Université Paris Sorbonne (Paris IV) Université de Californie à Berkeley. Complexité et Changement Climatique :

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1 Université Paris Sorbonne (Paris IV) École Doctorale 5 : Concepts et langages Université de Californie à Berkeley Department of Environmental Science, Policy, and Management Doctorat en cotuelle : Disciplines : Philosophie, sciences de l environnement Jennifer Lynn Wells Complexité et Changement Climatique : Une étude épistémologique des théories de la complexité Transdisciplinaires et leur apport aux phénomènes socio-écologiques Thèse soutenue en vue de l obtention du grade de docteur le 23 juin 2009 Jury : M. Daniel Andler Professeur à l Université Paris-Sorbonne, co-directeur de thèse Mme Amy Dahan-Dalmedico Directrice de recherche au CNRS et au Centre A. Koyré M. Jean-Pierre Dupuy Professeur à l Université de Stanford, Directeur de recherche honoré au CNRS Mme Catherine Larrère Professeur à l Université Panthéon-Sorbonne M. Pierre Livet Professeur à l Université de Provence Mme Carolyn Merchant Professeur à l Université de Californie à Berkeley, co-directrice de thèse

2 Université Paris Sorbonne (Paris IV) École Doctorale 5 : Concepts et langages Université de Californie à Berkeley Department of Environmental Science, Policy, and Management Doctorat en cotuelle : Disciplines : Philosophie, sciences de l environnement Jennifer Lynn Wells Complexité et Changement Climatique : Une étude épistémologique des théories de la complexité Transdisciplinaires et leur apport aux phénomènes socio-écologiques Thèse soutenue en vue de l obtention du grade de docteur le 23 juin 2009 Jury : M. Daniel Andler Professeur à l Université Paris-Sorbonne, co-directeur de thèse Mme Amy Dahan-Dalmedico Directrice de recherche au CNRS et au Centre A. Koyré M. Jean-Pierre Dupuy Professeur à l Université de Stanford, Directeur de recherche honoré au CNRS Mme Catherine Larrère Professeur à l Université Panthéon-Sorbonne M. Pierre Livet Professeur à l Université de Provence Mme Carolyn Merchant Professeur à l Université de Californie à Berkeley, co-directrice de thèse 1

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4 This dissertation is dedicated to William S. Wells, Doris J. Wells, Christopher G. Wells, Karen Wells, Rebecca S. Wells, Ronald Powers, Catherine E. Bostock, David Bostock, Andrew Bostock, William Bostock, and Samuel Powers 3

5 Comité Doctorale de l Université de Californie à Berkeley Carolyn Merchant Professeur de philosophie, éthique et histoire environnementale, Department of Environmental Science, Policy and Management (ESPM), à l Université de Californie à Berkeley (U.C. Berkeley) David Winickoff Professeur de Bioéthique, ESPM, U.C. Berkeley Richard B. Norgaard Professeur d Environnement, Energy and Resource Group, U.C. Berkeley Daniel Andler Professeur de Philosophie, à l Université Paris-Sorbonne, Catherine Larrère Professeur de Philosophie, à l Université Panthéon-Sorbonne 4

