“Complexipacity” What is it? Do we need it? Can we get it?

Citation

Pearce Snyder, D. (2010), "“Complexipacity” What is it? Do we need it? Can we get it?", On the Horizon, Vol. 18 No. 1. https://doi.org/10.1108/oth.2010.27418aaa.001

“Complexipacity” What is it? Do we need it? Can we get it?

Article Type: Guest editorial From: On the Horizon, Volume 18, Issue 1

In 1970, at hearings on the future of information and knowledge convened by the Science Committee of the US House of Representatives, British cyberneticist Stafford Beer testified that “the over-arching challenge of our age would be managing modern complexity” (Beer, 1970). Nearly 40 years later, an American designer discovered the personal meaning of Professor Beer’s prescient prediction. I happened to be “present” on that occasion. One night, four years ago, I received a phone call from that designer – my brother Tom – who announced in an uncharacteristically glum tone, “I think I’ve exceeded my complexipacity.”

Although I had never heard the word before, I immediately understood what he meant. I surmised that he had encountered some sort of intractable problem, almost certainly having to do with work. Since Tom had been solving design problems for firms like Arvin Industries, and Seagate Technology for several decades, I assumed that a client had presented him with an unusually complex set of specifications. I offered to assist him in any way I could.

As it turned out, Tom’s project involved the design and installation of an interactive web site using a new piece of software. Weeks of labor had been rewarded with repeated failures and confusing and unexpected results. Starting over for the third time with yet a third web-designing software had produced only more unanswered questions and suspicions of faulty hardware, software or “wet ware.” The experience had not only been frustrating, it had put a “dent” in my brother’s personal sense of professional competency. He was in this desultory state of mind when the concept of “complexipacity” first popped into his head.

After Tom explained his circumstances, I offered him my sympathy and best wishes. This was about all I could offer, since he is considerably more computer-competent than I. At the end of our conversation, he had my moral support and I had his “word”.

A neologism for our times

I began to use “complexipacity” in the strategic briefings that I conduct. I started to ask my audiences – typically senior managers and executives – whether their decision-making environments had become more complex in recent years. (Essentially everyone agrees that it has.) I then ask if they think that daily life is becoming more complex. On this, there is always an immediate consensus; everything, they agree – from commuting to work to planning for retirement – is more complicated that it used to be.

Finally, I ask “How many of you have encountered a problem or situation that exceeded your complexipacity?” After a brief pause while the audience digests the new word, hands begin to go up around the room as people recognize that this term – one they had never heard before – actually describes their own unarticulated concerns. Once those misgivings were given a name, they immediately gained validity. Complexity emerged from the shadows of their subconscious to become an expressed concern. And, if growing complexity is a problem, everyone agrees, increased complexipacity would clearly be a good thing to have.

Yes, Virginia, there IS more complexity

Since there are no standardized measures of complexity, it is not possible to make a definitive statement that “the world is becoming more complex at a rate of n% per year.” On the other hand, the media are filled with references to complexity, whether the topic is finance, business, economics, lifestyles or politics – foreign or domestic. Even higher education. MIT’s Sloan School of Management recently announced it was establishing a new master’s program in finance, “because the field has become too complex to be adequately covered as part of a traditional MBA program” (Lohr, 2009). Complexity has become a leitmotif of daily news analysis. The Financial Times, for example, recently quoted the IT director of a major British investment firm:

Information technology is becoming massively more complex. We have more servers and more applications. Our “safety net” back-up systems are even more complex. Some CIOs doubt that these complex back-up systems could recover from a disaster fast enough to save the company (Pritchard, 2009). [Author’s note: This suggests that, just as there are companies that are “too big to fail,” there may also be systems that are too complex to fix.]

Growing complexity is clearly not a phenomenon experienced only by systems engineers and senior decision makers. There have been a number of media reports, for example, on the growing demand for “patient advocates” in an otherwise poor labor market. The New York Times explained that “patient advocates have been around for decades, but in the last few years, the profession has gained momentum because even a person well-versed in medical lingo can be overwhelmed by the complexity of the healthcare system” (Alderman, 2009). (In his paper for this Special Issue, futurist Richard Maynard describes how he developed the necessary personal complexipacity to manage his own encounter with a complex medical issue.) At every level of human enterprise, decisions involve an increasing number of considerations, options and potential outcomes that pose uncertain consequences.

Both the ongoing growth of human knowledge and the increasing power of our research technologies would appear to preordain an even more complex world. As we learn more about the intricate, subtle links between our economic processes and the natural environment, self-interest and common sense dictate that, when we discover that we are poisoning ourselves or damaging the ecosphere that sustains us all, we will – sooner or later – take steps to modify or prohibit such activities. Chemicals are banned, equipment is replaced, inspections required, and data gathered and recorded. Most of these discrete, incremental increases in complexity can be routinized as minor complications over time. But there are forces of conceptual change working their way through the global marketplace of ideas that pose a real potential for making the decision-making environment much more complex for everyone.

