15 2 The Epistemological Predicament Entailed by Complexity This chapter has the goal of clarifying a misunderstanding that often affects the debate about how to handle, in scientific terms, the challenge implied by sustainable development. The misunderstanding is generated by confusion between the adjectives complicated and complex. Complicatedness is associated with the nature and degree of formalization obtained in the step of representation (the degree of syntactic entailments implied by the model). That is, complicated is an adjective that refers to models and not to natural systems. Making a model more complicated does not help when dealing with complexity. Complexity means that the set of relations that can be found when dealing with the representation of a shared perception is virtually infinite, open and expanding. That is, complex is an adjective that refers to the characteristics of a process of observation. Therefore, it requires addressing the characteristics of a complex observer-observed that is operating within a given context. Dealing with complexity implies acknowledging the distinction between perception and representation, that is, the need to consider not only the characteristics of the observed, but also the characteristics of the observer. Scientists are always inside any picture of the observer-observed complex and never acting from the outside. In scientific terms, this implies (1) addressing the semantic dimension of our choices about how to perceive the reality in relation to goals and scales; (2) acknowledging the existence of nonequivalent observers who are operating in different points in space and time (on different scales), using different detectors and different models and pursuing independent local goals; and (3) acknowledging that any representation of the reality on a given scale reflects just one of the possible shared perceptions found in the population of interacting nonequivalent observers. To make things more difficult, both observed systems and the observers are becoming in time, but at different paces. 2.1 Back to Basics: Can Science Obtain an Objective Knowledge of Reality? The main point of this chapter is that understanding complexity entails going beyond the conventional distinction between epistemology and ontology in the building of a new science for sustainability. To introduce such a basic epistemological issue, I have listed quotes taken from the paper “Einstein and Tagore: Man, Nature and Mysticism” (Home and Robinson, 1995), which is about a famous discussion between Einstein and Tagore about science and realism. • “In classical physics, the macroscopic world, that of our daily experience, is taken to exist independently of observers: the moon is there whether one looks at it or not, in the well known example of Einstein.”…“The physical world has objectivity that transcends direct experience and that propositions are true or false independent of our ability to discern which they are.” (pp. 172–173). • “The laws of nature which we formulate mathematically in quantum theory deal no longer with the elementary particles themselves but with our knowledge of the particles.” “The nature of reality in the Copenhagen interpretation is therefore essentially epistemological, that is all meaningful statements about the physical world are based on knowledge derived from observations. No elementary phenomenon is a phenomenon until it is a recorded phenomenon.” Einstein declared himself skeptical of quantum theory because it concerned © 2004 by CRC Press LLC Multi-Scale Integrated Analysis of Agroecosystems16 “what we know about nature,” no longer “what nature really does.” In science, said Einstein, “we ought to be concerned solely with what nature does.” Both Heisenberg and Bohr disagreed: in Bohr’s view, it was “wrong to think that the task of physics is to find out how nature is. Physics concerns what we can say about nature” (p. 173). • Quote of Tagore: “This world is a human world—the scientific view of it is also that of the scientific man. Therefore the world apart from us does not exist. It is a relative world, depending for its reality upon our consciousness” (p. 174). • Quote of Einstein: “The mind acknowledges realities outside of it, independent of it. For instance nobody may be in this house, yet that table remains where it is” (p. 174). • Quote of Tagore: “Yes, it remains outside the individual mind, but not the universal mind. The table is that which is perceptible by some kind of consciousness we possess…. If there be any truths absolutely unrelated to humanity, then for us it is absolutely non-existing” (p. 175). At the end of this paper, three positions related to the question “Does reality exist and can science obtain an objective knowledge of it?” are summarized as follows: 1. Einstein’s position—Science must study (and it can) what nature does. Entities do have well-defined objective properties, even in the absence of any measurement, and humans know what these objective properties are, even when they cannot measure them. 2. Bohr’s position—Science can study starting from what we know about nature. Objective existence of nature has no meaning independent of the measurement process. 