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The United States emerged from World War II as the world’s leader in research, after which we initiated an experiment in governmentfunded, civiliancontrolled military research. After 50 years, with the changes in pressures and goals of the Army in transformation and the military taking on multiple roles in modern warfare, it is time to reexamine the management of science and engineering human resources. Herein we argue that the human management resource procedures put in place following Vannevar Bush’s remarkable 1945 report, do not properly take the complex human aspects of research into account. In particular we use the data on such metrics as the number of published articles and citations to those articles to show that the Six Sigma program for managing resources, so successful in manufacturing, is counterproductive in a research environment. Using these data we suggest, using the Pareto Principle, how to overcome the present day distortion in the evaluation of the overall quality of research being done within federal laboratories and by government researchers.
Why Six-Sigma Science is Oxymoronic Dr Bruce J West, ST US Army Research Office, AMSRD-ARL-RO Information Sciences Directorate, AMSRD-ARL-RO-I Research Triangle Park, NC 27709 919-549-4257 DSN 832- 549-4257 bruce.j.west@us.army.mil Summary: The United States emerged from World War II as the world’s leader in research, after which we initiated an experiment in government-funded, civiliancontrolled military research After 50 years, with the changes in pressures and goals of the Army in transformation and the military taking on multiple roles in modern warfare, it is time to reexamine the management of science and engineering human resources Herein we argue that the human management resource procedures put in place following Vannevar Bush’s remarkable 1945 report, not properly take the complex human aspects of research into account In particular we use the data on such metrics as the number of published articles and citations to those articles to show that the Six Sigma program for managing resources, so successful in manufacturing, is counterproductive in a research environment Using these data we suggest, using the Pareto Principle, how to overcome the present day distortion in the evaluation of the overall quality of research being done within federal laboratories and by government researchers Science in government The Army is in the process of transforming itself from what it was in the late 1990’s into the Future Combat Force The intent is for science and technology to make the fighting forces more mobile, lethal and survivable, so they can successfully carry out today’s less conventional missions Under the rubric of network-centric warfare, the Army hoped to use computers and communications networks to connect all weapons, logistics and command networks and give our soldiers and commanders advantages in situational awareness and decision-making This transformation process has been dependent at every stage on science and technology, but it has apparently been assumed that the procedures used for the application of science and technology to this daunting task and the management of the associated human resources would remain essentially unchanged or even become more efficient during this period The arguments presented herein not address the general problems of managing human resources within the Army and the Department of Defense (DoD), but rather the more restricted set of problems associated with the management of human resources within the Army’s science and technology community and how these problems may have been impacted by the Army’s transformation Prior to World War II there was no large-scale scientific effort within the federal government, much less within the Army After World War II, Vannevar Bush, who had presided over the government research effort during the war as Director of the Office of Scientific Research and Development (1941-47), responded to a request from President Roosevelt regarding the transition of war research to the private sector with the report Science-The Endless Frontier [1] In this now legendary report V Bush argued that the United States needed to retain the scientific advantage achieved during the war years and laid out the reasons for building a civiliancontrolled organization for fundamental research with close liaison with the Army and Navy to support national needs and with the ability to initiate basic research V Bush emphasized that historically scientists have been most successful in achieving breakthroughs when they work in an atmosphere relatively free from the adverse pressure of convention, prejudice, or commercial necessity This freedom from hierarchical structure stands in sharp contrast to military tradition He believed that it was possible to retain an alternate organizational structure, outside the more traditional military, but working in close collaboration with it Such an organization would foster and nurture science and the application of science to new technologies, through engineering In Bush’s words [1]: …such an agency … should be … devoted to the support of scientific research… Industry learned many years ago that basic research cannot often be fruitfully conducted as an adjunct to or a subdivision of an operating agency or department Operating agencies have immediate operating goals and are under constant pressure to produce in a tangible way, for that is the test of their value None of these conditions is favorable to basic research Research is the exploration of the unknown and is necessarily speculative It is inhibited by conventional approaches, traditions and standards It cannot be satisfactorily conducted in an atmosphere where it is gauged and tested by operating or production standards Basic scientific research should not, therefore, be placed under an operating agency whose paramount concern is anything other than research His vision materialized through the development of the Office of Naval Research in 1946, the Army Research Office in 1951 (as the Office of Ordnance Research), the Air Force Office of Scientific Research in 1950 (as the Air Research and Development Command), and the National Science Foundation in 1950; albeit, none of these organizations followed all his suggestions regarding the management of scientific personnel and the support of science These agencies continue to support research conducted on university campuses While these agencies were being established a substantial number of research laboratories were stood up by the services to house government scientists and engineers that would collaborate with academic and industry scientists and engineers The voice of V Bush concerning the incompatibility of fundamental research and mission agencies was prophetic The dire consequence of that incompatibility was held off for 50 years, however, by a set of checks and balances put into place in order to insulate basic research (6.1) from the