Growing numbers of Fortune 500/FTSE 100 firms, under pressure from activist institutional stockholders, have expanded their Annual Reports to reflect corporate performance measured against three different sets of criteria: financial, social and environmental; perspectives whose interests and priorities are often perceived to be in conflict with each other. The adoption of carbon trading markets and carbon “footprint” ratings, along with green building standards and alternative energy tax credits, will sustain this trend toward “triple bottom line” accounting, which can be seen as a practical attempt by business to “manage modern complexity,” just as Stafford Beer foresaw back in 1970.

There is an even more ambitious movement under way to expand the number of factors associated with our economic decisions. A coalition of leading economists and political leaders from around the world have launched a global campaign to dramatically increase the number of criteria by which economic performance and social progress are measured: the gross domestic product (GDP).

“One number,” argue Nobel laureate economist Joseph Stiglitz and French President Nicolas Sarkozy, “simply cannot account for the legitimate interests of all the world’s peoples” (Hall, 2009). These heterodox economic thinkers want to adjust the way GDP is calculated, to add new measures of well-being and happiness, and to include metrics that indicate financial and environmental sustainability. Their proposals are intended to change how marketplace economies behave by making them transparent and inclusive of both indirect costs (e.g. environmental degradation) and benefits (e.g. increased human longevity). These are truly transformational ideas, and as revolutionary as anything Marx ever wrote. However, in light of the fundamental reassessment of global financial arrangements following the recent credit crash and recession, the proposals of the Commission on the Measurement of Economic Performance and Social Progress are timely, and they are attracting attention.

Even if the GDP reform movement does not “get traction” right away, the globalization of the world’s 200+ national economies into a single interdependent marketplace has been – and will continue to be – a complexifying factor for the world’s public and private sector decision makers (Friedman, 2006). The ongoing avalanche of new info-com technologies into the marketplace is adding new tools for workers, managers and consumers, and poses new, complex sets of options for organizing, managing and marketing (Selbe, 2009). The rise of multi-tasking is seen by some observers as a spontaneous response to the growing complexity of life, while others describe the multi-tasking made possible by personal technology as a cause of the increased complexity in life. It is, almost certainly, both. (Complex phenomena are like that.)

While the evidence may only be anecdotal or episodic, it seems overwhelming. There is a growing awareness and acknowledgement of complexity in our lives, from financial markets to the carbon footprint of a pound of rutabaga and the instructions for cleaning up a broken compact fluorescent light bulb. When I discuss this issue with my audiences – at association meetings, executive training sessions, public lectures, etc. – most people seem to think about complexipacity in terms of “juggling,” as in “I’m juggling my schedule so that I can pick up my daughter after her karate class,” or “How many balls can Obama juggle at one time?” While there is widespread appreciation in both the mass media and the academic press that some issues or problems are more qualitatively complex than others, the common popular understanding of complexity is largely defined quantitatively:

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  a complex machine has lots of parts;

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  a process complex has lots of steps; and

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  a complex equation has lots of variables.

I must confess that, even though I was familiar with complexity science and chaos theory, I began to think about the complexipacity problem almost exclusively in quantitative terms myself. Research from the University of Queensland has recently shown that human information processing capacity is typically limited to handling four moving variables at once, a serious quantitative limitation (Halford et al., 2004), but a group of Minnesota School Superintendents prodded me to refocus on the multiple dimensions of complexity. At a strategic thinking workshop, I asked the superintendents to develop a new mission statement for public schools based on a set of ten-year demographic, econometric and technologic forecasts they had just been given. At the end of the exercise, one group of superintendents had printed on their flip-chart page:

Prepare students to live in a complex world.

Underneath that, someone had scribbled:

What skills do we teach?

The call for papers

This practical, nuts-and-bolts approach to complexipacity provoked a lively discussion in Duluth, and ultimately led to this Special Issue of On the Horizon. Plainly stated, if life is truly becoming more complex for everyone, schools should be preparing young people for that reality. Our February, 2009 Call for Papers solicited contributions that addressed one or more of the following questions:

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  Is the decision-making environment really becoming more complex?

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  What do we mean by “complexity?”

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  Are there specific skills that can improve an individual’s ability to deal with complexity?

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  Can such skills be taught, and if so, how and at what age?

Current academic journals and the education press increasingly report that K-12 and post-secondary school students are using blogs, wikis, simulations and games to acquire higher-order cognitive skills – e.g. systems thinking, team-work, problem solving, self-directed learning, etc. These are the kinds of skills that job content research has concluded will, from now on, be required by the increased complexities of most work (Levy and Murnane, 2004). The Editorial Board of On The Horizon assumed that our Call for Papers would attract responses that reflected that rapidly growing – and notably upbeat – body of literature.

The Editorial Board (including this author) were wrong! We initially received no proposals from authors in education. In particular, we received nothing regarding K-12 education. The majority of proposals came from the management field, including consultants and leadership development experts. While each author proposed to approach complexipacity from a different perspective, all of our respondents agreed that the world was becoming more complex, by which they meant that institutional decisions necessarily involve more and more variables, considerations, stakeholders and constraints – as well as more uncertainty and risk. Our authors were clearly concerned with qualitative complexity as well as quantitative.

