Prominent theories of value-based decision making have assumed that choices are made via the maximization of some objective function (e.g., expected value) and that the process of decision making is serial and unfolds across modular subprocesses (e.g., perception, valuation, and action selection). However, the influence of a large number of contextual variables that are not related to expected value in any direct way and the ubiquitous reciprocity among variables thought to belong to different subprocesses suggest that these assumptions may not always hold. Here, we propose an interactive activation framework for value-based decision making that does not assume that objective function maximization is the only consideration affecting choice or that processing is modular or serial. Our framework holds that processing takes place via the interactive propagation of activation in a set of simple, interconnected processing elements. We use our framework to simulate a broad range of well-known empirical phenomena-primarily focusing on decision contexts that feature nonoptimal decision making and/or interactive (i.e., not serial or modular) processing. Our approach is constrained at Marr's (1982) algorithmic and implementational levels rather than focusing strictly on considerations of optimality at the computational theory level. It invites consideration of the possibility that choice is emergent and that its computation is distributed. (PsycINFO Database Record (c) 2020 APA, all rights reserved).