Early one morning, I received a LinkedIn notification from a job candidate we had just rejected—rarely a good sign. But this candidate’s message was different: “Job hunting can be hard and processes frustrating,” she wrote. “While I didn’t land my dream job yet, I discovered firms making a difference in how they recruit. Affective Advisory’s process was remarkably different: well-informed, transparent, and appreciative, using behavioral science insights.” 

In an era where rejected candidates frequently vent frustrations on LinkedIn or Glassdoor, here was a rejected candidate publicly endorsing an organization that had turned her down. She became an advocate, even though she didn’t get the job.

Today, many organizations claim to put their people first, proudly declaring a commitment to their employees. Yet have a look at typical hiring processes—the first and most decisive touchpoint between the organization and its potential employees—and you'll find a starkly different reality. 

What you’ll find (and have likely experienced firsthand) is a bureaucratic quagmire that fails to live up to the expectations of the people trying to get hired or the people doing the hiring. 

Those searching for jobs have to navigate incomplete information about the role, the team, and the organization. They have to interpret ambiguous hiring criteria and opaque decision-making processes. They have to put up with automated, boilerplate communication, if communication comes at all. 

Those tasked with hiring often aren’t any better off. They have to muddle through poorly defined hiring protocols and timelines. They regularly receive little to no guidance on how to review applications fairly or conduct unbiased interviews. They have to sit through tedious evaluation rounds that can take months.

Today, many organizations claim to put their people first, proudly declaring a commitment to their employees. Yet have a look at typical hiring processes, and you'll find a starkly different reality.

Beyond the frustration, the costs of mis-hiring are staggering: According to Gallup, voluntary turnover drains $1 trillion annually from United States businesses alone, with a recent study from Deloitte estimating replacement costs as high as 1.5–2.0 times the employee’s annual salary. 

What if organizations decided to treat their entire hiring process (not just who they hire), as a competitive advantage rather than a wearisome chore? 

Over the past eight years, as the leader of a growing behavioral science consultancy, Affective Advisory, I’ve made it my mission to create a better way to hire. And in the course of building our hiring process, my team and I have learned a number of important lessons. We’ve surfaced the key failure modes and developed solutions based on the best research and best practices. And, perhaps most importantly, we’ve learned how organizations can reimagine and redesign their hiring processes, so that it becomes a value add and trust building exercise for everyone involved. 

After hundreds of candidate evaluations and eight full-time hires, I believe these are lessons that can help small companies like ours and larger organizations hire more effectively. Hiring doesn’t need to be a bureaucratic burden. It can be your best chance to demonstrate—not just declare—your values.

Deciding there is a better way to hire

When I decided to make my first hire five years ago, the stakes were high, as they are for any small team or company. The hiring process would require considerable time and resources, of which I had very little. New employees would need training once they joined. And their work would significantly impact whether the then-young company succeeded financially and culturally. At such a critical stage, there was simply no room for low performance; no cushion to absorb the impact of a poor fit, no possible plan B. 

In addition to these practical considerations, there were more philosophical ones as well. I founded the firm with the vision to support public, private, and nongovernmental organizations incorporate the best behavioral science research and methods into their work. Our aim was to help clients analyze, understand, and change behavior in a scientifically and ethically sound way. In our work, we constantly advocate for rigorous, evidence-based methods. So when it came to hiring, we couldn't just fall back on the status quo. It was important that we practiced what we preached.

For the first hire, I brought in four advisors with experience in HR and behavioral science to help. We started with an in-depth review of how companies hired and what candidates experienced in order to figure out where existing hiring processes were going wrong. We identified five fundamental failures that begin at the start of the hiring process and cascade through every interaction, eroding (rather than building) trust in the company, team, and role. Here is what we found:

Unaligned hiring teams: Most organizations involved multiple people in hiring decisions, yet few defined clear roles and responsibilities for those involved. For some organizations, it was not clear who managed the hiring process, who was supposed to contribute and how, and who had the ultimate decision-making authority. This lack of role clarity colored the entire hiring process, leading to impulsive, unstructured decision-making and poor internal and external communication.

Ambiguous job descriptions and unclear criteria: We found that organizations too often failed to define the role and candidate profile with enough detail to create meaningful evaluation criteria. Time-pressed managers might pass on the responsibility of defining job descriptions to HR teams who, working without context, defaulted to generic templates. As a result, job postings brimmed with buzzwords and wishful requirements while revealing nothing about the role’s day-to-day responsibilities or performance metrics.

Amplified bias: The way hiring teams evaluated candidates rarely respected established behavioral science research. This often meant that their processes amplified biases in their decision-making rather than reducing them, including biases around race, gender, and class. This reduced the size of their talent pool and increased their risk of building teams that were too ideologically and experientially similar.

Uncomparable interviews: We found that interviews often varied considerably from one candidate to the next. For example, the questions different interviewers asked, how they scored responses, and the length of the interview lacked the standardization needed to make useful comparisons. The result was that the process of evaluating candidates became negotiations based on interviewers’ opinions rather than decisions based on objective assessments.

Communication voids: Because hiring teams often operated without agreed-upon timelines, they rarely communicated reliably to candidates. Even an acknowledgement that an application had been received often arrived late or never. Candidates, after months of going through the hiring process, might receive impersonal, automated, and boilerplate rejections without any substantive feedback. Hiring software, which promised administrative ease, often exacerbated these kinds of issues.

When organizations fail to define roles and procedures, set unclear timelines, and communicate poorly, they erode trust with their potential employees all before their first day at work.

When organizations fail to define roles and procedures, set unclear timelines, and communicate poorly, they erode trust with their potential employees all before their first day at work. (Not to mention signaling that this kind of work is acceptable at the organization.) 

Redesigning the hiring process around trust

Trust was central to the system I wanted to build. We know from research how trust factors into team performance and how trust and alignment with the company’s purpose are associated with higher employee incomes, longer job tenure, greater job satisfaction, less chronic stress, improved satisfaction with life, and higher productivity. 

But building trust doesn’t start on an employee’s first day; it starts during hiring. Every interaction, from the first job posting to the final decision, can either build or erode trust. This determines not just who joins but how they feel, interact, perform, and advocate for your organization’s values in the future.

For our hiring process, we defined six core components. Our goal was to build a structured process that addressed the deficiencies we surfaced during our review, incorporated behavioral science research and best practices, and allowed us to cultivate trust among our team and candidates at each step.

1: Assembling the hiring team: We start by assembling an evaluation team of at least four team members or outside advisors. Members of the hiring team are selected based on who possesses expertise in the domain we are recruiting for, who will work with the new hire, and who can provide complimentary perspectives to one another. The makeup of the team is crucial for the success of the following five steps, as they drive the hiring process from start to finish. 

2: Defining role clarity: The hiring team begins by listing the potential required competencies and day-to-day responsibilities and for the new role. From these lists, the hiring team defines the role and desired profile, distinguishing mandatory from optional criteria across four dimensions: academic qualifications, professional experience, technical skills, and intrinsic motivation. 

This clarity helps candidates self-assess fit when deciding whether to apply and gives evaluators concrete criteria for assessment. The evaluators can avoid the temptation to alter criteria midprocess, and it gives the team a shared understanding of what a successful candidate looks like for a specific role. It also enables us to provide candidates with clear feedback against the established guidelines, which helps build trust when progressing or rejecting candidates during the hiring process. Finally, rejecting candidates can be stressful for the hiring team; clarity makes the difficult, but necessary, step of rejection easier on everyone.

3: Systematic bias reduction: To reduce bias as we evaluate candidates, we’ve implemented a number of evidence-based practices. We remove demographic information from all incoming CVs, similar to blind orchestra auditions, in order to prevent ourselves from favoring people with similar social or educational backgrounds, age brackets, or special interests, to name just a few. This follows from research on unconscious discrimination in resume screening, which shows how identical skillsets with different names and profile pictures receive different ratings by evaluators. 

In our first year, this meant manually copying information from CVs into an Excel spreadsheet. In following years, we streamlined this process through a dedicated submission page on our website that automatically generates comparable profiles organized by relevant dimensions while masking personal identifiers. 

Additionally, those evaluating applications review groups of up to 30 shortlisted candidates in a randomized order. The aim is to prevent the ranking effects that can compromise the quality and fairness of evaluations.

4: Structuring team decision-making: Structured team decision-making is crucial for accurate evaluations and for maintaining the integrity of the process for those doing the hiring in groups. First, each team member scores qualified candidates independently on predefined metrics across all four dimensions using a five-point scale. Importantly, each team member submits their assessments before any collective discussion to prevent anchoring and confirmation bias. 

We found that team members who participated in hiring developed genuine ownership over creating an inclusive, high-performing environment.

When teams convene, aggregate scores drive the conversations, not the hierarchies or personalities within the hiring team. Every team member’s score, regardless of seniority, carries equal weight. This helps the team avoid potentially subjective debates and grounds their discussion in the evidence gathered in the application. If there are outliers, hiring teams discuss why different evaluators arrived at different ratings for the same candidate, rather than dismissing them.

5: Designing interviews and assessments: Our goal during interviews and assessments is to better understand candidates’ performance and behaviour in as realistic of a context as possible. And as with team decision-making, a clear structure and consistent criteria is essential to evaluating and comparing candidates accurately. In our hiring process, candidates participate in two interviews conducted by two individual assessors, and they complete one on-site case-study assessment with our team. We combine the two methods to increase inter-rater reliability and reduce bias in evaluation. 

Our interview questions focus on a candidate’s actual behavior. We ask things like: “Give me an example when you successfully contextualised and translated [a scientific finding or theory] into business practice?” or “How would your former work colleagues describe your approach to [a specific task, e.g., engaging stakeholders in workplace experiments]?”

The case study assessments involve real challenges candidates would face if hired. This provides us with a more realistic look at their performance, in addition to allowing us to observe how they problem solve, incorporate feedback, and communicate.

Crucially, interviewers and case evaluators maintain independence during the process.   Interviewers score interviews independently and don’t discuss candidates’ informally between sessions. For the case studies, evaluators use predefined metrics, so that they are evaluating candidates on the same criteria, and they only compare ratings after all assessments of each round are completed.

6: Building trust through systematic communication: Throughout a candidate’s entire journey—from their application submission through our final decision—we make communication clear and systematic. 

At the start, we share the detailed steps of our hiring process with all qualified candidates and commit to honoring timelines consistently. After each round, we update candidates on what happened, what we decided, what to expect next, when candidates will hear from us, and what we need from them. We add valuable information about how many people applied and how many progressed to each of the rounds. (See an example email below.)

For candidates that reach the final round, but who don’t receive an offer, the managing director provides them with personal feedback, transforming rejection from a dead end into a development opportunity. 

This approach honors investment by both candidates and our recruiting team, recognizing that every interaction shapes people’s perceptions of us and our market reputation. By treating each touchpoint as an opportunity for transparent, respectful engagement, we aren’t just evaluating candidates rigorously; we are building relationships founded on trust that can endure regardless of whether they join the company at that time or not.

What were the results?

How did a better hiring process change our company? 

First, and perhaps most importantly, we were able to build a highly qualified and diverse team of behavioral scientists. Every new team member successfully completed their probation period. Our retention rates surpassed industry standards. Moreover, we achieved  gender balance across all hires since 2022, not through quotas and targets but through systematic bias reduction and standardized evaluation.

We found that team members who participated in hiring developed genuine ownership over creating an inclusive, high-performing environment. We saw them grow into culture carriers, responsible not just for selecting candidates but for embodying the values we promoted in candidate onboarding and retention.

New team members arrived already aligned with these cultural norms, having experienced them firsthand as candidates. Most significantly, the trust established during hiring set expectations that permeated our daily operations. Every team member expects and provides the transparency, fairness, and structured communication they experienced as candidates. This created a self-reinforcing culture where behavioral science principles weren’t applied only to recruiting but became the foundation for how we work together. 

Our hiring process surpassed our expectations in boosting our reputation. We found that rejected candidates became voluntary ambassadors. Messages like the one we received on LinkedIn became part of an emerging pattern, not an exception. 

Every organization claims to put people first. The ones that succeed are those whose processes prove it.

We recognize, of course, that these are our own results from a small consultancy making eight full-time hires over five years. Our sample size is limited, and our industry is specific. But our approach represents what’s possible when how you hire is treated as seriously as who you hire. 

