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The history of predicting the future

对未来的预测的发展史

Humans have long tried to determine the shape of what’s to come. But even the most advanced technology can’t solve the fundamental issues with predictions.

长期以来，人类一直试图确定未来是什么样的，但即使是最先进的技术也无法解决预测的根本问题。

The future has a history. The good news is that it’s one from which we can learn; the bad news is that we very rarely do. That’s because the clearest lesson from the history of the future is that knowing the future isn’t necessarily very useful. But that has yet to stop humans from trying.

未来是有历史的。好消息是，我们可以从中学习；坏消息是，我们很少这样做。这是因为从未来的历史中所学到的最明显的教训是，了解未来对当下并不一定很有用。但这并没有阻止人类的尝试。

Take Peter Turchin’s famed prediction for 2020. In 2010 he developed a quantitative analysis of history, known as cliodynamics, that allowed him to predict that the West would experience political chaos a decade later. Unfortunately, no one was able to act on that prophecy in order to prevent damage to US democracy. And of course, if they had, Turchin’s prediction would have been relegated to the ranks of failed futures. This situation is not an aberration.

以彼得·图尔钦对2020年的著名预测为例。2010年，他发展了一种被称为派系动力学的历史定量分析，这使他能够预测十年后西方将经历政治混乱。不幸的是，没有人能够按照这一预言行事，以防止对美国民主的损害。当然，如果他们真的这样做了，图尔钦的预测就会被归入失败的未来行列。这种情况并非反常。

Rulers from Mesopotamia to Manhattan have sought knowledge of the future in order to obtain strategic advantages—but time and again, they have failed to interpret it correctly, or they have failed to grasp either the political motives or the speculative limitations of those who proffer it. More often than not, they have also chosen to ignore futures that force them to face uncomfortable truths. Even the technological innovations of the 21st century have failed to change these basic problems—the results of computer programs are, after all, only as accurate as their data input.

从美索不达米亚到曼哈顿的统治者都在寻求对未来的了解，以获得战略优势，但他们一次又一次地未能正确解读未来，或者未能理解提供未来的人的政治动机或投机局限性。他们往往也选择忽视迫使他们面对令人不安的真相的未来。即使是21世纪的技术创新也未能改变这些基本问题——毕竟，计算机程序的结果与数据输入一样准确。

There is an assumption that the more scientific the approach to predictions, the more accurate forecasts will be. But this belief causes more problems than it solves, not least because it often either ignores or excludes the lived diversity of human experience. Despite the promise of more accurate and intelligent technology, there is little reason to think the increased deployment of AI in forecasting will make prognostication any more useful than it has been throughout human history.

有一种假设是，预测方法越科学，预测就越准确。但这种信念造成的问题比解决的问题多，尤其是因为它往往忽视或排除了人类生活经验的多样性。尽管有更准确、更智能的技术前景，但几乎没有理由认为人工智能在预测中的部署增加会使预测比人类历史上更有用。

People have long tried to find out more about the shape of things to come. These efforts, while aimed at the same goal, have differed across time and space in several significant ways, with the most obvious being methodology—that is, how predictions were made and interpreted. Since the earliest civilizations, the most important distinction in this practice has been between individuals who have an intrinsic gift or ability to predict the future, and systems that provide rules for calculating futures. The predictions of oracles, shamans, and prophets, for example, depended on the capacity of these individuals to access other planes of being and receive divine inspiration. Strategies of divination such as astrology, palmistry, numerology, and Tarot, however, depend on the practitioner’s mastery of a complex theoretical rule-based (and sometimes highly mathematical) system, and their ability to interpret and apply it to particular cases. Interpreting dreams or the practice of necromancy might lie somewhere between these two extremes, depending partly on innate ability, partly on acquired expertise. And there are plenty of examples, in the past and present, that involve both strategies for predicting the future. Any internet search on “dream interpretation” or “horoscope calculation” will throw up millions of hits.

