Chapter 7

Balancing short-term and long-term reward: temporal interaction evaluation

So far my book has examined many cases of collective learning of health behaviors through conditional-interaction.  We can apply the same conditional interaction modeling approach at the individual level. Just as collectives learn to coordinate their individual actions, so any single individual learns to coordinate his own actions over time. In fact individuals can learn very complex sequences of conditional-interaction rules. This is quite a feat because there are a practically infinite number of possible combinations of conditional-action sequences one might perform, and much of human health behavior involves learning rather complex sequences of conditional-actions.  Moreover, there is a fundamental problem of coordinating immediate intentions with long-term intentions. Many health goals involve achieving long-term outcomes that require long-sequences of actions, even if this means repeating the same actions over a long period. Immediate needs arise, and individuals must cope with these as well. How do individuals learn to pursue long-term goals, given short-term needs that arise? This is the topic of this chapter, which explores my Temporal Interaction Evaluation model, based on some of what we know about neural learning. It is remarkable how evolutionary processes of selection have given rise to a parallel evolutionary processes of learning at the neural level.