We consider portfolio decision problems with positive interactions between projects. Exact solutions to this problem require that all interactions are assessed, requiring time, expertise and effort that may not always be available. We develop and test a number of fast and frugal heuristics – psychologically plausible models that limit the number of assessments to be made and combine these in computationally simple ways – for portfolio decisions. The proposed “add-the-best” family of heuristics constructs a portfolio by iteratively adding a project that is best in a greedy sense, with various definitions of “best”. We present analytical results showing that information savings achievable by heuristics can be considerable; a simulation experiment showing that portfolios selected by heuristics can be close to optimal under certain conditions; and a behavioral laboratory experiment demonstrating that choices are often consistent with the use of heuristics. Add-the-best heuristics combine descriptive plausibility with effort-accuracy trade-offs that make them potentially attractive for prescriptive use.

我们考虑项目之间存在积极互动的投资组合决策问题。确切的解决方案要求评估所有交互，这需要时间，专业知识和精力，而这些可能并不总是可用的。我们开发并测试了许多快速而节俭的启发式方法-心理上合理的模型，这些模型限制了要进行的评估的数量，并以计算上简单的方式将它们组合在一起-用于投资组合决策。提议的“最佳”启发式方法族通过迭代添加贪婪意义上最佳的项目（具有“最佳”的各种定义）来构建投资组合。我们提供的分析结果表明，通过启发式方法可以节省大量信息。模拟实验表明，在某些条件下，通过启发式方法选择的投资组合可能接近最佳；行为实验室实验表明，选择通常与启发式方法保持一致。最好的启发式方法将描述性的合理性与工作量准确性的权衡相结合，使它们对于描述性使用具有潜在的吸引力。