We do also recognize that this approach requires more upfront investment in time and human resources. Five years in, it takes four people about one day to define the role and criteria in detail, as well as select and onboard the hiring team. It takes another day to respond and provide feedback to all candidates. 

We think this investment is worth every minute, especially when we contrast the positive spillovers on team culture and cohesion with hidden costs of traditional hiring: rehiring for poorly filled roles, trouble retaining talent, damage to an organization’s brand, and erosion of culture. We believe this structured approach pays for itself within a year through reduced turnover alone, not counting the reputational benefits and the value of ongoing engagement with former candidates.

Talent represents the most valuable asset of any firm, and candidates evaluate employers as rigorously as vice versa. AI threatens to further depersonalize human interactions. To thrive in an era that threatens to erode human interactions, organizations must create consistently valuable experiences. 

The competitive advantage isn’t in fighting harder in the “war for talent” but in building systems that cultivate trust, performance, and, with it, an employer brand at scale. Every organization claims to put people first. The ones that succeed are those whose processes prove it.

The post The Trust Equation: It’s Not Just Who You Hire, It’s How You Hire appeared first on Behavioral Scientist.

Like most people, you probably feel like you have too much to do and that your organization actively conspires against you ever getting it all done. There are too many items on your to-do list, and thanks to endless interruptions and crises, you spend more time reprioritizing them than getting them done. This situation likely afflicts not just you but your entire company; deliverables are late, projects are behind, and everyone seems to be constantly scrambling to meet their objectives.

If life in the modern organization feels fundamentally misguided, you are correct. Many studies have shown that taking on too much work—a phenomenon called overload—hurts both individual and organizational performance. Psychologists have long understood that too much stress reduces the ability to perform and learn.

Similarly, studies of research and development teams show that taking on more projects than they can handle causes people to regularly switch from one task to the next depending on which project is currently getting the most pressure or is facing an imminent deadline. Task switching degrades productivity and leads to error. One recent study suggests that people spend almost 10 percent of their week just switching between different apps on their computers and phones.

Far too many leaders mistakenly believe that their organizations thrive under constant pressure. They reward managers who deliver under duress and revel in a culture in which the path to the top involves a huge to-do list, long hours, and a willingness to cut corners to get things done. At one large company we worked with, employees regularly told the story of the CEO berating a member of his leadership team for not answering email over the weekend. “If you don’t want to work on weekends, I’ll find someone who will,” he allegedly barked.

The tendency to overload represents perhaps the largest gap between research and practice in the world of management.

The tendency to overload represents perhaps the largest gap between research and practice in the world of management. Psychologists have long argued for the idea of optimal challenge, the notion that humans perform best when they are challenged just a bit beyond their current abilities. With too little challenge, we get bored and tend to fill in the gaps with our own projects or other all-too-available distractions; conversely, facing a challenge far beyond our current ability creates the frustration and anxiety of being held accountable for more work than can possibly be done. And many management scholars have shown the benefits of matching the amount of work in the system to the current capability. But despite the science, little has changed in practice.

We have come to realize that the propensity to overload work systems is deeply rooted in a fundamental misconception about how work systems perform. Ask a manager who is overloaded what they need, and the answer is always more budget and more people (or its complement, fewer things to do). This answer presumes that overload is solely a capacity problem. But that’s only part of the story.

Being overloaded changes how people manage their work, creating inefficiency and frustration due to constantly changing priorities and task switching. Eliminating these inefficiencies improves productivity to the point that capacity is often no longer a problem. In our experience, until you fix the design, adding additional resources will be largely ineffective. Adding more people without first fixing the design usually makes small problems into major ones. An already ineffective and chaotic system then becomes even bigger and more inefficient.

Being overloaded changes how people manage their work, creating inefficiency and frustration due to constantly changing priorities and task switching.

Dynamic work design focuses on getting the work to flow. A system in which work moves quickly is flexible and its direction can easily be adjusted to accommodate a changing world. An overloaded system, in contrast, is slow and difficult to redirect. And the more overloaded it is, the longer each email, product, or patient will take to work its way through.

To understand the process of learning to regulate for flow, imagine you control the number of cars that enter a long stretch of multilane highway. Your job is to maximize the flow of cars. It’s easy to see that if you allow only one car to enter, its entire journey will be unimpeded, going as fast as physics and safety allow. Something similar often happens when organizations face crises. Only mission-critical projects are allowed into the system, and they flow to completion without interruption. Such a strategy would not, however, optimize the flow of traffic.

As every driver knows, adding more cars only helps get more people where they are going up to a point. Once that threshold is exceeded, each additional car slows progress and reduces overall output and increases travel time. In the extreme case, putting the maximum number of cars on the road—every inch of concrete on the highway is now covered by a car—creates a huge traffic jam and nobody gets anywhere.

Once the traffic jam emerges, the overloaded highway becomes a prime target for expediting, further limiting performance. We are all comfortable getting out of the way for an ambulance, even if it slows our trip home. But how would you feel, as Nelson once witnessed, about a bus full of kids going to a hockey game with a police escort, thanks to one of the dads being the mayor? Everybody hates the person who works around the rules and drives in the breakdown lane, inducing others to make similar departures. Something similar happens in knowledge work. When a system is overloaded, critical work is increasingly expedited, which creates more congestion, slows the progress of every other task, and creates the need for more expediting.

Prior to the lean revolution in manufacturing in the 1990s, when companies started to focus on workflow, expeditor was a recognized job title in many factories. Expeditors started each day with a list of items that were at risk of missing their shipment dates and then hand carried those jobs through the process, interrupting other work already underway at every step, to make sure they got done. Though expediting has largely been purged from the world of manufacturing, it remains almost universal in work done off the factory floor.

Consider your own work. What do you do when you have an important task that requires another person’s input and you don’t want to risk it being lost in his inbox? You send a text, make a phone call, or stop by his office. Though you feel proactive, an extra text, phone call, or visit both takes your time and reduces the recipient’s productivity by forcing him to switch from whatever they are working on to what you want them to do.

No one would ever ask a cardiac surgeon to stop an operation midstream because something supposedly more important popped up—we know this is a bad idea—yet we ask knowledge workers to do something similar multiple times a day because the immediate impact doesn’t seem onerous.

Implementing the regulate-for-flow principle means acting like our hypothetical highway planner. To optimize the flow of traffic, you continue to let cars on the highway until you start to notice congestion. After that, you regularly monitor the system for emerging traffic jams and adjust the flow of new cars accordingly.

In knowledge work, if the work is flowing smoothly, you can safely let an additional task into the system. If, however, congestion starts to emerge and key tasks wait for resources (akin to waiting for a clear lane), then the system is probably getting overloaded. The key is to learn your way into the optimal mix of speed and cars on the highway that will deliver what your organization needs.

The key is to learn your way into the optimal mix of speed and cars on the highway that will deliver what your organization needs.

Spotting congestion in knowledge work can be harder than spotting it on the highway, so the regulate-for-flow principle can take different forms, depending on the nature of the work. In each case, though, it boils down to identifying a set of vital signs for how the work is moving.

Monitoring vital signs can be as straightforward as watching the work move (think visual management). Or it can require monitoring metrics like cycle time (how long is it taking to get a piece of work done versus how long would it take if there were no other piles of work waiting?) or staying in close touch with key people in the process and assessing how often they are switching tasks or are forced to expedite. Alternatively, if one person is perpetually overloaded, then she may be the bottleneck.

Developing ways to spot congestion is also critical in identifying which problem to solve next. Returning to the highway analogy, if every time you let more cars in, the backup occurs in the same place, then you know there is a problem with that part of the highway. Similarly, if congestion in your product development process always emerges at the testing phase, then it’s time to do a deep dive into that part of the process. In contrast, when a system is completely overloaded—it’s a traffic jam—then start at the customer and work backward, clearing congestion as you go. You will generate results more quickly and fixing one bottleneck after another allows you to gradually work off the backlog and increase the flow at every step.

Regulating for flow is a fundamentally dynamic process. Doing it effectively requires constantly evaluating the state of the system and making ongoing adjustments to learn your way to the right amount of work.

Excerpted from There’s Got to Be a Better Way: How to Deliver Results and Get Rid of the Stuff That Gets in the Way of Real Work by Nelson P. Repenning and Donald C. Kieffer. Copyright © 2025. Available from Basic Venture, an imprint of Hachette Book Group, Inc.

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A fairness myth we frequently encounter is what one of the leading decision-making and negotiation scholars, Max Bazerman, calls the “mythical-fixed-pie mindset”: the often-incorrect assumption (also called zero-sum thinking) that one person’s gain has to equal another person’s loss, or that there is only a finite amount of resources to go around. 

In some cases, this is, of course, true. The number of people who can win in the Olympics is finite; the number of spots available in most legislatures is fixed; and when haggling with a merchant, a lower price for you equals less money for them. 

But in most workplace situations, more issues are at play, allowing for creative solutions where all parties end up being better off. And the more we are stuck in the fixed-pie mentality, the harder it is to spot the opportunities to expand the pie for everyone’s benefit.

While genuine zero-sum situations are relatively rare, zero-sum beliefs are not, and much research suggests that our zero-sum beliefs can be limiting. In three studies, men’s zero-sum thinking increased when they were exposed to information highlighting the gains women have made in education, politics, and the workplace over the last century. This, in turn, increased sexism and lowered men’s support for workplace policies promoting gender equity. 

Another study supporting these findings showed that people’s zero-sum beliefs about gender at work tend to reflect broader zero-sum beliefs about women’s gains coming at the expense of men across different domains, including politics and family life; such beliefs were more pronounced among men than women.

And yet the United States economy has benefited tremendously from being able to draw from a larger talent pool today than, say, in the 1960s, when 94 percent of all doctors and lawyers were white men. 

It is an unequivocally good thing that today, all law school graduates can go on to practice law—in contrast to Sandra Day O’Connor, who would go on to become the first female justice on the U.S, Supreme Court but who could only get a job as a legal secretary when she graduated from Stanford Law School at the top of her class in 1952. 

Up to 40 percent of economic growth per person post-1960 is due to a better allocation of talent. As former U.S. commerce secretary Gina Raimondo stated in the context of America’s infrastructure workforce, “It’s an economic necessity to figure out how to attract, train, and retain women into these fields if we’re going to get the job done.”

The more we are stuck in the fixed-pie mentality, the harder it is to spot the opportunities to expand the pie.

As another example of how fairness is not about taking away from one group and giving to another, consider the Americans with Disabilities Act of 1990, which prohibits discrimination on the basis of disability and aims to ensure that people with disabilities have the same opportunities as anyone else to participate in everyday life. 

Among many provisions, the law includes regulations about making public spaces more accessible for people with mobility challenges, such as by having elevators, installing access ramps, and lowering curbs. At the time of the law’s introduction, approximately 4 percent of the U.S. adult population—over 8 million people—had a mobility disability and directly benefited from these changes. Today that share has grown to more than 12 percent, or approximately 30 million people. 

But actually, everyone benefited. Have you ever taken public transportation with a suitcase in tow? Pushed a child in a stroller or wheeled your groceries in a cart? Walked your bike while out and about? A redesign of the physical environment that was inspired by the experiences of people who differed in their ability turned out to make life better for everyone because it built fairness into how we do things.

There is extensive evidence in behavioral economics and social psychology of the positive impact fairness has on cooperation, trust, performance, and many more desirable outcomes. So if we finally figure out what a truly fair employee experience looks like and if we move from lamenting the lack of progress on fairness to actually making some, it will be a win-win for the world: a world where talent—no matter what it looks like or where it comes from—can flourish; a world where each of us can interact with and learn from our fellow humans; and a world where organizations can benefit from a historical wealth of previously overlooked talent. 

Making work fair can expand the pie for all of our benefit.

Note that while we want to debunk the myth of a zero-sum world, this is not a call for making the business case for fairness. As organizational behavior scholars Robin Ely and David Thomas write in a Harvard Business Review article aptly titled “Enough Already with the Business Case,” the business case sends the message to traditionally underrepresented colleagues that they are only valuable if they improve the organization’s bottom line. 

