Much of the real world is controlled as much by the ‘tails’ of distributions as means or averages: by the exceptional, not the commonplace; by the catastrophe, not the steady drip.... We need to free ourselves from ‘average’ thinking. (Nobel Laureate P. W. Anderson, 1997: 566)
The material and points of view presented in this unplugged paper have grown, partly by accretion and partly by modification of our previously held views, over several years. Pierpaolo’s discovery of power laws was accidental. Biologist Brian Goodwin gave a seminar at the London School of Economics in 1999. Since he was in London anyway and had read Goodwin’s previous book, How the Leopard Changed its Spots (1994), Pierpaolo went to the seminar expecting to hear about evolutionary problems and complexity. Instead, most of the seminar was on the asymmetric distributions of events. Pierpaolo was not impressed. The argument seemed to boil down to the fact that common events are, as might well be assumed, more common than rare events and that their relative frequencies seem to be connected by a number of relationships.
A couple of years later Pierpaolo heard Barabâsi give a talk in Boston and suddenly it all became clear. He realised that a power law (PL)  Vilfredo Pareto was born in 1848, in Paris, of a French... is the indication of diversity’s tendency to catalyse itself. Diversity in social and natural systems leads to further diversity, or in Kauffman’s words: ‘Diversity probably begets diversity; hence diversity may help beget growth’ (Kauffman, 1995: 292).
The history of the universe, societies and technologies show it. Requiem to Equilibrium, limited variance frameworks, and the self-correcting Invisible Hand! We started looking at the tail of extreme events and quickly became convinced of the inadequacies of gradualist and equilibrium-based frameworks (McKelvey & Andriani, 2005). Some of the material and reflection from our initial research has seeped into the Hollywood case presented in this article. When Anderson published his book in 2006, it became clear that there are two long tails and that any unconstrained market is fully Paretian.  PL relationships link two variables [such as rank (e.g.,... The first tail is about extreme events, that is, high-impact, low-frequency events, like the Lehman Brothers bankruptcy or a major earthquake. Taleb (2007) calls these events ‘black swans’. The complexity literature has focused mostly on the extreme-event tail. The second tail is about high-frequency, low- impact events. The homeless case shows the relevance of the first tail, while the long-tail case and the illycaffè case show the implications of the second Pareto tail for managers.
Despite mounting evidence against the Gaussian-based view, however, we struggled to publish our first paper. The objections we received ranged from minor details to a request for an entirely new statistical toolkit in place of what we teach in a traditional statistics class. We indirectly answered these objections by pointing out that the main problem is not methodological (i.e., lack of a toolkit for database analysis), but ontological: what type of reality do we assume we are dealing with? Hence, shifting paradigms make it possible to adopt a different take on problems. The first journal to which we submitted the full paper rejected all but some 200 words and dismissed the one reviewer who said ‘publish it’. Four revisions preceded publication. The good side to this, however, is that we will end up with five papers published from the first version rather than just one!
Working for a while in the Baroque city of Lecce in Southern Italy, we discovered Gladwell’s case on the homeless. This reveals in a very practical way the potential dormant in the paradigm switch to which we alluded above. Around the same time (2006) I had the chance of interviewing Ernesto Illy, the Chairman of illycaffè (Andriani & Detoni, 2008). Illy was a scientist, an entrepreneur and a man of immense culture and curiosity. He was also very knowledgeable on the subject of complexity and was close to both Prigogine’s group in Brussels and the Santa Fe Institute’s group in New Mexico. Ernesto Illy was unaware of PLs but knew a lot about scalability  This means, as Anderson shows, that the entire market,... and extreme events induced by so-called tiny initiating events (TIEs). He told us how illycaffè changed the Brazilian coffee market by amplifying small, existing instabilities into scalable macro-effects. We succinctly tell this story in our fourth case.
Bill (second author) has been a railroad and train enthusiast since the age of two. He is an expert on designing and building operating model steam engines and has built bridges, tunnels, switches and tracks for the Los Angeles Live Steamers Railroad Museum (which operates live- steam model engines and trains in the Griffith Park of Los Angeles). Unlikely as it may seem, if he had not been admitted to the PhD program at MIT after his MBA, his other choice was to become a steam- locomotive engineer on the Union Pacific Railroad. The Union Pacific was still running the full-size version of its newest and best steam engine—the one he was dreaming of operating. Needless to say, this historical and rather personal attachment to the Union Pacific made him especially sensitive to the operational and management disaster we discuss in our Union Pacific-Southern Pacific-merger example.
