Information Rich, Attention Poor: Understanding Information Overload

“What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention, and a need to allocate that attention efficiently among the overabundance of information sources that might consume it.”

—Herbert Alexander Simon, Economist and Nobel Laureate

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1: Introduction

We humans enjoy an unlimited capacity for creating new combinations of words and ideas, and we long, it seems, to let them out into the world. Consider that the total amount of data created, captured, copied, and consumed each year in the world is forecast to increase to somewhere around 150 zettabytes by the year 2024. A zettabyte is a measure of storage capacity and is 2 to the 70th power bytes (or 1 sextillion bytes). One zettabyte is approximately equal to a thousand exabytes, a billion terabytes, or a trillion gigabytes. Not too long ago, “one gig” was a wealth of information. Today, it is but a tiny fraction (one trillionth) of the total information produced in one year.

Every day, ideas are born and released into the world—some to fade into obscurity, and some to change society forever. For a business leader, most of that new information is easily ignored. On the other hand, some of that information is critical to innovation, market leadership, or simply enterprise survival.

The executive’s challenge is, of course, how to tell one from the other. How do she know if any information she encounters signals a sea-change in our environment, is a critical imperative, or just a rehash of tired notions gift-wrapped in new terms? How do leaders decipher the information that comes at them every day from books, articles, lectures, webinars, magazines, peers, consultants, conferences, white papers, and websites? Is that even possible in today’s world, and if not, what are the consequences?

While it is tempting to say “it’s not” and move on, this position is not an option for most business leaders today. Ignoring information is a dangerous game, and executives should not just seek to understand how increasing content loads impact their decision-making but also how the distinction between information and insight can help address this growing problem.??

2: Information Overload: A Definition

Information overload (“IO”) is a common term to most managers, but few of us have ever stopped to consider what the term means and its impact on our decision-making process. Let’s start our discussion, then, by defining the term as clearly as we can with the help of Prof. Peter Roetzel, Chair of Management Accounting & Information Systems at Aschaffenburg University, who has studied IO extensively (and especially in the context of decision-support systems):?

Information overload is a state in which a decision maker faces a set of information (i.e., an information load with informational characteristics such as an amount, a complexity, and a level of redundancy, contradiction and inconsistency) comprising the accumulation of individual informational cues of differing size and complexity that inhibit the decision maker’s ability to optimally determine the best possible decision. The suboptimal use of information is caused by the limitation of scarce individual resources. A scarce resource can be limited individual characteristics (such as serial processing ability, limited short-term memory) or limited task-related equipment (e.g., time to make a decision, budget).1

There is a lot to unpack in Roetzel’s statement, so let’s take it one step at a time. The first part of his definition posits that IO arises when the information an individual receives crosses certain thresholds, making it difficult, if not impossible, to process it correctly. These thresholds, in his view, are the following:?

???????Quantity

???????Complexity

???????Redundancy

???????Contradiction

???????Inconsistency

Roetzel does not claim this is an exhaustive list, and I can add at least three other factors that could trigger IO:

???????Velocity: when the information's obsolescence rate makes it difficult or even impossible to process.

???????Accessibility: if the information can only be processed by acquiring specialized skills.

???????Bias: when information is consciously (or unconsciously) overloaded to drive a specific outcome.

Whether considering technical innovations like generative AI or social movements for environmental justice, business leaders are surrounded by issues that, unless addressed with care and intelligence, will trigger IO within even the most sophisticated executive. One might be tempted to assume that IO is simply a “fact of life” and can be ignored, but that’s hardly the case for a reason illustrated in Figure 2 below.

The figure above illustrates a critical point: as information loads approach the point at which they can no longer be processed effectively (for whatever reason), decision-making performance begins to deteriorate. This is because the decision-maker is able to process less and less of the total information available to help make good decisions. In other words, imagine a scenario where making the right choice requires someone to process ten pieces of information, the maximum amount manageable by that individual. Suddenly, because of a technical innovation, the necessary information load increases from ten to twenty pieces. In this case, the decision maker who was maxed out at ten analytical tasks (100%) is forced to shift and make a decision at only 50% capacity. As the required information count climbs higher, the processing capacity percentage drops, thus generating a greater probability of making a bad choice.