pressures of applied research (6.2) The separation of the research being supported through the services but done on university campuses (basic) from the research being done within government laboratories (both basic and applied) has maintained, by and large, the separation between fundamental and applied research However, V Bush’s cautionary voice is now being echoed in a report [2] authored by members of the National Research Council Congress directed DoD to have the National Research Council study the nature of the basic research being funded by the DoD The findings of the report of most relevance to the present discussion are [2]: A recent trend in basic research emphasis within the Department of Defense has led to a reduced effort in unfettered exploration, which historically has been a critical enabler of the most important breakthroughs in military capabilities… Generated by important near-term Department of Defense needs and by limitations in available resources, there is significant pressure to focus DoD basic research more narrowly in support of more specific needs….The key to effective management of basic research lies in having experienced and empowered program managers Current assignment policies and priorities (such as leaving a substantial number of program managers positions unfilled) are not always consistent with this need, which might result in negative consequences for the effectiveness of basic research management in the long term Two recent studies [4] focused on the present day efficacy of the 700 laboratories and research centers constituting the Federated Laboratory System John H Hopps Jr., Deputy Director of Department of Defense Research & Engineering and Deputy Undersecretary of Defense in the Department of Defense, introduces the 2002 document with the observation that our “defense laboratories should have the same attributes as our transformed uniformed military forces.” He specifically pointed out that scientific research should share the characteristic of the modularity of the joint forces with the parallel attributes of [5]: ….productivity; responsiveness and adaptability; relevance, programming, and execution and application; and perpetuation of knowledge In opposition to this notion of modularity it is interesting to recall V Bush’s remarks [1]: Science is fundamentally a unitary thing….Much medical progress, for example, will come from fundamental advances in chemistry Separation of the sciences in tight compartments….would retard and not advance scientific knowledge as a whole The variability in research and creativity is fundamentally at odds with uniformity and regimentation One does not create on demand; it is the exploration of new alternatives, going down blind alleys, and even failing that is at the heart of innovative research Again quoting from V Bush [1]: Basic research is a long-term process - it ceases to be basic if immediate results are expected on short-term support [1] So what are the specific management problems in the Army science and engineering community? One of the more significant problems is the age distribution, which is heavily weighted towards the senior ranks with fewer junior scientists and engineers being attracted into and retained by the Army This problem was also identified by V Bush who observed that [1]: The procedures currently followed within the Government for recruiting, classifying and compensating such personnel place the Government under a severe handicap in competing with industry and the universities for first-class scientific talent His comments are as true today as they were in 1945 A second significant problem is the morale of those scientists and engineers staying within the Army A symptom of this dissatisfaction is the fact that 15-20% of the Army’s senior research scientist (ST) population left the Army in 2004 [5] I believe we have arrived at the present situation through a failure to properly take into account human nature in the management of scientists and engineers To support this proposition let us examine the evidence regarding the ‘unfair’ nature of social organizations and how this unfairness can and should influence human resource management The evidence I present concerns such apparently disconnected phenomena as the distribution of income in western societies, the distribution of computer connections on the World Wide Web, the frequency or publications of, and citations to, research articles, as well as, the description of other complex phenomena involving the multiple interactions of human beings [6] However, we begin our investigation on more familiar ground, before venturing out onto the frontier, where understanding is rare and useful ideas are few and far between The modern theory of human resource management can be traced back to Joseph M Juran who wrote the standard reference work on quality control [7] It is his work on management [8] that evolved into the Six Sigma program which is the basis of quality management worldwide The Six Sigma program has achieved a certain currency within the DoD and elsewhere because of its emphasis on metrics and the importance of being able to quantify the problems being addressed within an organization What we propose to establish in the following sections is that the Six Sigma approach to management is fundamentally incompatible with the goals of science Moreover, six sigma is counterproductive when applied to any metrics of the quality of the research done by scientists and engineers Gauss versus Pareto 2.1 Myth of Normalcy The name Six Sigma is taken from the bell-shaped probability distribution, see Figure 1, constructed by the mathematician Johann Carl Friedrich Gauss (1777-1855), to quantify the variability observed in the results of experimental measurements taken in the physical sciences In the Gaussian worldview the average value of an observable is the single most important variable for characterizing a phenomenon and the fluctuations around that average value are due to errors of measurement and random fluctuations of the environment The parameter sigma (σ) quantifies these fluctuations and in statistics, sigma is called the standard deviation; the smaller the value of σ relative to the average, the better the average represents the data Consequently, in this view, the world consists of networks made up of linear additive processes, whose variability is described by a bell-shaped curve, centered on the average value, with a width determined by sigma The Gauss model is very useful in manufacturing, where the specifications for the production of a widget can be given to as high a tolerance as can be achieved on a given piece of machinery Of course no two widgets coming off the production line are exactly the same, there is always some variability Let us suppose that the widget can be characterized by a diameter and the variation around the average value of the diameter is given by a bell-shaped curve If all the measured diameters are divided by the standard deviation in a given production run