More practically, the business case can be counterproductive. Oriane Georgeac and Aneeta Rattan found that the business case—and, to a much lesser extent, the fairness case—can cause underrepresented groups to anticipate a lower sense of belonging in the companies. As a result, LGBTQ+ individuals, women in STEM, and African Americans were less interested in joining those organizations. 

The authors summarize: “Don’t justify your commitment to diversity at all. . . . If you don’t need an explanation for the presence of well-represented groups in the workplace beyond their expertise, then you don’t need a justification for the presence of underrepresented groups either.”

It is only the people not in power today . . . who are asked to make the case for why they deserve to be there.

We had an epiphany about this while watching the documentary RBG, in which the second woman on the U.S. Supreme Court, the late justice Ruth Bader Ginsburg, describes her experience of starting Harvard Law School as one of only nine women in a class of over five hundred men in 1956. At a dinner the dean hosted for these nine women, recalled Ginsburg, “He asked each of us to stand up and tell him what we were doing taking a seat that could be occupied by a man.”

The realization hit us like a lightning bolt: Men have never been asked to justify their presence in any position of power. It is only the people not in power today—including women and members of other underestimated groups—who are asked to make the case for why they deserve to be there. This is fundamentally unfair and fundamentally wrong. 

Until the day that someone makes the business case for why white men, who in the United States constitute 30 percent of the population, should occupy nearly 90 percent of Fortune 500 CEO positions and approximately 60 percent of both the U.S. Congress and corporate leadership, we will take the rationale for fairness as a given. As scholars Sonia Kang and Sarah Kaplan powerfully note: “To make progress in achieving gender equality, we must declare the discussion on whether and why we should pursue equality to be over.”

We could not agree more. Making work fair is unequivocally the right and smart thing to do—and a win-win for us all.

From the book Make Work Fair: Data-Driven Design for Real Results By Iris Bohnet and Siri Chilazi Copyright 2025 by Iris Bohnet and Siri Chilazi. Reprinted by permission of HarperCollins Publishers.

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The post How Zero-Sum Beliefs Get in the Way of Fairness appeared first on Behavioral Scientist.

Occasionally, you might discover that your actual work is making a mockery of your mission. To see how that could happen, consider the experience of Ryan Davidsen.

On July 15, 2022, Davidsen bought a new pickup truck. A Ram 1500. To celebrate, he planned to take it on a camping trip the next weekend.

Then, a few days after he drove it off the lot, the survey shakedown began.

The dealership texted him on July 20 asking for a rating from 0-10 on the “overall buying experience.” He texted back a 9.

On July 21, Brittany—a customer care assistant—emailed him, asking, “Is there anything that we could have done better?” The next day, a Friday, she sent another email, wondering, “How could we have made your experience more pleasant?” The next Monday, undeterred by his nonresponse, she sent a third email: “There is just one thing that we would like to know. How could your purchasing experience have been better?”

That same Monday, another person from the dealership, Megan, texted him to see if he needed anything. And his salesperson texted too. (You get the sense that if a loved one died, the salesperson would send flowers and a survey.)

On Wednesday, the dealership texted him again, suggesting “our team would really appreciate your positive responses.”

Davidsen could sympathize with the dealership’s desire for feedback, because at the time he was in charge of customer experience for a health care software firm. He too had to keep tabs on customer satisfaction. He could relate.

So he took the time to fill out the survey. Gave some high scores, some low, reflecting his experience. He left thoughtful comments so the dealership could understand his ratings. He also replied to Brittany, who’d been desperate for that “one thing” that would have improved his experience.

He wrote, “I liked the people I worked with—my salesperson and my finance specialist. They were easy to work with and made it an enjoyable experience.” Then he elaborated on what could have gone better:

“Things I didn’t like was the experience of seeing all of the additional items that get added to the final purchase price that I then have to negotiate out or negotiate down with a lot of back and forth as everyone had to check with a higher up while I wait. I hated that.”

Still, he concluded on a positive note: “I’d buy from you again and intend to stick with this dealership for service. Thanks for asking.”

He never heard from Brittany again.

Within hours of submitting his survey, his salesperson texted him again: “Hey Ryan did I do anything wrong? I got a low score.” Davidsen wrote back “Not at all—had nothing but great things to say about you. I hope they let you read it.” The salesperson replied: “I understand that but every single score mattered.”

Since then, Davidsen said, the dealership “lost” the paperwork on the bed cover he bought with his truck. “Two months later I still don't have a bed cover and every text and email I sent have gone unanswered, which leaves me feeling it is ‘payback’ for the fair and helpful survey I sent,” he said.

Let’s lift our view of Davidsen’s horror story to the systems level. Because this is a story of change gone wrong. Badly wrong.

The leaders at Stellantis (the multinational that owns Dodge, Chrysler, Jeep, and Ram) were surely not intending to produce stories like this one. No doubt their original intent was pretty respectable: We want to create a great car-buying experience for our customers! That mission is big, long-term, and somewhat diffuse. What kind of shorter-term goal might serve that long-term mission? Boosting customer-satisfaction scores.

And my guess is that the team at the dealership where Davidsen bought his truck did, in fact, make progress toward that short-term goal. Probably their customer-satisfaction scores were outstanding! But their success was due not to their passion for improving the customer experience but rather to their prowess at browbeating customers into providing falsely inflated scores. That’s a sad kind of excellence.

And for even sadder excellence: When Davidsen was still in the process of buying his truck, the sales rep brought over a framed version of the survey that Davidsen would eventually be asked to fill out. The sales rep walked him through every question on it and told him what the “right” answer to each question should be! For instance, on the question “How would you rate your sales consultant overall?” he instructed Davidsen to give him a 10 out of 10. The sales rep also thoughtfully gave Davidsen a laminated take-home copy of the survey, with right answers filled in, for further study and reflection.

* * *

You don’t want to fall into the dealership’s trap of relentlessly chasing a goal and triumphantly moving the numbers only to discover (whoops) that it was all misdirected energy. One simple way to avoid that misalignment—a goal that’s inconsistent with the real mission—is to ask a simple but powerful question: What’s the goal of the goal?

The British advertising guru Rory Sutherland gave a great example of this idea in action. In 2007, Eurostar had finished upgrading the rail route from London to Paris, shaving the travel time from 2 hours 35 minutes to 2 hours 15 minutes. The total price tag was roughly 6 billion GBP, and the goal of the investment had been to speed up the route. Mission accomplished. But what was the goal of the goal?

If the goal, ultimately, was to make things better for the passengers, was that investment well spent? Sutherland was dubious. “For 0.01 percent of this money, you could have put wi-fi on the trains, which wouldn't have reduced the duration of the journey, but would have improved its enjoyment and its usefulness far more,” he said in a 2011 speech. “For maybe 10 percent of the money, you could have paid all of the world's top male and female supermodels to walk up and down the train handing out free Château Pétrus to all the passengers. You’d still have 5 billion pounds in change, and people would ask for the trains to be slowed down.”

We can get so fixated on a goal that we miss the bigger picture. And when we lock into a particular goal too quickly, we blind ourselves to alternate routes forward that might have been better and easier.

When we lock into a particular goal too quickly, we blind ourselves to alternate routes forward that might have been better and easier.

In early 2023, I studied a group of people as they set New Year’s Resolutions and began to work toward them. Marisa Lavars, an Australian mother of two, had initially declared this as her goal: “I’d like to get fit, or fitter. To be able to go about my daily activities without being breathless and to lose a bit of weight so I can fit into my pre-covid clothes.”

Lavars had significant health challenges. She was struggling with heart problems triggered by COVID, and as if that weren’t enough, she had recently been diagnosed with breast cancer. Her reference to being “breathless” was no exaggeration: Everyday activities like showering could exhaust her. Her heart rate sometimes spiked to 180 even when she was lying down.

After Lavars submitted her resolution to get fitter, the survey prompted her to consider why that goal was important to her. (In other words, what was the goal of her goal?) Here’s what Lavars wrote: “Absolutely my kids. I want to be the best Mum I can be for them and part of that is being healthy, but it also means modelling for them living my best life.”

Then, the survey asked her: What are 10 different ways you could achieve or approach that aspiration, other than by following your initial resolution? Suddenly, her answers changed dramatically. Some of her alternate pathways included:

• “Things have been pretty serious for us this past year. I could find some ways to bring back some fun into our lives as fun at times has been in short supply.”

• “We used to have some pretty locked-in routines in the evening, where things stopped and we did some reading, writing, and maths and then I would read to them. This has been pretty pared back or hasn’t occurred at all. This would be important to get back to doing.”

• “I would like to bring back music into our lives.”

Lavars realized, immediately, that these ideas would get her closer to “success” than the fitness-oriented goals. “I already feel a sense of light-heartedness through going through this process,” she wrote. “It feels more connected to the why.”

You don’t want to fall into the trap of relentlessly chasing a goal and triumphantly moving the numbers only to discover (whoops) that it was all misdirected energy.

Within a few months, she had dreamed up a variety of ways that she could create moments with her twin 7-year-old boys, despite her poor health. She’d bought some rocking chairs for the veranda. They would sit outside and rock and watch their guinea pigs play. She bought the Harry Potter 6,000-piece LEGO set—they’d been opening one bag of the kit each week.

She had also started a tradition of playing board games. Even at her worst—lying on the couch with an ice pack, trying to bring her heart rate down—she could still participate. She’d call out, “Who’s gonna roll the dice?” She’d tease, “Did you cheat?”

“Even if I’m vomiting, I’ll still be playing the bloody board game,” she laughed. “It just feels really doable. And it’s great for the kids too—they’re just so much more settled because they know that mum is still mum.”

She looks back with amusement on her initial resolution. She had been thinking, “I have to get fit, because I have to spend more time with the kids. . . . I look back now and go, I would never have actually done that, or I would have felt bad about it.”

By reconsidering her first goal—and contemplating instead the “goal of the goal”—she was able to ensure that the progress she made amounted to something meaningful for herself and her kids.

* * *

Achieving clarity on the way forward is not an incremental victory. It is transformative. It can mean the difference between stuck and unstuck.

A group of federal government leaders experienced this transformation several years ago when they rethought the goal of a program that served people with disabilities, including veterans. Some context: Anyone with a “total permanent disability” can, by law, have their federal student loans discharged. But thousands of veterans didn’t take advantage of the program. This was a disappointment to many government leaders, whose goal was simple: Make it easy for veterans to apply for the benefits they deserve.

What was holding back participation in the program? To some extent it was knowledge: Many simply didn’t realize they were eligible for forgiveness. Others got derailed by the cumbersome application process.

The stakes were high: Some of these borrowers were actually in default—potentially having their social-security-disability payments garnished to make loan payments. The government was reaching into their pockets to claim money for loans that they shouldn’t have owed!

So what could be done? In 2016, a team at the Department of Education thought: Rather than make the borrowers responsible for discovering this benefit, let’s proactively tell them about it!

They hatched a plan that led them to compare the databases at several agencies, including the Department of Education and the Department of Veteran Affairs (VA). The Department of Education database could tell you: Who has student loans? The VA database could tell you: Which veterans are permanently disabled? Anyone who matched both databases was eligible for a loan discharge.

By mid-2018, the VA and the Department of Education had identified around 50,000 veterans who qualified for forgiveness. (Thousands of them were in default on those loans-they-didn’t-really-owe.) Letters were sent to all of them, informing them that they were eligible for forgiveness and specifying how to claim it. So far, so good.

Ah, but then came another problem. The notification sent to veterans came under the letterhead of a private organization: the student loan servicer. The letter directed people to file their applications at a URL called “disabilitydischarge.com.” To recipients, it smelled a little fishy. Uh, why isn’t this coming from the VA? Why isn’t it a .gov address? Is this story going to end with someone absconding to Cabo San Lucas with the remainder of my checking account?

As one government insider told me, “Veterans just wouldn’t do it. They were scared. I mean, it was every red flag you could possibly imagine.”

If your goal, in their shoes, was to encourage veterans to file their applications for forgiveness, how could you make that happen? Well, maybe send a less suspicious letter. Or set up a .gov website. Maybe some tweaks would ease the veterans’ concerns.

But in 2019, the agency leaders stopped trying to iterate a broken process and took a hard look at the goal of the goal.

What’s our goal? To help veterans apply for the loan forgiveness they’ve earned.

And what’s the goal of the goal? To improve their financial security.