长期以来，人们一直试图了解更多关于未来事物形态的信息。这些努力虽然针对同一目标，但在时间和空间上有几个显著的差异，最明显的是方法论，即如何做出和解释预测。自最早的文明以来，这种做法中最重要的区别是具有预测未来的内在天赋或能力的个人与提供计算未来规则的系统之间的区别。例如，神谕、萨满和先知的预测取决于这些人进入其他存在层面并获得神圣灵感的能力。然而，占星术、手相术、命理学和塔罗牌等占卜策略取决于从业者对复杂的基于理论的（有时是高度数学化的）系统的掌握，以及他们将其解释和应用于特定案例的能力。解释梦境或亡灵巫师的实践可能介于这两个极端之间，部分取决于天生的能力，部分取决于后天的专业知识。在过去和现在，有很多例子涉及预测未来的两种策略。任何关于“解梦”或“星座运势计算”的网络搜索都会获得数百万次点击。

In the last century, technology legitimized the latter approach, as developments in IT (predicted, at least to some extent, by Moore’s law) provided more powerful tools and systems for forecasting. In the 1940s, the analog computer MONIAC had to use actual tanks and pipes of colored water to model the UK economy. By the 1970s, the Club of Rome could turn to the World3 computer simulation to model the flow of energy through human and natural systems via key variables such as industrialization, environmental loss, and population growth. Its report, Limits to Growth, became a best seller, despite the sustained criticism it received for the assumptions at the core of the model and the quality of the data that was fed into it.

在上个世纪，技术使后一种方法合法化，因为信息技术的发展（至少在某种程度上由摩尔定律预测）为预测提供了更强大的工具和系统。在20世纪40年代，模拟计算机MONIAC不得不使用实际的彩色水箱和水管来模拟英国经济。到20世纪70年代，罗马俱乐部可以转向World3计算机模拟，通过工业化、环境损失和人口增长等关键变量，对人类和自然系统中的能源流动进行建模。其报告《增长的极限》成为畅销书，尽管它因模型核心的假设和输入数据的质量而受到持续的批评。

At the same time, rather than depending on technological advances, other forecasters have turned to the strategy of crowdsourcing predictions of the future. Polling public and private opinions, for example, depends on something very simple—asking people what they intend to do or what they think will happen. It then requires careful interpretation, whether based in quantitative (like polls of voter intention) or qualitative (like the Rand corporation’s DELPHI technique) analysis. The latter strategy harnesses the wisdom of highly specific crowds. Assembling a panel of experts to discuss a given topic, the thinking goes, is likely to be more accurate than individual prognostication.

与此同时，其他预测者不再依赖技术进步，而是转向众包预测未来的策略。例如，民意调查公众和私人意见取决于一件非常简单的事情——询问人们打算做什么或他们认为会发生什么。然后，它需要仔细的解释，无论是基于定量（如选民意向调查）还是定性（如兰德公司的DELPHI技术）分析。后一种策略利用了高度特定人群的智慧。人们认为，召集一个专家小组讨论某个特定的主题可能比个人预测更准确。

This approach resonates in many ways with yet another forecasting method—war-gaming. Beginning in the 20th century, military field exercises and maneuvers were increasingly supplemented, and sometimes replaced, by simulation. Undertaken both by human beings and by computer models such as the RAND Strategy Assessment Center, this strategy is no longer confined to the military, but is now used extensively in politics, commerce, and industry. The goal is to increase present resilience and efficiency as much as it is to plan for futures. Some simulations have been very accurate in predicting and planning for possible outcomes, particularly when undertaken close to the projected events—like the Sigma war game exercises conducted by the Pentagon in the context of the developing Vietnam War, for example, or the Desert Crossing 1999 games played by United States Central Command in relation to Saddam Hussein’s Iraq.

这种方法在许多方面与另一种预测方法——战争游戏产生了共鸣。从20世纪开始，军事实地演习和演习越来越多地被模拟所补充，有时甚至被取代。这一战略由人类和兰德战略评估中心等计算机模型共同实施，不再局限于军事，而是现在广泛用于政治、商业和工业。我们的目标是提高目前的弹性和效率，就像为未来做计划一样。一些模拟在预测和规划可能的结果方面非常准确，特别是在接近预测事件的情况下进行的模拟，例如五角大楼在发展中的越南战争背景下进行的西格玛战争游戏演习，或者美国中央司令部在1999年与萨达姆·侯赛因的伊拉克有关的沙漠穿越游戏。

As these strategies have continued to evolve, two very different philosophies for predicting communal futures have emerged, particularly at the global, national, and corporate level. Each reflects different assumptions about the nature of the relationship between fate, fluidity, and human agency.