Roughly half of our initial proposals addressed complexipacity in organizations, while the balance were more concerned with developing the complexipacity of individuals. None of them, however, mentioned young people. When I approached these authors about the possibility of expanding their proposed papers to apply their knowledge and insights to childhood education, I was earnestly assured that young people could not understand the concept of complexity, which they said requires “adult sensibilities and experience”. This was a startling assertion with stunning implications: IF adult life is becoming more complex, and IF young people cannot understand complexity until they become adults, this calls into question whether schools will be able to prepare students for the future.

Two of the leadership development experts who responded to our Call for Papers commented that candidates for top executive positions typically display disparate capacities for dealing with the ambiguity, uncertainty and risk associated with truly complex decision situations. In particular, each observed, independently, that it was their impression that an unusually high proportion of senior staff who exhibited natural complexity – e.g. fluid mind frames, integrative thought processes and tolerance for uncertainty – reported attending Montessori schools. This insight led the Editorial Board to invite Wayne Jennings, a former teacher and administrator with both public and Montessori schools, to contribute a paper on complexipacity and early childhood education to the Special Issue. All the remaining papers in this issue were attracted by our original Call for Papers.

Taken together, the ten articles in this issue of On The Horizon present ten disparate perspectives on the phenomenon of complexity in modern life. Such a diversity of viewpoints might be expected to produce an inconsistent, or even self-contradictory, narrative; much like the legendary fable of the “six blind men and the elephant.” Remarkably, while our authors represent a variety of disciplines and different levels of sophistication, they are all clearly addressing the same recognizable phenomenon: complexity. The presence of common themes – and insights – in all of the papers, strengthens the impression that dealing with complexity will be an increasing challenge for society, business and governance in the years ahead. Happily, several of our authors report that it is possible to teach young people skills that they will need to deal with that complexity.

Inside “complexipacity”

The opening article, “Complexity, wisdom and education”, explores the multiple manifestations of complexity in modern life. In particular, this paper by On The Horizon’s editor, Tom Abeles, places the issue of complexity in context with the growth of knowledge and human progress. To begin his exploration, he introduces simple but serviceable working definitions of “complicated” versus “complex” systems, with which our other authors (generally) agree. Complicated systems are defined as involving “closed, largely linear processes with fixed, discrete elements and constrained dynamics.” Mechanical and digital systems, we are assured, can be very complicated, but are never complex.

(Note: My brother was pleased to discover that the programming problem which had originally “exceeded his complexipacity,” was, in fact, merely a “complicated” problem. As it turned out, he had been working with a slightly down-level version of the web software and using a computer whose operating system had not been sufficiently upgraded. Once these problems had been corrected, the task became quite tractable, the web site came together and my brother’s self-confidence was restored.)

Truly complex systems involve largely non-linear processes that are open to random externalities capable of generating rapid transformational change. This means that the behavior of complex systems – which include everything from terrestrial ecology to national economies, stock markets, corporations, and families – can never be forecast with certainty. Decisions relating to any complex system will always involve ambiguity and risk, including (as Edward Tenner has reminded us) unintended consequences (Tenner, 1996). This definition of complexity clearly transcends the simplistic, quantitative notion of “more balls to juggle.”

Our authors make it clear that they are talking about “qualitative complexity,” starting with the simple fact that there is ever more “knowledge to acknowledge” – about everything. And, because it is never possible to gather all the information relevant to the behavior of complex systems, even the best-informed decision maker will still be forced to deal with risk and uncertain outcomes. To deal with such ambiguities purposefully, Abeles tells us, people will need “meta-cognitive skills” – systems thinking, problem solving, statistical literacy, self-directed learning, etc. And, these process-oriented skills, he argues, are best learned though interactive experience rather than through passive classroom instruction. In this conclusion, Tom frames a theme that is reprised by most of the other contributors to this volume.

As a part of his review of the current “state of complexity,” Tom addresses the ultimate manifestation of complexipacity – the Singularity. He acknowledges society’s increasing need to rely on artificial intelligence (AI) to aid all sorts of human decision making, and he speculates on the ultimate form of a future, “blended,” man-machine decision maker. But he urges educators to pursue more experiential real-world learning as a means of increasing the general levels of complexipacity in society, to provide us with an “insurance policy” against becoming overly dependent on artificial intelligences whose own complexities will make them, by definition, unpredictable.

Our second article, “1, 2, a few, and many,” is by Paul Schumann, an engineer and complexity consultant. He opens with a concise and useful history of complexity science. Paul is quick to point out that the study of complexity is a new discipline – scarcely 50 years old – and is still evolving as it grows. He reports that there is as yet no commonly-agreed description of the field, nor even an accepted scientific definition of what is meant by “complexity”. In spite of this, Paul is able to describe a number of commonly agreed-on characteristics of complexity, and where they occur in our daily living and working environments.

