using chatgpt to make better decisions
运用 chatgpt 做出非常好的抉择计划
by viktor mayer-sch?nberger
can chatgpt help executives make better decisions? the large language model everyone has been talking about for months also has an eloquent answer to this question: “yes, i can support you in management decisions by providing information, facts, analysis, and perspectives that can help you make an informed decision.” chatgpt immediately follows up with a limitation of its own competence. “however, it is important to note that my advice and recommendations are based on an algorithmic analysis of data and information, and you, as a human being, still have to make the final decision based on your experience, knowledge, and assessment of the situation.”
fair enough. but despite this dose of modesty — or because of it — large language models like chatgpt can become powerful decision-making tools for managers and for companies. their promise isn’t in providing us answers, but in helping us go through a more systematic decision-making process than is often the case today, even with important management decisions.
很公正。可是，尽管有这种谦善——或许正因为如此——像 chatgpt 这样的大型言语模型可以变成打点者和公司的健壮抉择计划东西。他们的承诺不是为咱们供给答案，而是协助咱们结束比今日更体系的抉择计划进程，即就是重要的打点抉择计划。
three phases characterize well-informed decisions. first, we must define our goals and context. what exactly is the decision about, and based on which goals, values, and preferences? this way, we define the decision-making problem and set the decision-making framework. the second step is to develop choices: what decision-making options are available to us? the goal here is to generate many different alternatives and not, as is all too often the case, to focus just on the obvious options. only when we have developed sufficient options from the decision-making framework can we evaluate them and make a well-informed decision in a third step.
used skillfully, chatgpt can already provide valuable services in all three phases for business decisions in its current training state. in practice, this means we can enter into a dialogue with the system on any of the three phases of a well-informed decision-making system. when evaluating decision-making alternatives, we can ask, for example: what mistakes do managing directors of large, medium-sized companies in mechanical engineering make when they decide to expand into new markets? and what were the success criteria for a successful expansion?
chatgpt then does not provide us with a template with which we can weigh the options perfectly in our case. but it can help us uncover our own biases and challenge preconceived notions. using chatgpt cleverly can be like a de-biasing tool that has seemingly read daniel kahneman and amos tversky intensively. it thus offers food for thought to better reflect on how we can evaluate the options in a more well-informed way.
然后，chatgpt 不会为咱们供给模板，经过该模板咱们可以完满地权衡咱们事例中的选项。但它可以协助咱们发现自个的成见，应战先入为主的观念。奇妙地运用 chatgpt 就像一个去成见的东西，如同现已深化阅览了 daniel kahneman 和 amos tversky。因而，它供给了值得沉思的粮食，以非常好地思考咱们如何以更知情的方法评价这些选择。
the system is already even more valuable today when it is employed to work out additional options that we can not think of or easily come up with. this way, it broadens our decision-making horizons, and we understand that there are many more and more far-reaching decision-making options than we realize.
how do we reduce our dependence on china and diversify a supply chain? a managing director and his team may never have dealt with this decision-making question before. chatgpt, however, may be able to offer up many of the strategies documented on the internet by companies in a comparable situation and may come up with more original ideas than simply relocating production to vietnam. this is because the system has access to a part of the publicly available treasure trove of options in the industry or company class.
large language models can also help set goals and preferences, evaluate the decision-making circumstances, and select the decision-making framework. again, dialogue is key. with the right questions, we become the interlocutor to better understand the context of a decision. for example, with chatgpt, we can quickly see suggestions of what typical goals other companies might have had in mind in a comparable decision-making situation. for example, a prompt might look like this: “hi chatgpt, i am the head of a successful, mid-sized tooling manufacturer outside columbus, ohio. i am having difficulties attracting new talent, especially engineers. what may be the reasons for this? what strategies are similar manufacturing companies employing to cope with the talent shortage?”
the bottom line is: chatgpt is becoming an increasingly intelligent conversation and sparring partner. it does not relieve us of defining the decision-making framework, working out a wide range of options, and evaluating them. however — and here, the self-assessment from the beginning of this article is correct — it does provide interesting perspectives.
a large language model has several advantages compared to a human sparring partner: it does not pursue its own interests and does not want to please the top decision-maker, for example, to promote its own career. it is not subject to internal group thinking and bureaucratic politics and is also much cheaper than external management consultants or internal strategy departments. this also means that chatgpt may make the preparation and assistance of decisions for smaller companies cheaper, leveling the playing field.
与人类陪练火伴比较，大型言语模型有几个利益：它不寻求自个的利益，也不想取悦最高抉择计划者，例如，为了前进自个的作业生计。它不受内部集体思维和官僚政治的影响，也比外部打点参谋或内部战略部分廉价得多。这也意味着 chatgpt 可以会使小公司的抉择计划预备和协助更廉价，然后创造公正的竞赛环境。
the future of case studies
budding managers at business schools are already indirectly learning about decision-making through a large number of case studies. the aim is to acquire a repertoire of decision-making models by developing and evaluating possible options for action within a decision-making framework. of course, case studies do not contain a solution in the form of a perfect answer to a specific decision-making situation. in case studies, questions are raised, decision-making frameworks are presented, and decision-making options are outlined. not only can prospective managers learn from and with these case studies, but they can also be used to train large language models. however, this has not yet happened.
chatgpt’s programmers could only feed their model a fraction of publicly available case studies. the real treasure trove of data is exclusive and stored at the major prov
iders such as harvard business publishing (hbr’s parent company), with over 50,000 case studies or the non-profit case center. if the custodians of these business case studies team up with the makers of large language models, a language assistant for programming, copywriting, and customer inquiries could turn into a powerful decision-making assistant for companies.
this will also get easier in the future because the learning algorithms are becoming more and more efficient, and thus “medium-sized language models” will also be possible, in which it is no longer necessary to feed half the internet and entire libraries, but above all the texts and documents relevant to the specific field. it is only a matter of time before this happens. in any case, the economic incentive for more informed business decisions is excellent and will propel the transition from today’s chatgpt to an even more powerful future we might dub “decisiongpt.”
the great strength of chatgpt and similar systems is to compare and contrast similar situations. this is precisely the most important need in many management decisions. very few of the decisions managers face are unique. thousands, sometimes even millions, of managers before them have had to make a similar choice. the better it is described in human language how they set the decision-making framework, weigh the options, and make their decision, the easier it is for decisiongpt to become a powerful tool for more informed decision-making.
eventually, many such management decisions could be automated. robo-managers could be deployed sooner and more often than many executives in their corner offices may believe today.
in the meantime, though, the advantage will go to managers who use currently available tools to improve their decision-making process. don’t ask models like chatgpt for answers; probe them to each stage of the decision-making process.
但与此一起，运用其时可用东西来改进抉择计划进程的打点人员将获得优势。不要向像 chatgpt 这样的模型寻求答案;将他们勘探到抉择计划进程的每个期间。
a successful decision-making process has three steps: framing the decision, generating alternatives, and deciding between them. large language models can help at each stage of the process. but while it may be tempting to merely ask chatgpt for answers, the real power of llms is how they can assist at each stage. ask for help thinking of considerations you might be missing, or alternatives you might not have considered. llms can be a de-biasing tool, helping you frame and make the decision yourself.
viktor mayer-sch?nberger is professor at oxford. his new book with thomas ramge, reinventing capitalism in the age of big data, is being published by basic books in february.
viktor mayer-sch?nberger是牛津大学教授。他与托马斯·拉姆格（thomas ramge）合著的新书《大数据年代的重塑本钱主义》（reinventing capitalism in the age of big data）将于二月份由basic books出书。
from harvard business review