Since 2013 the Circular Economy (CE) movement towards “an industrial economy that is restorative or regenerative by intention and design” has gained a lot of attention in industry. Agent-based models are emphasized as highly capable to fill the gap between diffusion-based penetration of information and resulting behavior in the form of purchase decisions. In addition to the tool itself the results of this research indicates the need for systematic marketing strategies which emphasize CE value propositions in order to accelerate customer acceptance and shorten the transition time from linear to circular. The tool integrates socio-demographic factors, product utility functions, social network structures and inter-agent communication in order to comprehensively describe behavior on individual customer level. This research presents the first agent-based tool that can assess customer behavior and determine whether introduction of new business models will be accepted or not and how customer acceptance can be influenced to accelerate CE implementation. The implementation of the model has been tested using the practical business example of a washing machine. The model has been developed using an agent-based modeling approach which delivers results based on socio-demographic factors of a population and customers’ relative preferences of product attributes price, environmental friendliness and service-orientation. This tool will support decision makers in determining the impact of introducing new (circular) business models. The purpose of this research is to bring forward a quantitative analysis tool for identifying proper marketing and pricing strategies to obtain best fit demand behavior for the chosen new business model. However, tools or methods are missing to capture customer behavior to assess how customers will react if an organization introduces a new business model such as leasing or functional sales. In a CE customers are perceived as integral part of the business and therefore customer acceptance of new business models becomes crucial as it determines the successful implementation of CE. (You can use on-change logic for Parameters in your own models, and need to do so when altering a parameter requires some 'adjustments' to the rest of the model i.e., in situations where the change doesn't 'just work' due to other bits of the model reading the new value after the change point.This paper introduces an agent-based approach to study customer behavior in terms of their acceptance of new business models in Circular Economy (CE) context. It is only this triggered logic (internal to the Source block) which causes the Source to 're-evaluate' the rate properly.

You can set an AnyLogic Parameter directly (via just assigning a value as you did), but they also all have a set_ method (function) which should really always be used instead because this triggers any internal on-change logic for this Parameter.

(All AnyLogic's Process Modeling blocks are actually themselves Agents developed by AnyLogic, and thus with Parameters, Variables, etc.) Rate is effectively a Parameter (in the AnyLogic sense) of the Source block. Instead of using source.rate=5 use t_rate(5) To expand on Felipe's answer with an explanation:

Anylogic reaction on parameter change