6 Acknowledgments This dissertation spans continents, universities, languages, cultures, disciplines, the science and ethics of climate change, and of course, complexity theories. This has created quite a network of support and inspiration! I am forever indebted to Carolyn Merchant. Carolyn s outstanding scholarly qualities and intellectual mentorship have prepared me for a life s work. I will cherish the many memories of our time together. Moreover I have been very fortunate to work with David Winickoff, who s agile, rigorous, and critical thinking has greatly influenced me. I am grateful to the transdisciplinary maverick Richard B. Norgaard for his wonderful support over the years, helping me to feel fully at home in the Bay Area, and even making gourmet lasagnas for the cross-campus complexity discussion group we held in his living room. Aside from his invaluable support since 2001, Daniel Andler has given me one of the most valuable lessons of my doctoral years, to argue with someone of a slightly different philosophical perspective, who is able to remain as open-minded as he is erudite and rigorous in his thinking. Daniel put me in touch with Catherine Larrere, which has been a most fortunate encounter. Catherine has been a great intellectual inspiration for me personally and a pioneer of environmental ethics in France. I offer her my heartfelt thanks for her mentorship and friendship since As for my doctoral defense jury in France, I would like to offer my warmest thanks to Amy Dahan-Dalmedico, Jean-Pierre Dupuy, and Pierre Livet. Thanks to Amy Dahan-Dalmedico for her intellectual leadership in science studies and climate change, her critiques of my work, and for welcoming me into her doctoral dissertation working group, where I learned so much about critiquing and conducting research at the doctoral level. Likewise I thank Jean-Pierre Dupuy for his work in areas so close to my heart, and for his generosity in providing me with many texts and commentaries early on in my graduate studies, giving me the hope that I could actually attempt such a topic. I offer a special thanks to Pierre Livet, a pioneer of complexity thinking in social systems, for joining my jury sight unseen. In institutional support I have been unusually fortunate. Numerous departments at University of California at Berkeley have welcomed me, notably the philosophy department where I enjoyed work and conversation with Samuel Scheffler (qualifying exam member), Alan Code, Hubert Dreyfus, and others. In France, I have been a visiting scholar for one full year with the environmental group PROSES at the University Sciences Politiques, I am very grateful to have been welcomed over the years into the wonderful community at Ecole Normale Supérieur, and for the steady support of the Sorbonne, Paris IV. Thanks to the Yale Fox Fellowship, the Hixon Center, and many others for generous funding. 5

7 I d like to thank everyone at my home department of Environmental Science, Policy, and Management in Berkeley, including Richard Battrick, Rosalyn Farmer, Doty Valrey, and all the wonderful faculty and graduate students at Society and Environment. A wonderful unintended consequence of this dissertation is that I have become personally acquainted with an extraordinary group of scholars. I would like to especially thank Edgar Morin, Timothy F.H. Allen, and Henri Atlan for their friendship, conversations, and comments; likewise climate scholars Paul Baer and Stephen Schneider, environmental ethicist Andrew Light, SFI faculty and community Geoffrey West, Doyne Farmer, Neo Martinez, Debora Hammond, Timothy Foxon, and Yaneer Bar-Yam of NECSI. Finally, I hope to meet Kurt Richardson, whose prolific work at the journal E:CO has greatly benefitted this dissertation. I thank complexity luminary Alfonso Montouri for hiring me as Assistant Professor at the California Institute of Integral Studies starting in August I thank my extraordinary new colleague Bradford Keeney. I am grateful to my family, friends, and communities! I have dedicated this dissertation to my family. In the Bay Area I have been blessed with friendships with Alastair Iles, Kamal Kampadia, Paul Baer, and many others, too numerous to mention but no less appreciated. I am eternally grateful to Juan Roy, who helped to inspire me to begin this dissertation, and to complete it through thick and thin. Finally, I thank Peggy Touvet, Francesco Colonna, Chloe Manfredi, Emeline LeGoff, Fanny Verrax, and especially Kent James, whose support has helped me to complete it! 6

8 Table of Contents Acknowledgments 5 Table of Contents 7 Table of Lists 9 Introduction 11 Part I Complexity Theories: A transdisciplinary Survey 23 Chapter 1 Elucidating Complexity Theories 25 Chapter 2 Complexity and the Natural Sciences 91 Chapter 3 Complexity and the Social Sciences 131 Chapter 4 Complexity and Social Theory 179 Chapter 5 Complexity, Transdisciplinary Theory, and the Philosophy of Science 211 Part II Complexity and Climate Change 271 Chapter 6 Chapter 7 Complexity in Climate Change and International Assessments Complexity, Ethical Theory and Climate Change: Implications for Climate Ethics and Policy Conclusion 415 Bibliography 425 7

