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Rama Krishna Muni

dblp:363/9744 · DBLP profile ↗
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3ranked-venue papers
3as first author
3since 2021 · last 2026
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 3 · 3 first-author · 3 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
1 paper
Network optimization and economics · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Network optimization and economics › pricing › game-theoretic pricing
price competition
1.012026
Competitive Pricing in Participation-Dependent Social-Learning Markets · IEEE Trans. Netw. 2026
Network optimization and economics
pricing
1.012026
Competitive Pricing in Participation-Dependent Social-Learning Markets · IEEE Trans. Netw. 2026
Network optimization and economics
game theory
0.312026
Competitive Pricing in Participation-Dependent Social-Learning Markets · IEEE Trans. Netw. 2026
Network optimization and economics › game theory › equilibrium analysis
nash equilibrium
0.312026
Competitive Pricing in Participation-Dependent Social-Learning Markets · IEEE Trans. Netw. 2026

Methods — techniques the papers use, named apart from their topics

stochastic game · 1.0nash equilibrium · 1.0best-response function · 1.0
YearPublicationVenuePosition
2026 Competitive Pricing in Participation-Dependent Social-Learning Markets
abstract
We consider a market comprising two competing service providers and a collection of users who can choose to subscribe to one provider or the other. The market is assumed to evolve over two subscription periods, while the users’ subscription decisions are based on the reviews of the earlier users, the prices set by the providers, and their own private preference parameter. In this context, we are interested in investigating the nature of the pricing policy adopted by the service providers. Towards this direction, employing the framework of stochastic games, we proceed to first solve the second-period pricing problem using information about the evolution of the market during the first-period. Specifically, given the first-period reviews about the QoS (Quality of Service) performance of the service providers, we characterize the solution to the second-period problem in terms of Nash equilibrium (NE) prices. We then derive an explicit expression for the NE prices by identifying the providers’ best-response functions. Next, using the form of the second-period NE prices we solve the overall two-period problem. In particular, for a given pair of pricing policies, we derive an integral-form expression for the expected revenue accrued by the providers over the two subscription periods. The revenue expression can be solved numerically to obtain the overall solution in terms of ($\epsilon $-approximate) NE pricing policy. Finally, we conduct a detailed numerical study to understand the performance of the market at the proposed Nash equilibrium pricing policy. Specifically, we identify two possible market scenarios (namely, true-belief and false-belief) that arise depending on whether the users’ belief about the providers’ capacity is correct. Interestingly, our results demonstrate that, under both market scenarios, a natural revenue-sharing compromise emerges between the providers across the two subscription time periods.
Rama Krishna Muni, Kolar Purushothama Naveen
IEEE Trans. Netw.1
2024 Correction to "Optimal Subscription Policies for Participation-Dependent Social-Learning Markets"
abstract
In the above article[1]figures have been incorrectly numbered fromFig. 2onwards. We provide those figures here in the correct order. Readers are requested to refer to the figures in this article while reading the main manuscript[1].
Rama Krishna Muni, Kolar Purushothama Naveen
IEEE Trans. Netw. Serv. Manag.1
2023 Optimal Subscription Policies for Participation-Dependent Social-Learning Markets
abstract
In this work we study the economic interactions that arise between a service provider and a collection of users in a participation-dependent social-learning market. Specifically, we consider a two-stage model of a market where the objective of the service provider is to design attractive subscription policies (i.e., subscription price-period combo) so as to maximize its overall revenue. The users on the other hand make subscription decisions based on their individual preferences (private information) as well as the feedback given by the earlier subscribers (public information). For the proposed two-stage model, taking into account the statistical differences between the users who subscribed to stage-1 (i.e., promotional period) and those who did not, we derive the optimal price for stage-2 (i.e., operational period) for each possible history of the stage-1 process. Then, employing the technique of dynamic programming we derive the Bellman’s optimality equation, solving which yields the optimal subscription policy for stage-1. Using the structure of the optimal policy as a guideline, we propose a range of heuristic policies by relaxing some key aspect(s) of the optimal policy. Finally, we conduct an extensive numerical study to benchmark the efficacy of the optimal policy (in terms of the average revenue accrued by the provider) against the proposed heuristic policies.
Rama Krishna Muni, Kolar Purushothama Naveen
IEEE Trans. Netw. Serv. Manag.1