Paul devotes particular attention to two key concepts of complexity science: “critical” systems and “chaotic” systems. Chaotic systems are orderly complex systems that have been disordered by some combination of random events. And, we learn, many orderly complex systems – including national economies and the global climate – operate in a continuously “critical” state, in which just one or two random events can cause chaos. The potentials for economic or climatic chaos have provided complexity science’s most provocative hypotheses. But Schumann argues that the discipline poses a much more fundamental challenge for public policymakers and for educators. Specifically, he is concerned that the general public is largely unaware of the commonplace role of complexity in the world around us, and is thus unprepared for the random, unexpected dysfuctionalities that complex systems may exhibit. In particular, when untoward events occur, an electorate that is ignorant of complexity is likely to misplace blame or to support inappropriately simplistic responses that may make matters even worse.

As complexity science matures, Schumann believes, the trans-disciplinary nature of the field can be expected to transform the traditional structure of the classical sciences. Meanwhile, he believes we will almost certainly get better at making (at least) some kinds of complex choices easier through the use of “smart” computers. But before we can have a meaningful dialogue with a chatty AI system about planning a career path or choosing a health insurance provider, Schumann continues, we need to have a basic understanding of complexity, uncertainty and risk. In closing, he argues persuasively that middle and high school students can be introduced to complexity most effectively through the use of computer simulations and the study of fractals.

While Schumann is concerned simply with introducing young people to the complex nature of the modern world, our next authors (Viacava and Pedrozo) propose a complete transformation of current business school curricula. The authors, Brazilian organizational development consultants, prescribe a course of study designed to produce MBAs who think of their organizations and their employees as complex, multi-dimensional and continuously adaptive systems that are mutually supportive in all ways, in order to possess sufficient complexipacity to function competitively in a “triple bottom line” environment. They postulate the need to replace current hierarchic, deterministic bureaucracies with “self-organizing holarchic open systems (SOHOs)”, which are depicted as much better able to function effectively in a complexly changing world. In a startling reversal of long-standing organizational wisdom, Viacava and Pedrozo propose replacing anticipatory management with adaptive management. Since the future performance of complex systems cannot be predicted with certainty, they reason, managers should be prepared to deal with plausible unexpected developments, which futurist Peter Schwartz calls “inevitable surprises” (Schwartz, 2003).

While Viacava and Pedrozo do an impressive job of describing and validating an entirely new paradigm of management education to deal with complexity, the next article, “Finding and reducing needless complexity”, does an equally effective job of demonstrating why traditional management systems are likely to be overwhelmed by increasing complexity. Olson, Reger and Singer detail the formal steps and processes used by IBM to eliminate unnecessary procedural complexity. The authors – two of whom are senior IBM staff – concede at the outset that complexity is an intrinsic characteristic of both modern technology and modern organizations. Over time, however, large organizations inevitably accumulate operational complexity that adds no value and is not necessary: outdated legacy procedures, non-essential layers of review, redundant back-up measures, etc. The “needless complexity diagnostic” process is IBM’s answer to this common problem.

Those who have spent any time working inside a large corporate or public bureaucracy will recognize the style and mind-set of the IBM process. “Needless complexity” is initially classified into one of four types, each with its own characteristic features. The authors then describe a five-step process for finding and reducing needless complexity. This process uses a five-point numerical scale for weighing subjective group judgment and prioritizing specific targets for action. Much ad hoc data must be gathered, as well as input from multiple stakeholders; meetings must be held at each step of the process. The authors caution that eliminating even needless complexity can be politically problematic. However, they also offer assurances that, in spite of potential opposition, the process is a win-win proposition that can add real value to the business bottom line.

Readers of Olson et al. who are unfamiliar with the administrative workings of large organizations may, on the other hand, be struck by the irony of what they are reading: a complex process for eliminating un-necessary complexity. In fact, to be specific, the IBM diagnostic process is actually targeted at what systems engineers call “Detail Complexity,” which is characterized as involving known, finite, intra-systemic elements (Calvano and John, 2004). To complexity scientists, however, this definition describes “complicated” phenomena, not complex.

Truly complex problems – which systems engineers call “Dynamically Complex” – involve unknown, indefinite, multi-systemic parameters and uncertain outcomes for which standard bureaucratic processes are problematic, and probably unworkable. Even so, the needless complexity diagnostic process provides illustrative insight into the kinds of elaborate procedures that traditional bureaucracies will have to develop in order to cope with a more dynamically complex operating environment. The patent limitations of the hierarchical management model reflected in this article adds weight to Viacava and Pedrozo’s arguments that the traditional management paradigm must be re-invented if large enterprises are to succeed in an increasingly complex world.

The next four articles shift the focus of our exploration from institutional complexipacity to individual complexipacity. The author of “On becoming more complex” is currently director of research and development for the Institute of Complexity Management. Prior to this, Thomas Owen Jacobs was long-time distinguished professor of Behavioral Science at the US National Defense University. Dr Jacobs draws from longitudinal research on executive development to describe a series of phased interventions for enhancing a decision maker’s capacity to deal with complex problems. His processes are based on Elliott Jaques’ definition of seven levels of organizational complexity and I.W. Fishcher’s three-stage model of cognitive development. He further correlates his psychometric findings with evidence from neuroimaging and EEg patterns.

