Wealth distribution is a complex and critical aspect of any society.
Information exchange is considered to have played a role in shaping wealth
distribution patterns, but the specific dynamic mechanism is still unclear. In
this research, we used simulation-based methods to investigate the impact of
different modes of information exchange on wealth distribution. We compared
different combinations of information exchange strategies and moving
strategies, analyzed their impact on wealth distribution using classic wealth
distribution indicators such as the Gini coefficient. Our findings suggest that
information exchange strategies have significant impact on wealth distribution
and that promoting more equitable access to information and resources is
crucial in building a just and equitable society for all.

研究使用基于模拟的方法，调查了信息交流对财富分配的影响，并发现促进更公平的信息和资源获取对建立公正、平等的社会至关重要。

个体信息交换策略对社会财富分配的影响

The impact of individual information exchange strategies on the  distribution of social wealth

Chatbots are more and more prevalent in commercial and science contexts. They
help customers complain about a product or service or support them to find the
best travel deals. Other bots provide mental health support or help book
medical appointments. This paper argues that insights into users' language
ideologies and their rapport expectations can be used to inform the audience
design of the bot's language and interaction patterns and ensure equitable
access to the services provided by bots. The argument is underpinned by three
kinds of data: simulated user interactions with a chatbot facilitating health
appointment bookings, users' introspective comments on their interactions and
users' qualitative survey comments post engagement with the booking bot. In
closing, I will define audience design for conversational AI and discuss how
user-centred analyses of chatbot interactions and sociolinguistically informed
theoretical approaches, such as rapport management, can be used to support
audience design.

该论文认为可以通过对用户的语言意识和与聊天机器人互动的期望进行分析，从而确定聊天机器人对话的目标人群，以支持公平访问。同时通过三种数据展示了如何使用口语管理等理论方法来支持受众设计。