SONNETS: Scalability Oriented Novel Network of Event Triggered Systems

SONNETS -面向事件触发系统的新型网络-采用全新的方法进行下一代计算机建模和人工智能。为了实现这一目标，我们有一个非常重要的研究目标，这一目标在全国范围内都很重要，也很有挑战性：英国金融风险的实时建模。事后看来，2008年的金融危机是由太多的银行购买了太多的高风险抵押贷款造成的。当危机还在蔓延的时候，所有的信息都是新的：没有人知道有多少银行拥有高风险的抵押贷款。当时人们认为，抵押贷款违约的可能性不大。最后，假设少数银行的损失不会影响国民经济。问题是缺乏对全国情况的了解和了解：每家银行似乎都有一个可控的风险水平，但英国的大多数银行都暴露在同一个潜在的风险因素下，因此一旦抵押贷款开始违约，大多数银行就开始亏损，一场完美的金融风暴随之而来。我们当时需要、现在仍然需要的是国家层面的风险建模，这种建模可以在风险发生时考虑银行间的风险。为一家银行建模是一个难题，而为整个英国建模则困难得多。银行有复杂的不断变化的投资组合，因此建立一个“谁拥有什么”的图片意味着每天跟踪数百万笔交易。即使我们有了这样的图景，我们仍然需要以某种方式评估风险，但这需要预测未来：我们必须先发制人地识别潜在的情景，然后估计每种情景下的损失。目前，监管机构使用“压力测试”来识别国家风险--他们定义一个可能具有挑战性的经济情景，然后要求所有银行估计他们可能损失多少。然而，这是一个缓慢的过程--整个过程需要几个月--而且是有限的--他们只探索了一种非常严重的情景，而这可能不是导致问题的情景。SONNETS将创建一个实时执行国家级风险分析的系统，方法是构建英国金融体系的“数字孪生模型”，并利用它不断生成合理的未来情景并评估其风险。然后，我们使用人工智能来了解风险场景是什么样的。这为监管机构提供了全新的工具：-每天查看英国当前的国家风险，而不是等待数月的压力测试;-能够前瞻性地识别和减轻以前未知的风险，而不是等待金融危机来揭示它们。我们通过解决三个主要领域的挑战来解决这个问题：-计算：创建和运行程序的新范例，利用分布在云中的多种类型的计算机硬件;-人工智能：用于持续学习的方法，可以分为多个部分，以便学习过程可以更接近他们正在学习的数据;-建模：自动场景生成的理论和工具，再加上评估英国金融机构大规模模型风险的能力。这三个领域紧密相连，新的计算模式支持新人工智能的执行和云中建模，以及系统建模和模型学习之间的协同关系。这三个领域的基础是事件触发计算的思想，其中程序被分割成相互发送消息的小片段。使用这种事件触发的方法，我们可以扩大风险分析系统，以支持国家一级的风险分析。它将不断评估英国目前的风险程度，同时试图预测未来可能导致金融危机的情景。SONNETS将提供一个强大的工具，在金融风险积聚时检测和减轻金融风险，而不是在金融危机发生时试图做出反应。

SONNETS - Scalability Oriented Novel Networks of Event Triggered Systems - takes a clean-slate approach to next-generation computer modelling and artificial intelligence. To drive this we have an over-arching research goal that is both nationally important and challenging: real-time modelling of UK financial risk.It is easy to identify underlying risks after they cause a financial crisis. With hindsight, the 2008 financial crash was caused by too many banks buying too many risky mortgages. Whilst the crisis was unfolding it was all new information: no-one realised how many banks owned the risky mortgages. Then it was assumed that mortgage defaults were unlikely. Finally, it was assumed that losses in a few banks would not affect the national economy. The problem was a lack of visibility and understanding of the national picture: each bank appeared to have a manageable risk level, but most banks in the UK were exposed to the same underlying risk factor, so once mortgages started defaulting most banks started losing money and a perfect financial storm developed. What we needed then, and still do now, is national-level risk modelling that can consider risk across banks as it occurs.Modelling risk for one bank is a difficult problem, and modelling the entire UK is much harder. Banks have complex constantly changing portfolios, so building a picture of "who owns what" means tracking millions of trades per day. Even if we have that picture we still need to somehow assess risk, but that requires anticipating the future: we must pre-emptively identify potential scenarios, then estimate how much is lost in each scenario. Currently regulators use "stress tests" to identify national risk - they define a possible challenging economic scenario, then ask all the banks to estimate how much they might lose. However, this is both slow - the process takes months - and limited - they only explore one very severe scenario, which probably isn't the one that causes the problem.SONNETS will create a system that performs national-level risk analysis in real-time, by building a "digital twin" of the UK's financial system and using it to continually generate plausible future scenarios and assess their risk. We then use artificial intelligence to learn what risky scenarios look like. This gives regulators completely new tools:- A day-by-day view of the current national-risk of the UK, rather than waiting months for stress tests;- The ability to look forwards to identify and mitigate previously unknown risks as they develop, rather than waiting for a financial crisis to reveal them.We tackle this problem by addressing challenges in three main areas:- Computing: new paradigms for creating and running programs, exploiting multiple types of computer hardware distributed across the cloud;- Artificial Intelligence: methods for continual learning that can be split into multiple pieces, so that learning processes can be moved closer to the data they are learning from;- Modelling: theory and tools for automatic scenario generation, plus the ability to assess risk over large-scale models of the UK's financial institutions.These three areas are tightly linked, with the new computing paradigms supporting execution of the new AI and modelling in the cloud, and a synergistic relationship between the modelling of the system and learning about the model.Underpinning these three areas is the idea of event-triggered computing, where programs are split up into small fragments which send messages to each other. Using this event-triggered approach we can scale the risk analysis system up to support national-level risk analysis. It will constantly assess how risky the UK currently is, while trying to anticipate what scenarios might lead to financial crises in the future.SONNETS will provide a powerful tool to detect and mitigate financial risk as it is building up, rather than trying to react to a financial crisis once it happens.