Meanwhile, Bill lives in California, a land where 16,000 level-1 to level-4 earthquakes occur every year. He has also suffered the two level=6+ quakes to have occurred in the past 30 years and has studied why some buildings fail while others do not, how builders violate the building codes and where people are most likely to be killed (in the most recent 6+ quake, 60 out of the 63 people killed lived on the first floor of apartment buildings). In short, California is the only US state that focuses on the long Pareto-tail of the earthquake distribution. For people living in quake-land, Pareto distributions are what we worry about every day: when will the next level-8+ quake happen? With Pareto distributions burned into our brains, it doesn’t take much to get one wondering about where else Pareto distributions—and then PLs— thrive. Predictably, they thrive in living systems that have Pareto rank/ frequency-distributed predator/prey environments and consequently require internal requisite fractality  Scalability refers to the ability of a system to achieve... to survive (McKelvey, Lichtenstein, & Andriani, 2011). Requisite fractality ranges from biological species and niches to companies in competitive environments.
In the past few years the complexity-theory literature has paid increasing attention to PLs, long tails, extreme outcomes, fractals and other Pareto-related effects (Schroeder, 1991; West & Deering, 1995; lannaccone & Khokha, 1996; Barabási, 2002; Newman, 2005). Entirely new fields such as econophysics and sociophysics have arisen (Mantegna & Stanley, 2000; Chatterjee, Sudhakar, & Chakrabarti, 2005; Chatterjee & Chakrabarti, 2006, 2007) based on recognition of the fact that the nonlinear interdependences among people give rise to a more complex world in which PLs are the signature of scale-free dynamics. Aside from a few exceptions (De Vany, 2003; Anderson, 2006; Taleb, 2007), the management, organisation-theory and economics and business literatures are trailing behind, but at the same time they offer a very rich field of inquiry to researchers who want to explain the origin and dynamics of the extreme diversity of business structures. On the one hand, we see extreme outliers at one end of a Pareto long-tailed distribution: positive extremes such as GE, Microsoft, Walmart and
Google, and negative extremes such as the Challenger and Pioneer disasters, LTCM, Parmalat, Enron, Countrywide, IndiMac, Bear Sterns, Fanny Mae/Freddie Mac and Lehman. At the opposite end, we see the (other) Pareto long tail Chris Anderson writes about: a tail consisting, for example, of Amazon’s book sales or 17 million small, family-owned stores, many of which thrive in idiosyncratic micro-niches.
Previous papers (Andriani & McKelvey, 2007, 2009) have reviewed the literature on PLs, demonstrated the ubiquity of PLs in the social sciences and more particularly in the organisational world, applied Pareto- driven ideas to methodology and research in organisation science and developed a framework to explain the emergence of PLs in the social sciences (which we call scale-free theory). In this article we focus on a different aspect: given that the world in which organisations live is frequently Paretian, what types of changes in thinking and practices are required in the fields of management and strategy to help managers to prosper in a Paretian world? How to transform the new understanding of scalability and scale-free theories into tools that may help us to anticipate and govern the transformation of TIEs into extreme outcomes, either to shape the emergence of new business market and/or organisational structures favourably or to avoid the potentially lethal consequences of such a development? (Royer, 2003) Can scale-free theories help identify TIEs, which Holland calls ‘small “inexpensive” inputs’ or ‘lever point phenomena’ (2002: 29)?
These are major questions that require a reorientation of management and organisation theory. In this paper we show how the practice of management can drastically change if a practising manager adopts a Paretian course of action. We show in five short cases that the shift from Gaussian to Paretian involves more than a change in the tools used for statistical analysis. We base four of our illustrative examples on cases developed by other authors, while the illycaffè case was developed by Andriani. It signifies a shift in perspective that enables the manager to look at old things with new eyes. This change of perspective reveals novel strategic possibilities and operational approaches that were hidden from view under Gaussian management. In practice, rather than conceptualising a phenomenon by squeezing its diversity into a Gaussian distribution and working out the properties of the representative agent by calculating its mean and variance, managers should look at the entire unbounded distribution and pay particular attention to its tails. We show that by focusing on the tails, managers can find novel strategies and solutions. We believe that this unplugged article represents a first attempt to apply Paretian ideas to strategic management; we hope that it will stimulate other researchers to extend it. We begin with a review of basic ideas that differ between the Gaussian and Paretian approaches. We then discuss five specific cases in which the Paretian standpoint offers new insights and effective strategies. A discussion and conclusion follow.
