When making decisions about information privacy, people do not always act rationally according to their best interests. It is thus important to understand why people express concerns about privacy, but often act contrary to their stated intentions. This research investigates individuals' privacy decisions, from a behavioral economic perspective, by: 1) investigating how two systems of thinking in human minds, affective (system 1) and cognitive (system 2), operate during privacy decisions; 2) developing affective-cognitive algorithms to mathematically describe the operation of systems 1 and 2 in privacy decisions; and 3) testing and evaluating the accuracy of these algorithms. This is the first step in forming a new mathematical theory of privacy that can describe how people actually make privacy decisions versus how they are expected to make such decisions.
Using techniques from discrete mathematics, theoretical computer science and behavioral economics, and adapting the concept of bounded rationality, the PIs will develop affective-cognitive algorithms to model human experienced-utility in privacy decisions (i.e., risks and benefits they perceive in disclosing privacy). This is fundamentally different from the existing mathematical models of privacy decisions that assume humans have stable preferences and always choose the option with the highest expected utility (i.e., the option with the maximum privacy). The PIs use techniques from behavioral game theory to rigorously test and evaluate the accuracy of their algorithms. The PIs apply findings of behavioral economics, mathematical psychology, and previous research on information privacy to translate operation of systems 1 and 2 into mathematical models. This research is intended to bridge three streams of research, namely information privacy, theoretical computer science, and behavioral and experimental economics. The impacts of this research have translational potential for application to real online environments and implementation in decision support tools, such as recommender systems.
