MULTI - SCALE INTEGRATED ANALYSIS OF AGROECOSYSTEMS - CHAPTER 8 docx

231 8 Sustainability Requires the Ability to Generate Useful Narratives Capable of Surfing Complex Time* This is the last chapter dealing with epistemological issues. Actually, after reading Chapters 6 and 7, in which the concepts of mosaic effects across levels and impredicative loop analysis were introduced, the reader fed up with epistemological discussions can skip this chapter and move directly to Part 3. The question answered by this chapter is: If we refuse the charge that the expression “sustainable development” is an oxymoron, then should we not be able to describe what it is that remains the same (sustainable) when the system becomes something else (development)? We understand that to some practitioners this question could appear too theoretical. However, the message proposed so far is that those analysts willing to deal with the issue of sustainability cannot just apply formal protocols. Complexity requires the adoption of flexible procedures of analysis that always imply an explicit semantic check. For this reason, we believe that those who are serious about developing analytical tools for dealing with sustainability should address first—as done in this chapter—the peculiarity of this predicament. In this chapter, Section 8.1 introduces a few concepts that can be used to better frame the challenge implied by sustainability. The basic rationale proposed by Holling when representing evolutionary patterns (using the concepts of resilience, robustness and the cyclic movement among interrelated types—the adaptive cycle) is briefly introduced and translated into the narrative adopted so far in this book using the vocabulary presented in Part 1. Then Section 8.2, which is a technical section, deals with the concept of essence—something that cannot be formalized and that can be associated with the existence of multiple identities. The concept of essence requires a special discussion, since this is the elusive concept generating the epistemological predicament implied by complexity. In this section, first we provide several examples to show the relative unimportance of DNA in the definition of essences in biological systems. Then, using theoretical insights provided by the work of Rosen and Ulanowicz, we propose a mechanism that can be used to obtain a formal reading (images) of the unformalizable concept of essence within the analytical frame provided by network analysis. The final section, Section 8.3, deals with the definition of useful narratives in relation to the concept of complex time. Building on the concepts discussed in the previous two sections, we claim that useful narratives can only be defined by and in relation to a given complex observed-observer. Because of this, they have to be continuously updated during the never-ending process of evolution, which includes both the observer and observed system. In particular, the requirement of careful timing for updates becomes crucial when dealing with the reflexivity of human systems, that is, when observer and observed are at the same time observed by the observed and observing another observer, in a reciprocal process of interaction. This situation implies that both sides of the observer-observed complex can suddenly change their identity, implying that validated narratives can suddenly lose their usefulness. 8.1 What Remains the Same in a Process of Sustainable Development? 8.1.1 Dissipative Systems Must Be Becoming Systems Dissipative systems are necessarily becoming systems (Prigogine, 1978), since they have to continuously negotiate their identity with their context in time. As discussed in the previous chapter, the very existence (success in preserving its own identity) of a dissipative system implies the local destruction of * Kozo Mayumi is co-author of this chapter. © 2004 by CRC Press LLC Multi-Scale Integrated Analysis of Agroecosystems232 favorable gradients on which its metabolism depends (a consequence of the second law of thermodynamics). Therefore, dissipative systems tend to destroy the expected stable associative context to which their current identity (type) is associated. Because of this, a reliable and predictable associative context must be a context that is stabilized by another process of dissipation (which requires in turn favorable boundary conditions on a higher level) occurring elsewhere (see Figure 7.18). This is the first mechanism that generates trouble in the representation of these systems. As noted before (Chapter 7), the stabilization of the identity of these systems can only be obtained through impredicative loops in which processes of dissipation occurring in parallel on different levels should be considered in terms of reciprocal entailment among identities defined on nonequivalent descriptive domains. When representing these systems, we have to select one (among many possible choices) window of three levels (triadic reading) and assume that (1) on the lower-lower level, structural stability is given and (2) on the higher level, favorable boundary conditions are stabilized by some benign process ignored by the model. To make things more difficult, adaptive dissipative systems must use templates (e.g., DNA or social institutions) to guarantee the stability of their own identity (elements across levels). This implies the required stability of types expressed over a time window larger than the life span of individual components providing structural stability to the functions expressed by types. Realizations of an equivalence class have a shorter life span than that of the validity of the template used for making them. That is, organized structures sharing the same template undergo a process of turnover within a given set of expected types. Unfortunately, a mechanism of replication based on templates generates an additional problem of sustainability. Self-replicating dissipative systems are affected by an innate “Malthusian instability,” the expression coined by Layzer (1988). As soon as a dissipative pattern associated with the existence of favorable boundary conditions finds a good niche (room for expansion), it