What are alternate pathways for achieving that goal, other than having them apply for forgiveness? Um, well, why don’t we forget about the whole application process? Why do they need to apply at all? We can figure out who they are! Let’s tell them that we can just forgive their loans. And then after their “okay,” we’ll let them know it’s done. (They needed an “okay” first, because there was a tax issue in some states: Forgiven loans might be treated as income, meaning that the vet would get an income tax bill for the forgiven loan. That’s why the loan couldn’t be forgiven automatically.)

To make sure your work aligns with your mission, ask yourself, “What’s the goal of the goal?”

“There was a recognition that the letters clearly weren't working and there was a big push to make this automatic,” a senior official at the Department of Education said. “It would be good for the borrowers but it could also be good for us—it could save us the time of processing a bunch of applications, sending them back again if they need to be corrected. Could this be a win-win for everybody?”

It was a radically simple idea, but it was no easy feat to implement. Just getting permission from different government agencies to compare databases—essential for forgiving loans—required a blizzard of paperwork that had taken months and months to push through.

The ripple effects of this policy shift have been broad and profound: Tens of thousands of disabled veterans—many of whom, again, had been in “default”—had their financial lives transformed. One disabled Navy veteran saw her loan balance go from over $72,000 to … zero. “I felt immense relief,” she told a reporter. “I mean, I cried about it. I ugly cried about it.”

The program was so successful, in fact, that it has since been expanded to other situations where people have earned (but not claimed) loan forgiveness.

This success was earned by a set of government leaders who refused to treat their original goal—increasing loan forgiveness applications—as sacrosanct. After reconsidering the work, they hatched a new (and superior) plan.

You can do the same. To make sure your work aligns with your mission, ask yourself, “What’s the goal of the goal?” Answering the question can you help uncover better, simpler routes to the destination you care about.

Adapted from Reset: How to Change What's Not Working by Dan Heath. Published by Avid Reader Press. Copyright © 2025 by Dan Heath. All rights reserved.

Disclosure: Evan Nesterak and Heather Graci of the Behavioral Scientist served as editorial consultants on Reset. Behavioral Scientist’s Visual Editor Liam Speranza designed the cover of Reset.

The post What’s the Goal of the Goal? appeared first on Behavioral Scientist.

Changing entrenched work culture is hard. I had a front-row seat to a project that tried. In 2018, the behavioral design firm ideas42 kicked off a project to better understand what drives people to overwork and to test interventions that would improve individual work-life conflict and well-being.

My colleagues at ideas42 set out to talk to employees at nonprofit organizations who wanted to reduce work-life conflict. Nonprofits are often classic overwork and burnout cultures. People work “hero hours” because they’re passionate about the issues and see the work as mission-driven. Nonprofits also tend to be woefully understaffed and underresourced, so people aren’t paid much and don’t have a lot of support, which intensifies the workload. And if the work, barring radical change, likely won’t be done in a lifetime, it’s difficult to know when to call it a day.

One thing struck me immediately. In conversation after conversation, workers said they spent their days being super busy, rushing from one meeting to the next, jumping on and off the phone, and plowing through their email. It was only at what should have been the end of the paid workday that they realized they hadn’t gotten to the one big thing they really needed to do. Their work hours were constantly interrupted and filled with busywork.

Researchers have found that desk workers in an office setting tend to be interrupted about every three minutes. And after that colleague has dropped by or we’ve switched screens to check email, texts, social media, or a pinging notification, it can take, on average, twenty-three minutes and fifteen seconds to get back to where we were. Over and over and over throughout the day.

Then there are the meetings. One survey found that executives spend, on average, twenty-five hours a week in meetings, half of which could disappear without any negative impact. Those lower down the corporate ladder spend about ten hours a week in meetings and say 43 percent are a waste of time. But they go because they have to, or they fear they’ll miss out, or they want to show their managers how busy and committed they are. This means huge swaths of the workweek are a huge waste of time and money. One Bain and Company study found that after accounting for meetings, excessive emails, office chitchat, or unproductive conversations and low-value administrative tasks—all unproductive busywork—the typical middle manager has only six and a half uninterrupted hours a week to do their actual work.

I have come to think of work in three ways. There’s the “real” work—the tasks and outcomes that create value for the organization and give employees a sense of meaning and pride. There’s the “work around the work”—all the emails and meetings, some necessary, many not, that can consume entire days. And then there’s what I call the “performance of work”: sending late-night emails or Slack messages, showing up at the office or online early and staying late, shaming those who take lunch breaks, giving the appearance of super productive busyness when you’re really wrapped up in low-value tasks or focusing on email rather than making progress on a big project. Authors Anne Helen Petersen and Charlie Warzel call the phenomenon “live-action-role-playing the job.” In overwork work cultures, those who appear to perform well are often the ones who are rewarded.

It was only at what should have been the end of the paid workday that they realized they hadn’t gotten to the one big thing they really needed to do.

Yet when we’re busy and have that high-octane, panicked feeling that time is scarce—what one participant called the “sustained moment of hecticness” through the workday—our attention and ability to focus narrow. Behavioral science researchers call this phenomenon “tunneling.” And like being in a tunnel, in that heightened state of time scarcity and busyness, we’re only able to concentrate on the most immediate and often low-value tasks right in front of us. (Research has found we lose about 13 IQ points in this situation.)

“If you’re in this firefighting state of time pressure and tunneling, you’re not making time to meet long-term goals. You’re not dealing with any of the root causes that led to the firefighting in the first place,” said Matthew Darling, then ideas42 vice president and project lead. “The tendency is to do the stuff that’s easy to check off. That’s all you have the bandwidth for.” So the high-value work spills into what should be time off.”

After hearing from workers about being caught up in busyness culture, my ideas42 colleagues started thinking about a new “mental model” of an ideal worker with work-life balance. To paint this new picture, ideas42 designers at the time Dan Connolly and Uyhun Ung worked with participants to imagine a workplace that valued work-life balance and how that would change the social signals people sent. At work, all people see are others working. When they see late-night emails or texts, they often assume that their coworker or boss has been working all day and night without interruption, when in fact they might have been walking the dog or having dinner with their families. That life outside work doesn’t register because they don’t see it. (Often people don’t want to share their lives outside work with coworkers and bosses to preserve the busyness myth that they’re always working.)

“You end up miscalibrating,” Darling explained, thinking that people are working more than they actually are, so you automatically think you have to do the same to keep up. For an ideal work-life balance workplace, designers imagined leaders who were open about taking lunch breaks, working flexibly, going on vacation, and sharing more about their lives and families outside of work.

Having envisaged this new type of leader, my colleagues then designed, implemented, and tested practical interventions focused on four key pain points: long hours, endless and often pointless meetings, the guilt people felt about vacations (which they often didn’t take), and email. The goal of the project was to help organizations take a more thoughtful and proactive approach and to pilot different types of interventions that could help their employees find better work-life balance.

Humans already have a tough time estimating how much time and effort are needed to accomplish things. The busyness imperative only exacerbates the tendency to underestimate and overpromise. So one intervention asked workers to schedule big blocks in their calendars for their most important uninterrupted work, rather than, as many of us do, expecting it to just happen at some point during the workweek. Then the designers encouraged workers to create open space or slack in their calendars every week so that, instead of getting to the end of the day or week without having done that big project, they had a built-in cushion in case they had underestimated the time or an unanticipated emergency cropped up. “You almost always need a lot more slack than you think you will,” Darling explained, “and it is actually markedly important for doing good work.”

My colleagues designed interventions focused on four key pain points: long hours, endless meetings, the guilt people felt about vacations, and email.

Intentional scheduling that you share with coworkers makes it much more apparent that calling a meeting involves a trade-off. What are you not doing because you’re being asked to go to a meeting? And is the meeting the better use of time? The intervention also called for organizations to design and enforce better “meeting hygiene.” Meetings account for about 15 percent of an organization’s time, a share that has increased steadily in recent years. Many are not run well and are a waste of productive time that adds to the busy overwork culture. In the intervention, an agenda would be circulated before every meeting listing clear goals to keep things short and focused on discussion, debate, and decisions. Those calling the meeting were encouraged to be judicious about who needed to be in the room or on the call rather than putting out a blanket invitation, which can wind up being a time suck and an impediment to getting the most important work done. One study, for instance, found that one organization’s routine weekly executive meeting cost a total of three hundred thousand hours a year.

To encourage time for rest and vacation, some organizations experimented with nudging workers to put their summer breaks on their calendars as early as January or February, when the summer months were likely to be clear, rather than a few weeks beforehand, when calendars are cluttered with meetings and obligations. Planning early would force teams to map out how to meet deadlines and delegate tasks so people would no longer feel compelled to work on vacation. My colleagues helped design vacation prep checklists, an activity that recognizes the work involved in planning time off and gives team members the tools to prepare so that they can all successfully unplug. They encouraged organizations to experiment with allocating two vacation “transition days”—one before vacation and the other the first day back in the office, with the only expectation being that they use the time to disconnect and reconnect with work.

One organization adopted a “vacation roulette” intervention. The HR team found every person who hadn’t used their vacation days over a 90-day period and sent them a note reminding them of their vacation balance and copying their manager. They then sent them an invitation to take a random Monday or Friday off and signed the note, “From your vacation fairy godmother.” Often, the managers would encourage workers to take a break. Another organization experimented with closing down between Christmas and New Year’s. That changed the conversation, one participant told me, from the usual snarky, “Going on vacation? Must be nice for some,” to a more welcoming, “Tell me about your break and I’ll tell you about mine.” Research in Sweden found that when everyone takes vacation at the same time like that, there’s less guilt, a big boost to mental health, and the entire organization—or in Sweden’s case, the entire country—benefits from “collective restoration.”

My ideas42 colleagues also designed a tool to enable workers to pause their email inboxes in an “It Can Wait” campaign. They sent reminders to people at the end of each workday asking whether they had paused their inboxes. They sought to amplify the effort with a campaign to affirm people’s identities outside of work, sharing postcards showing happy people running, cooking, painting, reading a book to a child, and so on.

Some of these interventions showed promise. Vacation-taking interventions in particular led to encouraging results. For instance, during the “vacation roulette” intervention—where managers were copied on an email encouraging employees with high vacation balances to take a day off—participating organizations saw a boost in days off for over 20 employees, and the highest rate of vacation taking for India-based employees in 5 years. Some employees used the day to treat themselves to an experience they didn’t commonly make time for, like a visit to the spa. Others simply used the time to relax at home. “Having that day with family and coming back fresh helped me put things into perspective for the workshop I’ve been preparing,” one employee said.

The interventions that didn’t work were those aimed at individuals taking individual action to improve their personal work-life balance. Those that were more successful involved a change to the entire system.

Interventions to ease the pain of email were, perhaps not surprisingly, among the least successful. My then-ideas42-colleague Antonia Violante reflected on why some interventions had caught hold and others had not. The ones that didn’t work were those aimed at individuals taking individual action to improve their personal work-life balance. Those that were more successful involved a change to the entire system. “You can’t design a work-redesign intervention aimed at one person,” Violante concluded. “For it to stick, people have to have the assurance that it’s aimed at the whole culture.”

Leaders at many of the organizations had the best of intentions. They expressed a desire for better work- life balance, if not for themselves then at least for their staff. But they, too, were caught up in the prevailing busyness culture. Some were among the worst offenders, texting at 9:00 p.m., emailing over the weekend, even in the middle of the night, and rarely taking vacation. Many leaders knew they weren’t walking the talk: “We do a poor job modeling” work-life balance, they would say. Yet they appeared powerless to change themselves or their busyness cultures.

“One of the things I found odd is that people were acutely aware of how bad they are with work-life balance and they wanted to be better, but they’ve almost accepted it, almost like, ‘It is what it is,’” Violante told me. While many workers we spoke to were clearly frustrated by how work encroached on their lives, they were also conflicted by the strong norm to brag about their overwork as a sign of their dedication, like wearing a badge of honor. Some reported that though they saw the benefit of work-life balance, they routinely overworked to the point of exhaustion. “If we all hated our jobs," one participant told us, “it would be much easier to create work-life balance.”

Adapted from Over Work: Transforming the Daily Grind into a Quest for a Better Life by Brigid Schulte. Published by Henry Holt and Co. Copyright © 2024 by Brigid Schulte. All rights reserved.

