Advice on resilience and crisis management in academia would be almost identical to any other field, with two distinctions.

The first distinction is that academia is a safer environment than many: work in academia rarely involves matters of life and death. You do not risk your life in your routine work: it is not about fighting crime, fire, or mad neighbors. Severe crises in academia are few and far between.

The second distinction is worse and might be inescapable, as it comes from the research goal. The main goal of the research is to find new knowledge, which has become very specialized, with researchers working in highly narrow fields.

This fact makes them more vulnerable than representatives of many professions.

For instance, a skilled lawyer, a surgeon, or an accountant can find a new job more quickly—i.e., in the same city or country—than an expert physiologist specializing in a particular type of receptor in the brain.

Her specialization is narrow. What she does cannot be easily converted into money or any other resource. In basic research, her employer is almost always a governmental institution, and there might be no demand for her skills (i.e., no salary) if there is any crisis in the country. {EXPERT FUNNEL, LIQUIDITY, CONSTRAINTS OF A SYSTEM}

Thus, while it's crucial to recognize your value, it's equally important to diversify your skills, expand your professional network, have emotional support, and maintain physical stamina. These measures can serve as a safety net in times of crisis.

{RESOURCE, SUSTENANCE}

We live in a world of limited resources, and their distribution might be far from ideal. {HETEROGENEITY, CONSTRAINTS OF A SYSTEM, SUBJECTIVITY}

Grants are one way to distribute resources, and this way is not perfect. This is also a frequent reason for people quitting science—to have more certainty in their lives. Getting money for work and getting money for oneself while trying to discover something important is exhausting. {PSEUDOMONEY, GRANTING IS NOT INVESTING}

Thus, as we discussed, I suggest getting into the lab with resources already.

{ABUNDANCE OF RESOURCES, GHOST RESOURCES}

It must be.

People skip it because sometimes it is just empty talk with meaningless routines. The same, by the way, can be said about lessons learned, which often degenerate into formal box-ticking.

However, if you are interested in getting to the endpoint, then thinking about what can affect you, what you would do if some risk materializes, and what resources you need can save you a lot of resources in the future.

{BUFFER, INFORMATION ASYMMETRY, EXPERT}

Unless you have numbers from earlier projects or can use similar projects as an analogy, never pretend to have such numbers or do any “math” on them: garbage in – garbage out. {GIGO, ANALOGY}.

A simple example: what is the probability that we will have another pandemic within five years?

Who knows.

Moreover, our subjective perceptions of such likelihood will differ, and all will likely be equally useless.

We can see that modern transportation—fast and far-reaching—enables dissemination on a scale that was impossible years before. (Global Transport Networks and Infectious Disease Spread; Human Mobility and the Global Spread of Infectious Diseases: A Focus on Air Travel)

Does this fact give us numbers that we can use? No, it does not.

Thus, let people develop their complicated models—they might truly enjoy the process, yet, at the same time, be aware that these models might be useless.

Another critical thing to remember is that all these analyses—quantitative being one of the approaches—are done solely to prepare for the thing (response strategies).

Then, we arrive at an essential parameter—the response potential. Simply put, it is the number of resources one can use to protect oneself in the case of a crisis.

As the COVID-19 pandemic has demonstrated, if we have no resources in the first place, we will be hit harder than those who have.

What does it mean? It means that many people can never prepare for certain things.

{CONSTRAINTS OF A SYSTEM, ABUNDANCE OF RESOURCES, SUSTENANCE}

No.

Do not spend time on absurd scenarios. If you do, do not make it public. And practice, practice, practice.

{COMMON SENSE, ADEQUACY, EXPERT, TRIAL AND ERROR}

Of course, they do.

Also, most (three out of four) are risk-averse and prefer to err on the safe side most of the time.

But, the general rule is to refrain from playing with any non-existent numbers.

Identify risks and consider what you will do if certain things happen. If possible, appoint risk owners—those who will take care.

{GIGO, COMMON SENSE, ADEQUACY}

The very fact that you use statistics and probabilities in your work is the best example.

There are derivatives of that. We think that "we stand on the shoulders of giants," but the reproducibility crisis hints that, maybe, not all of them were/are giants.

What if the frequentist approach in statistics is worse than Bayesian? How will the situation change for research in general?

{REALITY, CONSTRAINTS OF A SYSTEM, SUBJECTIVITY}

In projects, everything can go differently in the future from what we expect now.

In research projects, uncertainties can come from methods, people, hypotheses, etc.

We can explore all these further: people, for instance, might be less competent than we expect, less productive, and not work well together.

A similar “tree” grows from the method: your equipment might be faulty, worse than expected, or unavailable. {RESOURCE, LIQUIDITY}

However, essential resources are always time and money. You can solve many project problems with extra money and time.

Abundant resources are a prerequisite for any system’s normal functioning, like an engine’s lubricating oil. {ABUNDANCE OF RESOURCES}

With time, it is even more complex, as if the opportunity for your product disappears, you often cannot “repurchase” it.

{KAIROS}

I try to avoid the words “good” or “bad.”

It depends on what is your work mode. If you learn (i.e., explore), all these “failingforwards” are a part of the game. {FAIL FORWARD}

If you must deliver results as an expert (i.e., exploit your expertise), then clients, whoever they will be, will expect the results according to the agreed criteria {EXPLORE VS. EXPLOIT, ECPM, THE 5 EXCHANGES, SUCCESS AND FAILURE}

When it comes to research, the game changes. It’s all about learning, and in this context, unexpected results are not just a possibility, but the very aim of your work.

