CATONSVILLE, MD, September 23, 2019 - Chatbots, which use artificial intelligence to simulate human conversation through voice commands or text chats, incur almost zero marginal costs and can outsell some human employees by four times, so why aren't they used more often? According to new research, the main contributor is customer pushback. The machines don't have "bad days" and never get frustrated or tired like humans, and they can save money for consumers, but new research in the INFORMS journal Marketing Science says if customers know about the chatbot before purchasing, sales rates decline by more than 79.7%. The study authors, Xueming Luo and Siliang Tong (both of Temple University), Zheng Fang of Sichuan University, and Zhe Qu of Fudan University, targeted 6,000 customers from a financial services company. They were randomly assigned to either humans or chatbots, and disclosure of the bots varied from not telling the consumer at all, to telling them at the beginning of the conversation or after the conversation, or telling them after they'd purchased something.

As more enterprise firms and their customers start to question the use of data in analytics, research and artificial intelligence tools, the Institute for Operations Research and the Management Sciences (INFORMS) is offering an 18-point set of guidelines to ensure ethics in quantitative decision-making. Based in Catonsville, Md., INFORMS was established in 1995 following a merger between the Operations Research Society of America and The Institute of Management Sciences. Its ethics guidelines, which are available as a foldable printout, are grouped into three broad categories where operations research and analytics are likely to have the greatest impact. These include society, business organizations themselves and those working directly in professional roles involving quantitative decision-making. "We aspire to be . . . Questioning of whether there are more effective and efficient ways to reach a goal," one of the guidelines says, for example, as well as "realistic in our claims of achievable results, and in acknowledging when the best course of action may be to terminate a project."

Management Science, 35 (5), 527-50.

See also: Evaluating influence diagrams with decision circuits. In Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence (UAI2007)Operations Research, 34, 871-882

Operations Research, 21, 1071–1088.

This paper explores new approaches to the symmetric traveling-salesman problem in which 1-trees, which are a slight variant of spanning trees, play an essential role. A 1-tree is a tree together with an additional vertex connected to the tree by two edges. We observe that (i) a tour is precisely a 1-tree in which each vertex has degree 2, (ii) a minimum 1-tree is easy to compute, and (iii) the transformation on “intercity distances” cij → Cij + πi + πj leaves the traveling-salesman problem invariant but changes the minimum 1-tree. Operations Research, 18, 1138–1162.

Operations Research 14:699-719