01300nas a2200205   4500000000100000000000100001008004100002260001200043653001500055653001300070653001100083653001500094100002000109245007200129856009900201300001000300490000600310520076400316022001401080       2017                            d  c06/201710aSimulation10aLearning10aAgents10aComplexity1 aFriederike Wall00aDistributed Search Systems with Self-Adaptive Organizational Setups  uhttp://www.ijimai.org/journal/sites/default/files/files/2017/01/ijimai20174_4_11_pdf_14655.pdf  a88-950 v43 aThis paper studies the effects of learning-induced alterations of distributed search systems’ organizations. In particular, scenarios where alterations of the search-systems’ organizational setup are based on a form of reinforcement learning are compared to scenarios where the organizational setup is kept constant and to scenarios where the setup is changed randomly. The results indicate that learning-induced alterations may lead to high levels of performance combined with high levels of efficiency in terms of reorganization-effort. However, the results also suggest that the complexity of the underlying search problem together with the aspiration level (which drives positive or negative reinforcement) considerably shapes the effects of learning.  a1989-1660