Stochastic dynamic programming (SDP) models are widely used to predict opti
mal behavioural and life history strategies. We discuss a diversity of ways
to test SDP models empirically, taking as our main illustration a model of
the daily singing routine of birds. One approach to verification is to qua
ntify model parameters, but most SDP models are schematic. Because predicti
ons are therefore qualitative, testing several predictions is desirable. Ho
w state determines behaviour (the policy) is a central prediction that shou
ld be examined directly if both state and behaviour are measurable. Complem
entary predictions concern how behaviour and state change through time, but
information is discarded by considering behaviour rather than state, by lo
oking only at average state rather than its distribution, and by not follow
ing individuals. We identify the various circumstances in which an individu
al's state/behaviour at one time is correlated with its state/behaviour at
a later time. When there are several state variables,the relationships betw
een them may be informative. Often model parameters represent environmental
conditions that can also be viewed as state variables. Experimental manipu
lation of the environment has several advantages as a test, but a problem i
s uncertainty over how much the organism's policy will adjust. As an exampl
e we allow birds to use different assumptions about how well past weather p
redicts future weather. We advocate mirroring planned empirical investigati
ons oh the computer to investigate which manipulations and predictions will
best test a model. (C) 2000 The Association for the Study of Animal Behavi
our.