Decision makers in the abovementioned situation experience what the American executive Herbert Simon described as “a wealth of information [which] creates a poverty of attention.” This attention deficit creates “a need to allocate that attention efficiently among the overabundance of information sources that might consume it.”2

There are few senior executives who have not faced this dilemma. Perhaps an issue was manageable for years until a disruptive competitor suddenly created the need for more information to properly manage it. Or an entirely new social or environmental phenomenon appears, forcing executives to gather and process the information needed to consider correctly this new force when making decisions. In either case, managers are confronted with a high-stakes (and often exasperating) challenge to “get up to speed” quickly and, even more critically, correctly.

Addressing the challenge inadequately is all too easy. Executives can rely on poor information sources or waste months (or even years) of effort analyzing the wrong information. They can also process brand new content with out-of-date skill sets, only to find out later that their analyses missed critical elements and so reached incorrect conclusions. Roetzl notes that in these situations, managers can not only harm themselves but others:

Users seem to ignore possible side effects of information overload up to a very high level before retreating from these channels or platforms. From a bird’s eye perspective, this situation might be compared with the spread of a disease. Thus, people often act irrationally by infecting others (i.e., sending more messages, likes, news to other members of their network) instead of sparing themselves (i.e., making a rest/recovery from their overloaded status).

The biological metaphor is not unwarranted. Indeed, Edward Hallowell, a psychiatrist and expert on attention-deficit disorders, has observed what he calls an “attention deficit trait” in managers that presents similarly to the medical condition often seen in children and adults. Author Linda Stone, who coined the term “continuous partial attention,” noted that the inability to process something as simple as an e-mail inbox can lead to what she labels e-mail apnea: “The unconscious suspension of regular and steady breathing when people tackle their e-mail.”3

While the sheer volume of information is the main driver of IO, other factors are also at play. Researchers have noted that the trend over the last few decades to flatten organizations has increased the number of direct reports executives are forced to manage. Indeed, there are CEOs who now manage over a dozen individual executives directly, each overseeing a complex function. Each direct report creates yet another information flow to process, compounding the organizational information overload facing any executive arising from her functional responsibilities.?

3: The Impact of Information Overload

A few examples illustrate the impact of IO on executive decisions. Business leaders are constantly asked to recognize, categorize, and assess a host of new innovations and forces that might alter the global business landscape in significant and, in some cases, revolutionary ways. Getting this process wrong, however, can have disastrous consequences, as the following three examples illustrate.

3.1: AMAZON

Amazon launched in 1995 and by 1999 had become the largest online seller in the world, a position of dominance that has only grown in the 21st century. Amazon did not spring into life overnight, fully formed and ready to tear down traditional business models. Instead, it evolved slowly, learning and adapting along the way as it patiently absorbed and employed investment capital, realizing the ever-evolving vision of its founder and senior managers. Many theories have emerged about why so many competitors could not respond and react to Amazon’s domination successfully, and I'd like to offer a novel additional explanation in the context of our analysis of IO.

As Amazon grew and took more market share from traditional competitors, a significant amount of public information was generated about its strategy, operations, and future intentions. History shows that most executives who competed against Amazon (a) had access to this information but (b) failed to process it correctly. Was this failure one of information quality, processing quality, or both? It is often said that many of Amazon’s competitors failed because they were not smart or visionary enough to read the signals coming from Amazon’s growth. That may explain some of the failures, but does it explain all of them? Isn't it also possible that many executives across the retail industry could not process what they were seeing in front of their eyes?

As we see in Figure 3 below, Amazon was constantly investing, innovating, and acquiring, thereby creating a stream of information loads—about technical innovations, strategic shifts, and evolution in customer buying patterns, etc.—that was very difficult for many competitors to process correctly at the time.

I would argue that IO had much to do with Amazon’s rise to the top. Many competitors could not simply pause their operational duties long enough to correctly process what Bezos and Amazon were doing. Thus, they competed in a state of information overload that contributed to their demise.

If the hypothesis above sounds fanciful, consider the following description of the Amazon purchase process from a 1997 Slate article:?

After calling the stores, we connected to Amazon using Netscape Navigator 3.0 and a 28,800-baud modem. Amazon has a special page dedicated to the Turow book, complete with a picture of the cover and some unenlightening amateur commentaries from other Amazon users. The psychology text, not surprisingly, was listed with no description and no commentaries. Amazon said it would take one to two weeks to order.?

After clicking your purchases into a “shopping cart,” you are directed to a “secure Netscape server” that will encrypt your credit-card information. After this is done, you are told: “Finalizing Your Order Is Easy.” Nothing could be further from the truth. Lower down in the verbiage, Amazon concedes, “Though we have tried hard to make this form easy to use, we know that it can be quite confusing the first time.” Amazon users have to page through screen after screen of details about shipping charges, refund rules, and disclaimers about availability and pricing. Then you are told to allow between three and seven days for delivery after your book leaves Amazon’s warehouse. “Upgrading to Next Day Air does NOT [their emphasis] mean you’ll get your order the next day.”

