Copyright © 2023 The Author(s) Open Access Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License ( ), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. In conclusion, the dynamic model, facilitated by ANYLOGIC for the Bass New Product Diffusion Process, not only serves as a robust tool for analyzing the dynamic traits of product diffusion but also carries profound industry implications, laying a foundation for industries to gain deeper insights into market penetration, potentially leading to more strategic product launches and targeted marketing efforts in the future. This research sheds light on previously unexplored aspects, underscoring the potential of the model. People are globally influenced by advertizing and also can contact and influence each other. Each person is modeled as a separate active object an agent with two states: Potential Adopter and Adopter. Through meticulous construction and exhaustive validation via simulation, our model successfully captures the intricate dynamics of the Bass Diffusion Process, yielding highly accurate outcomes. An agent based version of the Bass model of product or innovation diffusion. This tutorial covers the agent-based modeling approach, successfully applied. The objective is to comprehensively investigate the underlying dynamics governing the diffusion of new products. Agent Based Model AnyLogic supports different modeling techniques. Pathmind policy is able to reduce costs by about 41 compared to the optimizer. You can create several experiments for the same model with alternative model settings. A group of model settings is called an experiment, and experiments are displayed at the bottom of the model branch in the workspace tree. The objective is to minimize advertisement cost while hitting at least 80,000 adopters in duration of 1.5 years. Model simulation has a set of specific settings. We will model repeat purchase behavior by assuming that adopters move back into the population of potential adopters when their first unit is discarded or consumed. Based off AnyLogic's Bass Diffusion tutorial, we compare the performance of a reinforcement learning policy to an OptQuest Optimizer. There, you can run the model or download it (by clicking Model source files). Demo model: Bass Diffusion Phase 1 Open the model page in AnyLogic Cloud. This study introduces a dynamic model of the Bass New Product Diffusion Process implemented through ANYLOGIC. The model we have created does not capture situations where the product is consumed, discarded, or upgraded, all of which lead to repeat purchases. The advertising effect is largest at the start of the diffusion process and steadily diminishes as the pool of potential adopters is depleted.
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