PARETO VS GAUSS: WHY POWER LAWS MATTER
Abbott (2001: 7) discusses how the ‘general linear model’ from Newtonian mechanics came to shape social scientists’ thinking:
The phrase ‘general linear reality’ denotes a way of thinking about how society works. This mentality arises through treating linear models as representations of the actual social world.... The social world consists of fixed entities (the units of analysis) that have attributes (the variables). These attributes interact... to create outcomes, themselves measurable as attributes of the fixed entities.
The ‘general linear reality’ (GLR) model has influenced not only the way researchers build models and the philosophical assumptions they use, but more broadly the way we conceptualise the world. GLR, Abbott claims, has transformed normal distributions from a tool relevant under a set of specific circumstances into a representation of the world as it is. By stressing the centrality of fixed, independent entities as objects, GLR fails to account for the emergent properties of systems that derive from connectivity within or among systems. The consequent interdependence among agents  Requisite fractality follows from Ashby’s (1956) classic... generates Paretian dynamics and PLs as its hallmark.
PLs seem ubiquitous: they appear in leaves, coastlines and music (Casti, 1994), and they characterise earthquakes and hurricanes. American, Japanese, Chinese and Indian cities, among many others (but clearly not all), follow a PL when ranked by population (Auerbach, 1913; Zipf, 1949; McKelvey, Lichtenstein, & Andriani, 2011). The structure of the Internet follows a PL (Albert, Jeong, & Barabási, 1999), as does the size of firms (Stanley et al., 1996; Axtell, 2001). We have collected over 100 examples of PLs in the social and organisational world (Andriani & McKelvey, 2009). Brock (2000) suggests that PLs are the fundamental feature of the Santa Fe Institute’s complexity science.
A Pareto distribution plotted as a double-log scale appears as an inverse PL, a negatively sloping straight line. PLs are ‘indicative of correlated, cooperative phenomena between groups of interacting agents’ (Cook, Ormerod, & Cooper, 2004: 3). As noted earlier, PLs often take the form of rank/frequency (R/F) (or size) expressions such as F ~ N-?, where F is frequency, N is rank and ?, the exponent, is constant.  Note that though a PL exponent is constant, the exponent... Most firms are R/F-distributed, with one CEO at the top and many workers at the bottom of a multi-level hierarchy. Most industries are R/Fs, with the largest (often giant) firms at the top (e.g., Microsoft or Walmart) and thousands or millions of small businesses at the bottom with many size levels in between. The mean represents neither the top nor the bottom. In ‘exponential’ functions the exponent is the variable and N is constant. Theories explaining PLs are also scale-free, i.e., the same explanation (theory) applies to several adjacent levels of analysis.
We argue that PLs and scale-free theories apply to management and organisations. Furthermore, there is good reason to believe that PL effects are ubiquitous in organisations and industries showing adaptive change, and have far greater consequences than current management theories presume. We argue that whenever tension and connectivity dynamics exist, the probability that PL-distributed phenomena will occur increases substantially. PL distribution is a natural attractor for interdependent phenomena characterised by potentially unlimited variability. From a mathematical standpoint, distributions fall between the extremes of the normal and the PL distribution. These are the only two stable distributions. The former constitutes the natural attractor for limited-variance phenomena (where the central-limit theorem applies), while the latter is the natural attractor for interdependent phenomena, characterised by potentially unlimited dynamic variability and scale invariance (fractal). To the extent that real social and economic phenomena fall either in the Gaussian, Paretian or the intermediate space, managers ignoring PL effects risk missing important aspects of the dynamics of business phenomena. Specifically, the extreme outcome at one end of the Pareto tail is typically N = 1, i.e., it is characterised by low frequency but has a disproportionate impact on adjacent systems. Extreme outcomes and radical innovations fall into this group. At the opposite end the N can run into the millions. The mode, mean and median do not overlap as they do in a normal distribution. Moreover, in many PL distributions the mean does not really exist. There is no typical scale and therefore the use of averages and standard deviations to represent the phenomenon is misleading. Methods of good management at one extreme do not apply to the opposite extreme: managing one of the millions of small, family-owned stores (officially defined as having no paid employees) is not the same as managing Walmart. Managing the median firm is not the same as managing at either extreme. As Axtell (2008) points out, ‘the typical firm does not exist’.
