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In the business world, failure is the rule rather than the exception. Most companies liquidate or cease to be independent after just a few years of existence. A look at the history of the S&P or Fortune 500 shows that even attaining size and success does not guarantee longevity. For example, in their paper on "Sustained Competitive Advantage" Wiggins and Ruefli analyze twenty five years of data covering 6,772 firms and conclude that "(1) while some firms exhibit superior economic performance, (2) only a very small minority do so, and (3) superior performance rarely persists for long time frames." The world of investment management is no different. As time passes, the percentage of active managers who have succeeded in outperforming their relevant index benchmark drops sharply, even before the effects of fees and taxes are taken into account. At a higher level, the lives of many financial products, including many narrowly defined index products, are also very short. Despite this, the number of business books that focus on the causes of failure remains a small fraction of those that claim to offer the secrets of success. To some extent, this undoubtedly reflects an essential and admirable aspect of human nature -- our optimism is critical to our willingness to take risks, and drive the evolutionary process of variation and selection that constantly renews our fitness to survive in an ever changing environment. Yet I have also long felt that the dearth of books about failure reflects a fear of fully confronting the true scale of uncertainty we face, and learning the lessons life's most painful chapters can teach us. With that in mind, I spent the summer re-reading some of the best of these books I've collected over the years. In this article, I'll highlight their key findings, and summarize the lessons I think they hold for investment managers.
Let me begin with this quote: "Economists at this moment are called upon to say how to extricate the free world from the serious threat...which, it must be admitted, has been brought about by policies which the majority of economists recommended and even urged governments to pursue. We have indeed at the moment little cause for pride: as a profession we have made a mess of things." Sounds like something that could have been said last week, doesn't it? It comes from the speech Friedrich von Hayek gave on December 11, 1974 when he received the Nobel Prize in economics. Back then, the serious threat came from accelerating global inflation. Hayek went on to offer his view of the root cause of the failings he cited. "This brings me to the crucial issue. Unlike the position that exists in the physical sciences, in economics and other disciplines that deal with essentially complex phenomena, the aspects of the events to be accounted for about which we can get quantitative data are necessarily limited and might not include the important ones...In the physical sciences, the investigator will be able to measure what, on the basis of prima facie theory, he thinks important. [In contrast], the social sciences often treat as important that which is accessible to measurement. This is sometimes carried to the point where it is demanded that our social science theories must be formulated in such terms that they refer only to measurable magnitudes. It can hardly be denied that such a demand quite arbitrarily limits the facts which are to be admitted as possible causes of the events which occur in the real world..."
"What looks superficially like the most scientific procedure is often the most unscientific...Confidence in the unlimited power of science is too often based on a false belief that the scientific method consists in the application of a ready-made technique, or in imitating the form rather than the substance of scientific procedure...It sometimes seems as if the techniques of science are more easily learned than the thinking that shows us what the problems are and how to approach them...The chief point we must remember is that the great and rapid advance of the physical sciences took place in fields where it proved that explanation and prediction could be based on laws which accounted for the observed phenomena as functions of comparatively few variables -- either particular facts or relative frequencies of events. This may even be the ultimate reason why we single out these realms as "physical" in contrast to those more highly organized structures which I have called essentially complex phenomena...As we advance from the realm in which relatively simple laws prevail into the range of phenomena where organized complexity rules, we find more and more frequently that we can in fact ascertain only some but not all the particular circumstances which determine the outcome of a given process. In consequence, we are able to predict only some but not all the properties of the result we expect. Often, all that we shall be able to predict will be some characteristic of the pattern that will appear. Yet these are still predictions which can be falsified, and are therefore of empirical significance. Of course, compared with the precise predictions we have come to expect in the physical sciences, this sort of pattern prediction is a second best...Yet the danger of which I want to warn is precisely the belief that in order to have a claim to be accepted as scientific it is necessary to achieve more. This way lie charlatanism and worse...If man is not to do more harm than good in his efforts to improve the social order, he will have to learn that in economics, as in all other fields where essential complexity prevails, he cannot acquire the full knowledge which would make mastery of events possible."
