学术不端！如何解决科学‘信誉问题’？

科学是真理的源头，而且其在塑造现代社会的作用上所体现的重要性如何去强调也不为过。但近几年，科学却出现了信任危机。

许多科学实验的结果很难复制，同行评议制度的缺陷也凸显出来。这都影响了科学的信誉度，促使科研人员提出新的措施来维护学术研究及其成果的权威性。

尤其在英国，政府一直对自己制订的科学推动政策引以为傲。政策制订通常采纳行为学研究结论，也就是传统意义上的“软科学”。英国行为洞察力团队——也被称为“推动单位”的负责人认为，经济学家可以“让世界变得更美好”。但是社会学家却质疑某些行为学研究的可靠性和可重复性，使得一些人怀疑科学推动政策具有局限性——过度依赖它可能会适得其反。

因此知名科学家们提出了改变科学知识的生产方式的一系列建议。这些建议包括提升研究设计的透明度，激励更多的实验室在实际研究前提交完整的设计和分析计划，被称作预登记制度。

然而，值得注意的是，目前为止经济学家们在这场信任危机中没有受到负面影响。因为经济学是一门专注于战略行为分析及为了获得理想的结果提出激励措施的科学。

我们的研究将担负起这项挑战，对提出的增加透明度和监测科学结论可靠性政策，惊喜理论效果检验方面的开创性研究。

尽管在公众心目中，无私的科研人员努力工作发现真理的高大形象根深蒂固，但学术研究的真实过程是不同的。经济学理论提出了不同的科学家激励模型，其中最突出的是个人攀登学术阶梯的愿望。

我们关注实行透明度政策的建议，这将阻止研究人员提交有问题的、解释不通的科研结论。

我们模型的主要结论是降低轻微的学术不端行为，如解释不了分析结论的重要细节，也会减少更严重的研究问题，如操控数据。这是因为有问题的研究实践就好像科学竞赛的“类固醇”，某种不恰当的行为的增加会导致更多的极端行为。因此，一项政策，把这些不当行为扼杀在萌芽状态，从而避免了更严重的后果。

我们检验了一种情景，科研人员恪守研究伦理或保持良好的职业声誉，但同时也关心在有限的几个顶级期刊上发表文章。后者是至关重要的，因为它引入了“经济的外部性”。

个别研究人员提交有问题的研究结论的可能性取决于其他研究人员的行为：情节轻微的学术不端行为会导致更高频率的学术造假行为(保证一个独一无二的结论并获得这个研究带来的赞誉)。

因此一项透明度的政策能够降情节轻微的学术不端，并不能，至少在一开始，导致更严重的学术造假行为。减少较轻学术造假行为的发生，将会降低在发表文章方面的竞争，从而缓解学术造假行为方面的压力。

其他的政策旨在降低更严重的学术不端行为，如在应用统计相关技术时进行数据造假。但这也可能会增加轻度学术不端行为的频率，使得科学可靠性的整体效果不尽如人意。

当他们提出政策变化不适宜直接实验时，数学模型就有了用武之地。因为这个理论能够架起理想和现实之间的桥梁。发现研究人员不当行为的直接实验要付出很大成本而且很难办到，但是改革建议的潜在效果可以利用经济学理论进行评价。

我们的模型告诉我们，应该对实施一个透明度的建议充满信心，这个制度将有助于科学履行其发现真理的目的。

（原文标题：如何解决科学“信誉问题”）

英文原文：

How do we solve science's 'credibility problem'?

Science is considered a source of truth and the importance of its role in shaping modern society cannot be overstated. But in recent years science has entered a crisis of trust.

The results of many scientific experiments appear to be surprisingly hard to reproduce, while mistakes have highlighted flaws in the peer review system. This has hit scientific credibility and prompted researchers to create new measures in order to maintain the quality of academic research and its findings.

This is particularly relevant in the UK, whose government prides itself on science-driven policy making. Policies are often drawn from behavioural research, traditionally considered a "soft science". The head of the UK's behavioural insights team – the "nudge unit" – argues that these days research economists can"change the world for the better". But social scientists have debated the reliability and reproducibility of some behavioural research, prompting some to wonder whether science-driven policy has its limitations – and whether over-reliance on it can even backfire.

So leading scientists have suggested a variety of proposals to change the way that science produces knowledge. These include promoting transparency concerning research designs, incentives for more experimental repetition and enforcing the submission of a full plan of the design and analysis prior to the actual study – known as pre-registration.

It is remarkable, however, that economists have so far been content to remain so silent on this credibility crisis. It is, after all, the science that specialises in the analysis of strategic behaviour and the provision of incentives to promote desirable outcomes.

Our research takes up this challenge and provides a first step in examining the theoretical effects of the proposed policies of increased transparency and monitoring on the reliability of scientific results.

Although the image of altruistic researchers working hard to discover the truth is strong in the minds of the general public, the actual process in which academic research is conducted is different. Economic theory models the various incentives of scientists, prominent among which is the desire of individuals to ascend the academic ladder.

We focus on proposals to impose transparency – which will stop researchers from committing the questionable practises which make scientific evidence difficult to interpret.

The main result of our model is that discouraging slight transgressions, such as failing to report important details of the analysis, will also reduce more severe questionable research practises such as outright data manipulation. This is because questionable research practises serve as the "steroids" of the scientific race, where the abundance of a given form of misconduct increases the incentives to engage in more extreme misconduct. Accordingly, a policy that eradicates mild forms of misconduct also discourages the use of stronger "performance enhancers".

We examine a setting where researchers are motivated to conduct research ethically or to maintain a good reputation, but are also concerned about being published in a limited number of top journals. The latter is crucial, as it introduces an "economic externality".

The likelihood of an individual researcher to commit a questionable research practice depends on the behaviour of other resarchers: more lighter transgressions will result in a higher frequency of outright manipulation – to guarantee a unique result and the corresponding acclaim which this brings.

Therefore a transparency policy that reduces lighter transgressions does not, as might be expected at first glance, lead to more severe misbehaviour. On the contrary, reducing the incidence of lighter misdemeanour will reduce competitiveness of the race to publication and thus ease the pressure of engaging in questionable practises.

Other possible policies could aim at reducing more severe transgressions – such as data fabrication – by using the relevant statistical techniques. But this could increase the rewards and frequency of lighter transgressions, making the overall effect on the reliability of scientific results unclear.

Mathematical models are especially useful when they address policy changes that are not amenable to direct experimentation. This is because it is the theory that bridges the gap between the current status quo and the proposed new one. Performing direct experiments on researcher misconduct is costly and difficult, but the potential effects of proposed reforms can still be evaluated by using economic theory.

Our model teaches us that we should feel confident that implementing the transparency proposals will help science fulfil its purpose of discovering the truth.