The Social Cost of Growth: Evaluating GMV-Centric and Welfare-Centric Strategies in Online Food Delivery Platforms

• URL: http://arxiv.org/abs/2410.16566v1
• Date: Mon, 21 Oct 2024 22:55:59 GMT
• Title: The Social Cost of Growth: Evaluating GMV-Centric and Welfare-Centric Strategies in Online Food Delivery Platforms
• Authors: Yukun Zhang,
• Abstract summary: GMV strategies drive rapid short-term transaction growth but lead to uneven welfare distribution. Welfare-centric strategies promote a more balanced and equitable distribution of benefits among consumers, restaurants, and delivery workers.
• Score: 3.4039202831583903
• License:
• Abstract: This paper develops a comprehensive theoretical framework to analyze the trade-offs between Gross Merchandise Volume (GMV) maximization and social welfare optimization in online food delivery platforms. Using a multi-agent simulation and a dual-model approach based on two-sided market theory and welfare economics, we evaluate the impact of GMV-centric and welfare-centric strategies on platform dynamics, including pricing mechanisms, stakeholder welfare, and market efficiency. Our results show that GMV maximization strategies drive rapid short-term transaction growth but lead to uneven welfare distribution, particularly disadvantaging delivery workers. In contrast, welfare-centric strategies promote a more balanced and equitable distribution of benefits among consumers, restaurants, and delivery workers, enhancing platform sustainability in the long run. These findings provide actionable insights for platform operators and policymakers to design strategies that balance growth with social welfare, ensuring both economic efficiency and fairness.

Related papers

• Evaluating Supply Chain Resilience During Pandemic Using Agent-based Simulation [0.8158530638728501]
  Future pandemics raise a dilemma for businesses owners between short-term profitability and long-term supply chain resilience planning. We propose a novel agent-based simulation model integrating extended Susceptible-Infected-Recovered (SIR) epidemiological model and supply and demand economic model. We find that a balanced approach to supply chain resilience performs better in both pandemic and non-pandemic times compared to extreme strategies.
  arXiv  Detail & Related papers  (2024-05-13T18:32:28Z)
• Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents [101.17919953243107]
  GovSim is a generative simulation platform designed to study strategic interactions and cooperative decision-making in large language models (LLMs) We find that all but the most powerful LLM agents fail to achieve a sustainable equilibrium in GovSim, with the highest survival rate below 54%. We show that agents that leverage "Universalization"-based reasoning, a theory of moral thinking, are able to achieve significantly better sustainability.
  arXiv  Detail & Related papers  (2024-04-25T15:59:16Z)
• Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation [50.01551945190676]
  Social robot navigation can be helpful in various contexts of daily life but requires safe human-robot interactions and efficient trajectory planning. We propose a systematic relational reasoning approach with explicit inference of the underlying dynamically evolving relational structures. We demonstrate its effectiveness for multi-agent trajectory prediction and social robot navigation.
  arXiv  Detail & Related papers  (2024-01-22T18:58:22Z)
• Financial Time-Series Forecasting: Towards Synergizing Performance And Interpretability Within a Hybrid Machine Learning Approach [2.0213537170294793]
  This paper propose a comparative study on hybrid machine learning algorithms and leverage on enhancing model interpretability. For the interpretability, we carry out a systematic overview on the preprocessing techniques of time-series statistics, including decomposition, auto-correlational function, exponential triple forecasting, which aim to excavate latent relations and complex patterns appeared in the financial time-series forecasting.
  arXiv  Detail & Related papers  (2023-12-31T16:38:32Z)
• An Optimistic-Robust Approach for Dynamic Positioning of Omnichannel Inventories [10.353243563465124]
  We introduce a new class of data-driven optimistic-robust bimodal inventory optimization (BIO) strategy. Our experiments show that significant benefits can be achieved by rethinking traditional approaches to inventory management.
  arXiv  Detail & Related papers  (2023-10-17T23:10:57Z)
• Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model [50.06663781566795]
  We consider a dynamic model with the consumers' preferences as well as price sensitivity varying over time. We measure the performance of a dynamic pricing policy via regret, which is the expected revenue loss compared to a clairvoyant that knows the sequence of model parameters in advance. Our regret analysis results not only demonstrate optimality of the proposed policy but also show that for policy planning it is essential to incorporate available structural information.
  arXiv  Detail & Related papers  (2023-03-28T00:23:23Z)
• Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models with Reinforcement Learning [151.03738099494765]
  We study a heterogeneous agent macroeconomic model with an infinite number of households and firms competing in a labor market. We propose a data-driven reinforcement learning framework that finds the regularized competitive equilibrium of the model.
  arXiv  Detail & Related papers  (2023-02-24T17:16:27Z)
• Balancing Profit, Risk, and Sustainability for Portfolio Management [0.0]
  We develop a novel utility function with the Sharpe ratio representing risk and the environmental, social, and governance score (ESG) representing sustainability. We show that our system outperforms MADDPG while improving on deep Q-learning approaches by allowing for continuous action spaces.
  arXiv  Detail & Related papers  (2022-06-06T08:38:30Z)
• Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation [93.52573037053449]
  H-MARL (Hallucinated Multi-Agent Reinforcement Learning) learns successful equilibrium policies after a few interactions with the environment. We demonstrate our approach experimentally on an autonomous driving simulation benchmark.
  arXiv  Detail & Related papers  (2022-03-14T17:24:03Z)
• Finding General Equilibria in Many-Agent Economic Simulations Using Deep Reinforcement Learning [72.23843557783533]
  We show that deep reinforcement learning can discover stable solutions that are epsilon-Nash equilibria for a meta-game over agent types. Our approach is more flexible and does not need unrealistic assumptions, e.g., market clearing. We demonstrate our approach in real-business-cycle models, a representative family of DGE models, with 100 worker-consumers, 10 firms, and a government who taxes and redistributes.
  arXiv  Detail & Related papers  (2022-01-03T17:00:17Z)

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.