Opinion formation dynamics -- Swift collective disillusionment triggered by unmet expectations

TL;DR

This paper introduces a microscopic model of opinion dynamics that captures how collective expectations about innovations can rapidly collapse into disillusionment due to information maturation delays, using a queuing system analogy.

Contribution

It presents a novel queuing-based framework incorporating information maturation delays to model the swift transition from hype to disillusionment in collective opinion formation.

Findings

Model successfully reproduces hype cycle dynamics.

Framework can detect disillusionment onset early.

Highlights importance of information delays in opinion shifts.

Abstract

We propose a microscopic model to describe how individual opinions shared between interacting agents initiate excessive collective expectations about a new idea or an innovation, followed by a swift collapse towards a dramatic collective disillusionment. The basic assumption which underlies the dynamics is that the information gathering process is not instantaneous but requires maturation. Agents steadily refine and update their personal opinion via a recurrent consultation of a public pool which stores information tokens (ITs). The expectation for the innovative idea is monitored in real-time by counting the number of stored ITs. The flow dynamics of ITs is assimilated to a single node queuing system (QS) with feedback loop. It incorporates the information pool (the waiting room), an IT inflow, and a service outflow that stylizes the information gathering process. Contrary to basic queuing theory, here the ITs roaming the QS are endowed with time-dependent internal variables. This additional dynamic information is used to construct the information maturation process. Such a maturation of the information introduces response delays into the dynamics, which ultimately generates the collective disillusionment trough. We illustrate the introduced generic modeling framework by considering in details the hype cycle dynamics, a key managerial topic when dealing with diffusion of innovation. In a second part of the paper, we introduce a stylized framework to detect, as soon as possible, the onset of the collective disillusionment phase, while minimizing the frequency of false alarms.