The Investment Acceleration Principle Revisited by means of a Neural Network

2026-10-02

AI Summary

This study addresses the limitations of the traditional investment accelerator principle, which struggles to accurately model investment time series from consumption sequences and fails to distinguish between innovation-driven and capacity-replicating investments. To overcome these challenges, this work introduces self-organizing system theory to reconstruct the investment accelerator model. Specifically, it employs a Kohonen neural network to simulate the decision-making processes of enterprise managers, formulating a disaggregated accelerator equation capable of identifying emerging technological patterns. The proposed approach successfully achieves the dynamic decoupling of the two investment types and establishes an investment dynamics model that reflects managers’ ability to recognize technological patterns. Ultimately, this research provides a novel paradigm for the intelligent modeling of micro-level decision-making within economic systems.

Abstract

The investment acceleration principle is a heuristic for modelling investment time series out of consumption time series. The model presented herein develops a disaggregated accelerator equation whose coefficients are the weights of a Kohonen neural net that represents firms' decision-making. According to this model, investments take place when managers recognise emerging technological patterns. Furthermore, a technique borrowed from the theory of self-organising systems is used in order to disentangle innovation-driven investments from plant-replication investments.

Problem

• Investment Acceleration Principle
• Investment Time Series
• Neural Network
• Innovation-driven Investments

Innovation

• Kohonen neural network
• investment acceleration principle
• self-organising systems
• disaggregated accelerator equation
• technological patterns