Dynamic Decision Making: Implications for Recommender System Design

We make decisions in increasingly complex, high-risk, and dynamic environments that evolve over time in unpredictable ways, and the options that we have available in our daily decisions has exponentially increased. For example, when shopping in a store the item diversity on the shelves are large, menus in the restaurants offer large variety, books to select from in the bookstore are large, etc. We are living a choice explosion era. Even more dramatic is the choice explosion in the cyber-world. Given no physical storage restrictions, the options to choose from in the cyber world is immense. More than ever before, these situations challenge our cognitive abilities to process information and to make accurate decisions. How do we choose from this large diversity of options and how do we decide which ones best match our preferences? In the physical world, we may get advice from people we know: experts, friends and family, or we may get help and support from technology such as while driving relying on a GPS. In the cyber world, we now rely on recommender systems that help to filter the large amounts of information and to reduce possible decision options by predicting preferences of a decision maker and offering best possible alternatives. Recommender systems vary in their approach and ways in which individual preferences are collected and the way in which information and alternatives are filtered for particular users. However, ultimately, all recommender systems aim at predicting human preferences and choice and the essence of every recommender system is the human decision making process. Furthermore, because human preferences are not static, recommender algorithms must be dynamic and adaptable to changes. Often preferences are constructed through past experience (choices and outcomes observed in the past) and through explicit information provided. These characteristics suggest that human preferences are dynamic and contingent to the decision environment. I suggest that Dynamic Decision Making (DDM) research may help to build recommender systems that learn and adapt recommendations dynamically and to a particular user's experience, to maximize benefits and overall utility of her choices. I present a conceptual framework for dynamic decision making that is different from the traditional view of choice in the behavioral sciences, summarize main behavioral results obtained from experimental studies in dynamic situations; and summarize a theory and a computational model that has demonstrated accuracy in predicting human choice in a large diversity of tasks, which may provide an …