tends immediately to expand its size by amplification (making more copies of the template). This means adding more individual organized structures belonging to the class sharing the characteristics of the type associated with the pattern. The sudden enlargement of the domain of activity of the pattern means jeopardizing the very survival of the mechanisms of replication. In fact, by making more copies of themselves (by making more of what seems to work under the existing perception of favorable boundary conditions), adaptive dissipative systems tend to amplify on a larger scale the rate of destruction of local favorable gradients. Probably a few readers have already recognized in this mechanism the ultimate driver generating the problems of sustainability of human affairs discussed in Chapter 1 (Jevons’ paradox leading to the generation of various treadmills). Any dissipative system that keeps growing in size just by amplifying the same basic process of dissipation will sooner or later get into trouble. We can recall here the story of Zhu Yuan-Chang’s chessboard: if you put one kernel of rice on the first square, two on the second, four on the third, and keeping doubling the number for each square, there would be an astronomical number of kernels required for one position even before the 64th square is reached. This metaphor says it all. Using the expression proposed by Ulanowicz (1986), hypercycles (positive autocatalytic loops), when operating without a coupled process of control (and damping), do not survive for long—they just blow up. The expected trouble at one level (too much of an efficient type) implies that part of the surplus has to be invested in exploring new types (even if not efficient) able to diversify the set of relations expressed by the whole (on different levels). Dissipative systems that use a template to replicate themselves, when taking advantage of existing favorable gradients, must reinvest a part of their energetic profit to become something else. This is the reason why mutations in DNA should not be considered errors, but a crucial mechanism associated with the ability of biological systems to evolve. We addressed this key feature of adaptive dissipative systems when representing (in Figure 7.8a) these systems as made up of two compartments: a direct compartment (where we can define the efficiency of the return on the investment) and an indirect compartment (where the system invests in adaptability). As noted in Section 3.6.3 (Figure 3.7), adaptive dissipative systems, to stabilize their own process of dissipation, have to balance their investments in efficiency (making stronger the actual set of identities) with investments in adaptability (expanding the option space of the set of virtual identities). This is what leads to the concept of sustainability dialectics. That is, it is not possible to formalize in a substantive representation of an optimizing function the expected trade-off between these two types of investments. Existing identities must not be too greedy; © 2004 by CRC Press LLC Sustainability Requires the Ability to Generate Useful Narratives 233 maximization of profit or efficiency implies reducing the option of expressing alternative virtual identities. The only certain point that can be driven home from the unavoidable process of becoming of complex adaptive system is that a strategy looking for a maximization of efficiency (obtained under the ceteris paribus hypothesis) is not the wise if one is concerned with the long-term stability of the system. 8.1.2 The Perception and Representation of Becoming Systems Require the Parallel Use of the Concepts of Identity and Individuality In the 1970s Buzz Holling proposed a few concepts for the analysis of the sustainability of changes in ecological systems. These concepts were resilience, resistance (or robustness) and stability. The use of these concepts to represent the issue of sustainability of ecological systems has remained very popular among those trying to make formal analyses of sustainability for both human and ecological systems— an overview given by Holling himself about the use of these concepts is available in Holling and Gunderson (2002). It should be noted, however, that in spite of the large popularity of this narrative, and the crucial importance of these concepts for the understanding of the evolution and behavior of ecosystems, very little effort has been invested by those using these concepts in getting engaged in an epistemological discussion of them. If you were to ask different ecologists about the definition of these terms, you would get different answers. By looking at the literature in this field, one can find several definitions of resilience that are nonequivalent and nonreducible. Very often they are even listed as a set of interchangeable, optional definitions, without their mutual incompatibility and exclusiveness being addressed. It is obvious that the success of these terms is associated with their deep ambiguity, which can handle the different meanings that ecologists attach to them. A mathematician, on the other hand, would ask you to better specify the mathematical meaning of concepts like stability or resilience before getting into any discussion of their syntax. Obviously, this is not the way to advance in a critical epistemological appraisal of them. If we keep the terms too ambiguous, anyone can use them without problems, but in this way, one has to resort to a discussion about their semantics (what external referent should be used to share the meaning about them?). On the other hand, if the definition is too formal (as done by the mathematicians), everything is reduced to syntax. But exactly because of this, after having done that, it is no longer possible to discuss the semantic usefulness of the relative concept. Robert Rosen spent a large part of his academic career dealing with the epistemology of such a discussion. Therefore, this section has as its goal to share with the reader some of Rosen’s insights. Any theoretical discussion about the epistemology of these terms requires first answering the following question: When dealing with