  Unambiguous identification of the rewards driving behaviours of entities
operating in complex open-ended real-world environments is difficult, partly
because goals and associated behaviours emerge endogenously and are dynamically
updated as environments change. Reproducing such dynamics in models would be
useful in many domains, particularly where fixed reward functions limit the
adaptive capabilities of agents. Simulation experiments described assess a
candidate algorithm for the dynamic updating of rewards, RULE: Reward Updating
through Learning and Expectation. The approach is tested in a simplified
ecosystem-like setting where experiments challenge entities' survival, calling
for significant behavioural change. The population of entities successfully
demonstrate the abandonment of an initially rewarded but ultimately detrimental
behaviour, amplification of beneficial behaviour, and appropriate responses to
novel items added to their environment. These adjustment happen through
endogenous modification of the entities' underlying reward function, during
continuous learning, without external intervention.