Disclosure: ideas42 provides financial support to Behavioral Scientist as an organizational partner. Organizational partners do not play a role in the editorial decisions of the magazine.

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A copywriter, an art director, and an advertising account man are boarding a plane to go to a client presentation. Slightly implausibly, they open the overhead locker and a genie pops out. The genie says, “I've been stuck in that bloody overhead locker for 10 years. As a reward, I've only got three wishes, but I'll give you one each.”

The genie turns first to the copywriter and says, “What's your wish?”

“I think I'd like the life and the prose style of Hemingway. I'd like to live that life. I'd like to write that way. I can't think of anything better.”

And poof, the copywriter disappears.

The genie turns to the art director, “What would you like?”

“It's got to be Picasso. Think about it. The locations, the lifespan, the eye, the beauty. I'd like the life of Pablo Picasso.”

And poof, the art director disappears. And so the genie turns to the advertising account man and says, “What about you?”

He says, “I want those two guys back. I've got an important meeting in two and a half hours.”

In that joke, there's a kind of analogy for the present day, which is that we've sometimes allowed the urgent to drown out the important. The short-term consideration drowns out the long-term consideration. But in the process, rather like that account man, we may also be ruining it for everybody else.

I'll come to that thought, but I’ll start with something which I always share for a very good reason—I think it might save somebody’s life. The image below shows how extraordinarily subjective our perception of time is.

You're all familiar with the thing on the inside. That's a speedometer, denoted in miles per hour.

The thing around the outside is an interesting thing, which has only recently been, not invented, but publicized. It’s a paceometer, which shows how many minutes at that speed it will take you to go 10 miles. Assuming you're going 10 miles at 10 miles an hour, it'll take you an hour.

Now, most of you will have noticed something a bit strange about this, which is that whereas the numbers on the inside are completely regular, the numbers around the outside are anything but. If you're going 10 miles, or 20 miles, or 30 miles, something in that order of magnitude, there’s a big time-saving by going at 30 miles an hour rather than 20 miles an hour. In fact, you'll save a whole 10 minutes just by accelerating about 10 miles an hour.

On the other hand, if you accelerate from 80 miles an hour to 90 for example, or 70 to 80, you basically save a minute.

Some of you may have noticed this if you’ve got a GPS in your car. You’re driving on the motorway at 60, you realize you're going to be five minutes late for an appointment, so you welly it. And after driving at an insanely fast and dangerous speed for about eight minutes, you suddenly realize your arrival time has only improved by one minute.

This is fascinating. Because to a physicist, they're exactly the same. But when I present the information about time and distance in a different way, your reaction is now completely different. What it effectively says is: going quite a bit faster when you're going slowly is a really big gain. Going very fast when you're already going fast is the action of a dickhead.

Basically, once you hit a comfortable 65 or 70 on the road, don’t bother. That’s enough. It’s a waste of time because the risk you encounter—the risk you incur on yourself, the risk you effectively impose on other people by going any faster—is utterly pointless in terms of time saved.

So that’s a useful application of reframing time. Here’s a slightly more mercenary application. It fascinates me that all rail ticket–buying applications assume that you’re in a hurry. It’s odd. Maybe you want to save money instead. Below are two tickets from London to Exeter. On the left is a ticket from Waterloo to Exeter St Davids with a stop in Salisbury. It takes about an hour longer than the direct route from Paddington. It’s very scenic.

But in order to find that vastly cheaper ticket, you have to search Waterloo to Exter St Davids and type in “via Salisbury.” Unless you do that, the algorithm will not show you that ticket. Not because it isn't cheaper—it’s a lot cheaper. Not because it isn’t nicer—it's a lot nicer. But because it’s slower.

* * *

What happens when you give a load of engineers a brief? I always ask the question: What would’ve happened if you hadn’t given the brief for High Speed 2 to a load of engineering firms who immediately focused on speed, time, distance, capacity? What if you'd given the brief to Disney instead?

They would've said, “First of all, we’re going to rewrite the question. The right question for High Speed 2 is: How do we make the train journey between London and Manchester so enjoyable that people feel stupid going by car?” That’s the right question. It’s not about time and speed and distance. Those things only obliquely correlate with human behavior, with human preference.

What would’ve happened if you hadn’t given the brief to a load of engineering firms who immediately focused on speed, time, distance, capacity? What if you’d given the brief to Disney instead?

Why does that question never get asked? Because it’s an open-ended question. And businesspeople, governments, and politicians aren’t looking to solve problems; they’re looking to win arguments. And the way you win an argument is by pretending that what should be an open-ended question with many possible right answers isn’t one. Make it enjoyable, have free booze on the train, put Wi-Fi on the train, have a ball pit on the train for kids—these are the Disney answers. Those are multiple and involve what you might call human judgment. You can’t win an argument with those.

What you do is pretend this is a high school math problem with a single right answer, you solve for the right answer using high school math, and then nobody can argue with you because apparently you haven’t made a decision. You’ve simply followed the data.

This is a massive problem in decision-making. We try to close down the solution space of any problem in order to arrive at a single right answer that is difficult to argue with.

* * *

There are brilliant examples all over the place of people tweaking time subjectively. One of my favorites is the Uber map. It doesn’t change how long you wait for the taxi. It changes the quality of the waiting time by reducing uncertainty. If you look at human emotions, although humans might say, “I don't like waiting for a taxi,” what makes them uneasy is the uncertainty of the arrival. It’s not actually the duration. Too often, we optimize for the numerical thing, time and speed. We’re not optimizing for the emotional state, which is disquiet or anxiety.

With advertising, you can rebrand time: “Good things come to those who wait.”

What was the one downside of Guinness? Bartenders hated it because it took sodding ages to pour. In fact, if you wanted to make bar staff hate you, you just put in a huge order for drinks and crisps and then end it with, “And a pint of Guinness.” And they think, “Shit. I could have been pouring that while you were telling me about the other crap.” Take a weakness, turn it into a strength.

We also regard time as a kind of commodity, as if it’s fungible, as if 10 blocks of 10 minutes is the same as one chunk of 100 minutes. In human terms, this is absolutely not true. “The mere consciousness of an engagement will sometimes worry a whole day.” That’s Charles Dickens. In other words, if you try and break up your day into lots of little chunks of time, your productivity is massively destroyed even though the time available is pretty much notionally the same.

One of the worst mistakes we ever made was making email instantaneous. We should have built in a two-hour buffer unless you flagged the email as time-sensitive or urgent. Why is that? Because now everybody has to check their email every 10 or 15 minutes on the off chance that someone has sent them a time-sensitive email. So the burden falls on the recipient (which means everybody) rather than the sender (which means one person) to sift the urgent messages from the important but not time-sensitive.

It has been a productivity disaster. A fundamental catastrophe. In fact, one of the greatest ways you can improve your productivity is by setting your server to only check for new emails every two to three hours.

* * *

There’s an extraordinary case of this bias toward time-saving, that faster must be better. Someone I know who is an expert at Transport for London found out that quite a lot of people, quite a lot of the time, actually enjoy commuting. They enjoy the commute home much more than the commute to work. Men enjoy it a bit more than women. (That’s because men are a bit like Sky Boxes—we’ve got a standby mode. We like a bit of staring. If you look at coarse fishing, 95% male. Why is that? Because coarse fishing is basically staring with equipment.)

Quite a lot of people enjoy their commute time. And there’s good behavioral evidence for this because economists have noticed that people live a bit further from work than they optimally should in order to create a chronological buffer between where they work and where they live. We like that decompression time.

So this person announces the research to the people responsible for transport modeling at Transport for London, and they say, “You must never tell anybody that. It's absolutely wrong for you to say that people might actually enjoy a train ride.”

“Eh? But it's true.”

“Maybe it’s true, but all our models that justify transport investment assume that travel time is always a disutility. In other words, the more time you spend in transit, the worse off you are. If you come along with fancy ideas suggesting that people may sometimes prefer slower to faster, it fucks up our whole model.”

So this is what's happened to the world: optimization trumps human preference. The people who want to win the argument are effectively prepared to ignore human truths to preserve the integrity of the artificial model.

The people who want to win the argument are effectively prepared to ignore human truths to preserve the integrity of the artificial model.

The Unaccountability Machine by Dan Davies is a fantastic book, which argues that people create these models because if you can reduce decision-making to an algorithm, or a formula, or a process, or a procedure, you avoid the risk of blame. Computer says no, effectively.

Instinctively, people love to codify things, and make them numerical, and turn them into optimization problems with a single right answer. Because the second you acknowledge ambiguity, you now have to exercise choice. If you can pretend there's no ambiguity, then you haven't made a decision, you can't be blamed, you can't be held responsible. And what's the first thing you remove if you want to remove ambiguity from a model? You remove human psychology, because human psychology, particularly around time, is massively ambiguous. 

* * *

In the nineteenth century when they finally built a railway to California—I'm not making this up—people actually said, “Imagine how much leisure we'll have if we can get to San Francisco in two and a half days rather than two weeks.” They imagined that your clients wouldn't know that the railway existed, so you could pretend you'd gone by ship, spend 10 days playing golf, and then turn up by train.

Unfortunately, that information became widely known, and you were expected to turn up in two days. And this leads to a problem, I think, which bedevils many technologies and many behaviors. It starts as an option, then it becomes an obligation. We welcome the technology at first because it presents us with a choice. But then everybody else has to adopt the technology, and we suddenly realize we're worse off than we were when we started.

You can see that with things like the fact that four in five motorists want to get rid of parking apps. It’s not just that parking apps were bad necessarily, it's because they went from being an option to being an obligation, to a point where people are installing them in basement car parks where you have no chance of a mobile signal. That's the kind of thing that happens. And when behaviors become universal, they affect everybody.

This is a shot of a concert. Now, you could argue that this urge to photograph everything prevents people from being in the moment. And you could say naively, “Well, that's an individual choice. If you want to watch the concert, watch the concert. If you want film the concert, film the concert.” But there's a problem there because even if you don't want to film the concert, the behavior of everybody around you is fucking up the concert. And the weirder thing is that, when I researched this more, the people it most ruins concerts for are the performers, who say, “We used to be performing to a live crowd. Now it feels as if we're just doing something to be sucked into people's telephones.”

So when one person does something, it's an option. It's something that somebody does. When these things become more widespread, they morph from being alternative options to being social norms, conventions from which you have no escape.

There's a great book about this by a German sociologist called Hartmut Rosa whose work is about social acceleration. He claims that, dating back to the Industrial Revolution, the acceleration of things has made us miserable because our choices are no longer sufficiently limited that we feel we can accomplish everything we want. We've created an acceleration and an explosion of choice, which will permanently leave us feeling fundamentally unsatisfied or under-optimized.

* * *

I often say that the opposite of a good idea can be another good idea. I mentioned the Guinness advertisement. You can turn the slow pour of a Guinness into a virtue. You can take a long train journey and you can turn it into a benefit. Go on that train from Waterloo and pack a hamper—it's a day out rather than a tedious journey.

The great thing about the human brain is it can process the same thing in two different ways. A mathematical model can never cope with that. So every mathematical model concerned with time will assume that faster is better. But there are certain things you shouldn't try to accelerate. Sex, for example. “3.25 minutes, that's my personal best.” Not a good idea.

I owe this insight to my colleague Colin Nimick, a brilliant copywriter at Ogilvy who said, “In New York, people speak fast. In the American South, they speak slowly. Both of them are a form of politeness, understood in a different way. In New York, you speak quickly because you respect the value of the other person's time and you don't want to take up too much of it. In the South, you speak slowly because you want to respect the person by showing how much of your own time you are prepared to give to them.”

These are two behaviors, which, depending on cultural context, are intended to attain the same end while being completely opposite. And I think human psychology is absolutely packed full of these things. A union of opposites.

We welcome the technology at first because it presents us with a choice. But then everybody else has to adopt the technology, and we suddenly realize we’re worse off than we were when we started.

If you read Influence by Robert Cialdini, what you realize is that many things are successfully sold by opposites. Everybody has one of these, so it must be good. Or: not many people have one of these, so it must be good. You can achieve the same emotional effect with opposite messages. There are two great ways to check into a hotel. One of them is totally automated, where you walk straight to the room and use your phone to unlock the door. The other one is where someone takes you up to the room and makes you a cup of tea. They're both great check-in experiences. They're completely opposite.