the analysis of the evolution of a given adaptive dissipative system, if we want to take measurements and make formal models about it, what remains the same when the system becomes something else? Just to get our discussion started, let us try to describe the three concepts of resilience, resistance (or robustness) and stability. Two nonequivalent ways of defining these terms are listed below: (1) the definitions found in a dictionary (Merriam-Webster on-line) and (2) the semantic meanings conveyed by these terms according to a narrative and vocabulary taken from the work of Robert Rosen (1985, 1991, 2000). Obviously, we do not claim that what is posted below is the “right” interpretation of these terms. This is not the issue here. These definitions are needed for sharing with the reader the meaning assigned to these terms (to share a common understanding with the reader) in the rest of the chapter. Resilience From the dictionary: The capability of a strained entity to recover its original condition (e.g., size, shape, structural characteristics) after a deformation caused by stress. Narrative (using Rosen terminology): Referring to the idea of multiple equilibrium states for a dynamical system. A given system has a certain identity. That system faces a perturbation (a nonadmissible environment) that makes its present state no longer viable (boundary conditions that are not compatible with the mechanism keeping the metabolism associated with a given type alive). The system can access alternative states (since it has multiple identities). In one of these alternative states the very same boundary conditions that were not admissible for the © 2004 by CRC Press LLC Multi-Scale Integrated Analysis of Agroecosystems234 previous identity become admissible. In this way, the system can preserve its individuality. This system must have the ability to switch among different viable states in relation to different definitions of admissible environment Thus, it can preserve the ability to get back to the original state (type) when the perturbation is over. Examples are a tree branch bending under heavy wind (when the relevant state considered for defining its identity is only the position of the branch), bacteria forming a spore (relevant state considered is the original organizational structure of the bacteria, which comes back when the perturbation is over), and an ephemeral plant making seeds when the environment becomes too dry (as before). Robustness (or Resistance) From the dictionary: Having or showing firmness (firm=having a solid structure that resists stress, not subject to change or revision, not easily moved or disturbed). Narrative: A given system has a certain identity. That system faces a perturbation (that would generate a nonadmissible environment). But the system can react to it, by fighting the process that is generating a hostile environment. This can be obtained by using a set of controls (a tool kit of alternative behaviors linked to anticipatory models based on previous experience of the same perturbation)—expressing behavior that is based on an anticipatory model “knowing” about the potential perturbation—or just having a size large enough or enough redundancy to overcome and dissipate the perturbation into an admissible noise. This requires also the ability to (1) expect possible perturbations and (2) control enough power (being able to express the dissipative pattern at a size large enough) to combat the exogenous perturbation. Examples are immune systems in mammals and storage of water in plants when facing a shortage of rain in the desert. Stability From the dictionary: The property of a body that causes it, when disturbed from a condition of equilibrium (or steady motion), to develop forces or movement that restore the original condition. Narrative: If we want to translate this definition into a narrative based on Rosen’s terminology, we should get into something very generic: the ability to retain your individuality in the face of perturbations no matter how you do it. This definition could be accepted as a variant of that of resilience or also as a variant of that of robustness. This is due to an open ambiguity in the definition of the terms used. To decide how to deal with this ambiguity, we should first be able to answer the following questions: What is the time threshold considered for retaining identity? What has to be considered a perturbation big enough to be distinct from normal noise? What defines a given individuality of a system that can change its identity in time? What defines a given type that is expressed by different individualities? Are we more interested in the preservation of types (same pattern stabilized by a turnover of lower-level structural elements) or individualities (path-dependent organized structures that changed their identity in time)? The impossibility to answer in a general (substantive) way the above questions implies that often it is not possible to make a substantive distinction between the concepts of resilience, robustness and stability. Depending on what is the subject of our analysis (an individuality or a type), we can find different threshold values for assessing recover time and for defining the degree of perturbation and different useful strategies. To make things more difficult, the specification of these concepts is virtually impossible in nested hierarchical systems in which each of these concepts has to be defined on different scales (space- time domains), even though the resilience, robustness and stability of each level are affecting the others. This deep epistemological ambiguity can explain why these concepts escape formalization. This also means that to better characterize this discussion in a different way, we have to introduce new epistemic categories. The introduction of new epistemic categories requires first of all the ability to share the meaning assigned to new labels and terms. This is the reason why this book invests a large part © 2004 by CRC Press LLC Sustainability Requires the Ability to Generate Useful Narratives 235 of its text in dealing with epistemological foundations and why, in the rest