Purpose: The objective of this paper is to propose a holistic dynamic model for understanding the behavior of a complex and internet-based kind of knowledge market by considering both social and economic interactions. Design/methodology/approach: A system dynamics (SD) model is formulated in this study to investigate the dynamic characteristics of complex interactions in a fee-based online question & answer (Q&A) knowledge market. The proposed model considers the dynamic, non-linear, asymmetric, and reciprocal relationships between its components, and allows the study of the evolution of the market under assumed conditions. Findings: Some illustrative results show that: this market is very sensitive to the prices that the customers choose; low-priced questions are as important as high-priced ones; gradually increasing experts' proportion of a question's price reduces customer satisfaction and experts' reputation; and training programs for experts result in higher customer satisfaction and researchers' reputation. Furthermore, three types of customers are identified and discussed. Practical implications: This model can be used to change, manage, and control this market and also helps to design new similar markets. In addition, the proposed model helps to observe the behavior of a market under one or more policies before applying to the real world. Social implications: Since GA was shut down in 2006, the implications of this research serve as a strategic tool (strategic evaluation software) for understanding and examining the effects of policies for many existing similar Q&A business models. Furthermore, the SD approach can provide new insights into the field of online Q&A knowledge markets and overcome traditional econometric treatment of data for understanding the dynamic behavior of these markets. Originality/value: Understanding the complex social and economic behavior of Q&A markets is one of the most important concerns for academics and practitioners in the areas of online markets' management. The paper shows how SD can provide attractive insights into the field of online fee-based knowledge markets based on a qualitative and quantitative modeling. However, the background literature lacks a holistic view of these kinds of markets. (Contains 8 figures and 3 tables.)