We have seen that it is an advantage to have a plan that can be easily adapted when unforeseen events occur, and where this is not possible, to use probability distributions to appropriately balance reward with downside risk. For longer-term decision-making, another valuable tool – which can be combined with both these approaches – is to carry out a plan in phases, adapting it as more information becomes available. The key here is to build in flexibility, the ability to adapt to a changing environment, and recognise the right time to adapt. Show
Dr Michel-Alexandre Cardin, a senior lecturer in the Dyson School of Design Engineering, develops decision-making techniques for major engineering projects such as the building of new aerospace, building, energy and transportation systems. These require financial investments in the millions or billions and significant physical resources. They are also highly vulnerable to a changing natural environment, economy and technological landscape, and events like the COVID-19 pandemic. Decision-makers sometimes tie themselves into major projects without building in flexibility, because they are attracted by the economies of scale achieved by committing all at once to significant investments, or they are confident about future projections of major uncertainty drivers like price or demand. However, recent studies have shown that building in flexibility can improve expected value to investors or society by 10–30% and sometimes more.
Dr Cardin says: “Incorporating flexibility into decision-making when we design critical systems is hugely important not only for maximising returns on investment, but also for making society more resilient to extreme situations like pandemics and terror attacks. It also supports sustainability by making better use of resources.” Dr Cardin offers the example of the Iridium satellite phone network, which launched 66 satellites in a single year in the 90s, anticipating major demand just as land-based mobile phone networks entered the mainstream. The unexpectedly low demand, combined with early engineering decisions that meant the satellites could not adjust their orbits, meant revenue failed to cover the huge $4 billion investment – and ultimately led to the company’s bankruptcy, despite its award-winning technology. This situation could have been mitigated, Dr Cardin says, if Iridium had adopted a flexible capacity expansion strategy by launching the satellites in phases and designing the satellites to change orbital configuration in space in line with new information on demand and coverage requirements. Many other engineering projects shared a similar story, for example the IUT Global waste-to-energy system in Singapore and Ghost Cities in China.
The mission is to create a new mindset,” says Dr Cardin. “We need to design systems in a way that enables flexible decision-making to face future uncertainty, threats, and opportunities." Real options analysis, a technique that became popular in the 80s and 90s, offers decision-makers a way to value a flexible project. A real option is the right, but not obligation, to take an investment decision such as expanding or reducing capacity or delaying or abandoning investment. To analyse real options, we break a project down into decision-making phases and, given the current state of the project (e.g. capacity) and expected future realisation, determine the value of exercising each option. This analysis can sometimes show that the value of expanding a system’s capacity in phases, for example the Iridium network, is greater than the value of expanding all at once. The phased approach can be more valuable because it makes it possible to expand capacity only when needed and to expand more than originally planned if conditions are favourable. Moreover, the analysis may value the project as a whole more highly than a valuation that does not break the project down into phases, an approach that might even give the project a negative expected value by failing to incorporate the value of being flexible and leaving options open. A limitation with the classical real options approach is that it assumes that if a parameter such as the price of the product we are selling increases by £10 and then in the next time period decreases by £10, the value of the asset or project is equivalent to it decreasing and then increasing by the same amount. This is often a bad assumption in industrial contexts, because it does not account for path dependencies, such as the fact that you are likely to expand capacity when the price is high, and that this expansion cannot always be reversed without a significant cost in case of downturns.
Dr Michel-Alexandre Cardin in the Dyson School of Design Engineering. Photo: Jason Alden
Dr Michel-Alexandre Cardin in the Dyson School of Design Engineering. Photo: Jason Alden Dr Cardin has developed a version of real options analysis better suited for industry practice, which uses decision-rules. These are rules that specify the best time to change the system to capitalise on an upside opportunity or reduce exposure to downside risks, so as to improve the expected value. By using decision-rules, we can account for path dependency by modelling the decisions that will be made in each phase and reflecting these in the values of the choice sets available in subsequent phases. This is computationally complex, and he is currently exploring the role that deep reinforcement learning could play to build and analyse effective models more efficiently than humans can. In addition to financial decision-making, Dr Cardin examines the engineering decisions – including the mechanical nuts and bolts – that need to be considered at the outset of a project to build flexibility into the design of a system: for Iridium, for instance, each satellite would have needed to be designed to change orbit. His approach is in line with the Dyson School of Design Engineering’s focus on systems thinking, which considers product design and engineering not only from the point of view of user needs, but decision maker’s needs along with other components of the system a product is embedded in. He is currently interacting with private equity firms in infrastructure, who need better data-driven decision support tools to enable them to make best use of their resources and increase value. “The mission is to create a new mindset,” Dr Cardin says. “We need to design systems in a way that enables flexible decision-making to face future uncertainty, threats, and opportunities. Think of future buildings – these will need to embed more flexibility so we can adapt to future health crises like the COVID-19 pandemic, or climate change. A lot of these decisions will not be possible unless systems are appropriately designed from the outset to build in flexibility.” Photo: The Delta-II Rocket, carrying the Iridium network's first five satellites, lifts off on 4 May 1997 in California. The satellite phone network was launched just as land-based mobile phone networks were entering the mainstream, leading to unexpectedly low demand. Credit: STR/AFP via Getty Images.