We've got to understand that sometimes as an option, self-checkouts are great. As an obligation, they're bad—because sometimes the time spent in the process is where the value comes from. You can see this because people on a Saturday love nothing better than to shop in the most inefficient way possible. That's basically what a farmer's market is—let's take a Tesco and reverse everything. You've got to go to seven different places to buy anything. You've got to have a chat with everybody you buy something from. It's the mirror image, but we enjoy them both depending on the context.

We see this problem of time in the whole economy. This is William Baumol's model of cost disease.

This basically explains the whole world since about 1920. Manufactured goods, where you can enjoy extraordinary efficiencies of production—you can compress the time and effort required to make something—have massively reduced in cost. Services, which are time-dependent, have become more and more expensive. If you think there's a hell of a lot weird with the world that was completely different when you were a kid, this is why. Like the fact that a television is almost an impulse buy, but you agonize about getting childcare. In 1920, it was the other way around. Agatha Christie had three servants in her early life but couldn't dream of being able to afford a car.

Now, here's my point. Most of you, if you were students, wrote essays or something like that as undergraduates, right? Fairly confident to say that nobody's actually kept them? Nobody re-reads them. In fact, the essays you wrote are totally worthless.

But the value wasn't in the essay. What's valuable is the effort you had to put in to produce the essay. Now, what AI essays do is they shortcut from the request to the delivery of the finished good and bypass the very part of the journey which is actually valuable—the time and effort you invest in constructing the essay in the first place.

Similarly, the valuable part of advertising is, to some extent, the process of producing it, not the advertising itself. Because it forces you to ask questions about a business which people mostly never get around to asking: What do we stand for? What's our function? Who do we appeal to? Who's our target audience? How do we present ourselves? How do we differentiate ourselves? How do we make ourselves look different and feel valuable to the people who encounter us?

There are things in life where the value is precisely in the inefficiency, in the time spent, in the pain endured, in the effort you have to invest.

I think there are things in life that you want to telescope and compress and accelerate and streamline and make more efficient. And there are things where the value is precisely in the inefficiency, in the time spent, in the pain endured, in the effort you have to invest. And I don't think we're going to differentiate between those things. Because I think, like my friend at Transport for London, the automatic assumption is going to be that faster is better. We need to understand when we need to go slow.

So I'll end with a very weird question: What does slow AI look like? We've automatically assumed that the way we interact with it is instantaneous. Are we sure that's right? Would it be interesting to be able to say to an AI, Look, over the next three or four months, can you give me some ideas about holidays in Greece? Do we want to make that decision immediately? I don't think we do. I think we want to see things, refine things, consider things. I think we want to mull them over. I think we want to discuss them.

The general assumption driven by these optimization models is always that faster is better. I think there are things we need to deliberately and consciously slow down for our own sanity and for our own productivity. If we don't ask that question about what those things are, I think we'll get things terribly, terribly wrong.

This essay was adapted from a talk given at Nudgestock 2024. View the talk here.

The post Are We Too Impatient to Be Intelligent? appeared first on Behavioral Scientist.

Taking BS to mean behavioral science, the answer to that question is, not quite. But most things are. Most things involve a heavy dose of behavioral science. I would argue, however, the contrary does not apply. I don’t want us to start thinking that BS is everything. It is necessary but not sufficient in many, many cases.

I’d add that BS (behavioral science) without creativity—indeed BS without a tiny little whiff of BS (meaning bullshit)—may be actually suboptimal. If you don’t use behavioral science to expand the potential solution space to a problem by adding a psychological dimension in addition to the other aspects or metrics you’re considering, you’re probably missing a huge opportunity.

This is the vital thing: to a great extent, I think everything is BS. There are huge numbers of problems that persist in the world that probably could be solved much more quickly if people would consider a behavioral or psychological dimension. At the same time, I don’t want us to make the opposite mistake—to immediately go in, look at a problem, and assume that it has to be solved exclusively by the application of behavioral science. A lot of things are a mixture.

If you look at medicine, one of the slightly strange things about it is that they subtract the placebo effect. Now, given that the placebo effect can contribute to a cure, or to the efficacy of a treatment, you’d think people would be trying to actually maximize the placebo effect. What medicine does is say, “We subtract the psychological component of the efficacy of a treatment from the overall efficacy and what remains is the science.” Now, that seems a dubious thing to do. It seems to be self-evident that there are lots of cases where a beneficial combination of a psychological placebo effect and medicinal treatment would be the best solution of all.

One of the great quotes I always use—which, when I first saw it on a PowerPoint slide, I thought was a bit banal, but I now realize is incredibly important—is Harry Truman’s quote, “It is amazing what you can accomplish if you do not care who gets the credit.” Quite a lot of science is so desperate to prove the efficacy of what it itself does—the drugs company can’t claim credit for the placebo effect, they can only claim credit for the medicinal component of the treatment—that we become fixated on proving what we can do alone at the expense of proving and optimizing what we can do together.

This is the really important point about creativity: there are far more good ideas out there we can post-rationalize than there are good ideas we can pre-rationalize.

Let’s take something really simple: a sale in a shop. Now, if you’re a mainstream economist you would look at a sale and you would say, “Well, this is perfectly clear and consistent with mainstream economic theory; you reduce the prices of things and demand goes up.” To some degree, that’s true. I think if you held a sale and you didn’t drop the prices, you could do all the other stuff, and I think people would be fairly pissed off.

But equally, I don’t think it’s fair to say that a sale only works through effectively tweaking the price-demand curve. There are lots of things going on with a sale—there’s scarcity value, the fear that other people might buy the shit that you want, social proof in that there are huge queues of people outside the shop waiting to get in, and an additional element of scarcity in that most sales, although there are exceptions to this, last for a finite length of time. There is a shop on Oxford Street that’s been holding a closing down sale uninterruptedly for about the last five years, but that caters to tourists who aren’t around to notice the inconsistency.

So the most important thing you can do with a sale is not purely the economic bit, and it’s not purely the behavioral bit. It’s both. And in medicine, the best thing you can do is probably combine the drug’s psychological and pharmacological sides.

We need to start making friends with lots of other people who understand that solving problems is more complex and leaves more scope for creativity than the standard models currently allow.

Let me show you what I mean by this. If I gave you and a team of people two candles and I said, “You have to boil this quantity of water using only the candles and a box of matches,” people would probably struggle. And then some shrewd people will come in and say, “Well, actually, this is impossible, because look, the calorific value of the candles is insufficient to heat this volume of water to a point where it actually boils.” So, you put a big tick in the box saying “cannot be done.” And you move on to the next question.

Of course, it’s not quite true, is it? Because the boiling point of water depends on altitude, you could take it to a very, very high place and the same calorific value might well boil the water. If you actually produced a vacuum around the water, it’d be easier still. Or, as another compromise, you could just move the water to France, where, as everybody knows, water boils at 50 degrees. That’s the only thing that explains the shit quality of the tea, to be honest.

There are lots of ways you can solve the problem by simply recontextualizing it and adding new variables to the mix. We rarely notice these things. A lot of people will say, “Okay, this is impossible,” because they’ve failed to add what you might call the altitude or the air pressure component to the mix of variables needing to be considered.

I think there are whole categories in business like this, where some of the world’s greatest geniuses have solved extraordinary engineering problems but have completely failed to take the psychological component into account.

Think of solar panels. I don’t think anybody can fail to be extraordinarily impressed by the improvements made with solar panels in terms of their efficiency, the lower cost to manufacture them, the lower weight. And improvements continue to be made, beyond the point people thought was feasible a few years ago. Engineers have solved that problem, and, geniuses as they are, they deserve credit for doing that. But businesses have missed what you might call air pressure in the equation. They’ve missed, “How the hell do we sell this to people so they put it on the goddamn roof.” It is assumed, still, that solar panels are sold in one irreversible decision to somebody who fixes it irremovably onto their roof with a one percent chance that everything could go totally shit; either your local electricity provider refuses to pay for the electricity you put in or your roof starts collapsing under the weight or you discover you’ve got some hideous problem with beetles.

Now, what we know about humans is they hate making irreversible, five-figure decisions. They really, really hate making those kinds of decisions. So you’ve solved all of the scientific problems, you’ve got the whole how-to-maximize-the-calorific-value-out-of-the-candle stuff done, but you’ve failed to spot the fact that there’s another problem out there that you still have to solve before you can actually have a meaningful effect on personal energy consumption and on mass energy generation.

Nuclear power, you could argue, is even more extreme in that they completely screwed the pooch by choosing the word nuclear, which was associated with bombs, to describe the form of power generation, which it has very little in common with. If you change the psychological frame in which people have to decide, if you change the context, if you change the story, it’s exactly the same as changing air pressure—you change what happens.

Obsessing about the extent to which something you try in the future must make sense in terms of data you have from the past is a massive constraint on innovation and experimentation.

The other point I make is when you’re dealing with the rules of physics, they’re written somewhere upstairs. You can’t change them. Data from the past is necessary and sufficient to explaining what the laws might be in the future, because the laws of physics don’t change (at least at the scale we experience the world). In contrast, the laws of human behavior are highly context dependent, and they change according to things as strange as fashion, whim, storytelling, and mood.

Obsessing about the extent to which something you try in the future must make sense in terms of data you have from the past is a massive constraint on innovation and experimentation. In physics, yes, if it didn’t work in the past, it probably won’t work in the future, and the laws tell you what is impossible.

In anything involving psychology, and, you might argue, certain things involving complex systems, actually trying the same thing again and again and again with the expectation that it might work one time is not necessarily a definition of insanity; it might be a definition of complexity.

Once you accept the fact that we can’t necessarily focus all our time on how to steer the ship in situations where we can also change the weather, or in which the weather can change in ways we don’t expect, we move away from this kind of deterministic, rationalistic obsession with things that make sense in advance.

That’s one of my final creative lessons for behavioral science. Don’t just test the things that make sense. Test the things that don’t make any sense. Then, if you find that they work, you’ve learned something valuable. Actually, you’ve learned something mega valuable, because it’s something that nobody else knows, because the odds are nobody else has been wacko enough to test it.

We did a charity mailing where we tested seven different variables. The one rational one that made sense to everybody was a goddamn disaster. It actually reduced donations by about 30 to 40 percent. All the ones that were wacko improved the level of donation. This is the really important point about creativity: there are far more good ideas out there we can post-rationalize than there are good ideas we can pre-rationalize. The subset of things that we can try that haven’t been tried before that makes sense in advance is very, very small compared to the subset of things that might work in different circumstances.

Don’t just test the things that make sense. Test the things that don’t make any sense. Then, if you find that they work, you’ve learned something valuable . . . because it’s something that nobody else knows.

The bizarre half-sister of creativity is testing and rigorous measurement. They seem like the most different things in the world but they’re actually interdependent. Let me explain how things actually make no sense at all in until you try them.

If I suggest to you that you should get two dishwashers, nearly everybody, either who doesn’t have two dishwashers or who doesn’t have an evangelist friend with two dishwashers, is going to look at me as if I am totally barking insane. But here’s the weird thing: if you have two dishwashers, you never need to unload the dishwasher, and you don’t actually lose any storage space. Because you have a dirty dishwasher, which is where the dirty stuff goes, and you retrieve stuff from the clean dishwasher, eat off it, put it in the dirty dishwasher. When the dirty dishwasher is full, you turn it on, you put the post-it-note that says “clean” on that dishwasher, and then you simply reverse the process. There’s no unloading necessary. There’s no storage lost.

What’s weird is that this occurs to absolutely nobody in advance. It is absolutely obvious in retrospect, but it’s completely nonobvious in advance. And this is why we need creativity and what I call the benign bullshit part of bullshit in behavioral science. Stop obsessing about temperature, the volume of water, and the calorific power of the candles—let’s look for some other variables we might want to introduce.

I didn’t learn any of this at university, I learned it all at Ogilvy. What university teaches you to do is reduce every problem into a two-body problem, which can be solved mathematically to a single, optimal, right answer. Pretend that the problem is that simple, solve for the pretend simplistic model, and then pat yourself on the back. It was only at Ogilvy that I learned, No, the trick here is to go and find some completely different variable that no one’s looking at and try messing around with that instead.