By meshing Jaques’ and Fischer’s respective taxonomies, Jacobs infers three levels – or “tiers” – of human complexipacity development. Tier 1 involves being able to understand a single system, while Tier 2 requires the ability to understand systems of systems. Tier 3 includes the capacity to understand multiple systems of abstractions, concepts or principles. Professor Jacobs and his sources see an individual’s accumulation of complexipacity as a natural process over time, albeit one that can be (and often is) stunted by bad personal experience at home, school and work. But Jacobs also shows that constructive intervention can accelerate the growth of personal complexipacity.

Because we accumulate complexipacity skills from infancy, Jacobs’ learning interventions for Tier 1 complexipacity can be applied to pre-schoolers (and will be familiar to the viewers of Sesame Street). He cites evidence that Tier 2 skills – for dealing with systems of systems – can be introduced by ages ten to 12. The natural accumulation of complexipacity skills, Jacobs reports, appears to be completed between ages 26 and 29, which, he points out, concurs with neuro-cerebral research showing that the executive functions of the human brain becomes fully developed. Personal complexipacity can be increased after 30, but it apparently takes strong experiential intervention.

Throughout his paper, Dr Jacobs reminds us that understanding complexity involves, first and foremost, conceptual skills that are characteristically learned by experience, in the context of stimulus, action, consequences and reflection. (“Reflection”, he observes, is particularly important in dealing with complexity.) His regimen of complexipacity enhancement for every age level largely takes the form of structured experiences. As he stresses in his closing paragraph, “Conceptual capacity cannot just be poured into the head of someone in a passive mode”.

While Thomas Jacobs describes a system of learning experiences by which teachers and trainers can increase the complexipacity of all age levels, our next author, Sheila Rossan, offers an account of how bad managing and bad teaching can suppress complexipacity in employees and students. Dr Rossan is a senior associate with the British consultancy “bioss”, and has spent over 20 years helping large organizations institute policies and practices that promote the development of individual “capability.” (As she explains, “capability” is the term that the Brunel Institute of Organization and Social Studies has used for 30 years to describe what our Special Issue is calling “complexipacity.”) Elliott Jaques originally chose “capability” to describe those natural aptitudes that contribute to an individual’s capacity to deal effectively with complex problems.

Jaques and others distinguish “capability” from “wisdom,” which he believed had its basis in a different – but over-lapping – set of aptitudes. As does Thomas Owen Jacobs, Sheila Rossan accepts Jaques’ assertion that an individual’s potential complexipacity is set at an early age – perhaps at birth. While they do not believe that these aptitudes can be taught to those who do not innately possess them, both write that employers and schools can adopt practices that permit workers and students to strengthen and develop their natural complexipacity by encouraging them to express and act on their (often) unique ideas and insights. Conversely, Rossan suggests, regimented instruction and authoritarian parenting will suppress the natural aptitudes associated with capability/complexipacity, including integrative thinking, imagination and creativity, and a reasoned tolerance for ambiguity, uncertainty and risk.

Validating her concern for the destructive potential of insensitive schooling, Ms Rossan draws on early research notes regarding two putatively “difficult” adolescent students whose career path diagnostic survey reflected a high conceptual capacity. While schools may incentivize high IQ (“gifted”) students with AP courses and college credits, Rossan observes, it is much harder for a school’s high complexity students, because they constantly think “outside the box,” raise endless questions and often appear either perverse or disengaged to teachers who “aren’t on their wave-length”. If the world truly is becoming a more complex place, she concludes, we are going to have to figure out how our regimented, mass production school systems can begin to nurture their students’ natural potential to deal with complexity. As a former classroom teacher, Rossan offers some practical instructional interventions, but she doubts that many of today’s over-extended, test-obsessed faculty will have the spare time or energy to provide special attention to students with high potential complexipacity.

In “Achieving complexipacity in schools,” Wayne Jennings presents a fully-orchestrated set of a variations on Sheila Rossan’s simply stated theme. He is a former teacher and administrator for both public and private schools. In the first portion of his paper, he reviews the history of failed research-based efforts to reform US schools since the Second World War, and the parallel growth of an increasingly regimented and intransigent educational system whose implacable resistance to change finally provoked enactment of the Federal “No child left behind” reforms. That “reform”, Jennings laments, accelerated the long-term march of classroom learning away from teaching the higher-order cognitive skills that complex decision making requires, including analytical thinking, problem solving, self-directed learning and collegial collaboration. Such skills, Jennings argues (along with most of our other authors) are instrumental to an individual’s ability to deal with complexity in a cogent manner, and cannot be learned effectively in a passive classroom setting. Moreover, the narrowing focus of classroom time on teaching test-driven lower order cognitive skills is reducing time spent on the humanities, art and music, which allow freer expression to nurture students’ natural complexipacity.