Remember, you will commit three to six years of your life, possibly the best years, to a Ph.D.

Be clear about why you do your Ph.D. and what you want. Other things will follow.

{WHO AM I? MAP YOUR DREAM, FOLLOW YOUR DREAM, ENDPOINT TIERS}

Let’s separate apples from oranges.

The general rule is that risk management is bunk in many areas. It means nothing and is based on nothing.

Such a situation can persist as most risks never materialize, and the charming people who develop extensive “risk management statements” count pure luck as the result of their efforts. {The Failure of Risk Management...}

Whenever you do risk management, you have to consider this general truth.

The specific thing is the target audience of your proposal.

I will not be surprised if the experts who evaluate your proposal know very little (or nothing) about risk management.

Thus, please give them the impression that you have thought about risks and are smart enough to neither forget about them nor overdo them. And that's it. {EXPERT, COMMON SENSE, COMMUNICATION PRINCIPLES, GRANTING IS NOT INVESTING}

Where can we find the best risk management practices? In insurance companies. There, people make money from their predictions.

Their existence depends on their ability to have the fullest datasets and the best models to use these data and calculate the possible losses and gains. At the same time, they can only charge reasonable prices, as there is always competition in the field.

The priority is being rational and not hindering your work with the “meta-work.” {ADMINISTRATIVE OVERHEAD, COMMON SENSE, ADEQUACY}

You plan the project to save time and increase your chances of success, not otherwise. If you think that something is a time-waster, do not do it.

But first, try the tools we studied to get experience in your field.

In general, there are two aspects to consider.

First, no one can predict anything for sure. And if somebody says she can, she is a liar. A large part of planning new things is based on guesses. {GUESSWORK}

Second, thinking ahead with thought experiments and modeling “what-if” situations is always beneficial.

Research is about thinking. “Risk management” thoughts about “actions and consequences” might bring unexpected insights. The wind blows where it wishes. {REALITY}

Also, if you have data from previous projects, not necessarily yours, you can see what can happen: risk management is “being smart about taking chances.” (Thanks, Douglas!)

{ANALOGY, EXPERT}

You manage the risk at the very start by selecting the right people and not letting in the wrong ones {WEED-OUT}.

You control this risk by keeping both communication networks—formal and informal—fit. {COMMUNICATION PRINCIPLES}

Should you document everything? In some fields, it is a must. Yet, it gives a substantial administrative overhead, so be sure it will benefit you. {ADMINISTRATIVE OVERHEAD, ADEQUACY}

There are way more uncertainties coming from people. They can lie about their competencies, stop delivering, steal from you, quarrel between themselves, disclose your secrets to a competing company, etc.

{PERMIES, GROUPS: A PARAMEDIC APPROACH, THE 5 EXCHANGES}

“Extreme” is not a way to say that a project is poorly run, experiences scope creep, or sucks any other way.

It is a way to say that the project is very complex, and you are adjusting your desired endpoint and the method to reach it during the project. {TRIAL AND ERROR}

You likely mean something different: changes along the project transformed everything unrecognizably, and this mess bothers you.

If something changes, it rarely happens instantly. And if it does happen instantly, you understand what happened: “Our project is in trouble because the inflation jumped to 20%, or because the war broke and the best coders relocated to another country, or a pandemic broke out, etc.”

Changes happen more often step by step, and you only recognize their importance once the number of steps becomes the quality of a failed project.

Why does it happen?

It happens because you did not notice those steps. You did not notice because you had no discipline to mark all the changes and question your assumptions. {SELF-DISCIPLINE, DAILY INCREMENTS}

You thought you had a clear goal, but for team members, it was not—you had not communicated it. {COMMUNICATION PRINCIPLES}

They did their job but did not trace the actions because there were no tools or requirements. A new worker took more time to perform than planned, and other things ultimately accumulated and became an undeniable problem.

You might remember Benjamin Franklin´s:

“For want of a nail, the shoe was lost.

For want of a shoe, the horse was lost.

For want of a horse, the rider was lost.

For want of a rider, the battle was lost.

For want of a battle, the kingdom was lost,

And all for the want of a horseshoe nail.”

Thus, it would be interesting to see how this happened. If the goal was clear, what made it lose clarity? However, how would you unravel without a project diary or tracker? Memory is a lousy mate here. {PROJECT DIARY, LEAVE TRACKS}

What can you do now? Try to approach the project as a paramedic. {PROJECT: A PARAMEDIC APPROACH}

Also, try to recover steps that led to the current state using the props you can.

There are no universally accepted guidelines for research projects. However, I suggest looking at any research project from a risk management perspective.

As a researcher, you travel into the unknown. You explore the world around you to better understand how it works.

Anything new means “uncertainty,” by definition. You can win, and you can fail. {FAIL FORWARD}

You have a guess—a hypothesis—and want to test reality using this construct. Your hypothesis may be wrong, but despite that, you probe reality and get results using it.

If you are a scientist and not an accidental lab worker, you will try to prove that your results are accidental and that you have not seen your hypothesis work. As you know, this concept is called the null hypothesis—the claim that the studied effect does not exist. (A perfect one on common misconceptions by Harvey J. Motulsky is here)

In research, you try to prove yourself wrong and always think in a “what-if” mode.

{TRIAL AND ERROR, GUESSWORK}