With three experiments, we study the design of financial markets to help spread knowledge about solutions to the 0-1 Knapsack Problem (KP), a combinatorial resource allocation problem. To solve the KP, substantial cognitive effort is required; random sampling is ineffective and humans rarely resort to it. The theory of computational complexity motivates our experiment designs. Complete markets generate noisy prices and knowledge spreads poorly. Instead, one carefully chosen security per problem instance causes accurate pricing and effective knowledge dissemination. This contrasts with information aggregation experiments. There, values depend on solutions to probabilistic problems, which can be solved by random drawing.

中文翻译：

---

资源分配、计算复杂性和市场设计

通过三个实验，我们研究了金融市场的设计，以帮助传播有关 0-1 背包问题 (KP)（一种组合资源分配问题）解决方案的知识。为了解决 KP，需要大量的认知努力；随机抽样是无效的，人们很少采用它。计算复杂性理论激发了我们的实验设计。完整的市场会产生嘈杂的价格，并且知识的传播很差。相反，为每个问题实例精心选择一种安全性可以实现准确的定价和有效的知识传播。这与信息聚合实验形成对比。在那里，值取决于概率问题的解决方案，可以通过随机抽取来解决。