of this chapter, the reader will find a lot of pictures and examples taken from daily life experience, used to introduce concepts. Without introducing new concepts with examples familiar to everyone, it is impossible to share the meaning of new epistemic categories. On the other hand, without using new relevant concepts to be considered in analysis of sustainability (concepts that are ignored in reductionist science), it would be impossible to discuss how to conduct integrated assessments of agroecosystems in an innovative way. Without a clear understanding of the differences in the meaning of concepts such as resilience, robustness and stability—or better, without having reached an agreement on the meaning that we want to assign to these labels or words in relation to the goals of our analysis—it is impossible to reach an agreement on how to represent the process of becoming (making analysis of sustainability), let alone to discuss strategies useful for improving the persistence of some of the characteristics of evolving systems that we (and who decides who is we?) would like to preserve. To conclude this overview of the widespread confusion found in the field of analysis of sustainability of becoming systems, we can list additional concepts (variants of the previous ones) often used in literature that are associated with the ability to resist perturbation 1. Redundancy/scale: Because of this quality, the system can first resist and then even thrive on smaller-scale perturbations. It does so by incorporating them into the identity as functional activities, e.g., the use of wild fires by terrestrial ecosystems. 2. Diversity: Because of this quality, the system has the ability to work with multiple options— in terms of both possible behaviors and organizational states. All the concepts listed so far are often confused, in their use, with each other, in the same way as the various strategies (redundancy, diversity and adaptability) are often ill-defined and used without a clear articulation of specific conditions and situations. Even worse is the situation with the term adaptability, which directly points to the process of becoming obtained by changing identity to preserve a given individuality. In a way, the concept of adaptability could also be associated with the concept of persistence (if only we were able to answer in formal terms the question: persistence of what?). Due to the relevance of the concept of adaptability (which implies a clear acknowledgment of the distinction and an innate tension between identity and individuality), we include below two nonequivalent definitions for adaptability and two metaphors useful for illustrating the concept: Adaptability From the dictionary: To make fit for a specific new use [goal] or new situation [context] often by modification. Narrative: The ability to adjust our own identity to retain fitness in face of changing goals and changing constraints. Fitness means the ability to maintain congruence among (1) a set of goals, (2) the set of processes required to achieve them and (3) constraints imposed by boundary conditions. Since adaptive dissipative systems are history dependent, they preserve their individuality if they manage to remain alive in the process of becoming (the series of adjustments of their identity in time). This definition can be confronted with the definition of sustainable development proposed in Section 4.2.2. Useful metaphors about adaptability (from the Bloomsbury Thematic Dictionary of Quotations, available on the Internet): • “If the hill will not come to Mahomet, Mahomet will go to the hill” (Francis Bacon). • “President Rabbins was so well adjusted to his environment that sometimes you could not tell which was the environment and which was President Robbins” (Jarrel Randall). The point to be driven home from all these examples of definitions is that the set of concepts proposed by Holling to deal with the evolution of adaptive systems entails an unavoidable severe epistemological © 2004 by CRC Press LLC Multi-Scale Integrated Analysis of Agroecosystems236 challenge. Such a challenge is linked to the dilemma about (1) how to define the identity of the system, (2) how to define its context and (3) how to handle the fact that they change on different hierarchical levels at different paces. What is especially relevant in this discussion is the implicit constant requirement of both a syntactic and semantic appraisal of the terms used in these statements. When talking of adaptability and resilience, everything depends on:(1) what is considered to be the relevant set of characteristics used to determine (identify, perceive, represent) the identity of the system in the first place through observable qualities—type definitions and (2) what is considered to be the individuality of the system. The same individuality can remain—persist—even when its identity changes in time, as illustrated in the example of Figure 8.1. The four pictures given in Figure 8.1 can be imagined to be four views of Bertha, the old lady in the bottom-left picture, referring to four points in time of her life. As noted in Chapter 3, a peculiar way of expressing individuality of a holarchic system requires a preliminary choice made by the observer about an identity to be assigned to that individuality to make sense of the perceptions (signals carried by incoming data) referring to a given descriptive domain. The particular identity selected to organize our perceptions about a given individuality must be useful for the goal of the analysis. Differences in the choice of identity can be related to a different choice of scale or to a different choice of relevant attributes (as discussed in Chapter 3, e.g., Figure 3.1). In the case shown in Figure 8.1, we have an individuality (Bertha) that goes through a predictable trajectory of identities (types). Whenever the observer knows ahead of time that this will occur, she or he has to select the right set of observable qualities (epistemic categories) associated with the right type (the expected set of observable qualities useful to describe the individuality at a given historic moment). This means