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10 List of Tables Table 1.1 Generalized Complexity Framework (GCF), p.35 Table 1.2 The Hierarchy of Constitution and Disciplines, p.69 Table 1.3 A Hierarchy of Systems Classified by Complexity of Feedback Modes, p.72 Table 2.1 Definitions of Complex Adaptive Systems in the Natural Sciences, p.98 Table 2.2 Definitions of Complex Adaptive Systems, p.99 Table 2.3 Examples of phenomena that only exist in natural science systems, p.99 Table 2.4 Key Complexity Terms and Founders in those fields, p.101 Table 3.1 Complexity Theory Approaches to Social Systems: Three Realms, p.134 Table 3.2 Information Estimates for Straight English Text and Illustrated Text, p.138 Table 3.3 Estimates of Complexity Primarily Based upon Genome Length, p.139 Table 3.4 Kline s Estimations of Degrees of Complexity at Different Scales, p.141 Table 3.5 Complexity Fundamentals and Major Thinkers, p.171 Table 4.1 Three Theses in Social Theory and the Main and Secondary Complexity Fundamentals Supporting these Theories, p.179 Table 5.1 Five Transdisciplinary Fields, Leading Scholars, and Major Foci of Each, p.218 Table 5.2 A Hierarchy of Systems Classified by Complexity of Feedback Modes, p.236 Table 5.3 Systematic Knowledge Concerning the Limits to Systematic Knowledge, p.261 Table 5.4 The Limits to Science, p.264 Table 6.1 Epistemological Fundamentals of Complexity and their Expression in the Climate Change Literature, p.276 Table 6.2 Comparison of Mainstream and Complexity Conceptual Frameworks, p.302 9

11 Table 7.1 Axes II and III of the Generalized Complexity Framework, p.348 Table 7.2 Relationship between Ethical Theories and Emissions Allocation Schemes, p.363 Table 7.3 Ethical Theories, and their Founders and Leading Proponents, p.367 Table 7.4 Interests in Kakadu National Park, Australia Great Northern Territory, p.374 Table 7.5 Harms if Mining is Allowed in Kakadu National Park, p.375 Table 7.6 Complexity Fundamentals and their Implications for Ethics and Policy Approaches, p.402 Table 8.1 Generalized Complexity Framework GCF (Duplicate of Table 1.1), p.416 Table 8.2 Complexity Fundamentals and their Implications for Policy Approaches (Duplicate of Table 7.6), p

12 Introduction "[T]he twenty-first century will be the century of complexity." Stephen Hawking i In the twenty-first century complexity is not a vague science buzzword any longer, but an equally pressing challenge for everything from the economy to cell biology. Albert-Laszlo Barabásí ii Make things as simple as possible, but no simpler. Albert Einstein As the urgency of climate change and other global issues has come to the forefront in recent years, many scientists and scholars have begun recasting these issues in terms of complex systems. This influential new perspective on social and environmental issues has major implications that have yet to be clarified. Climate change is an ideal case study of the utility of complexity theories, as it occurs at a planetary scale and involves interactions among complex systems essential to human civilization, such as food, energy, water, and economic systems. In the last ten years, just as mounting evidence has made climate change a top priority, literature on complexity theories has increased exponentially. A rising tide of books, journals and conferences focus on complexity indicating that yesterday s buzzwords chaos, nonlinearity, and networks are today s mainstream science and knowledge production. This presents an urgent need: to analyze advances in complexity theories and their implications in order to best inform decision-makers dealing with climate change. The goal of this dissertation is to analyze how complexity theories are useful in addressing climate change. As climate change is awesome in its transdisciplinary scope, I necessarily address what complexity means in diverse disciplines, with their widely divergent methodologies and worldviews. In lieu of a provable hypothesis, I explore a key proposition: complexity theories are likely unnecessary and inappropriate in some research, and yet substantial and useful in other areas, and I then assess this statement with respect to the case study of climate change science, ethics, and policy. 11

