Fuzzy Logic-Based Approach to Behavioral Economics: Mathematical Modeling of Consumer Decision-Making

Abstract

Even the mathematics that some people want to apply to this decision-making is limited, as consumer decisions are filled with uncertainty, subjective evaluation, and cognitive biases. In this paper, we develop and apply a model entitled Fuzzy Logic-Based Decision Model to evaluate consumers' preferences regarding smartphone selections in the presence of uncertainty. The proposed model utilizes fuzzy set theory, linguistic variables and IF-THEN rule-based inference systems to capture consumer evaluations on price, battery life, and brand reputation. Using four smartphone models as a case study, it shows the model's ability to embrace vagueness and ambiguity in consumers’ choices. From the outcomes Smartphone A proved to be the max preferable smartphone followed by Smartphone C and least favorable smartphones were B and D. The results are consistent with the predictions of behavioral economics, which suggest that consumers weigh competing attributes in their choices, rather than optimizing on a single dimension. IoT data was used to apply the fuzzy logic model to consumer behavior using Fuzzy Logic based Classification system The presented model was able to capture these trade-offs accurately and thus proved to be a realistic and flexible approach for the analysis of consumer behaviour. So, the study develops a Fuzzy Logic Heuristics based model that helps to overcome these limitations and provides the constructs which Fuzzy Logic itself overcomes in conventional attitudes towards consumer behaviour. The future extensions involving, market research applications, neuro-fuzzy systems, and machine learning integration and temporal modeling are suggested in this paper for the use of both academic research and in practice by e-commerce platforms and product recommendation system.