Making decisions is hard. Making decisions under uncertainty is doubly hard. Uncertainty abounds in the modern information age: Marketplaces shift, customer preference adapt to new trends, technologies get “disrupted” at every turn, industry best-practices get amended … the list of changing unknowns is endless. A decision making model is necessary for success in an uncertain world. In this post you will learn about the Control Based (CB) decision making model. It will help buffer against uncertainty by focusing on the entire decision process, not just the decision itself. By doing so, the goal is to reinstate your control so that you can increase your efficiency in the decision making process. As always, our team of psychology and neuroscience PhDs have reviewed more than forty academic studies in decision science, behavioral economics, and cognitive neuroscience and constructed this model from the body of research. Why your brain likes certaintyPeople are creatures of habit. And yes, routine is good for your brain because it takes away the cognitive load that would otherwise overwhelm us. It is much easier to rely on pre-existing mental patterns. Familiarity is safe, effortless, and easy. Unfamiliar situations, on the other hand, require more of your attention and effort. Consider an everyday example. When you brush your teeth, you can fill your mind with several things at once and clean your teeth at the same time. You may even busy yourself with a task simultaneously, like read an email or send a text. However, if you brush your teeth with the non-dominant hand, all your attention is given over to that novel sequence of actions. You don’t have a motor based “program” wired for that slightly different physical experience. And as a result, an automated, fluid response becomes effortful, cognitively demanding, and stressful. Same goes for the uncertainty that you encounter when making decisions. What is normally a seamless choice process, uncertain decisions are inherently more effortful. That in turn clouds your judgement and slows down your ability to think clearly. The lesson in all this is that decision making under uncertainty is often made worse than it really is. You can blame the brain’s default settings, which often lead to a spiral of anxiety and self-reinforcing negativity: You feel like you don’t have control → You get anxious about lacking control → Anxiety clouds your judgement → Your decision making is impaired → you feel like you don’t have control → and on and on it goes. It’s a vicious cycle that starts with the feeling of not having control; the emphasis here being on feeling. Since emotional states are evaluated based on subjective interpretation, you can put things in place to regain the feeling of having more control. A key point to applying the CB decision making model is that feelings of control are necessary for effective decision making. This brings up the question, what actually constitutes a decision? Decision making is like an iceberg. The actual moment of making a choice – what we typically think is a decision – is merely the tip of the iceberg. All the build-up beforehand happens underneath the surface, without you realizing. That said, the CB decision making model is designed to optimize for all that lead-up in the decision process. It’s meant to reinstate control so that by the time you arrive at the tip of the decision iceberg, you’ve done the legwork and lined up your dominos. There are 3 steps in the CB model:
Step 1: Evaluating the situation to reduce uncertaintyThe first step in the decision making model is to evaluate if uncertainty is at play. This is the first step to put you (back) in control. Here’s how:
Now, based on your evaluation, if the situation you’re in involves a high degree of uncertainty, then continue on with the following. Step 2: Regain control internally through personal managementWhen facing uncertainty in decision making, we often rely on heuristics. These are fast and effortless cognitive strategies that bypass conscious deliberate thinking. When deciding under uncertainty, we have the urge to:
Such default cognition gives a false sense of control, but has a negative impact on our decision making process. But the good news is, they can be reversed by focusing inward and relying on the sequence of steps of the CB decision making model. Personal management, as it’s called, uses a holistic approach in focusing on how you By challenging these three aspects, you can regain internal control so that when it comes time to make a choice, you feel like the decision point is driven internally rather than on a set of external (and often unknown) factors. Personal management deals in the human psychology triangle of thinking, feeling and acting. Due to the structure of our psychology and brain, you don’t have to tackle all of them at once. They are all interlinked; a change in one necessarily means a change in the other two. Regain internal control through flexible thinkingWhen things are uncertain, it’s easy for our default cognitive heuristics to skew our judgments and hinder our ability to make effective decisions. Be aware of the following biases. Override the confirmation biasWhat it is: you tend to look for and attend to information that confirms your beliefs while ignoring the information that challenges your beliefs. That way, you cut yourself off to important information that you need to make an effective decision. How to tackle it: Play the devil’s advocate by proving yourself wrong. For example, if you think that option A is the most beneficial, write down on a piece of paper all the possible reasons why you think it’s the best. Then, for each point, write down why it may not be true. Be critical. This strategy can help with thinking outside the box by increasing your creativity and leading to more definitive choice outcomes. Override the sunk cost fallacyWhat it is: You tend to stick to ideas that already took some of your time or money. It is a doubling-down on a plan even when doing so doesn’t make sense. Abandoning the existing plan and going with something new doesn’t get considered a viable option because of the inflexibility of the heuristic. How to tackle it: When you want to make an effective decision, draw a clear cut-off point for what you consider a success or failure as a follow-up to your decision. Every once in a while, check if your outcomes are above the cut off point. If they fall below, it is a clear signal for you to change your strategy and go for another option. Do not stick to the original one hoping that things will turn around at some point. Another tip: Advertise your cut-off point to others. Socializing as a pre-commitment can ensure you stick to the original plan. Regain internal control through calm feelingYou like to think that your decisions are rational, and that you logically considered all possible options. The truth is, you are both a rational and an emotional being and everything that you do, including decision making, has a strong emotional basis. Feeling out of control when making decisions can lead to high-arousal negative emotions. So, in order to optimize for effective decision making, you need to get control over those feelings. Here’s how: Progressive muscle relaxationThis is a very simple exercise that is linked with mindfulness. The beauty of it though is that it’s a body-based exercise. This means it’s effective for those people who don’t care for the sitting-still aspect of traditional mindfulness. This technique involves tensing and relaxing one muscle group at a time. You start, for example, by making a tight fist, and then letting your hand relax. Then, you move to your arm and do the same. Then your back, other arm, legs … Until you go through every major part of your body. This technique has proven to help reduce uncertainty and negative arousal in the body: reduction in bodily arousal leads to a calmer and more balanced emotional and cognitive state, which in turn leads to more effective decision making. Journaling helps calm your feelings
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