Stochastic logistic fuzzy maps for the construction of integrated multirates scenarios in the financing of infrastructure projects

Abstract In general, the development of economic infrastructure systems requires a behavioural comprehensiveanalysis of different financial variables or rates to establish its long-term success with regards to theEquity Internal Rate of Return (EIRR) expectation. For this reason, several financial organizationshave developed economic scenarios supported by computational techniques and models to identify.15 cmthe evolution of these financial rates. However, these models and techniques have shown a series oflimitations with regard to the financial management process and its impact on EIRR over time. Toaddress these limitations in an inclusive way, researchers have developed different approaches andmethodologies focused on the development of financial models using stochastic simulation methodsand computational intelligence techniques. This paper proposes a Stochastic Fuzzy Logistic Model (S-FLM) inspired by a Fuzzy Cognitive Map (FCM) structure to model financial scenarios. Where theinput consists in financial rates that are characterized as linguistic rates through a series of adaptivelogistic functions. The stochastic process that explains the behaviour of the financial rates over timeand their partial effects on EIRR is based on a Monte Carlo sampling process carried out on the fuzzy sets that characterize each linguistic rate. The S-FLM was evaluated by applying three financingscenarios to an airport infrastructure system (pessimistic, moderate/base, optimistic), where it waspossible to show the impact of different linguistic rates on the EIRR. The behaviour of the S-FLM wasvalidated using three different models: (1) a financial management tool; (2) a general FCM withoutpre-loaded causalities among the variables; and (3) a Statistical S-FLM model (S-FLMS), where thecausalities between the concepts or rates were obtained as a result of an independent effects analysisapplying a cross modelling between variables and by using a statistical multi-linear model (statisticalsignificance level) and a multi-linear neural model (MADALINE). The results achieved by the S-FLM show a higher EIRR than expected for each scenario. This was possible due to the incorporationof an adaptive multi-linear causality matrix and a fuzzy credibility matrix into its structure. Thisallowed to stabilize the effects of the financial variables or rates on the EIRR throughout a financingperiod. Thus, the S-FLM can be considered as a tool to model dynamic financial scenarios in differentknowledge areas in a comprehensive manner. This way, overcoming the limitations imposed by thetraditional computational models used to design these financial scenarios.