Your Mileage May Vary is an advice column offering you a unique framework for thinking through your moral dilemmas. It’s based on value pluralism — the idea that each of us has multiple values that are equally valid but that often conflict with each other. To submit a question, fill out this anonymous form. Here’s this week’s question from a reader, condensed and edited for clarity:

I’m soon to be a part of the legal profession. I went to law school to advocate for marginalized populations who seldom have their voices heard — people who are steamrolled by unethical landlords, employers, corporations, etc. I will clerk after law school, and then I’ll encounter my first major fork in the road: whether I pursue employment in a corporate firm or nonprofit/government. Corporate firms, ultimately, serve profitable clients, sometimes to the detriment of marginalized populations. Corporate firms also pay significantly better. Nonprofit or government work serves the populations I want to work for and alongside, but often pays under the area median income.

I’ll be 32 by the time I reach this fork, and I don’t know what to do. I’m extremely fortunate in that I won’t have law school debt — I was on a full ride. Still, I’m not “flush.” I want to buy a house one day, have some kids with my partner, feel financially secure enough to do so. I also want to have a morally congruent career and not enable (what I consider) systems of oppression. What do I do?

Dear Fork in the Road,

Your question reminds me of another would-be lawyer: a very bright American woman named Ruth Chang. When she was graduating from college, she felt torn between two careers: Should she become a philosopher or should she become a lawyer?

She loved the learning that life in a philosophy department would provide. But she’d grown up in an immigrant family, and she worried about ending up unemployed. Lawyering seemed like the financially safe bet. She got out some notepaper, drew a line down the middle, and tried to make a pro/con list that would reveal which was the better option.

But the pro/con list was powerless to help her, because there was no better option. Each option was better in some ways and worse in others, but neither was better overall.

So Chang did what many of us do when facing a hard choice: She chose the safe bet. She became a lawyer. Soon enough, she realized that lawyering was a poor fit for her personality, so she made a U-turn and became — surprise, surprise — a philosopher. And guess what she ended up devoting several years to studying? Hard choices! Choices like hers. Choices like yours. The kind where the pro/con list doesn’t really help, because neither option is better on balance than the other.

Here’s what Chang came to understand about hard choices: It’s a misconception to think they’re hard because of our own ignorance. We shouldn’t think, “There is a superior option, I just can’t know what it is, so the best move is always to go with the safer option.” Instead, Chang says, hard choices are genuinely hard because no best option exists.

But that doesn’t mean they’re both equally good options. If two options are equally good, then you could decide by just flipping a coin, because it really doesn’t matter which you choose. But can you imagine ever choosing your career based on a coin toss? Or flipping a coin to choose whether to live in the city or the country, or whether to marry your current partner or that ex you’ve been pining for?

Of course not! We intuitively sense that that would be absurd, because we’re not simply choosing between equivalent options.

So what’s really going on? In a hard choice, Chang argues, we’re choosing between options that are “on a par” with each other. She explains:

To concretize this, think of the difference between lemon sorbet and apple pie. Both taste extremely delicious — they’re in the same league of deliciousness. The kind of deliciousness they deliver, however, is different. It matters which one you choose, because each will give you a very different experience: The lemon sorbet is delicious in a tart and refreshing way, the apple pie in a sweet and comforting way.

Now let’s consider your dilemma, which isn’t really about whether to do nonprofit work or to become a corporate lawyer, but about the values underneath: advocating for marginalized populations on the one hand, and feeling financially secure enough to raise a family on the other. Both of these values are in the same league as each other, because each delivers something of fundamental value to a human life: living in line with moral commitments or feeling a sense of safety and belonging. That means that no matter how long you spend on a pro/con list, the external world isn’t going to supply reasons that tip the scales. Chang continues:

By that, Chang means that you have to put your own agency into the choice. You have to say, “This is what I stand for. I’m the kind of person who’s for X, even if that means I can’t fulfill Y!” And then, through making that hard choice, you become that person.

So ask yourself: Who do you want to be? Do you want to be the kind of person who serves profitable clients, possibly to the detriment of marginalized people, in order to be able to provide generously for a family? Or do you want to advocate for those who most need an advocate, even if it means you can’t afford to own property or send your kids to the best schools?

What is more important to you? Or, to ask this question in a different way: What kind of person would you want your future children to see you as? What legacy do you want to leave?

Only you can make this choice and, by making it, choose who you are to be.

I know this sounds hard — and it is! But it’s good-hard. In fact, it’s one of the most awesome things about the human condition. Because if there was always a best alternative to be found in every choice you faced, you would be rationally compelled to choose that alternative. You would be like a marionette on the fingers of the universe, forced to move this way, not that.

