Bias as Structured Selection: Memory, Reweighting, and the Dynamics of Evaluative Access

Decision biases are commonly interpreted as systematic deviations from rational evaluation, arising from distorted preferences, heuristics, or computational limitations. This article challenges that assumption by proposing that many biases are not errors, but outcomes of a structured process of evaluative selection operating under constraint. Within this framework, decision-making is modeled as a dynamic process in which alternatives are evaluated through the interaction of symbolic value, functional return, and constraint structures, modulated by context-sensitive reweighting. Crucially, evaluation does not operate over a fixed set of representations. Instead, the evaluative field is selectively constructed through memory activation, such that only a subset of representations becomes available for comparison at any given moment. Biases are therefore redefined as selection outcomes produced under asymmetric weighting and constrained evaluative access. This account explains why behavioral regularities such as loss aversion, present bias, cognitive dissonance, and status quo preference can appear both stable and context-dependent, without invoking systematic irrationality. The article further develops the empirical implications of this framework by proposing experimental designs that manipulate not only outcome structure but also representational access and weighting conditions. It predicts that biases can be attenuated, amplified, or reversed through controlled variation in memory activation, contextual framing, and threshold conditions governing selection. By shifting the focus from deviation to structure, the framework provides a unified account linking conceptual analysis, decision dynamics, and experimental design, offering a foundation for a selection-based theory of decision-making.