13 Complexity Theories Definition Circa 2000 Complexity is a broad term referring to an overall scientific and philosophical perspective, based upon a large set of subsidiary ideas, sometimes referred to as complexity theories. While the terms complexity and complexity theories continue to lack clarity for many, the considerable successes of the complexity theories proves their importance. To begin with, I present the cursory, provisional definition of the field as it is still accepted as the last iteration of definitions for these terms, synthesized by various scientists and scholars in the period of around While other views have grown considerably in the last ten years, the definition presented in 2001 by Stephen Manson and in similar accounts, is still broadly referred to, as it presents the most recent clearly articulated majority opinion of the field. iii Even this account was quite contentious at the time, yet it is perhaps the best general reference point against which to contrast the immense progress made in the last decade. The circa 2000 account of complexity science or complexity theories roughly divided the field into three areas of research: algorithmic complexity, deterministic complexity, and aggregate complexity. Algorithmic complexity refers to two things. One, the measure of algorithmic complexity calculates the effort required to solve a mathematical problem. Spatial statistics and geographic information science face this kind of complexity. Problems such as enumerating all the permutations in a resource allocation situation or finding the shortest path through a network are very hard to solve in non-trivial cases. However, this form of algorithmic complexity has been utilized as an essential guide for practitioners in these areas. The second form of algorithmic complexity lies in information theory, and is thus dubbed algorithmic information theory. It has been attributed to independent contributions of three somewhat simultaneous founders, Solomonoff, Kolmogorov, and Chaitin. iv This body of work identifies complexity as the simplest computational algorithm that can reproduce system behavior. With information theory, one may condense the myriad interactions between systems components into simple measures. The use of information theory ranges from classifying remotely sensed imagery to considering the role of ecological community structure on biodiversity. Algorithmic complexity may not be applicable to social or environmental phenomena as it may incorrectly equate data with knowledge. According to Manson, Vast realms of human endeavor, such as the lived experience and meaning given to it, lie beyond algorithmic expressions. Critics of geographic information science such as John Pickles claim that such shortcomings of computational representation are too great even for accurate analysis of spatial phenomena. v 12

14 The second main category of complexity theories is deterministic complexity, which has four key characteristics: 1) the use of deterministic mathematics and mathematical attractors; (2) the notion of feedback; (3) sensitivity to initial conditions and bifurcation; and (4) the idea of deterministic chaos and strange attractors. Prominent instances of deterministic complexity are chaos and catastrophe theories, which are quite successful with respect to some biophysical phenomena such as weather patterns and physical and chemical reactions. Nonetheless, some debate persists about the overall value of chaos and catastrophe theories. A large amount of time series data is required to prove that a system has deterministic complexity. Even when data exists, fewer systems than anticipated are in fact deterministically chaotic or catastrophic. It appears that the instances are restricted largely to physical and chemical systems. Characterizing a human system through a few simple variable or deterministic equations is often just too simplistic. vi There are hazards in conflating pattern with process. For instance, urban land use may have a fractal pattern, but this knowledge only goes so far in aiding our understanding of how the land came to be that way, what this means for land use, or if that implies anything for land management. Yet, effects such as sensitivity to initial conditions or strange attractors have spurred new thinking about everyday phenomena. This is often achieved by using these terms in an analogical manner. Postmodernists have embraced deterministic complexity this way (Hayles 1991). Deterministic complexity is characterized by contextuality, complexity and contingency; these themes have been said to exemplify postmodernism (Warf 1993). According to Nancy Cartwright, sensitivity to initial conditions and bifurcation undermine totalizing discourses by supporting unpredictability and the search for fragmentation and discontinuity. vii There are many other interesting parallels between discoveries in the complexity sciences and major concepts in postmodern theory, such as the use of scale, spatial hierarchy, boundedness, and economic attractors. Finally, the third category has been called aggregate complexity, or systems of linked interacting components. Algorithmic and deterministic complexity relies on simple mathematical equations and a number of assumptions on how complex systems work. Aggregate complexity instead assesses phenomena occurring at the scale of a system, resulting from the interaction of system components. Key aspects of aggregate complexity include: a key set of interrelated concepts that define a complex system including relationships between entities, internal structure and surrounding environment, learning and emergent behavior and the means by which complex systems change, adapt, and develop. The heart of aggregate complexity lies in relationships, the relationships between components. Self-organization is the property that allows a system to change its internal structure in order to better interact with its environment, to learn through piecemeal changes in that internal structure. 13

