abstract: A substantial effort has been devoted to various incentive Stackelberg solution concepts. Most of these concepts work well in the sense that the leader can get his desired solution in the end. Yet, most incentive strategies developed thus far include either the follower's control, which may not be realistic in practice, or delays in the state, which makes stabilization more difficult to achieve. In this paper, we obtain the team-optimal state feedback Stackelberg strategy (with no delays) of an important class of discrete-time two-person nonzero-sum dynamic games characterized by linear state dynamics and quadratic cost functionals.

66 citations

Proceedings ArticleDOI
02 May 2011
TL;DR: A novel technique based on a hierarchical decomposition and branch and bound search over the follower type space, which may be applied to different Stackelberg game solvers is presented, resulting in a new exact algorithm called HBGS that is orders of magnitude faster than the best known previous Bayesian solver for general StACkelberg games.
Abstract: The fastest known algorithm for solving General Bayesian Stackelberg games with a finite set of follower (adversary) types have seen direct practical use at the LAX airport for over 3 years; and currently, an (albeit non-Bayesian) algorithm for solving these games is also being used for scheduling air marshals on limited sectors of international flights by the US Federal Air Marshals Service. These algorithms find optimal randomized security schedules to allocate limited security resources to protect targets. As we scale up to larger domains, including the full set of flights covered by the Federal Air Marshals, it is critical to develop newer algorithms that scale-up significantly beyond the limits of the current state-of-the-art of Bayesian Stackelberg solvers. In this paper, we present a novel technique based on a hierarchical decomposition and branch and bound search over the follower type space, which may be applied to different Stackelberg game solvers. We have applied this technique to different solvers, resulting in: (i) A new exact algorithm called HBGS that is orders of magnitude faster than the best known previous Bayesian solver for general Stackelberg games; (ii) A new exact algorithm called HBSA which extends the fastest known previous security game solver towards the Bayesian case; and (iii) Approximation versions of HBGS and HBSA that show significant improvements over these newer algorithms with only 1--2% sacrifice in the practical solution quality.

66 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023551
20221,041
2021563
2020557
2019582
2018487