A competitor casually reading this article might have been reassured: Amazon is nothing to worry about and poses little threat to a traditional bookstore. It continues:

For ordering, Politics and Prose was by far the easiest. Heidi answered the phone on the first ring. She was chatty, but professional. The store had “many, many, many” copies of the Turow on hand, and she promised to send one out “right away, tomorrow morning at the very, very latest.” When asked about the psych text, Heidi apologized (“sorry, sorry”) for not carrying it, and offered to order it. Estimated time of arrival: four weeks. She took a name, address, credit-card number. The entire phone call took 2 minutes and 38 seconds.4

?Of course, a visionary bookseller would have been fascinated by Amazon’s online presence, the impact user comments could have on consumers, security features of website transactions, ease of direct shipping, and finally by Jeff Bezos’s personal history, which hinted that he was never a person to pursue foolish quests.

Having spoken with many retail leaders in the last ten years, I would argue that IO was a major factor in Amazon’s victory over many retailers, especially smaller ones. Many competitors were unable to pause their operational duties long enough to correctly process what Bezos and Amazon were signaling; moreover, Amazon’s technical innovations were difficult to comprehend by traditional retailers, who had grown up as merchandisers for the most part and thus lacked the computer science foundation that could enable them to process the technical challenges Amazon was in fact embracing. In short, I believe that the IO experienced by many of Amazon’s competitors during its rise to prominence was a serious competitive advantage, along with its brilliant retail model and disruptive technical innovations.

3.2: COVID-19

When the global pandemic hit the headlines in early 2020, it seemed to catch most business leaders by surprise. As a collective group, they could not imagine how this invisible organism would halt social life worldwide and change the working world. I have the good fortune to know more than one scientist working in the realm of infectious diseases, and it is fascinating to note that for many scientists, something like COVID-19 was not just predictable but pretty much inevitable. Figure 4 below explains why many of them felt this way.

As the graphic deftly illustrates, the virus clustering that begins around 2000 was a warning that a much more serious global threat that could arise at any time. Below is a quote from a 2007 WIRED magazine article about virus researcher Nathan Wolfe’s project to understand the danger of emerging global viruses:

The implications of those results, published in the Proceedings of the National Academy of Science in 2005, were astounding: Retroviruses similar to HIV were crossing from primates to hunters far more frequently than anyone had expected. The long-ago Cameroonian hunter who acquired SIV was no freak occurrence. Viruses, it turns out, are constantly spilling over from animals to humans. The only reason we don't have frequent pandemics is that most of those viruses have a hard time establishing themselves and then spreading. "There were already some hints of viruses emerging this way," says Burke, who coined the term viral chatter. "What I wasn't ready for was finding them on the order of 1 in 100 people. That means there are literally tens of thousands of people walking around in equatorial Africa harboring viruses in this state."5

Or consider this quote from a 2018 WIRED piece regarding a proposal to set up a predictive global virus tracking system:

“Globally we let viruses emerge and trickle through our net quite often,” says Peter Daszak, parasitologist and the president of EcoHealth Alliance. “That needs to stop. We need to start taking these things seriously.”6

?These articles, and there are others like them, were not found in specialist academic journals. They were published in the general business press yet largely ignored by most senior business leaders, even when COVID-19 started to make headlines. A reason for this phenomenon was the complexity of the information about the looming crisis. In other words, it is possible that many business leaders who read the WIRED articles were concerned about a possible pandemic but struggled to take the next step. A lack of training in virology and medicine made understanding the science behind the WIRED headlines difficult, making the risk difficult to grasp. As in the quantity overload case of Amazon, the complexity overload situation with pandemic risk pre-COVID illustrates the serious, even catastrophic, negative consequence of IO across the global business and social landscape.?