that the characterization, perception, and representation of a given individuality of a becoming system over a large space-time domain (e.g., Bertha over her life span) requires the skillful handling of different identities. The same will occur if we want to study the multiple types that such an individuality could take (e.g., an overview of various members of different ages that are found in Bertha’s family at a given point in time). Actually, when looking at the series of pictures given in Figure 8.1, we cannot know a priori if this series of pictures is representing the same person (individuality) at different points in time (e.g., taken at 30-year intervals) or if this series of pictures was taken in the same day looking at a genealogical line made up of a great-grandmother, her daughter, granddaughter and great-granddaughter. In both cases, we are dealing with a set of four types that are useful for describing a female human being. This implies that the selected type of identity used for the representation of a particular individual of female human being be appropriated to the goal of the analysis. This FIGURE 8.1 Sustainability of what? Sustainability for how long? Photos by Mario Giampietro. © 2004 by CRC Press LLC Sustainability Requires the Ability to Generate Useful Narratives 237 requirement translates into the need to use different models for representing and simulating the relative perception of changes associated with the selected type(s). Selecting just one among the possible relevant identities included in this set implies also selecting the relative appropriate model for simulating the expected behavior of the type. As already discussed in Part 1, formal models can refer to only one formal identity at a time. If we decide to represent Bertha when she is 90 years old, then we cannot imagine using a model that has been calibrated on the behavior of the type representing Bertha when she was 30 years old. In parallel, a model for simulating the behavior of a child cannot be used to simulate the behavior of an elderly person, even though they both represent women living in the Netherlands in the year 2000 (this is the homeland of Bertha). That is, only after having specified one of the possible identities (i.e., the particular choice of triadic reading and the set of relevant attributes used to define the system), can we look for a model able to catch the set of expected causal relations used to predict expected changes in attributes. The scientist can attempt to make sense of experimental data only after having selected a given formal identity for the system and an inferential system able to simulate perceived changes in this formal identity (see Rosen, 1985, the chapter on modeling relations). The data set consists of different numerical values taken by a set of variables selected to encode changes in a set of relevant attributes, which are observable qualities associated with the choice of a measurement scheme, which are associated with the selection of a given formal identity. Because of this long chain of choices, all models are identity specific, and therefore they are bound to clash against complexity. Real natural systems are individualities operating on multiple scales or multiple types expressed simultaneously by a population of individualities. This is what entails the existence of multiple nonequivalent ways of mapping the same natural system when considering as relevant different sets of observable qualities (see Chapters 2, 3, 6 and 7). As discussed in Part 1, the unavoidable existence of multiple valid models for the same reality is related not only to the complexity of the observed system, but also to the complexity of the observer. The existence of nonequivalent and nonreducible models for the same system is entailed by the simple fact that “life is the organized interaction of nonequivalent observers” (Rosen, 1985). In spite of being nonequivalent and nonreducible to each other, the various models used by nonequivalent observers can all be relevant for the study of the sustainability of becoming systems. The main point made by Rosen (1985) about complex time is that any formalization of concepts such as resilience, robustness and adaptability into a mathematical system of inference has to deal with the existence of at least three relevant but distinct time differentials. The complexity of time in the process of making and using integrated set of models related to sustainability issues has to be contrasted with the simple time that is operating (only) within the simplified representation of reality obtained within reductionist models (formal systems of inference), when used one at a time. The three relevant time differentials are associated with the following processes: 1. The time differential selected for the dynamics simulated by the set of differential equations (in differential equations called dt). 2. The expiration date of the validity of the set of models used to simulate causality and the set of variables used to describe changes in the state space in relation to a given selection of relevant identities adopted in the problem structuring. When dealing with becoming systems, we have to explicitly address the unavoidable existence of a time horizon determining the reliability of the set of epistemic tools used to perceive, represent and simulate their behavior. The causal relation among observable qualities does change in time due to the process of becoming of these systems. This implies that functional forms and relations adopted in any given set of differential equations useful to simulate a becoming system at a given point in time should be updated sooner or later. The ability to observe and measure changes in observable qualities also evolves in time. That is, better proxies and better measurement schemes can become available to encode changes in relevant qualities of the system. This is another reason that can require changing and updating of the procedures adopted in the process of modeling. We call the time differential dt, at which the validity of the choice done in the process of modeling becomes obsolete. © 2004 by CRC Press