Living organisms are usually faced with a number of competing demands during their lifetime. Generally, these demands extend to finding food, avoiding predators, securing a sexual partner, and so on. Coping efficiently with these demands has the consequence of increasing the likelihood that the genes of that organism will survive and propagate in future generations. This in turn insures that many of the attributes possessed by that organism for coping with these demands will also be passed on to future generations if they are genetically encoded. The implication of this process is that contemporary species of animals are likely to be those which have coped most effectively with this optimality problem, and are thus likely to possess relatively optimal behavioral and psychological processes for dealing with their life’s problems. Argued in this way, optimality can be seen as a general constraint that impinges on most living organisms: dealing efficiently with the acquisition of food leaves more time to be alert to predators, and so on. There are at least two ways of going about tackling these ideas as far as we are concerned in this volume. First, we can work out mathematically what might be the optimal solution to a particular problem that the animal faces (such as choosing when to leave one food source in order to investigate others), and see if the animal’s behavior does approximate this optimal solution. Second, we would be particularly interested in the role that learning plays in achieving optimal performance, and what kinds of mechanisms underlie this learning.