PL phenomena exhibit Paretian rather than Gaussian distributions; see Figure 1. The fundamental difference lies in assumptions about the correlations among events. In a Gaussian distribution the data points are assumed to be independent-additive. Independent events generate normal distributions, which sit at the heart of modern statistics. When events are independent-multiplicative (Limpert, Stahel, & Abbt, 2001) they generate a lognormal distribution. When events are interdependent-multiplicative, Paretian distributions dominate because positive-feedback processes leading to extreme outcomes occur more frequently than ‘normal’, bell- shaped, Gaussian-based statistics lead us to expect; normality in social, organisational, and industry distributions is not the norm.
Figure 1. Gaussian vs Power-law distributions
Several theories explain PLs. They typically hinge on interdependence among data points and a possible ensuing positive feedback or other scale-free process. Herein lies the problem for ‘normal’ science: most quantitative research involves the use of statistical methods presuming independence among data points and Gaussian ‘normal’ distributions. The many findings of natural and social PL phenomena, however, indicate that interdependence is far more prevalent than ‘normal’ statistics assume and the consequent extremes have far greater consequences than the ‘averages’ in between.
In reality, what are most important to managers are the extremes they face, not the averages. Yet, most academics’ research produces results based on averages in normal distributions and associated statistical significances (Andriani & McKelvey, 2007). We believe that research results stemming from Pareto-based science (what we now term ‘power-law science’; Andriani & McKelvey, 2011) could be of more value to managers. But how? How does the study of PL science contribute practitioner-relevant academic research and the practice of management?
We started our article by stressing Abbott’s claim that many disciplines in the social sciences are subtly influenced by the General Reality Model, which has reified the Gaussian view and given Gaussian methods a certification of nearly universal validity and applicability. In contrast, we observe that complex systems and their statistical hallmark, PLs, result from the establishment of a collective system of interdependencies among agents that give rise to a self-organising adaptive system. The system self-organises around some dynamical patterns that are amplified until they become a new form of collective order, a system efficaciously adapted to a competitive context. This is something that science can study, model and try to anticipate; needless to say that managers need to learn how best to exploit this process. In general, the precise prediction of single events remains outside the capability of current science, but the anticipation of major reversals in trends or the development of a cascade from TIEs via the study of the buildup of interdependencies in the system becomes possible. We claim that the Paretian view gives a more realistic representation of social dynamics. The emergent collective order of complex systems allows some limited forms of prediction. In particular, the tail and slope of PL distribution give important information about the nature of the phenomenon in question and provide important information to the decision maker.
The Paretian view, or what we call ‘power-law science’, (Andriani & McK- elvey, 2011) affects the way we look at and conceptualise the following general question: how does this change in perspective impact strategy and the practice of management? We offer a comparative view of Gaussian vs. Paretian views in Table 5. This is a major problem that involves a paradigm change in the Kunhian sense.
Table 5. Gaussian vs. Paretian responses to key questions
In this essay, the impact of the Gauss-to-Pareto transition is shown by means of five paradigmatic examples that serve to illustrate the aspects of: (1) a change in strategy and business practices resulting from the emergence of a constellation of profit-making micro-niches in industries based on intangible products and internet distribution channels; (2) the perception and management of risk in the movie business; (3) the management of an apparently intractable social problem such as the chronic homeless in a modern city; (4) opportunities hidden in Anderson’s (2006) missing (other) ‘long tail’ of a Pareto market and the strategy for turning TIEs into extreme events; and (5) lessons from ‘smouldering’ crises such as the Union Pacific example (Andriani & McKelvey, 2010) and what we can learn about the early perception and scalability of TIEs in the prevention of such disruptive crises. A synthesis of the cases is presented in Table 6
Table 6. Synthesis of the case studies
Our first three cases are examples of managers learning or needing to learn about the advantages of Pareto-based management. Managers who adopt the Paretian view can easily observe that the world in which they live is not dominated by well-behaved Gaussian means and variances but is instead dominated by the uncertainties characterising both of the long tails of the Pareto distribution, as well as the horizontal scalability dynamics buried in the R/F lying in between. Once one switches perspective, the unbounded nature of risk is immediately recognised. In the movie industry, the lack of correlation between primary production variables and box-office results becomes evident and management practices based on predictions of single events become questionable.