then the new variable is expressed in terms of the standard deviation, sigma (σ) In Figure the results of producing this hypothetical widget are shown The attraction of this approach is the universality of the shape of the distribution when the standard deviation is the unit of measure All processes that have Gaussian or Normal statistics can be superimposed on the given curve A well known property of the Normal distribution is that 68% of all widgets produced fall between plus and minus one sigma; 95% of all widgets produced fall between plus and minus two sigma; 99.7% of all widgets produced fall between plus and minus three sigma and so on Figure 1: The normal distribution in units of σ centered on the average The typical partitioning for grading in college is indicated with the distribution providing the relative number of students in each interval The Six Sigma program maintains that the variability seen in Figure is an undesirable property in the production of widgets and should be eliminated Uniformity of outcome very near the specified tolerance level is the goal of managing the manufacturing process To make the implications of this distribution more transparent let us assume that one million widgets are sold and 99.7% of them are within the specified tolerance limits On its face this would appear to be very good, with the production line functioning at the three-sigma level On the other hand, this also implies that the company will have 3,000 unhappy customers, because they receive widgets that not satisfy specifications If the widgets were armor vests slated to be used by soldiers in Iraq a three-sigma level would not be acceptable A six-sigma level of 99.9997%, with its approximately 30 faulty vests out of the million shipped would be the more acceptable number So this is what the Six Sigma program is all about How to take a manufacturing plant, an organization, or any other activity whose output has an unacceptable level of variability and reduce that variability to a six-sigma level; variability is assumed to be bad and uniformity is assumed to be good When first introduced the six-sigma argument seems to be rather good, with simple but acceptable assumption about the process being measured and ultimately controlled through management After all we have been exposed to arguments of this kind since we first entered college and experienced that every large class was graded on a curve That curve invariably involves the Normal distribution, where as shown in Figure between +1 and -1 lie the grades of most of the students This is the C range, between plus and minus one σ of the class average, which includes 68% percent of the students The next range is the equally wide B and D interval from one to two σ in the positive and negative directions, respectively These two intervals capture another 27% percent of the student body Finally, the top and bottom of the class split the remaining 5% equally between A and F (or is it E?) I remember that being graded on a curve made me uncomfortable, but I could not put my finger on the reason why Subsequently in upper level courses the classes were small and the problem disappeared At least I did not experience it again until in graduate school I began grading large Freshman Physics classes By that time I knew that grading on a curve was a gross distortion of what the students knew and understood and I even had a theory as to why such grading was wrong I confronted the professor in charge of the course about grading on a curve and explained to him my theory, with the simple arrogance of a graduate student who knows he is right The professor asked a simple question Do you have any data to show that such grading is wrong and that can verify your theory? Of course I did not have such data and therefore I continued grading on a curve Subsequently I learned that in the social sciences the bell-shaped curve introduced the notion of a statistical measure of performance relative to some goal An organization, or network, is said to have a quantifiable goal, when a particular measurable outcome serves the purpose of the organization A sequence of realizations of this outcome produces a set of data; say the number of research articles published per month by members of a research laboratory Suppose a laboratory’s ideal publication rate (goal) is specified in some way The actual publication rate is not a fixed quantity, but varies a great deal from month to month If 68% of the time the lab essentially achieves its goal, the lab is functioning at the one-sigma level If another lab has 95% of its realizations at essentially the ideal rate, it is functioning at the two-sigma level A third lab, one that seems to be doing extremely well, delivers 99.4% of the ideal rate and is functioning at the three-sigma level Finally a six-sigma lab delivers 99.9997% of its publications at, or very nearly the ideal rate [9] What six-sigma means is that the variability in the publication rate has been reduced to nearly zero and based on a linear additive model of the world this ought to be both desirable and achievable The logic of assessing the quality of the laboratory research is the same as that used to assess the quality of the students and relies just as much on the bell-shaped curve Recently I ran across a paper where the authors analyzed the achievement tests of over 65,000 students graduating high school and taking the University entrance examination of Universidade Estadual Paulista (UNESP) in the state of Sao Paulo, Brazil [10] In Figure the results of the entrance exam is recorded for high- and low-income students It is clear that the humanities data in Figure 2a seem to support the conjecture that the Normal distribution is appropriate for describing the distribution of grades in a large population of students The solid curves are the best fits of a Normal distribution to the data, and give a different mean and width between public and private schools In the original publication the data was represented in a variety of ways, such as between the rich and poor, but that does not concern us here, since the results turned out to be independent of representation In Figure 2b the data from the physical sciences is graphed under the same grouping as that of the humanities in Figure 2a One thing that is clear is that the distribution is remarkably different from the bell-shaped curve Figure 2c depicts the distribution of grades under the same grouping for the biological sciences The first thing to notice is that the distributions of grades for the biological sciences are more like those in the physical sciences than they are like those in the humanities In fact the distribution of grades in the sciences is nothing like that in the humanities In fact the distributions are so different that if they were not labeled one would not be able to detect that they refer to the same general phenomenon of learning So why does normalcy apply to the humanities and not to the sciences? One possible explanation for this difference in the