3.3: CLIMATE CHANGE

Many executives have come to accept that the global climate is changing and that these changes could soon have devastating consequences on how the business world operates. Yet what we still hear from many companies are not wholesale readjustments of their strategies and business models in response to these challenges ahead; rather, many reactions range from conducting business as usual to making statements of intended future change. As climate strategist Prof. Raz Godelnik at Parsons wrote a few years ago when asked if most global corporations are truly undertaking serious plans for a new climate world:

The short answer is: It is very difficult to know. These climate change plans and commitments usually provide information in a manner that makes it difficult to understand if they go far enough. First, companies tend to (over)use jargon and terminology...that could be very confusing, and second, it is not clear what constitutes a “good enough plan” to address the climate crisis. As a result, it is not too surprising that the lack of clear language and a comparable format together with vague benchmarks make corporate climate change plans the modern version of the Tower of Babel. Unfortunately, journalists and websites covering these plans do not help most of the time to clearly contextualize them, leaving the readers confused and having a hard time separating signal from noise.7?

Godelnik’s observations co-exist with conclusions such as this one from researchers at BI Norwegian Business School:

The purpose of this article is to report that we have identified a point-of-no-return in our climate model ESCIMO—and that it is already behind us. ESCIMO is a “reduced complexity earth system” climate model 5 which we run from 1850 to 2500. In ESCIMO the global temperature keeps rising to 2500 and beyond, irrespective of how fast humanity cuts the emissions of man-made greenhouse gas (GHG) emissions. The reason is a cycle of self-sustained melting of the permafrost (caused by methane release), lower surface albedo (caused by melting ice and snow) and higher atmospheric humidity (caused by higher temperatures). This cycle appears to be triggered by global warming of a mere + 0.5 °C above the pre- industrial level.8

Again, note that this conclusion was presented to the world by professors at a business school and thus is information shared with current corporate leaders. Yet even as the information about the impact of climate change continues to grow, many social and business leaders fail to take action. It may be possible that climate change IO affects many leaders who find it impossible or too painful to process all information available about our precarious environmental position, which results in inaction that is driving us closer and closer to disastrous environmental outcomes.

4: IO UNDER NEGATIVE ESCALATION

Intriguingly, there may be one other IO-related phenomenon at play in the examples above, and it is related to how IO affects decision-makers when their situation deteriorates. Roetzl and his colleagues Pedell Burkhard and Daniel Groninger looked at this question and found that when someone's course of action does not yield the desired results, IO has an increasingly negative effect, which in turn leads to further bad decisions. As their paper notes:?

The finding of a significant interaction between the type of feedback and the information load extends our knowledge about the role of information processing in decision making in escalation situations. Furthermore, we find that the type of feedback affects self- justification, and we find a negative and significant interaction between information load and self-justification in negative-feedback cases. 9

To understand the implication of this finding, let’s return to the Amazon case. Sensing that Amazon is a threat, a competitor begins to react in the ways they know: discounting, coupons, more advertising, etc. All the responses fail, and now the competitor finds himself trying to understand not just Amazon’s innovations but the reasons his responses failed. His information load has increased even more than when he was not responding. This increased level of IO can progress to a point where recovery is impossible, and the competitor becomes destined to fail completely. Much like an inexperienced pilot who becomes overwhelmed by cockpit alarms combined with maneuvers that do not correct an unplanned descent, it is precisely when strategies do not go as planned that we are most vulnerable to the negative, or even catastrophic, impacts of IO.?

5: The Keys to a Solution

Over years of research, I have interviewed scientists and researchers, spoken with senior executives in a cross-section of industries, and conducted learning projects with corporate and scientific research partners. I have reached a few conclusions about IO, which are presented below.?

1.?Signal and noise depend on the receiver and purpose.

For many executives, “noise” refers to facts or opinions with little or no intrinsic value. In this view, noise is less valuable than information. This point of view is understandable but wrong. There is no necessary qualitative difference between signal and noise without consideration of receiver and purpose, i.e., the difference between signal and noise arises only when the information must be used. For example, consider the closing value of the NASDAQ stock exchange on any given day. For the average person, this number is noise heard over a radio or television broadcast. However, for a programmer building a trading algorithm that responds to NASDAQ fluctuations, the closing value is a signal. Furthermore, depending on the nature of the algorithm, the signal may be foundational or merely useful, which means that the end use of the information establishes the degree to which any signal is valuable. The critical point is that when it comes to information, the question is the answer, i.e., whether a piece of information is signal or noise depends on who is asking and for what purpose.

2.?Source validation is complex and time-consuming but critical.