I had a wonderful experience when Daniel Kahneman emailed me and about six or seven other people with exactly the kind of problem we ought to be looking at it. Someone had told Daniel, “A friend of mine sells fruit and vegetables at a farmers market somewhere in Austin, Texas, and he makes pretty large margins”—because, I guess, he’s selling to hipsters, where the lumpier and more deformed the vegetables are, the more they’ll pay—“but the problem is that he can never quite sell out.”

As Richard Thaler rightly explained to the group, scarcity value applies with perhaps pallets of strawberries, and it applies with electrical goods, which are all identical. But with single vegetables and fruit, we tend to assume the last cucumber is probably a bit manky and unwanted, and no one wants to buy it.

Here you had a bunch of people all answering this fundamentally fascinating question about how to sell your last bit of fruit. There were various suggestions, all fascinating. At the very end, I realized that what we were all doing was the same thing that economists would have done. Economists would have said, “It’s very simple, you drop the price.” If you’re an economist, you’re looking at a particular set of variables, which are high-status variables. We were all looking at it through the lens of scarcity value.

Maybe it’s something completely different. Maybe it’s the end of the day and the bloke towards the end of the day is just losing interest a bit and he just doesn’t look that interested in his customers and he starts packing things into the van and then the table is now half covered, so it looks like you’re not supposed to be buying from him. It’s one of the things we have to explore. And it has to be tested as well.

When I wrote my book, I said that if you run a coffee shop, leave the chairs and tables outside even if it’s raining, because from 300 yards away the fact that you have tables and chairs on the pavement signals that you’re open, it means there’s coffee available, because if you closed you would have put the chairs away.

Someone wrote to me and said they used to use that exact insight in reverse. This person worked for a coffee shop and the last thing they wanted was bastards coming in in the last 15 minutes before closing time and ordering a coffee and then sitting around for 45 minutes. Coffee shop owners all over the world are going to hate me for sharing this—all you’ve got to do is take two chairs, stack them upside down on a table.

No one comes in and no one orders a goddamn thing. Simply having two chairs upside down— you haven’t said closed, you haven’t changed the sign—simply doing that tiny little thing is basically enough to prevent anybody coming into your coffee shop.

Everything is BS, but BS isn’t everything, and actually allow space for the other kind of BS. A little bit of bullshit when you’re dealing with a future you can’t predict or fully understand is highly permissible.

What we have to do, effectively, is an element of decision hygiene. We’ve got a very, very weird decision-making process, where the deterministic and the reductionist comes first and the creativity, if it’s admitted at all, comes last.

Look at how government proceeds. It starts off with legislation, then if legislation fails, it moves to economic incentives. If economic incentives fail, it moves on to persuasion. Well, you don’t have to be a libertarian to say that’s completely the wrong way around.

What we need is a long period of recontextualizing the problem through the lens of complexity, the lens of psychology, the lens of behavioral economics, the lens of economics, and the lens of legislation. Drug times placebo is much better than drug or placebo on its own. How do we multiply these various things to actually solve the problem without artificially narrowing it into terms of your imagined high-status solution and implementing that?

When I first met Daniel, in 2009, he said, “I have no hope that we’ll actually make human behavior more rational. What I do hope is that we get more conversation around how people make decisions.” See, people will start saying, “Well, I think I’m going to go on holiday, but I’m wondering if it’s sunk-cost bias, because I’ve already paid the deposit.” As Daniel said back then, if we can simply change how people gossip, if we can change the way people discuss problems so that opening it up to other fields and other variables is seen as a positive, not as a disruption, then we’ve made 90 percent of the progress we need.

I’ve occasionally sat on a board as a nonexecutive director. Do not put a marketing person on a board. Seriously, marketing people will hate me for this; have a separate board for these discussions. There are 10 people all going, “This product isn’t selling, so we need to drop the price.” I’m sitting at the back and what I want to say is, “Have you thought about making it pink?” I know the second I say this it’ll be like passing wind, it’ll be a social embarrassment, all my status will disappear.

If we can rebalance the status of different modes of problem solving, and we can operate these things in parallel rather than in series, we’ll make progress. Just add a bit of creativity, acknowledge that yes, everything is BS, but BS isn’t everything, and actually allow space for the other kind of BS. A little bit of bullshit when you’re dealing with a future you can’t predict or fully understand is highly permissible.

This essay was adapted from a talk given at Nudgestock 2021 and appeared in Behavioral Scientist’s award-winning print edition, Brain Meets World.

The post Is Everything BS? appeared first on Behavioral Scientist.

When we’re unsure of the path to a goal, we often seek directions from an expert guide. It’s a familiar mantra: if you want to be great, learn from the best. Take a master class in cooking from a top chef. Sign your kids up for a tennis lesson with a pro. Convince the biggest star in your field to be your mentor and learn how to follow in their footsteps. What could be better than taking your first physics class with Einstein?

Quite a bit. In a clever study, economists wanted to find out whether students really learn more from experts. They collected data on every freshman at Northwestern University from 2001 to 2008. They investigated whether freshmen did better in their second course in a subject if their introductory class was taught by more qualified instructors.

You might assume that students would be better off learning the basics from an expert (a tenure‑track or tenured professor) than a nonexpert (a lecturer with less specialized knowledge). But the data showed the opposite: students who took their initial class with an expert ended up with poorer grades in the next class.

The pattern was robust across fields: students learned less from introductory classes taught by experts in every subject. It held across years—with over 15,000 students—and in courses with tougher as well as easier grading. And the experts were especially bad at teaching students who were less academically prepared.

It turns out that if you’re taking a new road, the best experts are often the worst guides. There are at least two reasons why experts struggle to give good directions to beginners. One is the distance they’ve traveled—they’ve come too far to remember what it’s like being in your shoes. It’s called the curse of knowledge: the more you know, the harder it is for you to fathom what it’s like to not know. As cognitive scientist Sian Beilock summarizes it, “As you get better and better at what you do, your ability to communicate your understanding or to help others learn that skill often gets worse and worse.”

If you’re taking a new road, the best experts are often the worst guides.

That was Einstein’s curse in the classroom. He knew too much, and his students knew too little. He had so many ideas swirling in his head that he had a hard time keeping his lectures organized—let alone explaining to a beginner how gravity bends light. When he made his teaching debut in a thermodynamics course, despite being a rising star in physics, his lackluster teaching attracted only three students. His material was often over their heads, and after he failed to draw a larger group the following semester, Einstein canceled the class. Several years down the road, he was nearly denied another faculty position because the university president was underwhelmed by his teaching skills.

It’s often said that those who can’t do, teach. It would be more accurate to say that those who can do, can’t teach the basics. A great deal of expert knowledge is tacit—it’s implicit, not explicit. The further you progress toward mastery, the less conscious awareness you often have of the fundamentals. Experiments show that skilled golfers and wine aficionados have a hard time describing their putting and tasting techniques—even asking them to explain their approaches is enough to interfere with their performance, so they often stay on autopilot. When I first saw an elite diver do four and a half somersaults, I asked how he managed to spin so fast. His answer: “Just go up in a ball.” Experts often have an intuitive understanding of a route, but they struggle to articulate all the steps to take. Their brain dump is partially filled with garbage.

Even if your chosen expert can walk you through their route, when you ask for directions on yours, you’ll run into a second challenge. You don’t share the same strengths and weaknesses—their hills and valleys aren’t the same as yours. You might be heading for the same destination, but you’re starting far from their position. This makes your path as unfamiliar to them as theirs is to you.

Experts often have an intuitive understanding of a route, but they struggle to articulate all the steps to take.

Of course, you’ll get more personalized advice from a guide who knows you well. But as tempting as it is to turn to a trusted mentor for sage advice, no individual will have all the right directions. You can see this in a study of lawyers navigating the path to partner. Guidance from a single mentor didn’t make a difference in promotions. There were other upsides: lawyers who had a supportive mentor were more satisfied and committed than their peers who lacked one. But when it came to getting promoted to partner, what mattered was being guided by multiple mentors. Different mentors were able to share different tidbits on how to advance. It didn’t take a village, though—all it took was two or three mentors to help lawyers make the climb to partner rather than seeing their careers stall.

No one else knows your exact journey. But if you collect directions from multiple guides, they can sometimes combine to reveal routes you didn’t see. The more uncertain the path and the higher the peak, the greater the range of guides you’ll need. The challenge is to piece the various tips together into a route that works for you.

Learning from multiple guides is an iterative, interactive process. It’s not as simple as going to people and asking, “Can I pick your brain?” (Also, the image of picking a brain is gross.) The information isn’t just sitting there, waiting to be extracted. We don’t live in the Matrix. Guides can’t simply upload their insights for us to download.

The point of engaging guides isn’t to blindly follow their leads ... The goal is to get your guides to drop pins—the key landmarks and turning points from their climbs.

The point of engaging guides isn’t to blindly follow their leads. It’s to chart possible paths to explore together. To do that, you have to make their implicit knowledge explicit. Instead of asking to pick their brain, you ask them to retrace their route.

The goal is to get your guides to drop pins—the key landmarks and turning points from their climbs. To jog their memories of paths long forgotten, you might inquire about the crossroads they faced. Those could be skills they sought out, advice they took or ignored, or changes they made. It can also help to tell them about the roads you’ve taken so far. As they learn about your prior paths and current location, they may begin to notice avenues for progress that they didn’t think to point out originally.

The pins you gather won’t form an accurate map. Some won’t apply to you—one pin might lead you across a stream, and your bicycle makes for a terrible boat. Some may no longer apply at all—they’ll take you to a road that’s closed. You may end up doing plenty of loops before you find the right path. And your guides are likely to be unaware of bridges that have only recently been built. The good news is that to start moving, you don’t actually need an exact map. All you need is a compass to gauge whether you’re heading in the right direction.

From Hidden Potential by Adam Grant, published by Viking, an imprint of Penguin Publishing Group, a division of Penguin Random House, LLC. Copyright © 2023 by Adam Grant.

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The average person receives dozens or even hundreds of messages—emails, text messages, and so on—each day, and the average professional spends nearly one-third of their workweek reading and responding to emails. Those numbers don’t even account for all the other communications that professionals receive outside the workplace. For busy readers, handling this torrent of information and messages is like living in an endless game of Whac-A-Mole. Highly relevant updates about health and school can inadvertently get overlooked or whacked with the delete button.

And yet there is a distressingly widespread misconception among writers that more is better. Perhaps it stems from memories of being a student and pushing to reach the required word count of a ninth-grade essay. It might reflect the hope that writing a lot will make it seem like we are smart and have a lot to say. Conversely, it might reflect a fear that if we don’t write a lot we will leave out some critical piece of information. Whatever the cause of all this verbal excess, the reality is more writing leads readers to be less likely to read anything.

Imagine you open your inbox and you see the following two messages. From the subject lines and senders, you know that they’re work related. You don’t engage with the messages other than to quickly scan their lengths.

Which would you deal with first? Probably the Concise one, right? In a survey we conducted, that’s what 165 out of 166 professionals said, too.

Readers often interpret the length of a message as an indication of how difficult and time-consuming it will be to respond to, which is another reason why they might choose not to engage with a wordy communication.

In one study, we sent two versions of an email to 7,002 school board members across the United States requesting that they complete a short online survey. One email was 127 words; the other was 49 words.

The Concise email yielded nearly twice as many survey responses as the Wordy email—a 4.8 percent response rate instead of 2.7 percent. Some readers likely looked at the length of the Wordy email and chose not to engage with it at all. Others likely didn’t read all the way through it and missed the request at the end. In addition, some readers may have used the length of the email as a signal of how long it would take to complete the survey and decided to pass on the request (which they presumed would be taxing).

Both emails directed recipients to the exact same survey, which took approximately five minutes to complete. Yet, in a separate study, 29 percent of respondents who saw the Concise email believed the survey would take less than five minutes, compared to just 15 percent of respondents who saw the Wordy email. Most readers, but especially those who are pressed for time, are likely to be put off by messages and requests that they expect will be difficult to deal with.

Readers who drop out in the middle of a long message engage in what we call “early quitting.” They may skim the text and decide it is too much to deal with at that moment—too many topics, requested actions, or words—and move on before finishing, hoping to return to it later. Some of these early quitters will never actually return. Others may come back to the message later, but by then they may have missed a critical moment: a payment deadline may have passed, an insurance enrollment window may have closed, or all the available meeting times may have been filled.