15 Like deterministic complexity, aggregate complexity is explored in different fields via different means. While methodological difficulties have plagued aggregate complexity in the natural sciences, the development of computer simulation tools has allowed for substantial advancement in many areas of natural science, such as ecology. Once again, postmodern perspectives link aggregate complexity to issues of knowledge, language, and epistemology. Both aggregate complexity and postmodern theory have shown how entities and relationships within complex systems undergo constant interaction and development, supporting the postmodern view of localized, networked, social and political discourses. It seems that all three forms of complexity present advances and insights at a cost. The three forms seem to gain in their power and reach in the order presented. Algorithmic complexity appears to be the simplest, yet the least fruitful and with the least real world applications. Deterministic complexity appears to be somewhat more difficult, but also bearing more tangible fruits. Finally, aggregate complexity is clearly more challenging than the other two areas. There is a very large literature on aggregate complexity, and a vast number of real world applications already developed and deployed. Numerous institutions, journals, academic departments and societies have formed to advance aggregate complexity theories. Clearly, many complexity scientists and scholars argue, aggregate complexity offers valuable perspectives on emergence, self-organization, social issues and environmental dynamics. Many brilliant thinkers are currently devoting their careers to this field. At the same time, the field remains philosophically questionable. The relationship between the greater body of scientific knowledge and aggregate complexity remains elusive and uncertain. Despite new tools such as simulation models, aggregate complexity seems plagued with methodological shortcomings and deep misunderstandings. Complexity Theories Definition Today This set of definitions, compiled and discussed about ten years ago, around the year 2000, now seems quite outdated. In this dissertation, I subsume the notions of algorithmic complexity and deterministic complexity under the much larger umbrella of aggregate complexity. Indeed, I give the prior two scant explicit attention, although they continue to play a major role within the field. The main focus in this dissertation is on what was previously called aggregate complexity, and now is called simply, complexity. This has been the center of attention of what I conceive of as the main field of generalized complexity theories. I define generalized complexity theories as the most encompassing view of complexity as it has been developing throughout the disciplines. I expand on this definition in later chapters. 14

16 A greater deal of synthesis and rapprochement has taken place, not just between those involved in the proliferation of complexity theories in this last decade, but also due to considerable work to develop the field of generalized complexity theories, or complexity theories writ large. A growing number of scholars have tirelessly been drawing together the many precursors of complexity, the many disparate founding voices, and disparate fields, including: mathematics, cybernetics, biology, ecology, environmental issues, and various areas of postmodern analysis, social theory, and the philosophy of science. My task is to assemble and assess this great body of work put together in the last ten years, and thus to advance our understanding of complexity, its relationship to the rest of knowledge, and its potential. My motivating questions include: Why it is that complexity seems to appear and make sense in every discipline, in a way that no former theory ever has? Is there something unique that merits this category of complexity theories? If so, what is the power, utility, and potential of this realm of theory called complexity theories? I use the word theory here in a broad, simple, general sense. This dissertation is a sequel to the slurry of brief overviews of complexity written in the phase from around , when the importance and predominance of the field was becoming clear, but the sense of what it was or where it was going was anything but clear. At this point in the history of the field, I argue, it is possible and beneficial to greatly clarify major terms in the field, the parameters of the overall field, their relationship to each other, their relationship to science more generally, and their applicability to various real world issues. In search of a more recent, better informed, and more synthesized definition of the complexity field, one simple place to begin is with the notion of a complex system, which has been defined as: a set of parts (Leibniz 1666); a set of unities with relationships among them (Bertalanffy 1956); and a global unity organized by interrelations between elements, actions, or individuals (Morin 1994). The key idea added to the definition of complexity in the Twentieth Century was the global character of the relational trait of the system, which has furthered our understanding of emergent and self-organizing properties. Today, some stress the multidisciplinary and epistemologically plural aspects of complex systems viii, while others see complex systems as, reality, untainted by the simplicity of models and other simplifying devices. ix Mainstream notions associate complexity primarily with certain quite delimited instances from the natural sciences, which have been widely popularized. This includes captivating images such as fractals, strange attractors, and networks as in maps, food webs, or the internet. In fact, complexity has emerged in all the disciplines at different moments throughout human thinking, up until today. While some scholars still associate complexity theories more strictly with a few examples in the natural sciences, many others have begun to use the term more broadly to conceive of a large array of 15

