Collaborative learning in networks
Collaborative learning in networks
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DOI：
10.1073/pnas.1110069108
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发表时间：
2012-01-17
影响因子：
11.1
通讯作者：
Watts, Duncan J.
中科院分区：
文献类型：
--
作者：
Mason, Winter;Watts, Duncan J.
科学、商业和工程中的复杂问题通常需要在利用已知解决方案和探索新解决方案之间进行权衡，在许多情况下，有关已知解决方案的信息还可以通过正式或非正式的网络在单个问题解决者之间传播。之前对集体解决复杂问题的研究发现了一个违反直觉的结果，即低效网络，即传播信息相对较慢的网络，对于需要扩展探索的问题，可以比高效网络表现得更好。在这篇文章中，我们报道了一系列基于网络的256个实验，在这些实验中，16个人组成的小组共同解决了一个复杂的问题，并通过不同的通信网络共享信息。正如预期的那样，我们发现集体探索比独立探索提高了平均成功率，因为好的解决方案可以通过网络传播。然而，与以前的工作相反，我们发现高效网络的表现优于低效网络，即使在具有被认为有利于低效网络的定性性质的问题空间中也是如此。我们从个人层面的探索-开发决策的角度来解释这一结果，我们发现这一决策受到网络结构以及战略考虑和Maxima之间的相对收益的影响。我们通过讨论对现实世界问题解决的影响和可能的扩展来结束。
Complex problems in science, business, and engineering typically require some tradeoff between exploitation of known solutions and exploration for novel ones, where, in many cases, information about known solutions can also disseminate among individual problem solvers through formal or informal networks. Prior research on complex problem solving by collectives has found the counterintuitive result that inefficient networks, meaning networks that disseminate information relatively slowly, can perform better than efficient networks for problems that require extended exploration. In this paper, we report on a series of 256 Web-based experiments in which groups of 16 individuals collectively solved a complex problem and shared information through different communication networks. As expected, we found that collective exploration improved average success over independent exploration because good solutions could diffuse through the network. In contrast to prior work, however, we found that efficient networks outperformed inefficient networks, even in a problem space with qualitative properties thought to favor inefficient networks. We explain this result in terms of individual-level explore-exploit decisions, which we find were influenced by the network structure as well as by strategic considerations and the relative payoff between maxima. We conclude by discussing implications for real-world problem solving and possible extensions.