Eight years after Hayek gave his Nobel speech, Air Force Colonel John Boyd began a remarkable series of Pentagon briefings entitled "Discourse on Winning and Losing" which, in effect, presented a framework for explaining failure and success in navigating complex adaptive systems. Boyd began with the assumption that the purpose of strategy was "to improve our ability to shape and adapt to unfolding circumstances, so that we can survive on our own terms." Boyd proposed a continuous cycle of analysis and synthesis, interaction with the environment and isolation that has come to be known as the "OODA Loop." In the first phase, an individual or organization observes the world. Failure can result when scarce attention is focused on the wrong indicators. The most critical phase of the loop is orientation, in which we use our observations to make sense of our situations, and formulate alternative courses of action to achieve our goals. The causes of failure in this phase include developing a dangerously inaccurate picture of one's situation, and/or inappropriate courses of action. At this point a decision is made about the course of action to pursue. In essence, Boyd regards decisions as hypotheses about the likely evolution of the environment, and cause and effect relationships within it. Failure in this phase can be caused by delay in making a decision, or by choosing the wrong decision. Action, the last phase, implements the chosen plan, and, in effect, tests the hypothesis, and generates new observations that begin the process all over again. In this phase, failure can be caused by poor execution, or by randomness (i.e., bad luck). As competition between intelligent players or organizations unfolds over time (Boyd began to develop his ideas when analyzing the causes of pilots' failures and success in dogfights), the player that executes the OODA loop more quickly and more accurately gains an ever increasing advantage over opponents. Moreover, when a player "gets inside an opponent's decision cycle", he or she causes an exponential accumulation of disorder inside the opponent's organization, causing it to make more mistakes, and hastening its failure.
In 1985, seven years after Boyd began to give his briefings, Dietrich Dorner published The Logic of Failure, which I still consider the best book on this subject. In essence, Dorner added more detail to both sides of Hayek's argument -- both the nature of complex systems and humans' shortcomings when comes to accurate perception and goal achievement when complex systems are involved. Regarding the former, Dorner describes the by now familiar mix of positive and negative feedback loops, goal directed agents with varying degrees of interconnectivity, and the evolution of key relationships over time that together create a complex adaptive system. What I think makes Dorner's book particularly valuable, however, is his insights into the many reasons we fail to reach our intended objectives - that is, we fail - when operating in complex adaptive systems.
Dorner notes that in such systems, "we must keep track of constantly changing conditions and never treat any image we form of a situation as permanent. Everything is in flux, and we must adapt accordingly. The need to adapt to particular circumstances, however, runs counter to our tendency to generalize and form abstract plans of action. This is an example of how an important element of human intellectual activity can be both useful and harmful." Dorner also anticipates a lot of later writing about the problem of what is now known as "information overload." In a complex adaptive system, "anyone who has a lot of information, thinks a lot, and by thinking increases his understanding of a situation will have not less but more trouble coming to a decision. To the ignorant, the world looks simple. If we pretty much dispense with gathering information, it is easy for us to form a clear picture of reality and come to clear decisions based on that picture...Once we start gathering information, however, we run into trouble, because we realize how much we still don't know...The self-reinforcing feeling of uncertainty, anxiety and insecurity that results [from gathering information about a complex adaptive system]...may explain why some people deliberately refuse to take in information...We end up combating our uncertainty either by acting hastily on the basis of minimal information or by gathering excessive information, which inhibits action and may even increase our uncertainty. Which of these patterns we follow depends on time pressure, or the lack of it....Eventually we may pull back into a small cozy corner of reality where we feel at home [i.e., narrow our focus to that part of the system we think we understand] or, alternatively, escape vertically, by creating a more abstract model of reality."
With respect to understanding actions that unfold over time, Dorner highlights human beings default reliance on linear extrapolation of events based on a few hypothesized simple cause and effect relationships. As a result, "when we have to cope with systems that do not operate in accordance with very simple temporal patterns we run into major difficulties." In particular, Dorner notes that "in situations where feedback [about the results of our actions] is not frequent and where the intervals between action and feedback are longer, we can expect ritualization to wax luxuriant." In particular, most people tend to forget some simple guidelines: "The essence of planning is to think through the consequences of certain actions, stringing individual actions together in sequences, and seeing whether those actions will bring us closer to our desired goal...Try to understand the internal dynamics of the process. Make notes on those dynamics so that you can take past events into account and not be at the mercy of the present moment. Try to anticipate what will happen." Dorner notes that, when faced with the challenge of achieving a goal in a complex adaptive system, a frequent cause of failure is overplanning. He notes that "if we expect the unexpected, we are better equipped to cope with it that if we lay extensive plans and believe that we have eliminated the unexpected...In very complex and quickly changing situations, the most reasonable strategy is to plan only in rough outline and to delegate as many decisions as possible to subordinates" and instead focus maintaining an awareness of the evolving situation. Dorner also reemphasizes "a point first made by Clausewitz: 'In war everything is simple, but it's the simple things that are difficult'....Plans often fail because the planners have not factored in all the irksome little conditions, or 'frictions' as Clausewitz called them, that have to be dealt with if the plan is to succeed. The plan may be simple; carrying it out is the hard part." On the other hand, Dorner concedes that simple plans also "often give us something we sorely need, namely, optimism and courage. There are many tasks we would never dare to take on if we didn't first conceive of them in very simple terms."