LLC Multi-Scale Integrated Analysis of Agroecosystems238 3. The time horizon compatible with the validity of the problem structuring according to the weltanschauung of science and with the particular set of interests of the stakeholders in relation to a specific problem of sustainability. That is, any problem structuring implies a finite selection of (1) goals of the scientific analysis, (2) relevant qualities, (3) credible hypotheses about causal entailments, (4) observable qualities/selection of encoding variables, (5) related measurement protocols and data and (6) inferential systems—all of which must be compatible with each other. Out of a virtually infinite information space (including all the epistemological tools available to humans), scientists have to decide how to compress this intractable mass of information into a finite information space with which it is possible to do science (see Chapter 5). This process of compression of infinite to finite is called problem structuring, and it establishes an agreed-upon universe of discourse on which we apply our models to make sense of our potential actions. This choice will constrain what we perceive as happening in the world and what we decide to represent (actually what the scientists eventually represent) when defining the identity of the system to be investigated. As discussed at length in Chapter 5, this process of compression of infinite sets of identities, causal relations and goals into a finite set is in turn constrained by an underlying weltanschauung in which the scientific activity is performed and by the structure of power relations among the actors. The speed at which the basic weltanschauung is evolving (what the social consciousness defines as relevant issues and facts) can imply the obsolescence of some of the preanalytical choices associated with a given problem structuring. Changes at this level can imply important consequences on the speed at which the identity of the universe of discourse is evolving. This is especially clear in periods of paradigm shift. As noted in Chapter 4, the quality of the process generating a given problem structuring refers not just to the accuracy in the measurement and the calibration of models on data. The relevance of the set of qualities that should be included in the representation of system identity as well as the relevance of the set of causal relations that should be addressed by the model change in time. We call the pace of this process of evolution, in relation to the definition of complex time, a time differential, d?. When dealing with the perception and representation of sustainability, the relevance of this third time differential can become crucial. In conclusion, we can define complex time as the parallel existence of nonequivalent relevant time differentials to be considered explicitly by the modelers (both inside and outside the model) when dealing with the implications of changes occurring in the observer-observed complex in relation to the validity of the model. Why should a discussion about the existence of complex time be relevant for those reading this book? Because these concepts are crucial for discussing sustainability. It is very interesting to note that the distinction between identity (referring to the first two time differentials dt and dt) and individuality (referring to the second two time differentials dt and d?) has been discussed by Rosen (1985, p. 403) using the metaphor of suicide. Suicide is a person terminating her or his individuality to resist the pressure of the context that would force a sudden change in her or his current identity. For example, there are people who take their life to avoiding aging, life without a loved one or because they are facing a failure. What is interesting in this case study is that when dealing with the complex observed-observer, the preservation of the current identity (just one among a set of possible ones) is obtained by eliminating (freezing) the observer (blocking the time differential d?), since nothing can be done about the changes on the ontological side (reality is forcing changes on the observed). The fact is that all becoming systems (biological and social entities) are history-dependent systems observing and making models of themselves. They must change their identity in time on both sides of the observation process (as both observed and observer). When the speed of the process of becoming pushes too close to the various time differentials (especially dt and d?), then the predicament of postnormal science can become overwhelming. That is, the very identities of both the observed system and the observer system become fuzzy since they are affecting each other’s definition at a speed that makes it impossible to have a robust validation. This can represent a serious problem of governance, related to a relatively new plague (widespread by mass media), which we can call the butterfly effect or pheromone attention syndrome, determined by the hypercyclic interaction observed- observer. Media focus on what is of concern for stakeholders, and stakeholders are concerned with what © 2004 by CRC Press LLC Sustainability Requires the Ability to Generate Useful Narratives 239 is focused on by media. The result is that what is on the spot of the public attention or in the debate about sustainability is often randomly generated by lower-level stochastic phenomena—what happened to be the initial problem structuring of a given problem given by media. Then this original input is amplified by lock-in effects (someone with a camera happened to be in a specific place catching a relevant fact). Nobody, however, can check how relevant is that particular fact, which is amplified by the spotlights, compared with other relevant facts ignored in the debate simply because they happened in the shadows. 