3. Tagore’s position—Science is about learning how to organize our shared perceptions of our interaction with nature. Objective existence of nature has no meaning independent of the human preanalytical knowledge of typologies of objects to which a particular object must belong to be recognized as distinct from the background. The first two positions can be used to point at the existence of a big misunderstanding that some physicists have about the role of the observer in the process of scientific analysis. Quantum physics finally was forced to admit that the observer does play a role in the definition of what is observed, but still, the interference generated by the observer in quantum physics is only associated to the act of measurement. Put another way, it is the interaction between the measuring device and the natural system (an interaction required to obtain the measurement) that alters the natural state of the measured system. This is why smart microscopic demons could get rid of this problem. According to this view, if it were possible to look directly at individual molecules in some magic uninvasive way, one could get knowledge (measures) while at the same time avoiding the problem of the recognized interference observer-observed system. Unfortunately, things are not that easy. Epistemological problems implied by complexity (multiple scales, multiple identities, and nonequivalent observers) are so deep that, even with the help of friendly demons, it would not be possible to escape the relative basic epistemological impasse. In any scientific analysis of complex natural systems, the step of measuring is not the only step in which the observer affects the perception and representation of the investigated system. Another and much more important interference of the observer is associated with the very definition of a formal identity for the system to be studied. This is a type of interference that has been systematically overlooked by hard scientists. The nature of this interference is introduced in the next section, again using a practical example. A more detailed description of relative concepts is given in Section 2.2. 2.1.1 The Preanaiytical Interference of the Observer In a famous article, Mandelbrot (1967) makes the point that it is not possible to define the length of the coastal line of Britain if we do not first define the scale of the map we will use for our calculations. The smaller the scale (the more detailed the map), the longer will be the length of the same segment of coast. This means that the length of a given segment of the coast—its numerical assessment—is affected not only by the intrinsic characteristics of the observed system (i.e., the profile of a given segment of © 2004 by CRC Press LLC The Epistemological Predicament Entailed by Complexity 17 coast), but also by a preliminary agreement about the meaning of what a segment of coast is (i.e., a preliminary agreement among interacting nonequivalent observers about the shared meaning of “a segment of coast”). Put another way, this implies reaching an agreement on how a given segment of coast should be perceived and how it should be represented. This means that such a number will unavoidably reflect an arbitrary choice made by the analyst when deciding which scale the system should use to be perceived and represented (before being measured). To better explore this point, let us use a practical example, provided in Figure 2.1, which is based on Mandelbrot’s idea. The goal of this example is to explore the mechanism through which we can “see” different identities for the same natural system (in this case, a segment of coast) when observing (perceiving and representing) it in parallel on different scales. The arbitrary choice of deciding one of the possible scales by which the coast can be perceived, represented and observed will determine the particular identity taken by the system and its consequent measure. Imagine that a group of scientists is asked to determine the orientation of the coastal line of Maine, providing scientific evidence backing up their assessment. Before getting into the problem of selecting an adequate experimental design for gathering the required data, scientists first have to agree on how to share the meaning given to the expression “orientation of the shore of Maine.” Actually, it is at this very preanalytical step that the issue of multiple identities of a complex system enters into play. In fact, imagine that we give to this group of scientists the representation of the coast shown in Figure 2.1a. Looking at that map, the group of scientists can safely state (it will be easy to reach an agreement on the related perception) that Maine is located on the East Coast of the U.S. A sound statistical experiment can be easily set to confirm such a hypothesis. For example, the experiment could be carried out by calling from London and Los Angeles 500 Maine residents randomly selected from a phonebook during their daytime and asking them, What time is it? Using such input and the known differences in time zones between London and Los Angeles, it is possible to scientifically prove that Maine is on the East Coast of the U.S. However, if we had given to the same group of scientists a map of Maine based on a smaller scale for the representation of the coast—for example, a map referring to the county level, as in Figure 2.1b— then the group of scientists would