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

TL;DR

This paper presents PASER, a physics-inspired theoretical framework for real-time optimization and understanding of on-demand platforms, validated with detailed data and applicable to various services like Uber and Airbnb.

Contribution

It introduces a novel physics-inspired model that quantifies platform dynamics and enables real-time optimization, bridging theoretical insights with practical applications.

Findings

Strong predictive power demonstrated with detailed historical data

Framework generalizes to all cyclical on-demand service platforms

Provides practical metrics and tools for real-time operational adjustments

Abstract

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).