grade distributions between the humanities and the sciences has to with the structural difference between the two learning categories In the humanities is collected a disjoint group of disciplines including language, philosophy, sociology, economics and a number of others relatively independent areas of study We use the term independent because what is learned in sociology is not dependent on what is learned in economics, but are at most weakly dependent on what is learned in language Consequently, the grades obtained in each of these separate disciplines are essentially independent of one another, thereby satisfying the conditions of Gauss' argument In meeting Gauss' conditions the distribution of grades in the humanities takes on normalcy Figure 2: The distribution of grades for 65,000 student university entrance examination Universidade Estadual Paulista (UNESP) in state of Sao Paulo, Brazil [10]: (a) humanities; (b) physical sciences; (c) biological sciences On the other hand, every science builds on previous knowledge Elementary physics cannot be understood without algebra and the more advanced physics cannot be understood without the calculus, which also requires an understanding of algebra Similarly, understanding biology requires some mastery of chemistry and physics The different scientific disciplines form an interconnecting web, starting from the most basic and building upward, a situation that violates Gauss' assumptions of independence and undercuts the idea that the average value provides the best description of the process The empirical distribution of grades in science clearly show extensions out into the tail of the distribution with no clear peak and consequently no characteristic, with which to characterize the data Consequently, the average values, so important in normal processes, become irrelevant in complex networks A better indicator of complex processes than the average is one that quantifies how rapidly the tail is quenched The distinction between the distribution in grades in the humanities and sciences is clear evidence that the Normal distribution does not describe the normal situation The bell curve of grades is imposed through education orthodoxy and by our preconceptions and is not indicative of the process by which student’s master information and knowledge Thus, the pursuit and achievement of intellectual goals, whether in science or engineering, is not normal Consequently, the Six Sigma program, being crucially dependent on Normal statistics, is not applicable to complex phenomena such as learning or scientific research The variability targeted for elimination by the Six Sigma program under the assumption of normal statistics is invalidated by the long-tailed distribution observed in truly complex networks The tails indicate an intrinsic variability that is not contained in the simpler processes where normal statistics are valid and working to reduce the tail region may in fact remove the very property that makes the process valuable With this in mind let us turn our attention away from the now discredited distribution of Gauss, at least discredited as being a viable description of the outcome for complex phenomena, and examine the arguments of the first scientist to recognize the existence of the long tails depicted in Figure 2.2 A tale of tails We need to have at least a preliminary understanding of how human networks operate in order to determine how to manage scientists and engineers In order to achieve this primitive level of understanding let us sketch how members of a community choose from a large number of options Suppose a large cohort group has a hypothetical set of choices, say a large set of nodes on a computer network, to which they may connect If we assume that the choices are made independently of the quality of the node, or without regard for the selections made by other members of the group, the resulting distribution in the number of times a given node is selected has the familiar bell-shape1 In this case the selection process is completely uniform with no distinction based on personal taste, peer pressure or aesthetic judgment, resulting in a network of random links between humans and nodes or more generally between individuals The bell-shaped distribution describes the probable number of links a given node has in a random network Barabási [11] determined such distributions to be unrealistic; that is, using realworld data he was able to show that the number of connections between nodes on the Internet and the World Wide Web deviate markedly from the bell-shaped distribution In fact he, along with others [12,13], found that complex networks in general have inverse power-law rather than bellshaped distributions The inverse power law was first observed in the systematic study of scientific data on income in western societies as analyzed by the engineer/economist/sociologist Marquis Vilfredo Frederico Damoso Pareto (1848-1923) in the nineteenth century Subsequently, scientists recognized that phenomena described by such inverse power laws not possess a characteristic scale and referred to them collectively as scale-free, in keeping with the history of such distributions in social phenomena [6] Pareto worked an engineer until middle age from which he gained an appreciation for the quantitative representations of phenomena On the death of his father he left engineering and took a faculty position in Lausanne, Switzerland With his collection of data from various countries he became the first person to recognize that the distribution of income and wealth in a society was not random, but followed a consistent pattern This pattern could be described by an inverse power-law distribution, which now bears his name, and which he called “The Law of the Unequal The bell shape is here given by a Poisson rather than a Gauss distribution, but the two are qualitatively the same, and the former becomes the latter under certain limiting conditions Distribution of Results” [14] He referred to the inequality in his distribution more generally as a “predictable imbalance” which he was able to find in a variety of phenomena This imbalance is ultimately interpretable as the implicit unfairness found in complex networks So how we go from random networks with their average values and standard deviations to networks that are scale free; and more importantly, how does this all relate to the management of scientists and engineers? Figure 3: A schematic comparison is made between the bell-shaped distribution of Gauss and the inverse power-law distribution of Pareto The vertical axis is the logarithm of the probability and the horizontal axis is the variable divided by σ Let us examine the distribution of human achievement and consider a simple mechanism that explains why such distributions have long tails, such as shown in Figure In the next section we discuss the distribution of scientific publications and citations to scientific papers as exemplars of the many inverse power-law networks in the social network of