随着这些战略的不断发展，出现了两种截然不同的预测社区未来的哲学，特别是在全球、国家和企业层面。每一种都反映了对命运、流动性和人类能动性之间关系性质的不同假设。

Understanding previous events as indicators of what’s to come has allowed some forecasters to treat human history as a series of patterns, where clear cycles, waves, or sequences can be identified in the past and can therefore be expected to recur in the future. This is based on the success of the natural sciences in crafting general laws from accumulated empirical evidence. Followers of this approach included scholars as diverse as Auguste Comte, Karl Marx, Oswald Spengler, Arnold Tonynbee, Nicolai Kondratiev, and, of course, Turchin. But whether they were predicting the decline of the West, the emergence of a communist or scientific utopia, or the likely recurrence of global economic waves, their success has been limited.

将以前的事件理解为未来的指标，使一些预测者能够将人类历史视为一系列模式，在这些模式中，可以在过去识别出清晰的周期、波动或序列，因此可以预期在未来会重现。这是基于自然科学在从积累的经验证据中制定一般规律方面的成功。这种方法的追随者包括各种各样的学者，如奥古斯特·孔特、卡尔·马克思、奥斯瓦尔德·斯宾格勒、阿诺德·托尼比、尼古拉·孔德拉季耶夫，当然还有图尔钦。但无论他们是在预测西方的衰落、共产主义或科学乌托邦的出现，还是全球经济浪潮的可能重演，他们的成功都是有限的。

More recently, research at MIT has focused on developing algorithms to predict the future based on the past, at least in the extremely short term. By teaching computers what has “usually” happened next in a given situation—will people hug or shake hands when they meet?—researchers are echoing this search for historical patterns. But, as is often a flaw in this approach to predictions, it leaves little room, at least at this stage of technological development, to expect the unexpected.

最近，麻省理工学院的研究重点是开发基于过去预测未来的算法，至少在极短的时间内是这样。通过教计算机在特定情况下“通常”会发生什么——人们见面时会拥抱或握手吗--研究人员正在呼应这种对历史模式的探索。但是，正如这种预测方法中经常存在的缺陷一样，它几乎没有留下什么空间，至少在技术发展的这个阶段，可以预料到意想不到的事情。

Another set of forecasters, meanwhile, argue that the pace and scope of techno-economic innovation are creating a future that will be qualitatively different from past and present. Followers of this approach search not for patterns, but for emergent variables from which futures can be extrapolated. So rather than predicting one definitive future, it becomes easier to model a set of possibilities that become more or less likely, depending on the choices that are made. Examples of this would include simulations like World3 and the war games mentioned earlier. Many science fiction writers and futurologists also use this strategy to map the future. In the 1930s, for instance, H. G. Wells took to the BBC to broadcast a call for “professors of forethought,” rather than of history. He argued that this was the way to prepare the country for unexpected changes, such as those brought by the automobile. Similarly, writers going back to Alvin and Heidi Toffler have extrapolated from developments in information technology, cloning, AI, genetic modification, and ecological science to explore a range of potential desirable, dangerous, or even post-human futures.

与此同时，另一组预测者认为，技术经济创新的速度和范围正在创造一个与过去和现在有质的不同的未来。这种方法的追随者不是寻找模式，而是寻找可以推断未来的突发变量。因此，与其预测一个明确的未来，不如根据所做的选择，对一组可能性进行建模，这些可能性或多或少会变得更容易。这方面的例子包括模拟世界3和前面提到的战争游戏。许多科幻作家和未来学家也使用这种策略来描绘未来。例如，在20世纪30年代，H.G.威尔斯在英国广播公司（BBC）上呼吁“有先见之明的教授”，而不是历史教授。他认为，这是让国家为意外变化做好准备的方式，比如汽车带来的变化。同样，回到阿尔文和海蒂·托夫勒的作家们从信息技术、克隆、人工智能、基因改造和生态科学的发展中推断出一系列潜在的可取、危险甚至后人类的未来。

But if predictions based on past experience have limited capacity to anticipate the unforeseen, extrapolations from techno-scientific innovations have a distressing capacity to be deterministic. Ultimately, neither approach is necessarily more useful than the other, and that’s because they both share the same fatal flaw—the people framing them.

