Motivation is typically modeled either as a hierarchical organization of needs or as an additive integration of internal drives. These approaches assume that behavior results from stable internal priorities or the accumulation of independent motivational forces. This paper proposes an alternative framework in which motivation is understood as a distributed process of evaluative selection. Within this framework, behavior emerges from the dynamic evaluation of alternatives based on symbolic value, functional return, and constraint structures, under context-dependent weighting conditions. Evaluation is not confined to individual agents but is shaped through networked interaction, memory-constrained access to alternatives, and threshold-dependent transitions at the collective level. As a result, collective behavior is not reducible to aggregated preferences, but arises from interdependent evaluative dynamics distributed across agents. To formalize this structure, the paper introduces a quasi-formal representation that specifies evaluation at the agent level, incorporates network coupling effects, models constrained access through memory processes, and defines system-level dominance and transition conditions. The framework captures key features of collective behavior, including alignment without uniformity, abrupt transitions under gradual change, and path-dependent stability. The model can be operationalized for simulation and empirical estimation by mapping symbolic, functional, and network components onto observable indicators. Even partial parameterization enables the identification of reweighting dynamics, access constraints, and threshold effects in real-world systems. This approach shifts the analysis of motivation from static internal structures to distributed evaluative processes, providing a unified account that connects individual behavior, social interaction, and collective outcomes within a single theoretical framework.
Najm Abe Housh (Sat,) studied this question.