Murimi Renita M
Gupta College of Business, University of Dallas, Irving, TX, United States.
Front Big Data. 2021 Mar 4;4:603282. doi: 10.3389/fdata.2021.603282. eCollection 2021.
This paper studies contractual graphs, where the formation of edges between nodes result in dyadic exchanges. Each dyadic exchange is analyzed as a contractual agreement that is implemented upon fulfilment of underlying conditions. As these dyadic exchanges proliferate, the resulting population of these exchanges creates a contractual graph. A contractual framework for graphs is especially useful in applications where AI-enabled software is employed to create or automate smart contracts between nodes. While some smart contracts may be easily created and executed, others may contain a higher level of ambiguity which may prevent their efficient implementation. Ambiguity in contractual elements is especially difficult to implement, since nodes have to efficiently sense the ambiguity and allocate appropriate amounts of computational resources to the ambiguous contractual task. This paper develops a two-node contractual model of graphs, with varying levels of ambiguity in the contracts and examines its consequences for a market where tasks of differing ambiguity are available to be completed by nodes. The central theme of this paper is that as ambiguity increases, it is difficult for nodes to efficiently commit to the contract since there is an uncertainty in the amount of resources that they have to allocate for completion of the tasks specified in the contract. Thus, while linguistic ambiguity or situational ambiguity might not be cognitively burdensome for humans, it might become expensive for nodes involved in the smart contract. The paper also shows that timing matters-the order in which nodes enter the contract is important as they proceed to sense the ambiguity in a task and then allocate appropriate resources. We propose a game-theoretic formulation to scrutinize how nodes that move first to complete a task are differently impacted than those that move second. We discuss the applications of such a contractual framework for graphs and obtain conditions under which two-node contracts can achieve a successful coalition.
本文研究契约图，其中节点之间边的形成会导致二元交换。每一次二元交换都被分析为一项契约协议，该协议在潜在条件得到满足时实施。随着这些二元交换的不断增加，这些交换所形成的总体构成了一个契约图。图的契约框架在使用人工智能软件在节点之间创建或自动化智能合约的应用中特别有用。虽然一些智能合约可能很容易创建和执行，但其他智能合约可能包含更高程度的模糊性，这可能会阻碍它们的有效实施。契约元素中的模糊性尤其难以实现，因为节点必须有效地感知这种模糊性，并为模糊的契约任务分配适量的计算资源。本文开发了一种双节点契约图模型，其中契约具有不同程度的模糊性，并研究了其对一个市场的影响，在这个市场中，节点可以完成不同模糊程度的任务。本文的核心主题是，随着模糊性的增加，节点很难有效地遵守契约，因为它们在为完成契约中规定的任务而必须分配的资源数量上存在不确定性。因此，虽然语言模糊性或情境模糊性对人类来说可能不会造成认知负担，但对于参与智能合约的节点来说可能会变得代价高昂。本文还表明时机很重要——节点进入契约的顺序很重要，因为它们要着手感知任务中的模糊性，然后分配适当的资源。我们提出一种博弈论公式来审视率先完成任务的节点与第二个完成任务的节点受到的影响有何不同。我们讨论了这种图的契约框架的应用，并得出双节点契约能够实现成功联盟的条件。
