A Competition-based Pricing Strategy in Cloud Markets using Regret Minimization Techniques

• URL: http://arxiv.org/abs/2309.11312v1
• Date: Wed, 20 Sep 2023 13:38:43 GMT
• Title: A Competition-based Pricing Strategy in Cloud Markets using Regret Minimization Techniques
• Authors: S.Ghasemi, M.R.Meybodi, M.Dehghan, A.M.Rahmani
• Abstract summary: This work proposes a pricing policy related to the regret minimization algorithm and applies it to the considered incomplete-information game. Based on the competition based marketplace of the Cloud, providers update the distribution of their strategies using the experienced regret. The experimental results show much more increase in profits of the providers in comparison with other pricing policies.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cloud computing as a fairly new commercial paradigm, widely investigated by different researchers, already has a great range of challenges. Pricing is a major problem in Cloud computing marketplace; as providers are competing to attract more customers without knowing the pricing policies of each other. To overcome this lack of knowledge, we model their competition by an incomplete-information game. Considering the issue, this work proposes a pricing policy related to the regret minimization algorithm and applies it to the considered incomplete-information game. Based on the competition based marketplace of the Cloud, providers update the distribution of their strategies using the experienced regret. The idea of iteratively applying the algorithm for updating probabilities of strategies causes the regret get minimized faster. The experimental results show much more increase in profits of the providers in comparison with other pricing policies. Besides, the efficiency of a variety of regret minimization techniques in a simulated marketplace of Cloud are discussed which have not been observed in the studied literature. Moreover, return on investment of providers in considered organizations is studied and promising results appeared.

Related papers

• Artificial Intelligence and Algorithmic Price Collusion in Two-sided Markets [9.053163124987535]
  We examine how AI agents using Q-learning engage in tacit collusion in two-sided markets. Our experiments reveal that AI-driven platforms achieve higher collusion levels compared to Bertrand competition. Increased network externalities significantly enhance collusion, suggesting AI algorithms exploit them to maximize profits.
  arXiv  Detail & Related papers  (2024-07-04T17:57:56Z)
• Contractual Reinforcement Learning: Pulling Arms with Invisible Hands [68.77645200579181]
  We propose a theoretical framework for aligning economic interests of different stakeholders in the online learning problems through contract design. For the planning problem, we design an efficient dynamic programming algorithm to determine the optimal contracts against the far-sighted agent. For the learning problem, we introduce a generic design of no-regret learning algorithms to untangle the challenges from robust design of contracts to the balance of exploration and exploitation.
  arXiv  Detail & Related papers  (2024-07-01T16:53:00Z)
• Multi-Agent Imitation Learning: Value is Easy, Regret is Hard [52.31989962031179]
  We study a multi-agent imitation learning (MAIL) problem where we take the perspective of a learner attempting to coordinate a group of agents. Most prior work in MAIL essentially reduces the problem to matching the behavior of the expert within the support of the demonstrations. While doing so is sufficient to drive the value gap between the learner and the expert to zero under the assumption that agents are non-strategic, it does not guarantee to deviations by strategic agents.
  arXiv  Detail & Related papers  (2024-06-06T16:18:20Z)
• By Fair Means or Foul: Quantifying Collusion in a Market Simulation with Deep Reinforcement Learning [1.5249435285717095]
  This research employs an experimental oligopoly model of repeated price competition. We investigate the strategies and emerging pricing patterns developed by the agents, which may lead to a collusive outcome. Our findings indicate that RL-based AI agents converge to a collusive state characterized by the charging of supracompetitive prices.
  arXiv  Detail & Related papers  (2024-06-04T15:35:08Z)
• Tacit algorithmic collusion in deep reinforcement learning guided price competition: A study using EV charge pricing game [0.0]
  Players in pricing games with complex structures are increasingly adopting artificial intelligence (AI) aided learning algorithms. Recent studies of games in canonical forms have shown contrasting claims ranging from none to a high level of tacit collusion. We consider a practical game where EV charging hubs compete by dynamically varying their prices. Results from our numerical case study yield collusion index values between 0.14 and 0.45, suggesting a low to moderate level of collusion.
  arXiv  Detail & Related papers  (2024-01-25T16:51:52Z)
• Dynamic Pricing of Applications in Cloud Marketplaces using Game Theory [6.369406986434764]
  This paper is the quantitative modeling of Cloud marketplaces in form of a game to provide novel dynamic pricing strategies. A committee is considered in which providers register for improving their competition based pricing policies. The usage of the committee makes the game a complete information one, in which each player is aware of every others payoff functions.
  arXiv  Detail & Related papers  (2023-09-20T13:41:45Z)
• Approaching sales forecasting using recurrent neural networks and transformers [57.43518732385863]
  We develop three alternatives to tackle the problem of forecasting the customer sales at day/store/item level using deep learning techniques. Our empirical results show how good performance can be achieved by using a simple sequence to sequence architecture with minimal data preprocessing effort. The proposed solution achieves a RMSLE of around 0.54, which is competitive with other more specific solutions to the problem proposed in the Kaggle competition.
  arXiv  Detail & Related papers  (2022-04-16T12:03:52Z)
• A Sparsity Algorithm with Applications to Corporate Credit Rating [11.52337781510312]
  We propose a new "sparsity algorithm" which solves the optimization problem, while also maximizing the sparsity of the counterfactual explanation. We apply the sparsity algorithm to provide a simple suggestion to publicly traded companies in order to improve their credit ratings.
  arXiv  Detail & Related papers  (2021-07-21T18:47:35Z)
• Model-Free Online Learning in Unknown Sequential Decision Making Problems and Games [114.90723492840499]
  In large two-player zero-sum imperfect-information games, modern extensions of counterfactual regret minimization (CFR) are currently the practical state of the art for computing a Nash equilibrium. We formalize an online learning setting in which the strategy space is not known to the agent. We give an efficient algorithm that achieves $O(T3/4)$ regret with high probability for that setting, even when the agent faces an adversarial environment.
  arXiv  Detail & Related papers  (2021-03-08T04:03:24Z)
• Competing Bandits: The Perils of Exploration Under Competition [99.68537519404727]
  We study the interplay between exploration and competition on online platforms. We find that stark competition induces firms to commit to a "greedy" bandit algorithm that leads to low welfare. We investigate two channels for weakening the competition: relaxing the rationality of users and giving one firm a first-mover advantage.
  arXiv  Detail & Related papers  (2020-07-20T14:19:08Z)
• Hierarchical Adaptive Contextual Bandits for Resource Constraint based Recommendation [49.69139684065241]
  Contextual multi-armed bandit (MAB) achieves cutting-edge performance on a variety of problems. In this paper, we propose a hierarchical adaptive contextual bandit method (HATCH) to conduct the policy learning of contextual bandits with a budget constraint.
  arXiv  Detail & Related papers  (2020-04-02T17:04:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.