In the end, Jennings is hopeful that the increasing sophistication of instructional technology, plus the rising job market demand for complexipacity-related competencies, will ultimately force educators to embrace both innovative curriculum content and instructional delivery. But he also expects the traditional educational establishment to continue pushing back against any reduction of time spent on the “3 Rs”. The coming revolution in education is over-due, Jennings concludes, and it will be a long time until it’s over.

Up through Wayne Jennings’ article, the contributors to the Special Issue all deal with complexity in institutional contexts – business, the military, education, etc. In “Developing personal complexipacity”, author Richard Maynard approaches the issue from an entirely different perspective. Mr Maynard, a management consultant and futurist, describes a personal encounter with a circumstance that clearly meets our other authors’ criteria for “complex”: multiple dynamic variables, uncertainty, ambiguity, risk and novelty (i.e. inexperience). As he observes, such moments occur throughout our lives – e.g. picking a college, buying a first home, deciding whether to change jobs, how to invest an unexpected inheritance, making living arrangements for an elderly relative, etc. For Dick, it was having to choose from among several differing courses of treatment after having been diagnosed with cancer.

In response to his unexpected encounter with complexity, Dick first reflects that changing social norms had made life more complex by eliminating many traditional sources of trusted authoritative advice – family doctors, lawyers, bankers, patriarchs, etc. – who served our parents’ generation. Now that most of us purchase expert input regarding important legal and medical decisions from strangers in the marketplace, an individual decision maker is much more “on his/her own” in dealing with the uncertainty and risk of a new complex situation.

In his own quest for a confident understanding of his prognosis and its best course of treatment, Dick recruited a personal brain trust – a small cluster of colleagues who functioned like a combined doctoral advisory committed and patient support group. They scanned different media for relevant information and actively searched for specific data that would fill gaps in their knowledge base. In some respects, Mr Maynard created what human resource developers call a “community of practice.” Workplace researcher Etienne Wenger first used that term to describe the informal advisory group maintained by most skilled and professional workers during their working lives. Mr Maynard’s successful creation of a community of practice to address his personal, non-work-related issue suggest that there may be a wider role for this natural social technology as a means of acquiring personal complexipacity in an increasingly complex world.

It will occur to many readers that on-line search engines, social networks, RSS, and other on-line peer collaboration applications have potentially “info-mated” the process that Mr Maynard describes. But, his having shown that he was able to create an effective ad hoc research group on a social basis invites comparison with a nineteenth century “barn raising.” This also suggests an evolving long-term purpose for today’s wildly popular social networking sites as they become mature technologies. Such social networking, Maynard observes, may become a crucial personal “survival skill” in a world where there is ample evidence – e.g. Hurricane Katrina, the housing and credit bubbles, the GMChrysler bankruptcies, etc. – to suggest that many institutional decision makers are not doing a very good job of managing complexity on behalf of people who are, as a consequence, faced with the complex challenges of replacing their lost homes, savings and jobs.

In fact, Dick asserts, the social dimension of his search for clarity and confidence in the face of complexity was an important factor in his own eventual personal decision. Since a complex decision, by definition, involves uncertainty and risk, there are emotional components to such choices: fear, hope, “gut” instinct, faith, etc. The collective group judgment, Dick tells us, helped him temper his own emotional responses to the information the support group gathered and the conclusions they reached.

While Richard Maynard is the only one of our authors to present a personal perspective on complex decisions, his is not the only paper that reflects on the important role played by emotions in making complex decisions. One way or another, all of our authors make reference to the fact that complex decisions necessarily involve emotional input – feelings, intuitions, values, etc. This is because we can never have enough information about a complex situation to be absolutely certain about the outcome of our actions. Our emotions – including faith – compensate for that lack of certainty, and allow us to make complex decisions in spite of our uncertainty. In some circumstances, as several of our authors comment, the uncertainty or risk associated with a complex decision may find us so emotionally conflicted – either by the data or by a lack of it – that we are unable to make any choice.

This is why emotions merit critical consideration when we contemplate the possibility of delegating our complex decision to “smart machines”. The final two articles in our Special Issue address the potential future role of computers and artificial intelligence in helping humans “master modern complexity”.

In his opening article, Tom Abeles reflects on the conventional futurist notion of allocating the logical elements of complex decisions to computers while assigning emotional considerations to humans. (He likens this arrangement to the science fiction decision-making team of the logical Vulcan, Mr Spock, and the emotional human, Captain Kirk.) But, in his review of Moral Machines: Teaching Robots Right From Wrong, Dr Abeles points out that the authors, Wendell Wallach and Colin Allen, demonstrate that computer systems, in spite of their ever-increasing size and speed, will never have sufficient capacity to run through every branch of the complete logic tree for a truly complex decision. (The numbers of options offered by complex situations, Wallach and Allen observe, greatly exceed the options involved in playing chess, which is merely complicated, not complex.)

Thus, although computers clearly outstrip the ability of human beings to process data, there are limits to a computer’s logical capabilities, just as there are for humans. Neither is capable of being perfectly logical. When humans are faced with the need to make decisions on the basis of incomplete information, we use emotional inputs – beliefs, feelings, values, etc. – to fill the gaps in our logic. Our computerized decision-making systems, say the authors of Moral Machines, must do the same.

