Adaptive Interactive Profit Expectations and Small World Networks

The aim of this paper is to simulate profit expectations as an emergent property using an agent based model. The paper builds upon adaptive expectations, interactive expectations and small world networks, combining them into a single adaptive interactive profit expectations model (AIE). Understanding the diffusion of interactive expectations is aided by using a network to simulate the flow of information between firms. The AIE model is tested against a profit expectations survey. The paper introduces “optimal calibration model averaging” and the “pressure to change profit expectations index” (px). Optimal calibration model averaging is an adaptation of “model averaging” to enhance the prediction performance of multiple equilibria models. The px is a subjective measure representing decision making in the face of uncertainty. The paper benchmarks the AIE model against the adaptive expectations model and the rational expectations hypothesis, finding the firms may have adequate memory although the interactive component of AIE model needs improvement. Additionally the paper investigates the efficacy of a tuneable network and equilibrium averaging. Finding the tuneable network produces widely spaced multiple equilibria and the optimal calibration model averaging enhances calibration but not prediction. Further research includes disaggregating the AIE model, using an input–output table to reflect the intensity of interaction between firms of different divisions, and supplementing optimal calibration model averaging with runtime weighted model averaging.