Department of Psychological Sciences, Purdue University, West Lafayette, Indiana, United States of America.
PLoS One. 2013 May 6;8(5):e61712. doi: 10.1371/journal.pone.0061712. Print 2013.
From behavioral sciences to biology to quantum mechanics, one encounters situations where (i) a system outputs several random variables in response to several inputs, (ii) for each of these responses only some of the inputs may "directly" influence them, but (iii) other inputs provide a "context" for this response by influencing its probabilistic relations to other responses. These contextual influences are very different, say, in classical kinetic theory and in the entanglement paradigm of quantum mechanics, which are traditionally interpreted as representing different forms of physical determinism. One can mathematically construct systems with other types of contextuality, whether or not empirically realizable: those that form special cases of the classical type, those that fall between the classical and quantum ones, and those that violate the quantum type. We show how one can quantify and classify all logically possible contextual influences by studying various sets of probabilistic couplings, i.e., sets of joint distributions imposed on random outputs recorded at different (mutually incompatible) values of inputs.
从行为科学到生物学再到量子力学,人们会遇到这样的情况:(i)一个系统会对多个输入输出几个随机变量,(ii)对于这些响应中的每一个,只有一些输入可能“直接”影响它们,但是(iii)其他输入通过影响其与其他响应的概率关系为这种响应提供了“背景”。这些上下文影响在经典动力学理论和量子力学的纠缠范式中非常不同,这两种理论传统上被解释为代表不同形式的物理决定论。人们可以在数学上构建具有其他类型的语境的系统,无论是否在经验上可实现:那些形成经典类型的特例,那些介于经典和量子之间的,以及那些违反量子类型的。我们通过研究各种概率耦合集(即在不同(相互不兼容)输入值处记录的随机输出上施加的联合分布集)来展示如何量化和分类所有逻辑上可能的上下文影响。