8.1.3 The Impossible Use of Dynamical Systems Analysis to Catch the Process of Becoming Adaptive holarchies can retain their individuality only if they are able to keep alive the mechanism generating coherence in the expression of their identity across the three time differentials defined in complex time. This implies the ability to keep harmony in the pace at which the various identities and individualities and their perceptions and representations are changing in time. This requires a deep interlocking of ontological and epistemological interactions (Chapter 2). The term expression of an identity refers to the concept of self-entailment between (1) establishing processes able to realize viable equivalence classes of organized structures sharing the same template at different levels (an ontological achievement) and (2) integrated processes across hierarchical levels able to determine essences in terms of the validity of mutual information used by interacting agents, which is associated with the perception, representation and running of anticipatory models at different levels (an epistemological achievement). This is a mechanism that cannot be fully represented using conventional formal systems of inference. For example, the formalization of concepts such as resilience and stability is in general attempted from within the field of dynamical systems analysis. Actually, this field provides powerful images (e.g., basin of attractions) that are often used with semantic purposes. For example, the shape of the basin of attraction is a very popular metaphor. Resilient systems are depicted as having a shallow and large basin. Robust but fragile systems are associated with basins that are very deep and small in domain. An example of these two metaphors is given in Figure 8.2 (taken from Giampietro et al., 1997). These visualizations are certainly useful, but they do not avoid the original unsolved problem. Any formalization of resilience, robustness, stability or whatever label we want to use within the field of dynamical systems requires the previous definition of a given state space. By state space we mean a finite and closed (in operational terms) information space made up of variables, referring to observable characteristics of the system, that can be measured at a given point in space and time through a measurement scheme. The implications of this fact are huge. To represent a basin of attraction, you need numbers, which in turn requires assessments (measurement schemes), which in turn must refer to given typologies (types defined as a set of attributes), which are represented using a set of epistemic categories (variables). Put another way, if we plan to develop formal analytical tools to study the evolution of adaptive dissipative system, we need to measure key characteristics of them through an interaction within an experimental setting that makes it possible to encode observable qualities into numerical variables. These measurements are location specific. That is, they are and must be context and simple time dependent. Simple time is what is perceived from within the representation of reality (the model) obtained within a closed and finite information space, and what we generate within the artificial settings of an experimental scheme. Because of this, the dt of the model is reflecting (1) the choice of a triadic reading associated with our perceptions and (2) the filter on possible signals implied by the measurement scheme. That is, such a dt will reflect the preanalytical choices made when choosing the particular model. The validity of simple models requires two assumptions related to the definition of identity for the system: (1) The existing associative context will remain valid (e.g., the environment is and will remain admissible also at a different point in space and time). That is, the validity of the model implies the absence of changes in relation to dt. (2) The choice of relevant attributes used to define the identity is agreed upon by all the observers (e.g., it is impossible to find a relevant user of this model who does not agree with its assumptions). That is, the validity of the model implies that the general agreement about its usefulness and relevance does not change in relation to d?. However, considering these two assumptions valid—as required by dynamical systems analysis—puts the modeler in the unpleasant situation of defining concepts (resilience, robustness, stability) associated © 2004 by CRC Press LLC Multi-Scale Integrated Analysis of Agroecosystems240 with the identity of static dynamical systems perceived as operating out of complex time. These systems can have multiple attractors. They can even be able to switch from one attractor to another at command. They can jump; they can get chaotic and engage in any type of fancy mathematical behavior. But yet the identity of their information space does not evolve in time; see the work of Rosen (2000) and Kampis (1991) for a more elaborate discussion of this point. They are not alive, they are not becoming something else, and they are not adding new essences and new epistemic categories (emergence) to their original information space. Finally, and most important, they are not adding new meanings (for the observer) to their identity. Put another way, the real problem with complex systems is not that they are exhibiting nonlinear behavior. In fact, the technical feature of linearity or nonlinearity of dynamical systems refers only to changes occurring within the known state space and the simple time defined on dt. Even when moving away from dynamical systems analysis to more advanced inferential systems based on the use of computers (e.g., cellular automata), the problem of a sound representation of the behavior of complex systems is not fully solved. These new mathematical objects can establish bridges between patterns and mechanisms operating on different levels, and this is a major step forward. However, also in this case, the mathematical tools only makes it possible to better clarify the mechanism associated with emergence. They can explain how a pattern expressed at one scale can be associated with patterns defined on different scales. We can find, using the output given by a computer, new properties that can be interpreted by the modeler in terms of additional insight provided by the algorithm. But the real challenge, in this case, remains that of finding the “right” set of external referents that can provide meaning