In his book The Information, James Gleick discusses the relationship of information to its sources and environment:

…knowledge isn’t simply information that has been vetted and made comprehensible. “Medical information,” for example, evokes the flood of hits that appear when you do a Google search for “back pain” or “vitamin D.” “Medical knowledge,” on the other hand, evokes the fabric of institutions and communities that are responsible for creating, curating and diffusing what is known. In fact, you could argue that the most important role of search engines is to locate the online outcroppings of “the old ways of organizing knowledge” that we still depend on, like the N.I.H., the S.E.C., the O.E.D., the BBC, the N.Y.P.L. and ESPN. Even Wikipedia’s guidelines insist that articles be based on “reliable, published sources,” a category that excludes most blogs, not to mention Wikipedia itself.

Gleick’s point is that information always has a relationship to its origin or source. Returning to the NASDAQ example above, it makes a great deal of difference if we receive the closing value from a stranger standing in line with us at a supermarket or if we read it on a Bloomberg screen.?

The point above may seem obvious, but it has significant implications for our analysis of IO. In today’s world, executives not only have to process information and turn it into action; they must also validate the sources of the information they consume. Understandably, many business leaders rely on external technologies and advisors to do this for them; however, this is a risky strategy. The technologies may be flawed or biased in their consolidation of information. Advisors often have sales agendas that color (or even bias) the information they present, which means that any first-rate insight sources must also be a first-rate information validator, with the ability to accurately access not just information but the quality of the source producing it. This is a discipline unto itself and must be understood in that way.?

3.?Insights are a personal experience and cannot be shared.

Insight is a term used constantly by consultants and business gurus, but many executives don’t stop to consider what the term really means. In experimental psychology, the term is specific and refers to the moment when the solution to a problem is seen spontaneously, i.e., the sudden discovery of the answer to a question or solution to a problem, often after a continuous effort was put forth without success.

A good example of insight is found when we look below and ponder the following question: What single term connects these three words?

DESK – MOUNTAIN – TREE

This problem is an example of what psychologists call a Remote Associates Test. RATs can be solved with and without insight, which makes them a handy tool. In other words, there are computational methods to find a term that connects all three words, or we might, after some consideration, realize on our own that the answer is “Top.”?

Insight, for our purposes, is defined only as the sudden acquisition of the answer to a question or solution to a problem. This definition suggests it is impossible to transmit your insights to anyone else precisely because they are yours. You can share your conclusions, decisions, or findings, but you can never really share your insights—it is an oxymoronic concept. Real insights can only be created within the mind through a process of discovery, dialogue, or analysis and are not found ready-made in reports, presentations, or webinars.?

4.?Groups are better at producing insights.

We are all familiar with the concept of “groupthink,” wherein a collection of similar individuals cannot escape an erroneous belief or conclusion. However, many people are unaware that the opposite phenomenon also exists, whereby a group is better able to solve a problem than someone working alone. Indeed, a research team studying this effect found that when it comes to insight-generation and insight-dependent problem-solving, group interaction is associated with “enhanced performance with respect to both recall and problem-solving.” The researchers concluded that a driver of increased group performance is related to something called “fixation,” which is when a problem solver gets “stuck” on one idea or piece of information, and that stubbornness makes it impossible to solve the problem.

I can illustrate fixation with the following example. Look at this number sequence carefully for at least three minutes and determine the progression logic in your head:

2, 4, 6, 8, 10 ….

Once you have the logic clearly in your mind, consider the following extension for an additional two minutes exactly:

2, 4, 6, 8, 10, 13, 17, 21….

Now consider this last set of information for one final minute:

2, 4, 6, 8, 10, 13, 17, 21, 28, 37, 49, 55….

It is common that even teams of bright executives do not find the correct (and very simple) answer to this problem. It seems so obvious once you know it, yet people often become fixated on the idea that the answer is “a sequence that goes up by 2” when considering the first set of numbers. That fixation, if it takes hold completely, makes it impossible to solve the problem.10

Fortunately, the researchers found that members of a group experience fixation in different ways:

…fixated states were not experienced uniformly by group members, that is, all group members did not remember the associated clues following the incubation period (a likely outcome if one generalizes from the individual clue memory data).11?

In other words, the researchers suggest that because different group members experience fixation differently, it is easier for individual members to “unfix” themselves from an incorrect answer and experience a moment of insight. This finding may be one reason why psychologically diverse groups often have better results when solving complex problems.?

5: Conclusions?

In his famous short story, The Library of Babel, Jorge Luis Borges describes a single edifice within which all possible written words are contained. Each book wall in the Library holds five shelves, each displaying thirty-two matching books. Every book has 410 pages with 40 lines per page and about 80 characters per line. The cover of each book has a title, but the title has nothing to do with the book's contents. No two books in the Library are identical; thus, the story posits, the Library is “complete.” In other words, the Library’s vast collection contains every idea, thought, emotion, discovery, poem, speculation, etc., that was ever conceived or could ever be conceived by the human mind. The Library in the story is an effective metaphor for today’s information overload, for the overwhelming store of words leads the building’s caretakers to despair, superstition, or even the hope that someone with superpowers will come along and explain everything to them.