have organized their perceptions in a different way. Someone who is preparing computerized maps of Maine by using satellite images could have easily provided empirical FIGURE 2.1 Orientation of the coastal line: nonequivalent perceptions. © 2004 by CRC Press LLC Multi-Scale Integrated Analysis of Agroecosystems18 evidence about the orientation of the coastal line. By coupling remote sensing images with a general geo-referential system, it can be “proved” that the orientation of the coast of Lincoln County is south. What if we had asked another group of scientists to work on the same question, but had given them a smaller map of the coast of Maine from the beginning? For example, consider the map referring to the village of Colonial Pemaquid, in Lincoln County, Maine (Figure 2.1c). The scientists looking at that map would have shared yet another perception of the meaning to be assigned to the expression “orientation of a tract of shore of Maine.” When operating from within this nonequivalent shared meaning assigned to this expression, they could have provided yet another contrasting statement about the orientation of this tract of coastal line. According to empirical analyses carried out at this scale, they could have easily concluded that the coastal line of Maine is actually facing west. Also, in this case, such a statement can be scientifically “proved.” A random sample of 1000 trees can be used to provide solid statistical evidence, by looking at the differences of color on their trunks in relation to the sides facing north. In this way, this group of scientists could have reached a remarkable level of confidence in relation to such an assessment (e.g., p =.01). This new scientific inquiry performed by a different group of scientists operating within yet another distinct shared perception of the identity of the investigated system can only add confusion to the issue, rather than clarifying it. The situation experienced in our mental example by the various groups of different scientific observers given different maps of Maine is very similar to that experienced by scientists dealing with sustainability from within different academic disciplines. Our hypothetical groups of scientists were given nonequivalent representations of the coastline of Maine, and this pushed them to agree on a particular perception of the meaning to be assigned to the label/entity “tract of coastal line ” As will be discussed in more detail in the rest of this chapter, the existence of different legitimate formal identities for a natural system is generated by the possibility of having different associations between (1) a shared perception about the meaning of a label (in this case, “tract of coastal line ”) and (2) the corresponding agreed-upon representation (in this case, the nonequivalent maps shown in Figure 2.1). Differences about basic assumptions and organized perceptions are in fact at the basis of the problem of communication among disciplinary sciences. For example, a cell physiologist assumes that the biomass of wolves (seen as cells) is operating at a given temperature and a given level of humidity, whereas an ecologist considers temperature and humidity key parameters for determining the survival of a population of wolves (parameters determining the amount of wolf biomass). Neoclassical economists often assume the existence of perfect markets, whereas historians study the processes determining the chain of events that make imperfect actual markets. The mechanism assigning an identity to geographic objects implies that we should expect (rather than be surprised) to find new identities whenever we change the scale used to look at them. Getting back to our example, it would be possible to ask yet another group of scientists to clarify the messy scientific empirical information about the orientation of the coastal line of Maine. We can suggest to this group that, to determine the “true” orientation of the coastal line, sophisticated experimental models should be abandoned, getting back to basic empiricism. Following this rationale, we can ask this last group of scientists to go on a particular beach in Colonial Pemaquid to gather more reliable data in a more direct way (they should use the “down to Earth” approach). The relative procedure is to put their feet into the water perpendicular to the waterfront while holding a compass. In this way, they can literally “see” what the “real” orientation is. If they would do so on Polly’s Beach (Figure 2.1d), they would find that all the other groups are wrong. The “truth” is that Maine has its shore oriented toward the north. Such a shared perception of the reality, strongly backed by solid evidence (all the compasses used in the group standing on the same beach indicate the same direction), will be difficult to challenge. The point to be driven home from this example is that different observers can make different preanalytical choices about how to define the meaning assigned to particular words, such as “a segment of coast,” which will make them work with different identities for their investigated system. This will result in the coexistence of legitimate but contrasting scientific assessments. This example introduces a major problem for reductionism. Whenever different assessments are generated by the operation of