Bio-inspired optimization of Fuzzy Cognitive Maps for their use as a means in the pricing of complex assets

Fuzzy Cognitive Maps (FCMs) are well-suited techniques to model the behavior of complex systems. But, since they depend on the quality of human knowledge they are prone to subjective false estimations. Moreover, even domain experts can fully describe only less complex systems which, in turn, limits the effectiveness of FCMs in real world applications. Thus, a technique is needed that, on the one hand, eases the use and, on the other hand, optimizes the formal structure of an FCM. Against that background, this article elucidates the use of different bio-inspired algorithms to optimize the application of the FCMs: Evolutionary Strategies (ES), Particle Swarm Optimization (PSO), and Big Bang-Big Crunch (BB-BC). The proposed approaches are applied to a novel valuation technique using FCMs. Besides principal feasibility, we demonstrate its potential to facilitate the use of vague and incomplete expert knowledge in the valuation of complex investment opportunities such as, but not limited to, young high-technology ventures.