The emergence of the long tail of micro-niches is difficult to explain solely by means of traditional statistical tools. The problem with Linear Science (defined more fully as Abbot’s (2001) ‘general linear reality’ (GLR)) and normal statistics rests on the assumption of limited variance (Mandelbrot, 1963). This translates into the assumption that the world is complicated but not complex. Complex systems result from emergent properties, which are often comprised of multiplicative interactions rather than just additive ones. In a complicated world, the properties of systems may be derived from the properties of their constituent elements. For instance, in the neoclassical view of economics, the properties of the macro level are derived by aggregating the properties at the micro level (firms, institutions, individual actors, transactions, etc.). Similarly, in the neo-Darwinian account of evolution, the properties of an organism are set by the information in its genes (Dawkins, 1976). The causal chain runs bottom-up, from firms to economies and from genes to organisms. Ignoring emergent properties allows reality to be simplified.
The GLR assumption and the consequent methodology and methods have permeated entire disciplines within management and business studies, from decision-making to marketing and from logistics and supply-chain management to strategy. If reductionism is a valid strategy, the possible states of a system are finite and consequently both the heterogeneity of the agents of a social-economic system and its pattern of variability are finite both in principle and in practice. The purpose of the analyst is to identify the ‘atoms’ of the system. These can be a gene (Dawkins, 1976), a meme (Dawkins, 1976), a rational optimiser (Nash, 1950), a technique (Mokyr, 2002), a way of making a living (Vermeij, 2006), a routine (Nelson & Winter, 1982), or any fundamental units that may be aggregated to reconstruct a seemingly non-dynamical system. The result is that they show linear dynamics. In fact, if the evolution of a system follows a set of scale-independent universal laws, then the task of predicting the evolution of a system may be transformed into a modelling exercise of decomposing complicated systems down into individual parts, followed by the analysis of the behaviour of the system’s parts, and finally by the re-aggregation of the findings and predictions about the behaviour of the whole.
The GLR approach fails on two accounts: First, it ignores the reality that emergent properties increase the irreducible diversity and heterogeneity of agents at the specific level on which they operate. The emergence of company culture, or routines in social groups, is a result of the connections within networks (connectionist property) and cannot be ‘reduced’ to the properties of the agents. Second, the focus of GLR-based sciences is on discrete entities (objects) rather than on connections among entities.  Connections, and, in general, the context within which... Independent or weakly interdependent entities may be treated by means of Gaussian statistics, which reduces complexity in aggregating micro-level diversity through population parameters and thereby gives rise to the concept of the representative agent and related variability. The representative agent acts as a bridge between the diversity of the microlevel and macro-level aggregates. Assuming that diversity is finite and mostly contained within two standard deviations from the mean, attention focuses on the centre of the distribution, where most of the data are expected to fall. The rare outcomes that lurk in the tail of the distribution are treated as outliers and usually discarded. Reliance on central tendencies is particularly problematic in strategic and entrepreneurship studies. The normalisation of samples (i.e., winsorising) for statistical analyses aims to eliminate outliers, which in a gradualistic view of societal change are attributed to measurement errors or other spurious effects.
Management by averaging can be misleadingly simple and dramatically ineffective, as the chronic homeless case illustrates. Reliance on the idea of the mean-plus-limited variance as a correct representation of reality leads researchers to develop minimum-common-denominator strategies to maximise value around average agents; this is the one-size- fits-all approach. Size is determined by the average of the phenomenon.
For instance, in dealing with an epidemic, Gaussian managers assume constant contagion probabilities and adopt blanket vaccination policies. Dealing with the homeless, they reduce the diversity of homeless to a non-existent average homeless and provide a homogenous solution designed for a non-existent entity. Managers wanting to shift perspective have discovered that addressing the extreme cases out in the long tail may be cheaper and more effective. Ironically, the US Internal Revenue Service (tax-collection agency) has finally discovered that it can find vastly more ‘missed’ tax payments by the top 20% of corporations and wealthy individuals than by spending their audit costs on the average (or smaller) firms and less wealthy people (Bloomquist & Emblom, 2008). Unconstrained by the diversity-limiting paradigm, Pareto strategists realise, instead, that change happens in the tails and that the non-existence of a representative scale (or agent) creates opportunities for the emergence of innovative business models that rely on unbounded diversity.
The final two cases illustrate another typical aspect of Paretian systems, i.e. scalability. This refers to the fact that the same (or similar) dynamical processes apply on different scales and hence can trigger major runaway phenomena. In the illycaffè case we deal with the opposite issue, namely that of how TIEs can trigger the emergence of new business ecosystems, whereas in the Union Pacific case we deal with how to prevent TIEs from escalating into destructive extreme outcomes.