scientists and engineers Achievement is, in general, the outcome of a complex task A complex task or project is multiplicative and not additive because an achievement requires the successful completion of a number of separate subtasks, and the failure of any one of which would lead to the failure of the project As an example of such a process consider the publication of a scientific paper A partial list of those abilities that might be important for the publication of a paper is: 1) ability to think up a good problem; 2) ability to work on the problem; 3) ability to recognize a worthwhile result; 4) ability to make a decision as to when to stop and write up the results; 5) ability to write adequately; 6) ability to profit from criticism; 7) determination to submit the paper to a journal and 8) willingness to answer referee’s objections If we associate a probability with each of these abilities, then to some level of approximation, the overall probability of publishing a paper would, based on this argument, be the product of the eight probabilities The central limit theorem applied to such a process would yield a distribution for the successful publication of a paper that is log-normal or inverse power law at large scales [6] Other more mathematical arguments lead to inverse power laws throughout the domain of the variate So how does the inverse power-law distribution affect the evaluation of the scientific and engineering work force? Assume that a position has become available and a short list of candidates has been compiled Suppose further that there are eight criteria that are being used in the evaluation of a group of candidates, all with ostensibly the same level of professional achievement Using the 10 above argument on the multiplicative nature of complex processes, we see that if each of the criteria’s probabilities for an individual is reduced by the same small factor, say 10%, then that person’s total capability as perceived by the committee is reduced by 60% with the probability of being promoted reduced by the same amount In the additive world of Gauss the reduction would only be 10%, in fact, this is typically how such small influences are dismissively discussed In the world of the inverse power law, the world we live in, small changes in specific attributes can result in large changes in one’s career potential This model of the distribution of achievement is not a Gauss additive situation, where a strong prejudiced individual can oppose a promotion and bully others on the committee into going along with him/her This is a more subtle, multiplicative situation, where each rumor, innuendo and slur can detract in a cumulative way from a person’s potential being fully recognized Moreover, this suggests that significantly different evalutations of individuals or laboratories may not be attributable to any single cause, but may be the result of an uncounted number of overlooked and often forgotten impressions that not contribute on their own merits, but color the overall evaluation From this example of an inverse power-law, I draw your attention to the fact that the law of errors and the bell-shaped distribution are only appropriate for describing variability in simple systems, those that are linear and additive The fact that such a model is not appropriate for research was appreciated by the National Research Council [2]: Basic research is not part of a sequential, linear process, from basic research, to applied research, to development, and to application Unfortunately this is the way science and engineering is often viewed within the DoD, with its compartmentalization of research into 6.1, 6.2, 6.3 and higher and the bias towards applying the Six Sigma program within the research laboratory The NRC report goes on to say [2]: DoD should view basic research, applied research, and development as continuing activities occurring in parallel, with numerous supporting connections throughout the process Here we observe that the complexity of science and engineering is more likely to be described by inverse power-law distributions which capture the multiplicative character of the scientific enterprise and examine the more general conditions under which we would expect to observe such behavior Distributions in Complex Networks The understanding of inverse power-laws in the context of social networks began with small-world theory; a theory of social interactions in which social ties can be separated into two primary kinds: strong and weak Strong ties exist within a family and among the closest of friends, those that you call in case of emergency and contact to tell when you get a promotion Then there are the weak ties; such as with many of the colleagues at work with whom you chat, but never reveal anything of substance, friends of friends, business acquantences and most of our teachers Clusters form among individuals having strong interactions, forming closely knit groups; clusters in which everyone knows everyone else These clusters are formed from stong ties, but then the clusters are coupled to one another through weak social contacts The weak ties provide contact from within a cluster to the outside world It is the weak ties that are all-important for interacting with the world at large, say for getting a new job A now classic paper by Granovetter, The strength of weak ties [15], explains how it is that the weak ties to near strangers are much more important in getting a new job than are the stronger ties to one’s family and friends In this ‘small world’ there are short cuts that allow for connections between one tightly clustered group to another tightly clustered group very far away With relatively few of these long-range random connections it is possible to link any two randomly chosen individuals with a relatively short 11 path This has become known as the six-degrees of separation phenomenon [11-13] Consequently, there are two basic elements necessary for the small-world model, clustering and random long-range connections Small-world theory is the conceptual precursor to understanding inverse power-law networks Recent research into the study of how networks are formed and how they grow over time reveals that even the smallest preference introduced into the selection process has remarkable effects Two mechanisms seem to be sufficient to obtain the inverse power-law distributions that are observed in the world One of the mechanisms is contained in the principle that the rich get richer and the poor get poorer [11] In a computer network context, this principle implies that the node with the greater number of connections attracts new links more strongly than nodes with fewer connections, thereby providing a mechanism by which a network can grow as new nodes are added The inverse power-law nature of complex networks affords a single conceptual picture spanning scales from those in the World Wide Web to those within an organization As more people are added to an organization the number of connections between existing members depends on