Another source of failure highlighted by Dorner is the unwillingness to evaluate the consequences of our plans and actions. In effect, humans tend to ignore opportunities for learning in order "to preserve the illusion of our competence." Indeed, this psychological need is so strong that it "contributes significantly to shaping the direction and course of our thought processes...We often redirect our thinking from our actual goals to the goal of preserving our sense of competence."
More recently, in 2006 Stephan Fruhling, of the Strategic and Defense Studies Centre of the Australian National University, published a paper that built on many of the issues raised by Dorner. He noted that, "strategy in practice inevitably involves the forecasting of future cause-effect relationships. Five basic sources of uncertainty make it difficult to predict and test these relationships and the variables associated with them...Aleatory uncertainty refers to the uncertainty inherent in a stochastic, random phenomenon" - what most people call randomness or luck. "The second cause of uncertainty in strategy is the existence of dynamic systems caused by nonlinearity and complexity...A third source of uncertainty derives from the fact that humans are limited in their cognitive and physiological abilities to process information...Fourth, the enemy himself is a fourth major source of uncertainty in strategy...a battle of two or more wills seeking to achieve their respective goals...Finally, the difficulty in predicting non-linear changes is compounded by the fact that information about the current state of the system -- intelligence -- is inevitably limited and uncertain."
As you have no doubt concluded by now, failure is a far more popular topic among military analysts than it is among business writers, perhaps because its consequences are so much more serious in the former realm. Among the many good pieces of writing by military authors on the subject of failure is Cohen and Gooch's 1991 book Military Misfortunes, in which they used historical examples to illustrate three critical sources of organizational failure. These included failure to anticipate the future, failure to adapt in the present, and failure to learn from the past. In some cases, more than one of these causes was at work, leading to that they termed "compound" failures. Failure to anticipate has also been the subject of a series of books on surprise attack, including Roberta Wohlstetter's classic Pearl Harbor: Warning and Decision, Richard Betts' Surprise Attack: Lessons for Defense Planning, and Ephraim Kam's Surprise Attack: The Victim's Perspective. All of these books echoed the findings that would later be reached by the multiple commissions that studied the events leading up to the 9/11 attacks. In different ways, all of these books employed a version of Bayes Theory, which describes a methodology for updating a prior view in light of new evidence. In the case of surprise attack, failure can be caused by the difficulty of separating signals from noise, and in evaluating the diagnostic value and credibility of signals that often conflict. A more fundamental problem, however, lay in the nature of the prior views that were held before the attack. As more than one study of the 9/11 attacks concluded, the real problem was not so much a technical failure to "collect the dots" or an analytical failure to connect them, but rather a more fundamental failure to imagine a wide enough range of scenarios and possibilities to guide the search for the dots in the first place. In the face of information overload, which has been made orders of magnitude more challenging by technology, there are two ways to attack the sensemaking and warning problem. The first is to use technology to attack the problem with brute force, through software that can generate "novel insights from massive data sets." To use an investment analogy, this is equivalent to algorithmic trading, which uses machine learning software (e.g., neural networks and genetic algorithms) to inductively predict the evolution of data points in a time series from empirical relationships found in the historical data, rather than deducing them on the basis of theories about how a system should behave. The second approach is to attack the sensemaking and warning problem with imagination, generating a series of hypotheses (e.g., scenarios) and proactively seeking evidence that falsifies them (i.e., that you would not expect to see if the hypothesis was true). In an age of information overload, both of these approaches are more efficient than simply trying to "make sense" of a stream of incoming data.
