Cognitive Modeling of Behavioral Experiments in Network Science Using ACT-R Architecture

Abstract

The Network Science has dedicated a considerable amount of effort to the study of many distributed collective decision-making processes which must balance diverse individual preferences with an expectation for collective unity. Several works have reported their results about behavioral experiments on biased voting in networks individuals, however we will focus on the results reported on [1] on which were run 81 experiments, on which participated 36 human subjects arranged in a virtual network who were financially motivated in a heterogeneous manner and whose goal was to reach global consensus to one of two opposing choices. Multiple experiments were performed using diverse topological network configurations, different schemes of financial incentives that created opposing tensions between personal preferences, and finally different ratios of both inter and intra-connectivity among the network nodes. The corresponding analysis of the results demonstrated that changing those features of the experiments produced different kind of social behavioral patterns as a result. Thus, the purpose of this work is manifold: on the one hand, it aims to describe the possible structures that underlie the decision-making process of these experiments through the modeling of symbolic cognitive prototypes supported by a robust and complex cognitive architecture so-called ACT-R and, on the other hand, by applying modifications in the ACT-R parameters to find those subtle aspects that can either influence both the performance and speed of convergence of the experiments or cause the total inability to reach a global consensus in a reasonable amount of time.