Prioritizing Risk Factors in Media Entrepreneurship on Social Networks: Hybrid Fuzzy Z-Number Approaches for Strategic Budget Allocation and Risk Management in Advertising Construction Campaigns

• URL: http://arxiv.org/abs/2409.18976v2
• Date: Mon, 13 Jan 2025 01:18:17 GMT
• Title: Prioritizing Risk Factors in Media Entrepreneurship on Social Networks: Hybrid Fuzzy Z-Number Approaches for Strategic Budget Allocation and Risk Management in Advertising Construction Campaigns
• Authors: Ahmad Gholizadeh Lonbar, Hamidreza Hasanzadeh, Fahimeh Asgari, Elham Khamoushi, Hajar Kazemi Naeini, Roya Shomali, Saeed Asadi,
• Abstract summary: The proliferation of complex online media has accelerated the process of ideology formation. The media channels, which vary in cost and effectiveness, present a dilemma in prioritizing optimal fund allocation. To enhance marketing productivity, it's crucial to determine how to distribute a budget across all channels to maximize business outcomes.
• Score: 0.0
• License:
• Abstract: The proliferation of complex online media has accelerated the process of ideology formation, influenced by stakeholders through advertising channels. The media channels, which vary in cost and effectiveness, present a dilemma in prioritizing optimal fund allocation. There are technical challenges in describing the optimal budget allocation between channels over time, which involves defining the finite vector structure of controls on the chart. To enhance marketing productivity, it's crucial to determine how to distribute a budget across all channels to maximize business outcomes like revenue and ROI. Therefore, the strategy for media budget allocation is primarily an exercise focused on cost and achieving goals, by identifying a specific framework for a media program. Numerous researchers optimize the achievement and frequency of media selection models to aid superior planning decisions amid complexity and vast information availability. In this study, we present a planning model using the media mix model for advertising construction campaigns. Additionally, a decision-making strategy centered on FMEA identifies and prioritizes financial risk factors of the media system in companies. Despite some limitations, this research proposes a decision-making approach based on Z-number theory. To address the drawbacks of the RPN score, the suggested decision-making methodology integrates Z-SWARA and Z-WASPAS techniques with the FMEA method.

Related papers

• Adaptive Budget Optimization for Multichannel Advertising Using Combinatorial Bandits [9.197038204851458]
  We introduce three key contributions to the field of budget allocation in digital advertising. First, we develop a simulation environment designed to mimic multichannel advertising campaigns over extended time horizons. Second, we propose an enhanced bandit budget allocation strategy that leverages a saturating mean function and a targeted exploration mechanism with change-point detection.
  arXiv  Detail & Related papers  (2025-02-05T06:29:52Z)
• Hierarchical Multi-agent Meta-Reinforcement Learning for Cross-channel Bidding [4.741091524027138]
  Real-time bidding (RTB) plays a pivotal role in online advertising ecosystems. Traditional approaches cannot effectively manage the dynamic budget allocation problem. We propose a hierarchical multi-agent reinforcement learning framework for multi-channel bidding optimization.
  arXiv  Detail & Related papers  (2024-12-26T05:26:30Z)
• From approximation error to optimality gap -- Explaining the performance impact of opportunity cost approximation in integrated demand management and vehicle routing [0.0]
  We propose an explainability technique that quantifies and visualizes the magnitude of approximation errors, their immediate impact, and their relevance in specific regions of the state space. Applying the technique to a generic i-DMVRP in a full-factorial computational study, we show that the technique contributes to better explaining algorithmic performance and provides guidance for the algorithm selection and development process.
  arXiv  Detail & Related papers  (2024-12-18T13:46:46Z)
• LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models [75.89014602596673]
  Strategic reasoning requires understanding and predicting adversary actions in multi-agent settings while adjusting strategies accordingly. We explore the scopes, applications, methodologies, and evaluation metrics related to strategic reasoning with Large Language Models. It underscores the importance of strategic reasoning as a critical cognitive capability and offers insights into future research directions and potential improvements.
  arXiv  Detail & Related papers  (2024-04-01T16:50:54Z)
• Bridging the gap: Towards an Expanded Toolkit for AI-driven Decision-Making in the Public Sector [6.693502127460251]
  AI-driven decision-making systems are becoming instrumental in the public sector, with applications spanning areas like criminal justice, social welfare, financial fraud detection, and public health. These systems face the challenge of aligning machine learning (ML) models with the complex realities of public sector decision-making. We examine five key challenges where misalignment can occur, including distribution shifts, label bias, the influence of past decision-making on the data side, as well as competing objectives and human-in-the-loop on the model output side.
  arXiv  Detail & Related papers  (2023-10-29T17:44:48Z)
• Risk-reducing design and operations toolkit: 90 strategies for managing risk and uncertainty in decision problems [65.268245109828]
  This paper develops a catalog of such strategies and develops a framework for them. It argues that they provide an efficient response to decision problems that are seemingly intractable due to high uncertainty. It then proposes a framework to incorporate them into decision theory using multi-objective optimization.
  arXiv  Detail & Related papers  (2023-09-06T16:14:32Z)
• Welfare Maximization Algorithm for Solving Budget-Constrained Multi-Component POMDPs [2.007262412327553]
  This paper presents an algorithm to find the optimal policy for a multi-component budget-constrained POMDP. We show that the proposed algorithm vastly outperforms the policy currently used in practice.
  arXiv  Detail & Related papers  (2023-03-18T01:43:47Z)
• Learning to Incentivize Information Acquisition: Proper Scoring Rules Meet Principal-Agent Model [64.94131130042275]
  We study the incentivized information acquisition problem, where a principal hires an agent to gather information on her behalf. We design a provably sample efficient algorithm that tailors the UCB algorithm to our model. Our algorithm features a delicate estimation procedure for the optimal profit of the principal, and a conservative correction scheme that ensures the desired agent's actions are incentivized.
  arXiv  Detail & Related papers  (2023-03-15T13:40:16Z)
• Strategic Decision-Making in the Presence of Information Asymmetry: Provably Efficient RL with Algorithmic Instruments [55.41685740015095]
  We study offline reinforcement learning under a novel model called strategic MDP. We propose a novel algorithm, Pessimistic policy Learning with Algorithmic iNstruments (PLAN)
  arXiv  Detail & Related papers  (2022-08-23T15:32:44Z)
• Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning [156.5667417159582]
  This paper proposes a novel model of sequential information design, namely the Markov persuasion processes (MPPs) Planning in MPPs faces the unique challenge in finding a signaling policy that is simultaneously persuasive to the myopic receivers and inducing the optimal long-term cumulative utilities of the sender. We design a provably efficient no-regret learning algorithm, the Optimism-Pessimism Principle for Persuasion Process (OP4), which features a novel combination of both optimism and pessimism principles.
  arXiv  Detail & Related papers  (2022-02-22T05:41:43Z)
• Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions [80.49176924360499]
  We establish a framework for directing a society of simple, specialized, self-interested agents to solve sequential decision problems. We derive a class of decentralized reinforcement learning algorithms. We demonstrate the potential advantages of a society's inherent modular structure for more efficient transfer learning.
  arXiv  Detail & Related papers  (2020-07-05T16:41:09Z)

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.