Looking Through Glass Box
This work provides an incremental method for implementing fuzzy cognitive maps, potentially benefiting researchers working with causal inference and decision-making systems.
This paper introduces a neural network implementation of fuzzy cognitive maps (FCMs), called FHM. The FHM learns causality patterns from multiple FCM inputs and uses Langevin differential dynamics to inverse solve output node values based on a policy, which provides a modification criterion for user discretion.
This essay is about a neural implementation of the fuzzy cognitive map, the FHM, and corresponding evaluations. Firstly, a neural net has been designed to behave the same way that an FCM does; as inputs it accepts many fuzzy cognitive maps and propagates them in order to learn causality patterns. Moreover, the network uses langevin differential Dynamics, which avoid overfit, to inverse solve the output node values according to some policy. Nevertheless, having obtained an inverse solution provides the user a modification criterion. Having the modification criterion suggests that information is now according to discretion as a different service or product is a better fit. Lastly, evaluation has been done on several data sets in order to examine the networks performance.