Limitations and Usefulness of Computer Simulations for Complex Adaptive Systems Research

Complex adaptive systems in nature may produce something new, like structures, patterns, or properties, that arise from the rules of self-organization. These novelties are emergent if they cannot be understood as any property of the components but are a new property of the system. Emergence is a key property of complex systems. A popular method to better understand complex adaptive systems is the use of their computational representation, predominantly using the agent metaphor to produce emergence. The philosophy of science differentiates ontological and epistemological emergence. Ontological emergence produces something systemically new, while epistemological emergence produces new rules and laws, and as such can be reduced by gaining a better understanding of the system. The work presented here makes the case that emergence in computational complex adaptive systems cannot be ontological, as the constraints of computable functions do not allow for ontological emergence. As such, computer representations of complex adaptive systems are limited, as claims to produce systemically real emergence with computational systems contradicts some fundamental insights from computer science and philosophy of science. Nonetheless, they are useful to understand better the relationship of emergence and complex adaptive systems and conduct adductive research, which may be the best support of complex systems evaluation we can provide to complexity scientists to move the borderline between what is theoretically feasible to what is practically possible.