标题： PASER：一种受物理启发的理论，用于按需平台中的刺激增长和实时优化 显示英文标题

标题： PASER: A Physics-Inspired Theory for Stimulated Growth and Real-Time Optimization in On-Demand Platforms

摘要： 本文介绍了一个创新的框架，通过量化正向网络效应、信任、收入动态以及需求对按需平台运营的分钟级甚至秒级影响，来理解按需平台。 受到物理学的启发，该框架提供了理论和实用的视角，以图示和定量的方式展示按需平台如何创造价值。 它通过提供实用、具体且高度适用的指标、平台设计模板和实时优化工具，寻求简化这些平台复杂操作的神秘性，用于战略性的假设情景规划。 其模型具有强大的预测能力，并深深植根于原始数据。 该框架为理解像优步、爱彼迎和食品配送服务等多样化平台的运作提供了确定性的见解。 此外，它能够推广到建模所有具有周期性操作的按需服务平台。 它与机器学习、博弈论和基于代理的模型协同工作，通过提供一个基于原始数据、扎根于物理真理的坚实定量核心，能够为实时操作调整提供切实可行的预测。 通过严谨的历史数据验证，该框架的数学模型以接近100%的确定性检索到了高度详细的数据。 通过数据驱动的归纳法，在大数据集中的独特属性通过迭代过程被识别。 通过类比思维，建立了广为人知的物理系统元素、操作原理和动态行为之间的清晰且直观的映射，以创建一个受物理启发的优步观察视角。 这一新颖的定量框架被称为PASER（收入刺激排放产生的利润放大），与它的物理对应物LASER（光刺激排放产生的辐射放大）形成类比。 显示英文摘要

摘要： This paper introduces an innovative framework for understanding on-demand platforms by quantifying positive network effects, trust, revenue dynamics, and the influence of demand on platform operations at per-minute or even per-second granularity. Drawing inspiration from physics, the framework provides both a theoretical and pragmatic perspective, offering a pictorial and quantitative representation of how on-demand platforms create value. It seeks to demystify their nuanced operations by providing practical, tangible, and highly applicable metrics, platform design templates, and real-time optimization tools for strategic what-if scenario planning. Its model demonstrates strong predictive power and is deeply rooted in raw data. The framework offers a deterministic insight into the workings of diverse platforms like Uber, Airbnb, and food delivery services. Furthermore, it generalizes to model all on-demand service platforms with cyclical operations. It works synergistically with machine learning, game theory, and agent-based models by providing a solid quantitative core rooted in raw data, based on physical truths, and is capable of delivering tangible predictions for real-time operational adjustments. The framework's mathematical model was rigorously validated using highly detailed historical data retrieved with near 100% certainty. Applying data-driven induction, distinct qualities were identified in big data sets via an iterative process. Through analogical thinking, a clear and highly intuitive mapping between the elements, operational principles, and dynamic behaviors of a well-known physical system was established to create a physics-inspired lens for Uber. This novel quantitative framework was named PASER (Profit Amplification by Stimulated Emission of Revenue), drawing an analogy to its physical counterpart, the LASER (Light Amplification by Stimulated Emission of Radiation).