Suchergebnisse

Collective problem-solving networks are common in modern life. They often benefit from having diverse members with complementary skills and perspectives, however the benefits of information exchange and synthesis among them may be squandered if they self-select away from diverse counterparts and towards homogeneous groups or perceived competency. Building on the extensive tradition of "exploration and exploitation" agent-based models (ABMs), we initialize communicative networks with diverse groups of agents who solve a complex problem represented by Kauffman's NK problem space. We compare three types of ABMs, where the initial network and agent setups are identical but differentiated by agents' attachment proclivities: (1) diversity- seeking networks, where agents prefer ties with different-agents; (2) homophily-seeking networks, where agents prefer ties with similar-agents; and (3) merit-seeking networks, where agents prefer ties with agents who have found better solutions. We find that diversity-seeking networks perform well because diversity promotes more exploration for solutions, but it also fosters network structures that disseminate these higher quality solutions more effectively than merit-seeking and homophily-seeking networks.