how many links already exist In this way the status of the oldest members, those that have had the most time to establish links, grows preferentially Thus, some members of the organization have substantially more connections than the average, many more than predicted by any bell-shaped curve These are the individuals out in the tail of the distribution, the gregarious individuals that seem to know everyone and to be envolved in whatever is going on In a research context these are the individuals that members of the laboratory seek for scientific discussion and collaboration 3.1 The Pareto Principle In itself, determining that a given data set has a distribution of the inverse power-law type would not necessarily be important What makes the Pareto (inverse power-law) distribution so significant are the sociological implications that Pareto and subsequent generations of scientists were able to draw from its form For example, he identified a phenomenon that later came to be called the Pareto Principle, that being that 20% of the people owned 80% of the wealth in western countries It actually turns out that fewer than 20% of the population own more than 80% of the wealth, and this imbalance between the two groups is determined by the Pareto index The actual numerical value of the partitioning is not important for the present discussion; what is important is that the imbalance exists In any event, the 80/20 rule has been determined to have application in all manner of social phenomena in which the few (20%) are vital and the many (80%) are replaceable The phrase “vital few and trivial many” was coined by Juran in the late 1940s and he is the person that invented the name Pareto Principle and attributed the mechanism to Pareto The 80/20 rule caught the attention of project managers and other corporate administrators who now recognize that 20% of the people involved in any given project produce 80% of all the results; that 80% of all the interruptions come from the same 20% of the people; resolving 20% of the issues can solve 80% of the problems; that 20% of one’s results require 80% of one’s effort; and on and on and on Much of this is recorded in Richard Koch’s book The 80/20 Principle [13] This principle is a consequence of the inverse power-law nature of complex social networks Power laws are the indicators of self-organization, so that inverse power laws are apparently ubiquitous, with order emerging from disorder in complex networks In the transition from disorder to order, networks give up their uncorrelated random behavior, characterized by average values and become scale-free, where the network is dominated by critical exponents It is the power-law exponent that determines the rate of fall-off of the inverse power law and captures the global character of the network, the imbalance between the rich and poor, between those that publish rarely and those that are prolific, between those whose work goes unread and those cited 12 by everyone This behavior is implicit in Figure where we schematically show the dramatic difference between the bell-shaped curve of Gauss and the inverse power law of Pareto It is evident from Figure that all the action takes place in the vicinity of the peak of the bell curve (the average value), since a random network has no preferences In the inverse powerlaw network the action is spread over essentially all the available values to which the process has access, but in direct proportion to the number of links already present This preference of a new member to hookup with an old member, having established connections, is what makes a star A luminary in science (given a certain level of technical ability) is socially determined in substantially the same way as is a luminary in Hollywood (given a certain level of acting ability) From Figure we see how unfair social phenomena are in the real world If events happened according to Gauss, then everyone would be similarly situated with respect to the average value of the quantity being measured If the normalized variable were a measure of income, then everyone would make approximately the same amount of money If, rather than money, the distribution described the number of scientific publications, then the world of Gauss would have most scientists publishing the average number of papers, with a few above and a few below the average But the world of publications would be basically fair Applying Six Sigma to this world would have every scientist publishing exactly the same number of papers and since there is little variability in the resultant measure this would appear to be a desirable goal However, that is not the world in which we live In the real world, the world of the inverse power law, there may not even be an average value and people may have almost none of the variable, or they may have a great deal of it So we find people at the level of poverty and people making millions of dollars a year and all those in between The number of publications is no exception in this regard, the biophysicist Lotka [14], determined that the distribution in the number of scientists having a given number of publications is, in fact, inverse power law, just like that of income The difference between the two distributions is the parameter determining the inverse power-law fall off; the power-law index or Pareto index The law of Pareto was derived for the distribution of income, but there are other laws with the same inverse power-law form: the law of Auerbach on the distribution of the sizes of cities, the law of Zipf on the relative frequency of words in language, the law of Richardson for the distribution of the magnitudes of war, the law of Lotka on the distribution in the number of scientific citations and many others [6] All these distributions stem from the implicit multiplicative nature of the underlying phenomena that make them amenable to the Pareto Principle The essence of this principle is that a minority of input produces a majority of the results and this is the key to understanding the management of scientists and engineers The Pareto Principle results in the few being important, and in the present context, these few determine the methods, goals and direction of complex research teams, the research direction of laboratories and in fact the overall research direction of the country The principle also maintains that the many are irrelevant in the sense that they are not the scientific leaders, but this does not imply that they are not valuable Most of science and engineering is accomplished by the faceless majority that refines, improvises and brings to fruition the often nearly unintelligible ideas of the few Phenomena described by inverse power-law distributions have a number of interesting properties, particularly when applied to social phenomena In the case of publications, it is possible to say that most scientists publish fewer than the averge number of papers It is definitely not the case that half the scientists publish more than average and half publish less than the