但是，如果基于过去经验的预测预测不可预见的能力有限，那么技术科学创新的推断就具有令人沮丧的确定性。最终，两种方法都不一定比另一种更有用，这是因为它们都有一个致命的缺陷——设定它们的人。

Whatever the approach of the forecaster, and however sophisticated their tools, the trouble with predictions is their proximity to power. Throughout history, futures have tended to be made by well-connected, cis-male people. This homogeneity has had the result of limiting the framing of the future, and, as a result, the actions then taken to shape it. Further, predictions resulting in expensive or undesirable outcomes, like Turchin’s, tend to be ignored by those making the ultimate decisions. This was the case with the nearly two decades worth of pandemic war-gaming that preceded the emergence of Covid-19. Reports in both the US and the UK, for example, stressed the significance of public health systems in responding effectively to a global crisis, but they did not convince either country to bolster their systems. What’s more, no one predicted the extent to which political leaders would be unwilling to listen to scientific advice. Even when futures did have the advantage of taking into account human error, they still produced predictions that were systematically disregarded where they conflicted with political strategies.

无论预测者采用何种方法，无论他们的工具多么复杂，预测的问题都在于他们接近权力。纵观历史，未来往往是由人脉广泛的顺性男创造的。这种同质性的结果是限制了未来的框架，因此也限制了为塑造未来而采取的行动。此外，导致昂贵或不理想结果的预测，如图尔钦的预测，往往会被那些做出最终决定的人忽视。在新冠肺炎出现之前，近20年的大流行警告就是这样。例如，美国和英国的报告都强调了公共卫生系统在有效应对全球危机方面的重要性，但并没有说服任何一个国家加强其系统。更重要的是，没有人预测政治领导人会在多大程度上不愿意听取科学建议。即使期货确实具有考虑人为错误的优势，但它们仍然产生了与政治策略相冲突的系统性忽视的预测。

Which brings us to the crucial question of who and what predictions are for. Those who can influence what people think will be the future are often the same people able to command considerable resources in the present, which in turn help determine the future. But very rarely do we hear the voices of the populations governed by the decisionmakers. It’s often at the regional or municipal level that we see efforts by ordinary people to predict and shape their own communal and familial futures, often in response to the need to distribute scarce resources or to limit exposure to potential harms. Both issues are becoming ever more pressing in the presently unfolding climate catastrophe.

这就引出了一个至关重要的问题，即预测是针对谁和什么的。那些能够影响人们认为未来的人，往往是那些能够在当下掌控大量资源的人，而这些资源反过来又有助于决定未来。但我们很少听到决策者所统治的民众的声音。通常在地区或市一级，我们看到普通人努力预测和塑造自己的社区和家庭未来，通常是为了应对分配稀缺资源或限制潜在危害的需要。在目前正在发生的气候灾难中，这两个问题都变得越来越紧迫。

The central message sent from the history of the future is that it’s not helpful to think about “the Future.” A much more productive strategy is to think about futures; rather than “prediction,” it pays to think probabilistically about a range of potential outcomes and evaluate them against a range of different sources. Technology has a significant role to play here, but it’s critical to bear in mind the lessons from World3 and Limits to Growth about the impact that assumptions have on eventual outcomes. The danger is that modern predictions with an AI imprint are considered more scientific, and hence more likely to be accurate, than those produced by older systems of divination. But the assumptions underpinning the algorithms that forecast criminal activity, or identify potential customer disloyalty, often reflect the expectations of their coders in much the same way as earlier methods of prediction did.

从未来的历史中发出的核心信息是，笼统地思考“未来”是没有帮助的。一个更有成效的策略是思考更多具体的未来；与其说是“预测”，不如说是对一系列潜在结果进行概率性思考，并根据一系列不同的来源对其进行评估。技术在这方面发挥着重要作用，但至关重要的是要记住《世界3》和《增长的极限》中关于假设对最终结果的影响的教训。危险在于，带有人工智能印记的现代预测被认为比旧的占卜系统更科学，因此更可能准确。但是，支持预测犯罪活动或识别潜在客户不忠的算法的假设，往往反映了编码者的期望，与早期的预测方法非常相似。

Rather than depending purely on innovation to map the future, it’s more sensible to borrow from history, and combine newer techniques with a slightly older model of forecasting—one that combines scientific expertise with artistic interpretation. It would perhaps be more helpful to think in terms of diagnosis, rather than prediction, when it comes to imagining—or improving—future human histories.

与其纯粹依靠创新来规划未来，不如借鉴历史，将较新的技术与稍旧的预测模型相结合——一种将科学专业知识与艺术解释相结合的预测模型。当涉及到想象或改善未来人类历史时，从诊断而非预测的角度思考可能会更有帮助。

【Source】www.wired.com

【Translated by】Spark Liao (廖怀宝)

【Illustration】From Bing