Glancing around our information-saturated world can lead a business leader to become exasperated and imagine that a solution does not exist for the problem of IO. I think that there are a few strategies most leaders can deploy to combat this serious issue:

• Understand what IO is and how it operates. This article only summarizes the important work done by some of the best researchers in this field. Executives should become more familiar with the way in which IO affects decision-making and the decision-support systems and processes on which they rely.
• Improve one's filtering skills by consuming better, not more, information. Adding an information task to combat IO may seem paradoxical, but learning more about source quality is a great way to worry less about the information any given source provides. In other words, leaders should become curators, experts in information sources critical to their decisions, prioritizing the best and discarding the worst. In other words, the answer is to take in more information from fewer sources.
• Design incoming information flows so they do not overwhelm your capacity to absorb them. If you do not know your optimal information "flow rate", then you may be subject to IO. Thinking critically about how much information you can, and should, consume is not always high on a leader's agenda, but it should be considered as much as the information itself.

A final point before closing comes from my years of working with senior executives: Getting and staying connected is an important antidote to IO. As Bill Taylor, the founder of Fast Company, wrote:?

Finally, and most personally, successful learners work hard not to be loners. These days, the most powerful insights often come from the most unexpected places — the hidden genius locked inside your company, the collective genius of customers, suppliers, and other smart people who would be eager to teach you what they know if you simply asked for their insights. But tapping this learning resource requires a new leadership mindset — enough ambition to address tough problems, enough humility to be willing to learn from everyone you encounter. Nobody alone learns as quickly as everybody together.12

Perhaps in time, new artificial intelligence tools will help us better manage IO, and render it a problem of the past. For the foreseeable future, information overload is an issue that leaders should understand and consider as they design their organizations, teams, and management systems.

Notes

1: Roetzel, P.G. Information overload in the information age: a review of the literature from business administration, business psychology, and related disciplines with a bibliometric approach and framework development. Bus Res 12, 479–522 (2019). https://doi.org/10.1007/s40685-018-0069-z

2: Simon, Herbert A. Designing organizations for an information-rich world, Brookings Institute Lecture, 1969 https://zeus.zeit.de/2007/39/simon.pdf

3: Hemp, Paul. Death by Information Overload, Harvard Business Review (September, 2009 https://hbr. org/2009/09/death-by-information-overload

4: Chait, Jonathan and Glass, Steven. Amazon.con, Slate (Jan 05, 1997) https://slate.com/news-and-poli- tics/1997/01/amazon-con.html

5: Ratlif, Evan. The Plague Fighters: Stopping the Next Pandemic Before It Begins, WIRED (August 24, 2007). https://www.wired.com/2007/04/feat-firstblood/

6: McKenna, Maryn. The Race to Find the Next Pandemic—Before It Finds Us, WIRED (December 12, 2018). https://www.wired.com/story/the-race-to-find- the-next-pandemic-before-it-finds-us/

7: Godelnik, Raz. Are companies taking climate change seriously? A new tool helps you figure it out, Medium (Nov 12, 2020) https://razgo.medium.com

8: Randers, J., Goluke, U. An earth system model shows self-sustained melting of permafrost even if all man-made GHG emissions stop in 2020. Sci Rep 10, 18456 (2020). https://doi.org/10.1038/s41598-020-75481-z

9: Roetzel, P.G., Pedell, B. & Groninger, D. Information load in escalation situations: combustive agent or counteractive measure? J Bus Econ 90, 757–786 (2020). https://doi.org/10.1007/s11573-020-00987-x

10: The logic of the sequence is "any series of numbers that increase in value."

11: Smith, Christine M.; Bushouse, Emily; Lord, Jennifer. Individual and Group Performance on Insight Problems: The Effects of Experimentally Induced Fixation, Grand Valley State University (2009). https://scholar- works.gvsu.edu/cgi/viewcontent.cgi?article=1021&context=p- sy_articles

12: Taylor, Bill. Are You Learning as Fast as the World Is Changing? Harvard Business Review (January 26, 2012). https://hbr.org/2012/01/are-you-learning-as-fast-as-th

? 2023 Carlos Alvarenga