In the past several decades, the world's economy has become increasingly
globalized. On the other hand, there are also ideas advocating the practice of
``buy local'', by which people buy locally produced goods and services rather
than those produced farther away. In this paper, we establish a mathematical
theory of real price that determines the optimal global versus local spending
of an agent which achieves the agent's optimal tradeoff between spending and
obtained utility. Our theory of real price depends on the asymptotic analysis
of a Markov chain transition probability matrix related to the network of
producers and consumers. We show that the real price of a product or service
can be determined from the involved Markov chain matrix, and can be
dramatically different from the product's label price. In particular, we show
that the label prices of products and services are often not ``real'' or
directly ``useful'': given two products offering the same myopic utility, the
one with lower label price may not necessarily offer better asymptotic utility.
This theory shows that the globality or locality of the products and services
does have different impacts on the spending-utility tradeoff of a customer. The
established mathematical theory of real price can be used to determine whether
to adopt or not to adopt certain artificial intelligence (AI) technologies from
an economic perspective.

本文通过建立一个包含多个生产者、消费者的网络联系和与之关联的一个马尔科夫链模型分析了价格本质，从而研究全球性和本地性的消费对于消费者的影响，并提出了与人们直觉不同的购买建议。同时，该研究还可以被用于对人工智能技术在经济层面上的采纳进行评估。