17 phenomena and patterns that are now constellated into what many believe to be, in the Kuhnian sense, a new paradigm of human knowledge. In this sense, complexity theories emerged in all the disciplines, mostly since World War II. While complexity has established itself widely and deeply in the last thirty years, both critics and supporters in both the natural sciences and social theory often still sidestep complexity terminology, because they find the definitions abstruse. Various troubling claims are made. Anything that you cannot define clearly is not worth studying. There is no such thing as complexity; what you call complexity is just standard science. Complexity is just a rag-bag of everything. Indeed, some natural scientists remain wary or are condescending of the term complexity itself. x To these claims I would answer, many things we cannot define are clearly worthwhile, including love, hope, sustainability, healthy ecosystem services, and even the term science itself, which is actually also very difficult to define well. Complexity is in a sense an extension of standard science; but according to a vast new literature, there is more to the story than that. Therefore, complexity is not just a rag-bag of everything. Mainstream analysis of the field has emerged almost entirely from just a few natural science institutes scattered in industrialized countries, largely starting with the Santa Fe Institute in New Mexico. These scientists advance basic science and consider applications in various niches, from spin glasses, pendulums, and sand piles, to food webs, and certain restricted examples of social and economic phenomena. This perspective is valuable, but may mask the more extensive implications in the realms of social science and global change. However, again, rather than recap the default mainstream perspective, this dissertation involves a full survey of the field, leading to the most wide-reaching ideas about what complexity is, what it implies, and if and how it is valuable. This dissertation has been conducted, I believe, at just the right moment. By now, it is clear that complexity is here to stay, and the difficulty of defining it seems rather to indicate the breadth of its impact. Complexity has always been an aspect of our world, and there have always been complexity visionaries Heraclitus, Blaise Pascal, Ludwig von Bertalanffy, Edgar Morin and many others. Since World War II, there have been a series of impressive new fields born under the umbrella, I argue, of greater complexity theories. By the mid-twentieth century, many leading philosophers had made important insights into complexity. By the turn of the twenty-first century researchers in most all disciplines had compiled enormous information about complex systems. In the last ten years work on complexity throughout the social science and social theory disciplines has become increasingly explicit and a coherent field of social complexity studies has been developing. 16

18 Aim, Rationale, and Methodology of the Dissertation One of the main purposes of the first half of this dissertation is to connect the dots between various realms and disciplines, to explore just if and how complexity theories are transdisciplinary and useful. While the scope is necessarily transdisciplinary, the methodology I use is primarily philosophical analysis and interpretation. While I must tread at times into interpretation within different fields like the sciences, this dissertation is grounded in the philosophy of science and applied ethics. However, this dissertation also conforms to the views and methods of the following fields: environmental studies, environmental politics, science and technology studies, risk studies, and futures studies. It is no coincidence that such transdisciplinary fields have been proliferating in recent years; rather this trend has occurred directly in response to the increasing acknowledgement of the need to address greater degrees of complexity and transdisciplinarity in many of the major issues confronting societies and their environments today. This dissertation presents a novel synthesis of contemporary complexity theories. While scholars have already delineated important work on transdisciplinary complexity, never before, to my knowledge, has someone brought together the most disparate areas of complexity studies in one synthetic interpretation. I include in this many important theses that have been based only implicitly and not explicitly on complexity theories. I attempt to show that making the many fruitful links to articulate complexity theories, both within and between the disciplines, represents a great advance for the field of complexity theories and for contemporary scholarship. The novel contribution of this dissertation is the comprehensive, transdisciplinary, contemporary definition of complexity theories and its explicit application to climate change. There has been, notably, the transdisciplinary analysis of Edgar Morin. xi However, so much has evolved in just the last ten years that the two contributions presented here this contemporary definition and the application to global change are nonetheless notable advances. I expect that secondary benefits will also accrue. Currently, many scholars in dispersed domains are exploring how complexity is essential to understanding the larger dimensions of various phenomena, social, technological, and environmental. This dissertation shows important links between their work: 1) elucidating the commonalities between different fields, 2) showing how lessons can and cannot be usefully adapted from one domain to another, 3) highlighting what, despite common principles, makes these fields distinct, and 4) analyzing when certain individual disciplinary approaches are appropriate, and when it is appropriate to employ complexity principles, theories, methods and analyses, whether stemming from the natural sciences, social sciences, transdisciplinary studies, philosophy, or ethics. Moreover, it sheds light on issues such as: 17