Swiss Federal Institute of Technology Zurich, Zurich, Switzerland.
PLoS One. 2012;7(2):e30902. doi: 10.1371/journal.pone.0030902. Epub 2012 Feb 16.
We model an N-player repeated prisoner's dilemma in which players are given traits (e.g., height, age, wealth) which, we assume, affect their behavior. The relationship between traits and behavior is unknown to other players. We then analyze the performance of "prejudiced" strategies--strategies that draw inferences based on the observation of some or all of these traits, and extrapolate the inferred behavior to other carriers of these traits. Such prejudiced strategies have the advantage of learning rapidly, and hence of being well adapted to rapidly changing conditions that might result, for example, from high migration or birth rates. We find that they perform remarkably well, and even systematically outperform both Tit-For-Tat and ALLD when the population changes rapidly.
我们构建了一个 N 人重复囚徒困境模型，其中玩家具有特质（如身高、年龄、财富），我们假设这些特质会影响他们的行为。其他玩家并不知道特质与行为之间的关系。然后，我们分析了“偏见”策略的表现——这些策略根据观察某些或所有这些特质做出推断，并将推断出的行为外推到具有这些特质的其他载体上。这种有偏见的策略具有快速学习的优势，因此非常适合快速变化的情况，例如高移民率或出生率。我们发现，当人口快速变化时，它们的表现非常出色，甚至比以牙还牙和 ALLD 策略的表现还要好。
