using chatgpt to make better decisions
运用 chatgpt 做出非常好的抉择计划
by viktor mayer-sch?nberger
作者：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.”
chatgpt 能协助高管做出非常好的抉择计划吗？我们几个月来一向在谈论的大型言语模型也对这个疑问有一个雄辩的答案：“是的，我可以经过供给信息、实际、分析和观念来撑持你的打点抉择计划，协助你做出正确的抉择。chatgpt当即跟进其本身才能的捆绑。“可是，重要的是要留心，我的主张和主张是根据对数据和信息的算法分析，你作为一自个，仍然有必要根据你的经历、常识和对情况的评价做出究竟抉择。
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.
三个期间是正确抉择计划的特征。首要，咱们有必要断定咱们的方针和布景。抉择究竟是啥，以及根据哪些方针、价值观和偏好？这样，咱们界说了抉择计划疑问并设置了抉择计划规划。第二步是拟定选择：咱们有哪些抉择计划选择？这儿的方针是发生许多不一样的替代方案，而不是像常常发生的那样，只重视清楚明晰的选项。只需当咱们从抉择计划规划中开宣告满足的选项时，咱们才干评价它们并在第三步做出正确的抉择。
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?
奇妙地运用，chatgpt 现已可以在其其时培训状况下的一切三个期间为事务抉择计划供给有价值的效能。在实习中，这意味着咱们可以就知情抉择计划体系的三个期间中的任何一个期间与体系进行对话。例如，在评价抉择计划方案时，咱们可以问：机械工程领域的大中型公司的董事总司理在抉择拓宽到新商场时犯了啥差错？成功扩展的成功标准是啥？
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.
今日，当该体系用于拟定咱们无法想到或简略想出的其他选项时，该体系现已更有价值。这样，它拓宽了咱们的抉择计划视界，咱们理解，有比咱们知道到的更多、影响深远的抉择计划选择。
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.
咱们如何削减对我国的依靠并使供给链多样化？董事总司理和他的团队早年可以从未处置过这个抉择计划疑问。可是，chatgpt可以可以供给许多由处于类似情况的公司在互联网上记载的战略，而且可以会提出更多自创的主意，而不是简略地将出产转移到越南。这是因为体系可以造访作业或公司品种中揭露可用的选项宝库的一有些。
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?”
大型言语模型还可以协助设定方针和偏好，评价抉择计划环境，选择抉择计划规划。相同，对话是要害。有了正确的疑问，咱们就会变成对话者，以非常好地晓得抉择计划的布景。例如，运用 chatgpt，咱们可以快速查看其他公司在类似抉择计划情况下可以想到的典型方针的主张。例如，提示可以如下所示：“嗨，chatgpt，我是俄亥俄州哥伦布市以外一家成功的中型模具制造商的担任人。我很难招引新的人才，特别是工程师。可所以啥缘由？类似的制造公司采纳了哪些战略来应对人才短少？
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.
底线是：chatgpt正在变成一个越来越聪明的对话和陪练火伴。它并不能清除咱们断定抉择计划规划、拟定广泛的选择和评价它们的责任。可是——在这儿，这篇文章最初的自我评价是正确的——它的确供给了风趣的观念。
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
事例研讨的将来
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.
商学院锋芒毕露的打点者现已经过许多事例研讨直接地学习了抉择计划。意图是经过在抉择计划规划内拟定和评价可以的行为选择，获得一系列抉择计划方法。当然，事例研讨并不包括对特定抉择计划情况的完满答案方法的处置方案。在事例研讨中，提出了疑问，提出了抉择计划规划，并概述了抉择计划选项。将来的打点者不只可以从这些事例研讨大学习，还可以用于练习大型言语模型。可是，这没有发生。
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.
chatgpt 的程序员只能向他们的模型供给一小有些揭露的事例研讨。真实的数据宝库是独家的，存储在哈佛商业出书公司（hbr的母公司）等首要供给商处，具有跨越50，000个事例研讨或非盈利性事例中心。假定这些商业事例研讨的保管人与大型言语模型的制造商协作，那么用于编程、文案和客户查询的言语辅佐可以会变成公司的健壮抉择计划辅佐。
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.”
这在将来也会变得更简略，因为学?惴ū涞迷嚼丛礁咝В蚨爸行脱杂锬Ｐ汀币步涑煽梢裕浼洳辉傩枰话氲幕チ驼鐾际楣莨└澄铮钪匾氖怯胩囟煊蛴泄氐奈谋竞臀牡怠Ｕ庵智榭龇⑸皇鞘笨桃晌省２还苋绾危返纳桃稻裨窦苹木霉睦羌玫模⒔平咏袢盏腸hatgpt过渡到一个咱们称之为“decisiongpt”的更健壮的将来。
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.
chatgpt和类似体系的健壮之处在于比照和比照类似的情况。这正是许多打点抉择计划中最重要的需要。打点者面临的抉择计划很少是绝无仅有的。在他们之前，不计其数的，有时甚至是数百万的打点者不得不做出类似的选择。用人类言语描绘他们如何设置抉择计划规划、权衡选项并做出抉择越好，decisiongpt 就越简略变成更正确抉择计划的健壮东西。
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.
究竟，许多这样的打点抉择计划可以主动化。机器人司理的安设可以比今日许多角落单位的高管可以认为的更早、更频频。
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。
summary. 总结。
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.
一个成功的抉择计划进程有三个进程：拟定抉择计划，生成替代方案，并在它们之间做出抉择。大型言语模型可以在该进程的每个期间供给协助。可是，尽管只是向chatgpt寻求答案可以很诱人，但llm的真实力气在于它们如何在每个期间供给协助。寻求协助，思考您可以遗失的思考要素，或您可以没有思考过的替代方案。llm可所以一种消除成见的东西，协助您自个拟定和做出抉择。
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