But instead, you’re free — we’re free — and that is a beautiful thing. Because we get the precious opportunity to make hard choices, Chang writes, “It is not facts beyond our agency that determine whether we should lead this kind of life rather than that, but us.”

How should the UK government choose which projects to invest in? How can it avoid making catastrophic mistakes? Labour’s new 10-year Infrastructure Strategy, published in June, shows the immense amount of public investment our economy needs and the sacrifices that will have to be made to fund it. Meanwhile, as the government sets out its noble intentions, HS2 is costing the Treasury more than £7 billion a year. 

In June, the CEO and the responsible minister for HS2 told parliament that the unfinished high-speed rail project is over-specified, that costs are out of control and that construction is running years late. The most beneficial branches of HS2—which would have helped northern cites—have been withdrawn. What is left will duplicate existing railway lines. 

When HS2 was first proposed in 2009, the appraisal, carried out according to the Treasury’s method, suggested that the benefits of the project might justify its vast costs. It also found that HS2 was a risky undertaking that would take many years to produce any return. It turns out that the costs had been understated and the benefits have withered. If spent on alternative public investments, the money invested in HS2 would almost certainly give a much better return. With demands for growth-enhancing investment outstripping the limited supply of public funds, it is particularly important to get this right. We cannot afford to go on wasting so much resource on unproductive initiatives for which there is lots of hand-waving aspiration, but not enough supporting analysis. 

But the Treasury does have a method for doing just that. The department has a rainbow of official “books”, each with its own purpose: Red, Blue, Magenta, Aqua, Orange and now Teal. Not forgetting the Pink Book, which records statistics on the UK’s balance of international trade. We all watch the chancellor delivering budgets and spending reviews, but few study these supporting documents. That, however, is where the substance—and the devilish detail—is to be found.   

HS2 was recommended for investment under the methodology of the Green Book. This particular Treasury book provides guidance on how to appraise spending on policies, programmes and projects. All government departments are supposed to follow it. In principle—if not always in practice—it determines what taxpayers’ money gets spent on. At the recent Spending Review, the Treasury published its audit of the Green Book, which the chancellor commissioned in January.   

The Green Book has, in the past, suffered a bad rap. There is a common misperception that it is only about cost-benefit analysis; that it is a rigid and technocratic set of pseudo-scientific rules; that it gives undue weight to arbitrary monetary valuations; that it misses many important social dimensions of public policy; and that it is incapable of dealing with large, “transformational” proposals.  

When lobbyists and politicians proclaim which investments are the right ones, they can be disappointed when Treasury funding is not forthcoming. Often, they blame the Green Book. One argument goes that looking at value-for-money tends to show good returns to investment in productive, high-income regions (such as London and the southeast) and poor returns in regions that need help (such as the north). But that is a case of “shooting the messenger”. 

The results of the audit commissioned by the chancellor are thoroughly sensible. In essence, the audit found that the Green Book should be tweaked (naturally) but that in general it is just fine. The problems come not from the book’s analysis, but in the ways it can be used and misused.  

This is how it works: the Green Book sets out a framework for the considerations to be made when deciding on government investment—a “five case model”. The cost-benefit analysis, one the five cases, is the economic dimension. It asks, as the Green Book itself says: “What is the net value to society (the social value) of the intervention compared to continuing with business as usual?” The second case is commercial: “Can a realistic and credible commercial deal be struck?” (meaning, who bears the risks for the investment and what are the proposed procurement arrangements?). The third case is the financial aspect. The book asks: “What is the impact of the proposal on the public sector budget in terms of the total cost of both capital and revenue?”. The fourth case relates to project management: “Are there realistic and robust delivery plans?” 

These four sit with the fifth, strategic question: “What is the case for change, including the rationale for intervention? What is the current situation? What is to be done? What outcomes are expected? How do these fit with wider government policies and objectives?”

The reputation of the case that deals with economic value for money has been damaged because too much is expected of it. The question should not be “is it perfect?”, rather “is it useful?”. As Rachel Reeves’s audit points out, cost-benefit analysis is certainly not expected to grind out decisions like a machine. Sensibly used, this five-case model puts the weight of cost and benefits in a broader context. 

Robust analysis of the government’s potential investments will expose assumptions for scrutiny. It will bring the best available evidence to bear, facilitating comparisons within and across departments of state. If done consistently, cost-benefit analysis can be helpful in guiding choices between, say, a road safety measure and spending that same public money on reducing risk of death in the health service. 

Looking at costs and benefits systematically will protect against economically incoherent elephant traps, of which there are a number. It will help to stop the proponents of various schemes “double counting”, which is surprisingly difficult to avoid. A famous example is to claim financial benefits for the lower rate of crowding and faster journeys to a location achieved by introducing a new road or railway, as well as for an increase in the value of the land at that location. But the second is a consequence of the first; one can claim one or the other, but not both.  

