The advent of Swarm, along with other agent-based modeling techniques, has opened up a set of problems unapproachable by traditional equation-based modeling:
(1) Can we produce an existence-proof model, akin to von Neumann's model of self-reproduction, that exhibits open-ended evolution, with increasing diversity and complexity?
(2) Can we model the complex 'circuitry' of biological cells in ways that suggest useful interventions, such as elucidating the relation between a non-aggressive Gleason 3 prostate cancer
and a metastasizing Gleason 4 cancer?
(3) Can we build models that replace food webs ('who-eats-whom') with networks that take into account the indirect effects on behavior induced by cross-species signaling, as in the case of
(4) Can we produce a proof-of-principle, agent-based model of language-acquisition demonstrating that signaling (say between mother and offspring), under familiar cognitive mechanisms, is
adequate for the acquisition of grammar, without an innate ('wired-in') grammar template.
Well-formulated models aimed at these challenges should substantially advance our understanding in the corresponding disciplines, whether the results are positive or negative.