Readers often interpret the length of a message as an indication of how difficult and time-consuming it will be to respond to.

On average, a wordy message will be dealt with less quickly than a concise message. In the worst case, a wordy message will be relegated to the same fate as the hundreds of other messages that languish in inboxes, never to be read.

Even when ineffectively written communications are read, they impose an unkind tax on readers’ time. At an event we recently led on this topic, one participant wrote: “Lengthy emails in today’s work environment [are] disrespectful of the reader.” The longer the message, the larger the tax.

Imagine if you receive 120 emails every day (as many people do), and each is three paragraphs long. Reading them in their entirety would require four hours each day. Or flip the situation around and imagine you are sending a three-paragraph message to all 120 employees at your organization. You deliberate over every word; your high school English teacher would be so proud of you. But then it takes each employee two minutes on average to read what you wrote. Across 120 employees, your lovingly crafted message will impose a four-hour time tax. If you cut its length by just one paragraph, you would save eighty total minutes of employees’ time.

Writing concisely requires a ruthless willingness to cut unnecessary words, sentences, paragraphs, and ideas. It can be hard to delete the words that you spent time crafting—to “murder your darlings,” as advised in the classic lectures compiled in On the Art of Writing. But doing so increases the chances that your audience will read what you write. Nancy Gibbs, former editor in chief of Time magazine, would tell her staff that every word has to earn its place in a sentence, every sentence has to earn its place in a paragraph, and every idea has to earn its place in a text.

Spending a little more time up front to be concise saves readers and writers time by reducing follow-ups, misunderstandings, and requests left unfulfilled.

Most of us have never been trained in the skill of concise writing. Compounding the problem, most of us have not been trained in the skill of concise editing (or self-editing), either.

Researchers have found that people tend to add words and content while editing, rather than remove them. In one illustrative study by Gabrielle Adams and colleagues at the University of Virginia, test subjects were asked to read and summarize a short article about the discovery of King Richard III’s bones beneath a parking lot in Leicester, England.

After completing their summary, they were then asked to edit it and improve how well it captured the ideas in the article. In response, 83 percent of participants added words. The same pattern showed up across topics ranging from travel itineraries to patents: we tend to add ideas rather than subtract or remove them in the editing process.

Writing less is one of the six principles of effective writing that we discuss in our book Writing for Busy Readers. The additional effort required to write effectively is an investment. Busy readers are more likely to make time to engage with short, well-structured, skimmable messages. And if they do engage, they are then more likely to take away the most critical information.

Spending a little more time up front to be concise saves readers and writers time by reducing follow-ups, misunderstandings, and requests left unfulfilled.

Reprinted from Writing for Busy Readers by Todd Rogers and Jessica Lasky-Fink with permission by Dutton, © 2023 by Todd Rogers, Ph.D, and Jessica Lasky-Fink, Ph.D.

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At a conference last night, you were swayed by an emotional speech from the CEO of a prominent charity for impoverished children, and you decided to donate $1,000. You wire the money in the morning, feeling a certain sense of warmth. Then you head to the airport for your flight home. When you check in, you’re still feeling pretty good. You walk through the doorway of the plane and look for your economy-class seat. As you’re making your way down the aisle, you pass someone familiar sitting in first class. It’s the CEO of the charity you just donated to. How do you feel?

After seeing the CEO sitting in first class, you’d probably feel resentful and maybe a little mad. You might even regret sending that check, because you feel like all you did was help pay for the CEO’s first-class seat. You’re not alone. Many people don’t like charities with high levels of overhead costs—expenses that aren’t directly attributed to the primary objective, including the CEO’s travel expenses.

With a tunnel vision on overhead costs, we often don’t think about how effective the donation will be to the cause when making the decision to donate. With little time and energy for research, we base our decision largely on one question: What percentage of my donation will go to overhead? This concern highlights another question: Why are people so averse to paying overhead costs in the first place?

Philanthropy has always emphasized the enormous potential and impact of giving. Many of us believe in the power of giving to create meaningful change. In the United States alone, individuals donated close to $300 billion to charity in 2019. Even in the market of good intentions, however, it’s important to provide some economic incentives to drive organizations.

In a 2013 TED Talk, the activist and fundraiser Dan Pallotta called out the double standard that drives our broken relationship with charities. He argued that when it comes to nonprofits, we equate frugality with morality—we closely scrutinize nonprofits’ spending, rewarding them for how little they spend instead of what they get done. We seem to have one rulebook for the nonprofit sector and another for the rest of the economic world. We don’t judge CEOs of regular companies by how modest they are in their spending but by outcomes, such as the company’s profit. This rulebook discriminates against the nonprofit sector and keeps nonprofit organizations from realizing their full potential, Pallotta argued.

The double standard is especially prevalent in employee compensation. Making millions of dollars selling books or weapons in the private sector is okay, but if you made millions in personal income through your nonprofit trying to cure cancer, you’d be vilified and chastised from all sides. In the general population’s view, an MBA graduate making $400,000 a year working at a bank is okay, but the CEO of a charity making that much? No way—completely unacceptable. This skewed public perception drives talent away from the field of charity. People who could make a huge difference in the nonprofit sector end up choosing the for-profit sector because they’re unwilling or unable to make this lifelong economic sacrifice.

We seem to have one rulebook for the nonprofit sector and another for the rest of the economic world. We don’t judge CEOs of regular companies by how modest they are in their spending but by outcomes.

Back to overhead aversion—one of the reasons I like this topic is that as an economist, I understand that when giving, I should care about the overall impact of my dollars and not the overhead of the charity. However, as a human being, I would feel bad passing the CEO in first class on the way to my economy seat. In other words, although I understand that I should care about impact rather than overhead, in practice, I care about both. And I’m certainly not alone: studies have shown that donors strongly prefer charities with low overhead regardless of cost-effectiveness.

Two reasons are typically used to explain why people don’t like charities with high overhead costs. The first is that high overhead might imply that the organization is inefficient and that the people who run it are bad at their jobs. The second is that high overhead might suggest corruption within the charity, either through high consumption or embezzling. We’ve all heard of examples of these two types of problems in charities. Donors may therefore be wary and use a charity’s overhead spending as a signal of how much the charity is actually doing for its cause.

Although I recognize the first two reasons, I also propose a third one directly related to the donor’s feelings and inspired by our first-class CEO thought experiment: donors want their money to have a direct impact on the cause they support. They might feel that they’ve made a greater impact when they know their contributions went directly toward the kids’ meals as opposed to the CEO’s first-class seat. In other words, thinking their money went entirely to the kids amplifies their self-signal and reassures them that they are good people for helping the needy.

Although I understand that I should care about impact rather than overhead, in practice, I care about both.

Ayelet Gneezy, Elizabeth Keenan, and I asked ourselves: Could this feeling be why people are averse to paying for overhead? Which one of the three reasons just described is the primary driver of overhead aversion? Apart from simple curiosity, we thought a better understanding of the reason behind this overhead aversion might lead to a new way to increase donations. Our idea was motivated by a simple thought experiment: Imagine you are the CEO of a charity who just secured funds from a generous private donor to help launch a new fundraising campaign. How would you use this initial donation as an incentive designed to maximize contributions from other potential donors?

This isn’t just a hypothetical—board members in charity organizations ask this question all the time when they receive large donations. Traditionally, charities have used these initial financial gifts to solicit additional donations in two primary ways: (a) describing the initial donation as seed money (“a generous donor already gave us $10 million for the cause”) or (b) using a matching model in which the charity uses the initial funds to match every new dollar donated. These two uses of initial donations—seed money and matching grants—have been studied and proven to be effective in increasing donor contributions. In our experiment, we wanted to use incentives as a diagnostic tool, offering a new fundraising approach that will reveal why people hate paying for overhead. To do this, we proposed a third alternative incentive: telling donors that their donation would be overhead-free.

Think back to when you walked past that CEO in first class, and imagine instead that at the conference, he and his charity promised you that 100 percent of your dollars would go toward covering meals for the needy kids. Someone else had given the nonprofit the money necessary to cover all the overhead expenses, including compensation, travel, and other administrative costs. Now when you pass the CEO in first class, you don’t feel quite as bad—it wasn’t your money that put him there. Your money was put directly to good use. Would that knowledge help alleviate your negative emotions?

Seed money and matching grants have been proven to be effective in increasing donor contributions ... We proposed a third alternative incentive: telling donors that their donation would be overhead-free.

We thought it would. We tested this idea with a foundation specializing in education. The foundation purchased the right to send a one-time donation-request letter to forty thousand potential U.S. donors who had donated to similar causes in the preceding five years. All participants were informed about the foundation’s new initiative, were told that the cost of the new program was $20,000, and were asked to donate toward this goal.

Together with the foundation, we secured the funds needed for the incentives and created four different groups; each received a different type of incentive to donate. Specifically, each group, consisting of ten thousand individuals randomly sampled from the list of forty thousand past donors, was sent one of the following incentives:

• Control group: No additional incentives were offered.
• Seed group: Participants were told that the foundation had already secured $10,000 for the project from a private donor.
• Match group: Participants were told that the foundation had already secured $10,000 for the project from a private donor that would be used as a matching grant. This grant would match every dollar donated up to $10,000.
• Overhead-free group: Participants were told that the foundation had already secured $10,000 for the project from a private donor that would be used to cover all overhead costs. Thus, every dollar collected would go directly toward the program.

This field experiment allowed us to learn more about what dissuades donors from making charitable contributions to companies with large overhead costs: Is it the size of the overhead cost that turns donors off, or is it who pays for it? It’s important to diagnose the root cause of overhead aversion before we rush to come up with a solution. We hypothesized that if we covered all the overhead costs associated with the project, we would incentivize donors to give, because they would have the assurance that every dollar they give would go directly to the cause.

The figure below presents the overall donations collected from the ten thousand appeals in each of the four groups. The seed and match treatments were effective in increasing the donation amount above the control, but the overhead treatment proved to be even more effective.

The result was mainly driven by the fact that more people were convinced to donate in the overhead-free group. Incentivizing potential donors by informing them that overhead costs would be covered by an initial donation significantly increased the number of people deciding to donate and the total donation amount compared with the seed and matching incentive approaches.

These results helped us diagnose the reason behind overhead aversion; they showed that the alternative explanation was important: donors care not only about helping the cause but also about how doing so makes them feel. Understanding the reason behind overhead aversion is not just a theoretical exercise; it can also help increase giving. One approach would be to “educate” small donors who give $100 on why they shouldn’t care about overhead and instead concentrate on impact. However, given that there are typically many small donors, this approach would be very challenging. Furthermore, measuring a charity’s impact is hard.

Informing potential donors that overhead costs would be covered by an initial donation significantly increased the number of people deciding to donate and the total donation amount compared with the seed and matching incentive approaches.

Imagine instead that, as the person in charge of development in a hospital, you are facing a big donor who is about to give $5 million to your institution. You could tell the donor how the money will be used—on a new building or some state-of-the-art machines. Alternatively, you could try to convince the donor to use the donation to cover the overhead costs of raising money for hospital development. This conversation may allow the hospital to offer an overhead-free campaign for the smaller donors, all of whom care about whether they’re giving directly to the cause. Our research suggests that this approach—using the donation as an incentive in the form of overhead-free donations for smaller donors—may help increase the number of donations and the total donation amount. By allotting the money to cover overhead, the $5 million could go much further than if it were simply put toward a new building.

A prominent example of this approach at work is a nonprofit called charity: water, which splits into two separate organizations: “charity: water,” which accepts donations that go entirely to the cause, and “The Well,” which consists of a dedicated group of private donors who pay for all the overhead expenses.

The relative efficiency of paying for overhead demonstrates how important it is to control the framing of the story. The three incentives we tested in this field experiment were exactly the same from a traditional economic point of view. However, the source of overhead aversion is not just worrying about corruption, inefficiencies, and overspending. Emphasizing donors’ personal impact is important. Find the frame that people care about, and your incentives will go further than ever.

Excerpted from Mixed Signals: How Incentives Really Work by Uri Gneezy. Published by Yale University Press. Copyright © 2023 by Uri Gneezy. All rights reserved.

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