nonequivalent measurement schemes, linked to a logically independent choice of a nonequivalent perception/representation of the same natural system, it becomes impossible to reduce the resulting set © 2004 by CRC Press LLC The Epistemological Predicament Entailed by Complexity 19 of numerical differences just by adopting a better or more accurate protocol of measurement or using a more powerful computer. The four different views in Figure 2.1 show that there are several possible couplets of organized perceptions (the meaning assigned to the label “coastal line” ) and agreed-upon representations (types of map used to represent our perception of coastal lines ) that can be used to plan scientific experiments aimed at answering the question “What is the orientation of a tract of coastal line of Maine?” If we do not carefully acknowledge the implication of this fact, we can end up with scientifically “correct” (falsifiable through empirical experiments) but misleading assessments. For example, the assessment that Maine is on the East Coast (based on an identity of the coastal line given in Figure 2.1a and scientifically proved by a sound experiment of 500 phone calls) is misleading for a person interested in buying a house in Colonial Pemaquid with a porch facing the sun rising from the sea. For this goal, the useful identity (and the relative useful experiment) to be chosen is that shown in Figure 2.1d. At the same time, the information based on the identity of Figure 2.1a is the right one for the same person when she needs to determine the time difference between Los Angeles and Colonial Pemaquid to make a phone call at a given time in Los Angeles. So far, the story told through our mental example has shown the practical risk that honest and competent hard scientists can be fouled by donors who provide research funds to make them prove whatever should be proved (that the coast is oriented toward the north, south, east or west). Put another way, the existence of multiple potential identities entails the serious risk that smart and powerful lobbies can obtain the scientific input they need just by showing in parallel to honest and competent scientists a given map of the system to be investigated, together with a generous check of money for research. The set of four different views (couplets of perceptions/representations) of the coastline given in Figure 2.1 obviously can be easily related to the example of the four different identities of the same natural system (in that case, a human being) given in Figure 1.2. The same natural system is observable (generating patterns on data stream) on different scales, and therefore it entails the coexistence of multiple identities. The message given by these two figures is clear. Whenever we are in a situation in which we can expect the existence of multiple identities for the investigated system (complex systems organized on nested hierarchies), we must be very careful when using indications derived from scientific models. That is, we cannot attach to the conclusions derived from models some substantive value of absolute truth. Any formal model is based on a single couplet of organized perception and agreed- upon representation at the time. Therefore, before using the resulting scientific input, it is important to understand the epistemological implications of having selected just one of the possible couplets (one of the possible identities) useful for defining the system. The quality check about how useful the model is has to be related to the meaning of the analysis in relation to the goal and not to the technical or formal aspects of the experimental settings (let alone the significance of statistical analysis checked through p= .01 tests). The soundness of the chain of choices referring to experimental setting (e.g., sampling procedure and measurement scheme) in relation to the statistical test used in the analysis can be totally irrelevant for determining whether the problem structuring was relevant or useful for the problem to be tackled. Rigor in the process generating formal representations of the reality (those used in hard science) is certainly indispensable, but rigor is a necessary but not sufficient condition when dealing with complexity. Actually, a blind confidence in formalizations and algorithmic protocols can become dangerous if we are not able to define first, in very clear terms, where we stand with our perception of the reality and how such a choice fits the goals of the analysis. It is time to return to the original discussion about the “querelle” between Einstein and Tagore about science and realism. If we admit that the observer can interfere with the observed system even before getting into any action, during the preanalytical step, simply by deciding how to define the identity of the observed system, then it becomes necessary to discuss in more detail the steps and implications of this operation. The concept of identity will be discussed in detail in Section 2.2; for now it is enough to say that the definition of an identity coincides with the selection of a set of relevant qualities that makes it possible for the observer to perceive the investigated system as an entity (or individuality) distinct from its background and from other systems with which it is interacting. We can distinguish