Modeling personality is important both for understanding human traits, and for designing artificial systems. Both aims are challenging. Studies of human personality suggest that personality traits such as extraversion and neuroticism have multiple correlates relating to many qualitatively different aspects of neural and cognitive functioning. The tri-level explanatory framework of cognitive science provides a basis for personality description. Traits are expressed as biases in neural functioning, in the functional architecture supporting symbolic processing, and in high-level self-knowledge and motivation. Furthermore, traits relate to multiple biases of each type, so that traits are distributed across and within levels. Such a description supports fine-grained modeling of personality. A similar approach may be applied to modeling transient states such as emotions. However, the descriptive picture is incomplete. Personality also has a dynamic, adaptive aspect, as the system learns to fulfill its goals by acquiring contextualized skills appropriate to the external environment. Thus, in humans, observed differences in architectural parameters must be interpreted adaptively. In designing artifacts, there may be advantages to allowing personality to emerge in part from interactions between the artifact and the environments within which it is intended to function.