Identifiability of Linear Threshold Decision Making Dynamics

Abstract

The binary-decision dynamics of two types of individuals; coordinators who tend to choose the more common option among others and anti-coordinators who avoid the common option can be modeled using the linear (anti-)threshold model. Each individual has a time-invariant threshold and decides whether to choose an option by comparing his threshold with the proportion of the population who have already chosen that option. The resulting decision-making dynamics can be predicted and controlled, provided that the thresholds are known. In practice, however, the thresholds are unknown, and often only the evolution of the total number of individuals who have chosen one option is known. The question then is whether the thresholds are identifiable given this quantity over time, which can be considered as the output of the decision-making dynamics. Identifiability investigates the recoverability of the unknown parameters given the error-free outputs, inputs, and the developed equations of the model. Different notions of and methods to test identifiability exist for dynamical systems defined in the continuous state space. However, the decision dynamics of the linear threshold model is defined in the discrete state space. We develop the identifiability framework for discrete space systems and highlight that this is not an immediate extension of the continuous space framework. Then, we investigate the threshold identifiability of both coordinators and anticoordinators in the linear threshold model. For both the synchronous and asynchronous dynamics, we find necessary and sufficient conditions for the identifiability of coordinating and anticoordinating populations. The results open the door for reliable estimation of the thresholds and in turn prediction and control of the decision-making dynamics using real-world data.