Toyota has long been known for wanting its assembly line workers to shut down the entire line if they see something that is not right. Rochlin (1989: 167) notes that on the aircraft carrier Carl Vinson, ‘any critical element that is out of place will be discovered or noticed by someone before it causes problems’. Of course, not every random error or event scales up into an extreme outcome. But as Andriani and McKelvey (2009) show, scalability is much more prevalent than most people are willing to realise. In spite of this, we see no evidence that scalability has seeped into management or organisation theory textbooks. The Union Pacific Railroad example shows that the adoption of a Paretian approach, and learning how to look for negative TIEs, will help managers negate scalable TIEs before they spiral into extreme negative outcomes.
In what follows, we briefly show how scale-free theories  For a more exhaustive treatment of scale-free theories,... can be used to make sense of the scaling-up of TIEs into extreme outcomes over which managers could/should gain leverage.
normal distributions of different variables remain normally distributed if they are combined (even becoming more normally distributed, in fact, because of the central-limit theorem). But if several organisational processes producing somewhat skewed distributions happen to combine multiplicatively, their combined outcome distribution will become PL-distributed (Newman, 2005). The PL distribution acts as an attractor for the combination of non-normal distributions. Let us suppose that before the merger Union Pacific activities were normally distributed: things mostly worked as expected, with some random deviations because of events like the flu, storms, or random equipment failures. Thus, normally, train crews are on time; trains are on time; locomotives are at the right location on time; repair and dispatch crews are on time; locomotives and crews and other railroaders function effectively most of the time, etc. Then comes the merger. Now suppose each of the foregoing normal distributions becomes skewed. Since there are several of these, and since they interact with multiplicative effects, we see the emergence of dramatic scalability. The possible states of the entire system expand from the limited variability of a well-behaved normal distribution into the unbounded variability of a PL. Extreme events, such as a gridlock of the entire system, become highly likely and evolve rather quickly. Because of the high likelihood of cumulative multiplicative-interaction effects, spiralling out of control on a railroad having long sections of single-track mainline, one could easily predict the likelihood of extreme outcomes and then try to manage against them.
Preferential Attachment Theory
Given newly arriving agents in a system, larger nodes with an enhanced propensity to attract agents will become disproportionately even larger, resulting in the PL hallmark (Yule, 1925; Barabási, 2002). In the illycaffè case, we see the switch to quality occurring in an epidemic fashion. The success of the early farmers, who invested in quality, encouraged nearby farmers also to adopt quality. The epidemics spread along existing social networks. The more nodes switched to quality, the more they were collectively able to exploit scale and scope advantages, with the consequence that additional farmers also chose to adopt quality. When enough farmers adopted quality it became convenient to form Associations of quality producers. In the regions we studied (Andriani, Biotto, & Ghezzi, 2011) we observed preferential-attachment dynamics not only at the level of individual farmers but also at the farmers’ Association level. When farmers formed the first coffee Association, they were quickly imitated. The first Association started a cascade by showing the advantages for quality producers to form Associations and caused nearby areas to form their own Associations. Confirming the preferential attachment scheme, we find a significant concentration of Associations in specific regions and a virtual absence in others. Cascades of preferential attachments were also observed for other related aspects. The illycaffè Award, the TIE that unleashed the quality-transformation dynamics, became the early node that triggered an imitative cascade of other Awards. In the hope of enabling the same transition from commodity to quality that the ‘illy’ Award had caused, several local and national institutions in Brazil set up new Awards. Interestingly, if one maps the regulations of the various Awards, the illy Award clearly emerges as the ‘rich-get-richer’ node.
Preferential attachment theory suggests that as the Union Pacific and Southern Pacific social and work-related networks merged, some individuals would emerge as more ‘connectively’ important in getting the new system and new ways of getting things up and running. Prior dominant nodes could reasonably be expected to be replaced by new ‘social stars’ who rose to connectivity prominence because they offered relevant information, skills and solutions benefiting other employees scattered around the new railroad context. Instead of this, however, the old-guard railroaders kept themselves dominant and inadvertently kept the old pre-merger railroad networks dominant also: the old Union Pacific network trying for dominance over both railroads; the old Southern Pacific network in rebellion, passive resistance and slow-downs. Alternatively, both could have joined in with a collective reframing of a new combined network with new social stars emerging. Managers aware of this theory would expect network dynamics to change dramatically with the merger and would ‘manage’ to facilitate this outcome, which would be extreme in the sense that a few key social stars would come to dominate.