to this analysis on multiple levels. In our view, there is a big risk associated with this new generation of sophisticated formalisms. Many practitioners tend to apply them to the analysis of sustainability, under the incorrect assumption that more complicated models and more powerful computers could handle the complexity predicament just by providing more syntactic entailment. Put another way, the risk that we see is that this new frontier of development of more powerful inferential systems can represent yet another excuse for denying a relatively simple and plain fact: becoming dissipative systems organized in holarchies have, and must have, a noncomputable and nonformalizable behavior to remain alive (Rosen, 2000). Modelers should just accept this fact. 8.1.4 The Nature of the Observer-Observed Complex and the Existence of Multiple Identities Imagine that an extraterrestrial scientist belonging to an unknown alien form of life suddenly arrives on Earth to learn about the characteristics of holons—human beings. It would be confronted with the fact that humans can be classified in nonequivalent ways. These different ways could be seen as different attractor types using the vocabulary of dynamical system analysis, or different types associated with identities using the vocabulary developed in Chapters 2 and 3. For example, a given human being can FIGURE 8.2 Shapes of basins of attraction. © 2004 by CRC Press LLC [...]... with the realizations of this car in society That is, Italy in those years was full of: © 2004 by CRC Press LLC 2 58 © 2004 by CRC Press LLC Multi- Scale Integrated Analysis of Agroecosystems FIGURE 8. 11 Failure in the design of a type of organized structure; the equivalence class never generated an essence Trajectory of evolution of types referring to a valid essence (Courtesy of FIAT spa and ARCHIVIO... Unimportance of DNA in the Definition of Essences in Biological Systems: The Blunder of Genetic Engineering To introduce this discussion, let us start with an example dealing with the evolution of nonbiological essences.The two sets of identities given in Figure 8. 10 show two trajectories of evolution referring to © 2004 by CRC Press LLC 252 Multi- Scale Integrated Analysis of Agroecosystems FIGURE 8. 10 Evolution... where the displacement of the engine had to be increased and several additional changes in the body of the car became necessary.At a different level, we can think of a different lazy 8 adaptive cycle based on the movement through different models, all referring to the same essence of car © 2004 by CRC Press LLC 250 Multi- Scale Integrated Analysis of Agroecosystems FIGURE 8. 8 Evolution of models within the... of an Essence within the Frame of Network Analysis To see an image of an essence within the frame of network analysis, it is important to acknowledge the existence of a few hidden assumptions, which are required to represent biological systems in terms of © 2004 by CRC Press LLC 260 Multi- Scale Integrated Analysis of Agroecosystems dissipative networks Two old papers of Robert Rosen can be used to... implementation of the original design than © 2004 by CRC Press LLC 266 Multi- Scale Integrated Analysis of Agroecosystems with a real phenomenon of emergence.The substitution of an element A with an element E that does better in the niche of A is related to what in biology is called the survival of the fittest, when talking of natural selection But the definition of fittest is already related to the definition of. .. avoiding dangerous situations, controlling the inflow of required © 2004 by CRC Press LLC 262 Multi- Scale Integrated Analysis of Agroecosystems FIGURE 8. 13 Different view of the identities of elements of biological networks when shifting the triadic reading to lower levels 4 inputs) According to the specific characteristics of a particular element of the graph, we have to expect that each element not... the validity of the original idea, it is time to patch the original process of realization according to operational problems (scaling up) In this way, it is possible to occupy as © 2004 by CRC Press LLC 2 48 Multi- Scale Integrated Analysis of Agroecosystems FIGURE 8. 7 The lazy 8 in three dimensions (Courtesy of FIAT spa and ARCHIVIO STORICO FIAT.) much as possible the niche (take advantage of favorable... systems and ecosystems (components of the graph, the whole network, and subcomponents of components) have a common (the same) reference state for their process of dissipation/self-organization They all discharge heat as the final by-product of their metabolic process, meaning that the various epistemic © 2004 by CRC Press LLC 264 Multi- Scale Integrated Analysis of Agroecosystems categories used to characterize... available set of types used in pattern recognition (to categorize the individuals in the sample), an observation made at a particular point in time, but over a large space domain (e.g., on a particular day over an entire continent)—a synchronic analysis will provide a profile of distribution of individuals over © 2004 by CRC Press LLC 242 Multi- Scale Integrated Analysis of Agroecosystems FIGURE 8. 3 Gina’s... the process of aging © 2004 by CRC Press LLC 244 Multi- Scale Integrated Analysis of Agroecosystems FIGURE 8. 4 Respiration cycles within human cells (from BIO 301 Human Physiology syllabus (eastern Kentucky University)—Gary Ritchison http://www.biology.eku.edu/RITCHISO/301notes6.html.) 3 of a given person, and so on Obviously, in this case, we need a meta-model able to deal with the semantics of these . entire continent)—a synchronic analysis will provide a profile of distribution of individuals over © 2004 by CRC Press LLC Multi- Scale Integrated Analysis of Agroecosystems2 42 the given set of possible types as FIGURE 8. 6 The metaphor of lazy 8 applied to a car. (Courtesy of FIAT spa and ARCHIVIO STORICO FIAT.) © 2004 by CRC Press LLC Multi- Scale Integrated Analysis of Agroecosystems2 48 much as possible. by CRC Press LLC Multi- Scale Integrated Analysis of Agroecosystems2 44 of a given person, and so on. Obviously, in this case, we need a meta-model able to deal with the semantics of these relations.