Modeling Complexity in Multi-modal Adaptive Survey Systems

Modern survey data collection systems must balance cost and quality while supporting multiple response modes (paper, internet, telephone and personal interview) and addressing unpredictable respondent behavior. The next generation of survey systems utilizes adaptive methods to address these issues, but this may affect system behavior and introduces new issues. The paper discusses the development of a system model to analyze system behavior, determine the level of complexity present, define the conditions under which complex behaviors occur and explore approaches to manage complexity. The model, built in NetLogo, uses an agent representation for control, response mode management and the respondent. It not only represents control logic, particularly survey strategies, but also realistic stochastic respondent demographics and external influences which are independent of the system. The paper frames the problem as a potential complex adaptive system, discusses the approach and modeling of the system and reviews analysis of the model to date and its impacts on system design. Preliminary results indicate that basic system behavior is complicated (according to the Cynefin framework) but external influences can introduce unpredictable behaviors that make the system complex, requiring careful management in order to achieve system objectives.