average, but rather that the vast majority of scientists publish less than the average number of papers Consequently, if the number of papers were the metric of quality used by management to evaluate a laboratory, most labs would rate far below average However, this would be a distortion of the true quality of the lab and the research being done, since this application of the metric presupposes a linear worldview as the basis for comparison with other labs, or among members of a research group 13 Another inverse power-law distribution is the number of citations to published papers in any given year The cumulative values yield [15]: 35% of all papers published in the sciences have no citation; 49% of all such publications have citation; 9% have citations; 3% have citations; 2% have citations; 1% have citations and 1% have or more citations The average number of citation per year in the sciences is 3.2 and the inverse power-law distribution implies that 95% of all papers published are below average in the number of times they are cited So here again the manager that uses the number of citations as a direct measure of the quality of the research coming out of a lab is using a linear worldview and is not being fair to the lab Most of the research published is in alignment with the 95% [16] If the number of citations to the lab’s papers is average for the discipline then that research falls in the upper 5% category How a manager ought to use the information on publications, citations and other such criteria, for purposes of evaluation is therefore strongly nonlinear Suppose we applied Six Sigma to either the publication or citation metric Reducing the variability in the metric would imply eliminating 90% of the researchers, leaving only those 5% above and below the average value The rational for this action would be that the researchers were dead wood since they are not publishing or being cited The absurdity of this logical conclusion is frightening, since it actually paraphrases a remark made by a high level DoD official a few years ago A less Draconian response might be to give the 90% a substandard evaluation But this would imply that that the top 10% of the work force are judged to be average Neither The management of complexity The evaluation of a research laboratory is, all too often, a consequence of the manager being the product of our competitive system of education There is, of course, no a priori reason why everyone taking a college course, which has absolute criteria for mastery of the material, cannot receive an A The competition that arises in classes is all too often the result of the restriction in the number of top slots in the class Treating students as if they were independent entities, all struggling for the top grade, as one does with a Gauss model, is, in fact, a self-fulfilling prophecy of human behavior This educational model does not serve the reality of the multiply connected world of research into which the student will graduate On the other hand, working with students, promoting their interactions with one another enhances their interdependence and facilitates collaboration This latter model better prepares them for the future world of scientific research The interdependence of individuals in both science and the learning of science suggests a different way of thinking about how to facilitate the complex social interactions among scientists and engineers engaged in research The inverse power laws that measure the quantities of interest to the science and engineering community suggest that any attempt by management to equalize the reward structure within a research laboratory, such as imposing a Normal distribution on the evaluation process, will necessarily be counterproductive and disrupt the interactive modes that would ordinarily, adaptively develop among researchers This ‘unnatural’ equalization process is a consequence of the failure to recognize that unlike other professions, the work of scientists and engineers is judged not only by managers, but also, perhaps more importantly, by a professional community of peers who work both inside and outside of government The credibility of government science and engineering is judged by the same standards as those in the private and academic sectors It is critical that management recognize that the credibility of government science and engineering rests on the external credibility of its scientists and engineers Consequently, with the 80/20 rule in mind, the data coming out of the analysis and understanding of complex networks suggests that managers, evaluating the overall quality of Army research labs, should concentrate on the 20% that truly make a difference and only lightly 14 monitor the 80% that not significantly influence the operation of the lab Using this principle for guidance, the scientists and engineers should take cognizance of what motivates these individuals For example, scientists and engineers are stimulated by interesting and challenging work, but only the top 20% are sufficiently talented to basic research in the frontier areas The other 80% can often be guided to transition such fundamental research towards both long and short term Army needs The two groups are complementary to one another and a successful lab requires both in order to generate innovative ideas, to carry to completion the necessary research testing these ideas and to publish the results of the research in the appropriate journals, give talks at national and international meetings to disseminate knowledge and file for the necessary patents Two factors that often drive scientists and engineers away from Army labs are the lack of competitive salaries and the failure to have in place a creative rewards framework An example of a creative reward for a senior scientist or engineer might be a readily obtainable sabbatical at a top-level university The lack of such rewards might be offset, in the case of more junior scientists and engineers, through enhanced career advancement potential Rewards, as well as the quality of the laboratory equipment and support facilities made available to researchers, are indications of the level of respect with which management regards the work and therefore the scientists and engineers themselves A progressive and well-trained management, something that is certainly desireable, is one that involves the scientists and engineers in the determination of the laboratory’s vision and in the selection and replacement of top-level equipment, facilities and personnel Again taking cognizance of the 80/20 rule, it is clear that the 80% also desire high-caliber colleagues, for example, those who are well-published and whose work is well-cited in the peer reviewed scientific literature The inverse power-law nature of the number of publications and citations [15, 16] indicates the level of impact the cited research has on the science and engineering community and draws attention to the research activities of the Army labs Such 20% colleagues provide a stimulating environment