The engineering world has also published numerous studies of failure, often based on lessons learned from thorough investigations of aircraft crashes and industrial accidents. Perhaps the best known of these is Charles Perrow's 1999 book Normal Accidents, which concluded that two factors make a system highly susceptible to catastrophic failure. The first is "tight coupling", or the tendency in a system for events to be closely related to each other in time. The second is "interactive complexity", or systems in which there are interrelationships between elements, some of which are non-linear. This makes their behavior hard for operators and observers to fully understand and anticipate, while tight coupling reduces the time available to react to unanticipated events. In 2001, James Chiles published a fascinating book called Inviting Disaster: Lessons from the Edge of Technology. Among the many interesting points that emerge from Chiles' recounting of various disasters, three stood out for me. The first is the critical importance of learning from "near misses" and small "system anomalies" rather than dismissing them as "noise." Chiles stresses that more often than not, they provide early warnings of unanticipated pathways to potentially far more serious problems. This is also consistent with the findings of Mandelbrot and others that the behavior of some complex systems is "fractal" in nature, or displays similar power law distributions across different time scales. When confronted with a near miss, it always pays to ask, "what caused this problem? How could it have led to a much bigger problem?" The second point Chiles makes is that "part of the trick in high fear situations is knowing what needs to be done immediately, what can wait, and which actions cannot be reversed after second thoughts." This is why operators of complex systems employ elaborate checklists and substantial amounts of simulation training. Finally, Chiles stresses that "we know from technological disasters that transitions in their broadest sense - from aircraft landings to factory crew changes to start-ups after maintenance shut-downs - are the times of greatest danger for a complex system."
Psychologists, sociologists, and biologists have also contributed to the study of different failure modes. Examples include the dangers of groupthink (which Irving Janis describes as "a mode of thinking that people engage in when they are deeply involved in a cohesive in-group, when the members' strivings for unanimity override their motivation to realistically appraise alternative courses of action"), herding by unrelated individuals or organizations (for an investment oriented analysis of this source of failure, see "Thought and Behavior Contagion in Capital Markets" by Hirshliefer and Teoh), and the transmission of panic (see "Learning Fears by Observing Others" by Olsson, Nearing and Phelps). In our experience, one of the most useful frameworks for thinking about the sources of failure has been provided by Stuart Kauffman, who popularized the NKCS model, which captures the complex balance between internal and external sources of failure. The model assumes the existence of two or more systems, each of which is composed of different organizations (technically, "agents"). At the end of each time period, organizations with a "fitness level" (i.e., performance compared to one or more metrics) below a given minimum are removed from the game - that is, they fail. Internally, an organization is composed of N elements (e.g., strategic choices). Each choice is, on average, related to K other choices. The fitness impact of a decision to change the value of a given element depends both on the direct result of the change, plus the indirect impact on K other elements. Organizations also have an average of C connections with S other organizations that exist in other systems (technically, "ecosystems"). Hence, a change in just one of N elements in an organization can affect the fitness of S other organizations in other ecosystems, via their C connections with the organization making the original change (for example, think of the cascading consequences of credit contraction at large money center banks). Moreover, this process works both ways. The great power of this model is that it shows multiple pathways that can result in failure, not due to some exogenous shock, but rather due to changes in relationships within the system itself (i.e., due to endogenous changes). As Kaufman shows, the risk of failure (i.e., fitness below the failure threshold) increases with the degree of imbalance in the NKCS model. If the product N*K is greater than C*S, it produces excessive stability, which is one source of failure. If N*K is significantly less than C*S, it produces chaotic behavior (think of it as lurching from one new initiative to another, in an uncoordinated manner), which is another source of failure. On the other hand, when N*K and C*S are closely balanced, the organization is said to be in the region of maximum adaptivity and resiliency, with the lowest risk of failure.
Another biological concept that significantly bears on the causes of failure is "path dependence." This refers to the tendency of choices made in one period to constrain the range of possible choices that can be made in the face of evolving circumstances many periods into the future. A business example of this would be the impact of a substantial financial commitment to a given market or technology, or the multiperiod impact of poor customer experience and its reputational consequences in an age of ubiquitous communications. Indeed, as most CEOs can tell you, the interrelated effects of path dependence and randomness are far more powerful than most investors (and not a few boards) would care to admit.