For the purposes of their discourse, Wallach and Allen conceptualize a general purpose artificial intelligence – an autonomous moral agent (AMA) – which might be placed in charge of a hospital ICU monitoring station, an electric power distribution network or a pilotless attack drone. The bulk of Moral Machines, Dr Abeles reports, is devoted to describing how conjoint contributions from engineering, the sciences and the humanities will be needed in order to endow our AMAs with human sensibilities, values and ethics. The authors concede that, by equipping artificial intelligences with moral “judgment,” we will make it possible for such autonomous agents to have, in effect, “free will” and the potential to “act with intent.” But Wallach and Allen express confidence that, by pursuing a multi-disciplinary approach to creating AMAs – with inputs from the humanities and social sciences – we will foreclose the possibility of our autonomous intelligent systems “going rogue”. Our reviewer, however, is not so confident.

Before becoming Editor of On The Horizon, Tom Abeles worked with the Santa Fe Institute, widely regarded as the “home” of complexity science. Tom retains an abiding interest in the subject, and the Special Issue offered him a unique opportunity to explore some of the practical implications of an often abstruse topic. In his closing piece, Tom examines the evolving relationship between humans and their increasingly smart machines, and explores a range of potentialities, from the Kurzweil-Vinge vision of the hyper-productive man-machine collaboration of The Singularity, to an array of vaguely dystopian futures, in which our autonomous cyber-servants, programmed with our values and ideals and endowed with free will, decide to pre-empt their human masters. As Tom points out, such autonomous decision systems – like aircraft auto-pilots, programmed stock-market and currency trading systems, city traffic-light controllers, etc. – are already “emergent phenomena” throughout the world. And emergent phenomena, as several of our authors observe, are one of the characteristic random features of complex systems that can transform such systems … or make them unstable and chaotic.

To this array of uncertainties, Tom adds his own complexifying variable, which he calls “fast adders.” By itself, “adders” is a piece of IT hardware jargon which simply refers to the basic on-off, 0 or 1 binary switches that are at the heart of all electronic computing. The rapid growth of computing speed and capacity over the past 50 years rests on our ability to make adders smaller, faster and cheaper; but the rudimentary mechanism remains unchanged. For the foreseeable future, the continued ability of our electronic info-structure to process the decision-critical data required by an increasingly complex world will depend on our deploying ever more faster, smaller adders.

At the same time that they are functioning in the real world, Abeles points out, fast adders are also being programmed to create increasingly realistic virtual worlds – games, simulations, Second Life, etc. – simulacra of the real world, but without real world complexity. In the real world, where we are being stretched and stressed by growing complexity, the mass appeal of simpler virtual worlds – especially among high-use 25 to 40 year olds – may well reflect society’s need for some therapeutic “down time”. (Why else would 62 million people worldwide spend hours each day at the FarmVille web site, cultivating imaginary acreage and paying real money for virtual seeds and a tractor to harvest their virtual crops?)

The trillions of fast adders that permit our information systems to work are themselves value free; just switches. They can be mobilized in myriad ways to perform an incalculable variety of functions, all of which are putatively determined by scripted code and, ultimately, by programmers and system designers. But, Abeles argues, if computer operating systems are provided with a set of moral values, ethical standards, social priorities and the capacity to make judgmental decisions, there would be little to prevent the executive functions of autonomous systems from amending, contravening or repurposing the fast adders they control, either blatantly or secretively. Moreover, in its sensitivity to initial conditions (the so-called “butterfly effect”) and in its potential for rapid large-scale change, the population of fast adders is, in and of itself, a classic complex system – open, dynamic and non-linear – and, as hackers routinely demonstrate, constantly giving rise to pernicious new phenomena that can disrupt or transform the larger system.

While Abeles concedes that growing complexity will necessarily force humans to become increasingly dependent on computer-aided decision making, he also argues that our artificially-intelligent cyber-systems will be no more infallible than the Titanic was unsinkable. Moreover, the potential mutability of the complex large systems around us – the ecology, the economy, the polity, etc. – means that we must be attentive to all emergent phenomena (including those that are yet to emerge), as we seek to blend human and artificial intelligence to create the complexipacity we will need to preserve, promote and, perhaps even transform the species.

Managing modern complexity

In their diverse perspectives on complexipacity, our authors have provided a remarkably coherent portrait of complexity as an emergent reality in modern life. Each author, in his/her own way, depicts a decision-making environment that is becoming more complex. The continued growth of complexity is tacitly assumed, driven by a variety of underlying inertial trends – e.g. the scale of human habitation (6.6 billion and counting), the globalization of commerce, the increasing powers of technology, the accelerating growth of knowledge, etc. Moreover, our authors all agree that complex problems or questions are an order-of-magnitude more difficult to resolve than are merely complicated ones.