in which to challenging research and the enhanced visibiltiy of the lab increases the chances of the 80% to collaborate productively with their counterparts in industry and academia The succesful management of the Army science and engineering community can only be accomplished by enabling the 20% to focus on ‘unfettered’ basic research, who are unreservedly supported by the 80% Management must also bear in mind that this 20% does not always consist of the same individual scientists and engineers From one year to the next any individual can be one of the 20% or one of the 80%, but only if the research environment is structured in such a way as to facilitate that mobility Both scientific/engineering leadership and support are important in an effective research laboratory and enabling one at the detrement of the other, in the end, serves neither References http://www.nsf.gov/od/lpa/nsf50/vbush1945.htm Assessment of Department of Defense Basic Research (2005) http://www.nap.edu/openbook/0309094437/html/1.html.copyright 2005,2001 The National Academy of Sciences US Air Force, Office of the Chief Scientist of the Air Force, “Science and Technology workforce for the 21st Century”, Washington D.C., July 1999; US Navy, Office of the Assistant Secretary of the Navy (Research, Development, and Acquisition), Naval Research Advisory Committee, “Science and Technology (S&T) Community in Crisis”, Washington, D.C., May 2002 15 An Army Material Command Human Resources Representative indicated this phenomenon at an Army-wide Senior Research Scientist (ST) meeting in Warren, MI January 25, 2005 See for example, B.J West, Physiology, Promiscuity and Prophecy at the Millennium: A Tale of Tails, Studies of Nonlinear Phenomena in Life Science-Vol.7, World Scientific, Singapore (1999) J Juran, Quality Control Handbook, 5th Edition, J Juran, Managerial Breakthroughs, (1964) P Pande and L Holpp, What is Six Sigma?, McGraw Hill, New York (2002) H.M Gupta, J.R Campanha and F.R Chavorette, ''powere-law distribution in high schoold education: effect of economical, teaching and study conditions'', arXir.0301523v1 (2003) 10 A Barabási, Linked, Plume, New York (2003) 11 D.J Watts, Small Worlds, Princeton University Press, Princeton, New Jersey (1999) 12 S Strogatz, SYNC, Hyperion Books, New York (2003) 13 V Paeto, Cours d’Economie Politique, Lausanne (1897) 14 M Granovetter, “The strength of weak ties”, American Journal of Sociology 78, 13601380 (1973) 15 R Koch, The 80/20 Principle: The Secret of Achieving More with Less, Nicholas Brealey Publishing (1998) 16 A.J Lotka, Elements of Mathematical Biology, Dover , New York (1956) first published in 1924 17 D.J de Solla Price, Little Science, Big Science … and Beyond, Columbia University Press, New York (1986) 18 S Redner, “How popular in your paper? An empirical study of the citation distribution”, Eur Phys Jour B 4, 131-134 (1998) 16 [...]... (2003) 13 V Paeto, Cours d’Economie Politique, Lausanne (18 97) 14 M Granovetter, “The strength of weak ties”, American Journal of Sociology 78, 13 6 013 80 (19 73) 15 R Koch, The 80/20 Principle: The Secret of Achieving More with Less, Nicholas Brealey Publishing (19 98) 16 A.J Lotka, Elements of Mathematical Biology, Dover , New York (19 56) first published in 19 24 17 D.J de Solla Price, Little Science, Big Science. .. 2005,20 01 The National Academy of Sciences 3 US Air Force, Office of the Chief Scientist of the Air Force, Science and Technology workforce for the 21st Century”, Washington D.C., July 19 99; US Navy, Office of the Assistant Secretary of the Navy (Research, Development, and Acquisition), Naval Research Advisory Committee, Science and Technology (S&T) Community in Crisis”, Washington, D.C., May 2002 15 ... Pande and L Holpp, What is Six Sigma? , McGraw Hill, New York (2002) 9 H.M Gupta, J.R Campanha and F.R Chavorette, ''powere-law distribution in high schoold education: effect of economical, teaching and study conditions'', arXir.03 015 23v1 (2003) 10 A Barabási, Linked, Plume, New York (2003) 11 D.J Watts, Small Worlds, Princeton University Press, Princeton, New Jersey (19 99) 12 S Strogatz, SYNC, Hyperion... this would be a distortion of the true quality of the lab and the research being done, since this application of the metric presupposes a linear worldview as the basis for comparison with other labs, or among members of a research group 13 Another inverse power-law distribution is the number of citations to published papers in any given year The cumulative values yield [15 ]: 35% of all papers published... publications is no exception in this regard, the biophysicist Lotka [14 ], determined that the distribution in the number of scientists having a given number of publications is, in fact, inverse power law, just like that of income The difference between the two distributions is the parameter determining the inverse power-law fall off; the power-law index or Pareto index The law of Pareto was derived for the distribution... than money, the distribution described the number of scientific publications, then the world of Gauss would have most scientists publishing the average number of papers, with a few above and a few below the average But the world of publications would be basically fair Applying Six Sigma to this world would have every scientist publishing exactly the same number of papers and since there is little variability... criteria’s probabilities for an individual is reduced by the same small factor, say 10 %, then that person’s total capability as perceived by the committee is reduced by 60% with the probability of being promoted reduced by the same amount In the additive world of Gauss the reduction would only be 10 %, in fact, this is typically how such small influences are dismissively discussed In the world of the inverse... relatively few of these long-range random connections it is possible to link any two randomly chosen individuals with a relatively short 11 path This has become known as the six- degrees of separation phenomenon [11 -13 ] Consequently, there are two basic elements necessary for the small-world model, clustering and random long-range connections Small-world theory is the conceptual precursor to understanding inverse... out of a lab is using a linear worldview and is not being fair to the lab Most of the research published is in alignment with the 95% [16 ] If the number of citations to the lab’s papers is average for the discipline then that research falls in the upper 5% category How a manager ought to use the information on publications, citations and other such criteria, for purposes of evaluation is therefore... applied to social phenomena In the case of publications, it is possible to say that most scientists publish fewer than the averge number of papers It is definitely not the case that half the scientists publish more than average and half publish less than the average, but rather that the vast majority of scientists publish less than the average number of papers Consequently, if the number of papers were the