Following a great deal of research into failure in other disciplines, in recent years, the subject of failure has finally become, if not popular, than at least more interesting in the eyes of business book publishers (though success recipes still outsell them by a large multiple). An excellent example is Why Most Things Fail by Paul Ormerod. He notes at the outset that "within economics, we will look in vain for any satisfactory account of why firms fail." In response, Omerod asks "how can it be that not just failure, but patterns of failure, are so similar in biology and human organization when there is such a sharp contrast between the abilities to act with the conscious intent of improving one's prospects for survival?" Omerod begins with an exposition of the similarity between the distribution of the size and frequency of failures across multiple domains -- all of which are shown to follow similar power laws. He then delves into the causal processes that produce this outcome, comparing the impact of exogenous and endogenous shocks. He concludes that the latter are far more important when it comes to explaining failure. However, we found Omerod's most interesting conclusion to be the relatively low potential for superior information and cognition -- as one would expect an business organization to display -- to affect the chances of failure, once it has successfully survived the most dangerous (in terms of failure probabilities) early years of existence. As he notes, "despite the ability of humans and human institutions to act with intent, in reality it is if they operate close to the paradigm of the agent with zero cognitive ability. They do not have to mimic it completely, and a small amount of ability to translate intent into desired outcome is compatible with the evidence we observe, but no more than that...[Analysis of the historical record of failures leads one to conclude that] agents have very limited capacities to acquire knowledge about the true impact of either their strategies on others or of others on them...The future remains covered in a deep veil to all in complex dynamic environments which evolve over time."
In How the Mighty Fall, Jim Collins describes a five-step process of organizational decline. The first is "hubris born of success". According to Collins, "it sets in when people become arrogant, regarding success as virtually an entitlement, and lose sight of the factors that created success in the first place...Luck and chance play a role in many successful outcomes, and those who fail to acknowledge the role luck may have played in their success -- and thereby overestimate their own merit and capabilities -- have succumbed to hubris." This stage is also characterized by a decline in curiosity and learning. The next stage is "the undisciplined pursuit of more of whatever those in power see as 'success'...This often causes them "to stray from the disciplined creativity that led them to greatness in the first place, making undisciplined leaps into areas where they cannot be great or growing faster than they can achieve with excellence or both." In the third state, "denial of risk and peril" Collins concludes that "internal warning signs begin to mount, yet external results remain strong enough to 'explain away' disturbing data, or to suggest the difficulties are temporary...or that ‘noting is fundamentally wrong.' " In this stage, "leaders discount negative data, amplify positive data, and put a positive spin on ambiguous data...The vigorous fact-based dialogue that characterizes high performance teams dwindles or disappears altogether." By stage four, "grasping for salvation", the problems have become too visible to deny, and the organization seeks a magic bullet that will quickly reverse the decline. "The key point is that they go for a quick, big solution or a bold stroke to jump-start a recovery, rather than embark on the more pedestrian, arduous process of rebuilding long-term momentum." As Collins notes, "the signature of mediocrity is not an unwillingness to change. The signature of mediocrity is a chronic inconsistency...The longer a company stays in stage four, the more likely it will continue to spiral downward to stage five", which he terms "capitulation to irrelevance or death."
In Why Smart Executives Fail, Sydney Finkelstein highlights four broad causes of corporate decline, including brilliant execution of the wrong plan, hubris that suppresses dissent, a failure to face up to data that shows the need for change, and the personal shortcomings of corporate leaders. The latter include “seeing themselves and their companies as dominating their environments", identifying too closely with their company, thinking they have all the answers, "ruthlessly eliminating anyone who isn't 100 percent behind them", "becoming obsessed with the company's image", "underestimating major obstacles" to the implementation of plans, and "stubbornly relying on what worked in the past", regardless of mounting evidence to the contrary.
As I said at the outset, I have, over the years, been a keen student of failure. I have come to realize that failure is not simply the flip side of success. Rather, they are distinct phenomena, if by "success" one means achieving performance in the right tail of the distribution, as opposed to simply avoiding failure and delivering performance that is in the middle of the bell curve. For that reason, failure merits study as a critical phenomenon in its own right. After reading the studies summarized above, and others like them, I have developed my own theory of failure. My starting point is the observation that the fitness of all organisms and organizations can be measured according to three metrics: Effectiveness (the extent to which their actions result in achievement of their goals); Efficiency (the extent to which the resources acquired in a given period exceed the resources expended); and Adaptability (the ability to survive and thrive in the face of change). In my view, most of the causes of failure fall into these three categories.
Effectiveness results from the proper balancing of ends (i.e., the alignment of goals with the metrics driving selection in the environment), ways (i.e., the plan for achieving the chosen ends), and means (i.e., the resources available to execute the plan). The greater the degree of imbalance between these three elements, the higher the risk of failing to pass the selection test. This raises the question of what causes risk to be high. In some cases, this is a deliberate decision -- think of an army with its back to the wall, a manager who wants to stay on the good side of a domineering boss, or perhaps a fund manager trailing far behind her benchmark and fearing the loss of her job who takes on significant leverage to make a big bet that will vault her into the first performance quartile if it pays off. In other cases, organizations can back into high risk decisions without fully realizing what they are doing. For example, magic bullet solutions (which, one way or another, always assume extreme effectiveness relative to available resources) are inevitably predicated on magically accurate forecasts (even if this usually isn't explicitly acknowledged).