At the very least a Green Book appraisal forces proponents to write down and justify estimates of the basics, such as how much a project might cost, how many people might use it over the years and what the risks of the undertaking are. It is surprising how often people will assert that a project is a “good idea” without knowing these details. Often, a project’s champions will reel off an unquantified list of benefits from a scheme, giving little regard to how much public money it is going to cost, as if resources were free, and as if they could not be used in other, perhaps more beneficial, projects. Or even that they could remain in taxpayers’ pockets.

One criticism of cost-benefit analysis is that it can yield inequitable results, but there are ways of compensating for this, including to use a system of weighting. The chancellor’s audit recommends using unweighted analysis, which makes no pretence of dealing with equity, and then to discuss it as part of the strategic case and policy more generally. 

A fair charge is that cost-benefit analysis is less plausible for large, “transformational” schemes. But this is a reflection of the fact that large schemes are difficult to analyse, requiring a lot of data. The issue is whether an imperfect appraisal using the best available evidence is more helpful than the alternative—speculation.  

No cost-benefit analysis should be treated as definitive. But a poor estimated rate of benefit, in return for the costs at risk, should be heeded as a warning that there may be wiser ways of spending public money.  That was the view of the unjustly neglected independent Eddington Transport Study (2006, for HM Treasury and Department for Transport). For these reasons Eddington was sceptical of high-speed rail in UK conditions. How right he was.

The recent review of the Green Book rightly promises more thinking on the knotty issue of the future. At the point of decision on a project, how should the government account for benefit or cost that is expected to accrue in, for instance, 20 years’ time? Typically, once serious spending has started on a project, delays in completion are catastrophic for the overall social value. That is not a weakness of cost-benefit analysis, it is a reflection of a fundamental dilemma. To what extent should any society sacrifice the benefits of current consumption so that investment can be made to deliver benefits in the future?

The review made some other sensible recommendations. A committee will be established to develop guidance where several projects are inter-dependent. An editorial shortening of the Green Book and its many supporting documents is promised; it is asking a lot of national or local government officials, who may or may not be trained economists, that they get to grips with the existing mass of material. 

There is also a promise of more transparency, by publishing all the business cases for major projects and programmes. That can only improve the standard to which the work is done and it will facilitate holding government to account for decisions it makes—including explanations of its justification for proceeding with projects that don’t look like they represent good value for money. The problem in the past has not been too much reliance on mechanistic cost-benefit analysis. It has been a failure to show that available information on a proposed project has been given its due weight.

Success in the policy of investing to grow requires hard-headed analysis of the kind set out in the (revised) Green Book. This is how government can select the right projects for investment. We can no longer afford to squander resources on aspirational projects that do not have a firm, evidence-based justification.

Studies of neural metabolism reveal our brain’s effort to keep us alive and the evolutionary constraints that sculpted our most complex organ.

Introduction

The brain is not purely a cognition machine, but an object sculpted by evolution — and therefore constrained by the tight energy budget of a biological system. Thinking may make you feel tired, then, not because you are out of energy, but because you have evolved to preserve resources. This study of neural metabolism, when tied to research on the dynamics of the brain’s electrical firing, points to the competing evolutionary forces that explain the limitations, scope and efficiencies of our cognitive capabilities.

The Cost of a Predictive Engine

The human brain is incredibly expensive to run. At roughly 2% of body weight, the organ gorges on 20% of our body’s energetic resources. “It’s hugely metabolically demanding,” Jamadar said. For infants, that number is closer to 50%.

The brain’s energy comes in the form of the molecule adenosine triphosphate (ATP), which cells make from glucose and oxygen. A tremendous expanse of thin capillaries — an estimated 400 miles of vascular wiring — weaves through brain tissue to carry glucose- and oxygen-rich blood to neurons and other brain cells. Once synthesized within cells, ATP powers communication between neurons, which enact the brain’s functions. Neurons carry electrical signals to their synapses, which allow the cells to exchange molecular messages; the strength of a signal determines whether they will release molecules (or “fire”). If they do, that molecular signal determines whether the next neuron will pass on the message, and so on. Maintaining what are known as membrane potentials — stable voltages across a neuron’s membrane that ensure that the cell is primed to fire when called upon — is known to account for at least half of the brain’s total energy budget.

Measuring ATP directly in the human brain is highly invasive. So, for their paper, Jamadar’s lab reviewed studies (opens a new tab), including their own findings, that used other estimates of energy use — glucose consumption, measured by positron-emission tomography (PET), and blood flow, measured by functional magnetic resonance imaging (fMRI) — to track differences in how the brain uses energy during active tasks and rest. When performed simultaneously, PET and fMRI can provide complementary information on how glucose is being consumed by the brain, Jamadar said. It’s not a complete measure of the brain’s energy use because neural tissues can also convert some amino acids (opens a new tab) into ATP, but the vast majority of the brain’s ATP is produced by glucose metabolism.

