A Multi-factorial Innovation Model with Feature Interaction

2026-05-28

AI Summary

This study investigates the dynamic evolution and interaction mechanisms of old and new features in multifactorial innovation processes. To this end, it proposes a novel three-parameter Indian buffet process model that, for the first time, incorporates mean-field interactions among features and a self-reinforcement mechanism to characterize agents’ joint behavior in adopting existing features and generating novel ones. The model integrates reinforcement, forcing, and interaction effects, and—grounded in recursive stochastic dynamical systems theory—reveals multiple phase transitions driven jointly by interaction strength and external forcing, as well as an asymptotic synchronization effect in feature adoption under high interaction regimes. Furthermore, the work establishes strong laws and second-order asymptotic properties for aggregate quantities such as total and average feature counts, along with feature-specific measures, including a central limit theorem under competitive dynamics.

Abstract

We introduce an Indian-buffet-type model for multi-factorial innovation in which each arriving agent may exhibit both previously observed and new features. The number of new features follows a power-law behavior, while the probability of selecting an old feature combines self-reinforcement, depending on the feature-specific popularity, with a mean-field interaction term depending on the average popularity of all observed features. The model is governed by the usual innovation parameters (mass, discount and concentration), together with two additional parameters: one controlling the strength of reinforcement against a forcing input toward zero, and one regulating the intensity of feature interaction. Although the growth of the total number of distinct observed features has the same behavior as in the three-parameter Indian buffet process, the interaction mechanism produces new asymptotic regimes. For aggregate quantities, including the predictive mean, the averaged number of features per agent, the mean inclusion probability, and the mean feature popularity, the phase transition is determined by the comparison between the discount parameter and the weight of the forcing input. For feature-specific quantities, a further transition appears according to the comparison between the interaction level and a critical threshold. In particular, high interaction leads to an asymptotic synchronization of feature-specific inclusion probabilities. We establish strong laws and second-order asymptotic results, including central limit theorems in regimes where martingale fluctuations compete with deterministic or random terms. The analysis relies on novel general results for recursive stochastic dynamics, which may be useful beyond the present framework.

Problem

• multi-factorial innovation
• feature interaction
• Indian buffet process
• power-law behavior
• asymptotic regimes

Innovation

• feature interaction
• Indian buffet process
• phase transition
• asymptotic synchronization
• recursive stochastic dynamics