However, experience has taught me that more often than not, high risk bets are made inadvertently because the decision maker lacks an adequate understanding of the complex adaptive system within which he or she must act. Cohen and Gooch would call these failures of anticipation. As many other authors have noted, the root cause of these errors lies in fundamental aspects of human cognition and emotion and social interaction. On the one hand, I am confident that advances in agent based and network modeling are leading to better decision aids to help us more clearly understand the dynamics of complex adaptive systems. However, as Hayek noted 35 years ago, there are inherent limitations as to how far advances in this area can take us -- we can become better at recognizing patterns and preparing for different types of outcomes (i.e., become more effective by becoming more resilient), but we will generally not be able to improve the accuracy of our point forecasts. Still, there is evidence that even slight improvements in our mental models of complex adaptive systems can significantly improve effectiveness, and reduce the chance of failure (see not only Omerod, but also "Mental Models, Decision Rules, Strategies and Performance Heterogeneity" by Gary and Wood and RAND's work decision making under deep uncertainty). On the other hand, I am less sure that we will ever be able to overcome some of the emotional limitations (e.g., our aversion to uncertainty, and tendency to want to stick with the crowd when uncertainty is high) that limit our ability to deal with complex adaptive systems.
I see two broad sources of failure caused by falling short of the efficiency criteria. The first is derivative -- failures to adequately allow in plans for what Clausewitz termed "frictions" (and the Irish call "Murphy's Law") that result in resources that are insufficient to execute a plan as designed. Practically, this is what I view as a failure to learn from the past. Examples of this type of failure abound (e.g., see Bent Flyberg's excellent papers on the causes of major project cost overruns), as evidenced by the many rules of thumb suggested to help people avoid them (e.g., double your original estimate of the amount of time and money required to complete a project). The second cause of failure I have frequently observed in this area is Omerod's "exogenous shock." Perhaps the most famous recent examples of this are those related to oil, exchange rates, and credit -- though depending on how broadly you draw the boundaries system involved, they could also be termed endogenous (indeed, if you define the system broadly enough, only asteroid hits and alien landings would be deemed exogenous).
As I have grown older, however, I have come to realize that, assuming a company gets the basics right (and, as the failure statistics show, a surprising number don't), it is failure to adapt to a constantly evolving environment that most often results in an organization being selected out of independent existence. One cause of adaptation failures already noted is path dependency, or the tendency of previous decisions to limit your current options (which, of course, argues strongly for keeping options created and closed off clearly in mind when making decisions). However, I believe the far more important sources of adaptation failures lie in a range of individual (cognitive and emotional), group, and institutional (e.g., information flows and incentives) causes that have been described in many different ways by the authors we have cited in this article. One further source of adaptive failure, not often mentioned but critical nonetheless, is the widespread tendency to judge the models we use by how well they explain the past. Research has shown that in a complex adaptive system, this standard is guaranteed to produce prediction failures (see, for example, "The Good, The Bad, and The Ugly of Predictive Science" by Hemez and Ben-Haim, or, in the context of recent failures in economics and finance, Paul Krugman's article "How Did Economics Get It So Wrong?" or an excellent paper by Shojai and Feiger, "Economists' Hubris -- The Case of Asset Pricing"). Unfortunately, when confronted with their inability to accurately predict the future, too many organizations have sought not higher resiliency (i.e., robustness to future uncertainty), but higher efficiency, and in so doing significantly reduced their ability to adapt.
What then, are the lessons for investors, and asset allocators in particular, from this review of the causes of failure across a wide range of other domains? I think there are many, including:
| Feature Article: What Causes Failure? | September 2009 Economic Update | Product and Strategy Notes: Interesting Commodity Research; The Coming U.S. Muni Market Train Wreck; New Volatility Research; Harvard and Yale Endowment Results; and More Interesting Research | This Month's Letters to the Editor: Index's Position on Timber; Right Question to Ask a Fund Manager; Why not 7 Year Forecasts?'; Credit Beyond HYG | September 2009 Issue: Key Points | Global Asset Class Valuation Updates Detail | Global Asset Class Returns | Table: One Year Asset Class Valuation Conclusions and Recent Momentum | Table: Market Implied Regime Expectations | Uncorrelated Alpha Strategies Detail |