Beyond these two practical truths – the continued growth of complexity and the challenge to all decision makers – three authors actually see understanding complexity as the next threshold of human knowledge, transcending the disciplinary framework of scholarship established during the Enlightenment. Paul Schumann argues that complexity science will ultimately transform and subsume the traditional sciences. Viacava and Pedrozo do not devote their paper to adding a “complexity track” to the traditional B-School curriculum; they present a detailed plan for the complete transformation of the MBA degree program. And Dr Abeles, in his opening paper, suggests that coming to grips with the trans-disciplinary realities in complexity theory must invariably lead to the restructuring of academia.

In sum, the articles that follow depict complexity as a robust new reality that will be a pervasive and permanent feature of life and work from now on. Happily, there is a general consensus among the contributors that skills for dealing with complex situations can be acquired through experience and practicum. Five of the authors – Abeles, Viacava, Schumann, Jacobs and Jennings – cite evidence that structured experiential interventions can produce an increase in individual conceptual capacity. Basically, they are telling us that increasing student or employee complexipacity is an actionable objective, not just an speculative notion.

While most of our authors are optimistic about the potential for increasing human complexipacity, all of them acknowledge that the computer’s superior speed in analyzing masses of data means that IT will necessarily play an instrumental role in future human decision making – both personal and collective. At the same time, complexity science tells us that emergent phenomena – like the current rapid growth in social networks and smart machines – characteristically disrupt, and sometimes transform, complex systems like modern societies. Tom Abeles observes that smart machines might even alter the path of human evolution.

Acquiring greater complexipacity appears to be a human imperative. How we acquire it poses both a short-term issue and a long-term question. In the short-term, our authors offer a wide array of policies and practices for promoting complexipacity in students and employees. But there are many visions competing for the future of both education and enterprise, and as our authors suggest, complexity simply is not on most people’s radar. However, if complexity continues to grow as a common feature of daily work and life, it seems likely that necessity will eventually force schools and businesses to acknowledge this reality and adapt accordingly. Failure to do so will only lead to chaos in commerce and the classroom. Recent collapses of global financial and property markets, increasing breakdowns in regulatory oversight, more frequent infrastructure failures and environmental disasters all give evidence that a lot of things are already beginning to exceed our collective complexipacity. An improved general understanding of complexity would surely help society respond more constructively to such dysfunctions, and act to prevent their recurrence, rather than simply rail against “the system”.

For the long-term, complexipacity poses existential, philosophical and even religious issues. We are almost certainly predestined to engage in ever-closer collaborations with artificially intelligent autonomous agents – which will eventually be programmed with ethics, moral values, emotions and empathy, so that their decisions will be more sensitive to human concerns. Trans-humanists, as Dr Abeles reminds us, are prepared to go beyond merely collaborating with smart machines. Extropians, for example, envision immersing themselves in the virtual world, eventually transmigrating their (presumable eternal) consciousness into a computer, where they will be able to accumulate knowledge and wisdom forever (or at least until someone pulls the plug). Cyborgians, on the other hand, foresee the eventual commonplace use of cerebral prosthetics: brain implants with electronic memory and logic processing that will enhance human intellectual capacity. In either vision, the definition of what it means to be a human could change.

The OTH Editorial Board did not have any anthropological consequences in mind when authorizing our original Call for Papers on “complexipacity.” But that’s complexity for you. For me, this project has been an adventure in learning, and I sincerely hope that readers will find the content of our Special Issue a “triple bottom line” value.

Answering “complexity deniers”

Finally, something needs to be said with respect to the “complexity deniers” who responded to our Call for Papers. These earnest skeptics assert that things have always been as complex as they are now. The apparent increase in complexity, they argue, is the result of recent increases in the rate of innovation and change, largely due to new technology. Once these technologies have matured and their use has been subsumed into our institutions and our everyday routine, the world will feel much more manageable, like the USA back during the 1950s – the “golden years” of the Industrial Age.

This is a plausible argument. Because we have no standard measure of complexity (yet), the evidence of growing complexity is largely anecdotal and the arguments are primarily deductive. But Princeton polymath and Nobel laureate Freeman Dyson made an observation recently that plainly trumps the complexity deniers’ hypothesis. In Technology Review, Professor Dyson wrote that humankind had now entered the “post-Darwinian age” (Dyson, 2005). In the Darwinian age, species evolved on the basis of fitness for survival in local conditions. In the post-Darwinian age, Dyson reasons, the survival and perpetuation of every species of plant and animal on the planet will ultimately be determined by human decisions; by what we choose to do, and how we choose to do it.

The world may, in fact, be no more complex than it was before, but now we are responsible for it. All of it! Fulfilling that responsibility will require a lot more complexipacity on everybody’s part.

David Pearce SnyderContributing editor of The Futurist magazine and on the editorial boards of On the Horizon, and The Trend Letter

About the author

David Pearce Snyder is contributing editor of The Futurist magazine, and on the editorial boards of On the Horizon, and The Trend Letter. His office is in Bethesda, Maryland, where he can be contacted at: david@the-futurist.com or david_snyder@verizon.net; his web site is www.the-futurist.com