Jamadar’s analysis showed that a brain performing active tasks consumes just 5% more energy compared to a resting brain. When we are engaged in an effortful, goal-directed task, such as studying a bus schedule in a new city, neuronal firing rates increase in the relevant brain regions or networks — in that example, visual and language processing regions. This accounts for that extra 5%; the remaining 95% goes to the brain’s base metabolic load.

Researchers don’t know precisely how that load is allocated, but over the past few decades, they have clarified what the brain is doing in the background. “Around the mid-’90s we started to realize as a discipline [that] actually there is a whole heap of stuff happening when someone is lying there at rest and they’re not explicitly engaged in a task,” she said. “We used to think about ongoing resting activity that is not related to the task at hand as noise, but now we know that there is a lot of signal in that noise.”

Much of that signal is from the default mode network, which operates while we’re resting or otherwise not engaged in apparent activity. This network is involved in the mental experience of drifting between past, present and future scenarios — what you might make for dinner, a memory from last week, some pain in your hip. Additionally, beneath the iceberg of awareness, our brains are keeping track of the mosaic of physical variables — body temperature, blood glucose level, heart rate, respiration, and so on — that must remain stable, in a state known as homeostasis, to keep us alive. If any of them stray too far, things can get bad pretty quickly.

Theriault speculates that most of the brain’s base metabolic load goes toward prediction. To achieve its homeostatic goals, the brain needs to always be planning for what comes next — building a sophisticated model of the environment and how changes might affect the body’s biological systems. Prediction, rather than reaction, Theriault said, allows the brain to dole out resources to the body efficiently.

The Brain’s Evolutionary Constraints

A 5% increased energy demand during active thought may not sound like much, but in the context of the entire body and the energy-hungry brain, it can add up. And when you consider the strict energetic constraints our ancestors had to deal with, weariness at the end of a taxing day suddenly makes a lot more sense.

“The reason you are fatigued, just like you are fatigued after physical activity, isn’t because you don’t have the calories to pay for it,” said Zahid Padamsey (opens a new tab), a neuroscientist at Weill Cornell Medicine-Qatar, who was not involved in the new research. “It is because we have evolved to be very stingy systems. … We evolved in energy-poor environments, so we hate exerting energy.”

The modern world, in which calories are relatively abundant for many people, contrasts starkly with the conditions of scarcity that Homo sapiens evolved in. That 5% increase in burn rate, over 20 days of persistent, active, task-related focus, can amount to a whole day’s worth of cognitive energy. If food is hard to come by, it could mean the difference between life and death.

“This can be substantial over time if you don’t cap the burn rate, so I think it is largely a relic of our evolutionary heritage,” Padamsey said. In fact, the brain has built-in systems to prevent overexertion. “You’re going to activate fatigue mechanisms that prevent further burn rates,” he said.

To better understand these energetic constraints, in 2023 Padamsey summarized research on certain peculiarities of electrical signaling (opens a new tab) that indicate an evolutionary tendency toward energy efficiency. For one thing, you might imagine that the faster you transmit information, the better. But the brain’s optimal transmission rate is far lower than might be expected.

Theoretically, the top speed for a neuron to feasibly fire and send information to its neighbor is 500 hertz. However, if neurons actually fired at 500 hertz, the system would become completely overwhelmed. The optimal information rate (opens a new tab) — the fastest rate at which neurons can still distinguish messages from their neighbors — is half that, or 250 hertz.

Our neurons, however, have an average firing rate of 4 hertz, 50 to 60 times less than what is optimal for information transmission. What’s more, many synaptic transmissions fail: Even when an electrical signal is sent to the synapse, priming it to release molecules to the next neuron, it will do so only 20% of the time.

That’s because we didn’t evolve to maximize total information sent. “We have evolved to maximize information transmission per ATP spent,” Padamsey said. “That’s a very different equation.” Sending the maximum amount of information for as little energy as possible (bits per ATP), the optimal neuronal firing rate is under 10 hertz.

Evolutionarily, the large, sophisticated human brain offered an unprecedented level of behavioral complexity — at a great energetic cost. This negotiation, between the flexibility and innovation of a large brain and the energetic constraints of a biological system, defines the dynamics of how our brain transmits information, the mental fatigue we feel after periods of concentration, and the ongoing work our brain does to keep us alive. That it does so much within its limitations is rather astonishing.

Introduction

The advantages of solid-state cooling: No refrigerants, higher performance ceilings, simpler systems

The potential market for solid-state cooling technologies is enormous.

To enter the mainstream, solid-state cooling still has challenges to overcome

What are the pathways to market?

Solid state’s right to win in the cooling market