between semantic and formal definitions of identity; the former are sets of expected qualities associated with direct observations © 2004 by CRC Press LLC Multi-Scale Integrated Analysis of Agroecosystems20 of a natural system (e.g., a fish). This definition still belongs to the realm of semantics since it is open (e.g., the list of relevant and expected qualities of a fish is open and will change depending on who we ask). Moreover, a semantic identity does not specify the procedure that will be used to make the observations (e.g., what signal detectors will be used to check the presence of fish or to establish a measurement scheme useful for representing it with a finite set of variables). For example, bees and humans see flower colors in different ways, even though they could reach an agreement about the existence of different colors. A semantic definition of identity, therefore, includes an open and expanding set of shared perceptions about a natural system (see the examples given in Figure 2.2). A semantic identity becomes a formal identity when it refers not only to a shared perception of a natural system, but also to an agreed-upon finite formal representation. That is, to represent a semantic identity in formal terms (e.g., to represent a fish in a model), we have to select a finite set of encoding variables (a set of observable qualities that can be encoded into proxy variables) that will be used to describe changes in the resulting state space (for more, see the theory about modeling relations developed by Rosen (1986)). This, however, requires selecting within the nonequivalent ways of perceiving a fish (illustrated in Figure 2.3) a subset of relevant attributes that will be included in the model. In conclusion, we can make a distinction that will be used later in this book: • Semantic identity= the open and expanding set of potentially useful shared perceptions about the characteristics of an equivalence class • Formal identity= a closed and finite set of epistemic categories (observable qualities associated with proxies, e.g., variables) used to represent the expected characteristics of a member belonging to an equivalence class associated with a type By using this definition of semantic identity, we can make an important point about the discussion between Einstein and Tagore. The preliminary definition of an identity for the observed systems (associated with an expected pattern to be recognized in the data stream, which makes possible the perception of the system in the first place) must be available to the observer before the actual interaction between observer and observed occurs. This applies either when detecting the existence of the system in a given place or when measuring some of its characteristics, let alone when we make models of that system. This means that any observation requires not only the operation of detectors gaining information about the investigated system through direct interaction (the problem implied by the operation of a measurement scheme, indicated by Bohr), but also the availability of a specified pattern recognition, which must be know a priori by the observer (the point made by Tagore). The measurement scheme has the only goal of making possible the detection of an expected pattern in a set of data that are associated with a set of observable qualities of natural systems. These observable qualities are assumed to be (because of the previous knowledge of the identity of the system) a reflection of the set of relevant characteristics expected in the investigated system. An observer who does not know about the identity of a given system would never be able to make a distinction between (1) that system (when it is possible to recognize its presence in a given set of data in terms of an expected pattern associated with observable qualities of the system) and (2) its background (when the incoming data are considered just noise). The table in the room mentioned by Einstein in his discussion with Tagore can be there, but if the epistemic category associated with the equivalence class table is not in the mind of the observer—in the “universal mind,” as suggested by Tagore, or in the “World 3 of human culture,” as suggested by Popper (1993)—it is not possible to talk of tables in the first place, let alone check whether a table (or that table) is there. The concepts of identity, multiple identity and different perceptions/representations on different scales are discussed in more detail in the following section. The main point of the discussion so far is that scientists can only measure specific representations (using proxies based on observable qualities) of their perceptions (definition of sets of relevant qualities associated with the choice of a formal identity to be used in the model) of a system. That is, even when adopting sophisticated experimental settings, scientists are measuring a set of characteristics of a type associated with an identity assigned to an equivalence class of real entities (e.g., cars, dogs, spheres). This has nothing to do with the assessment of characteristics of any individual natural systems. © 2004 by CRC Press LLC The Epistemological Predicament Entailed by Complexity 21 In fact, it is well known that, when doing a scientific inquiry, any measurement referring to special qualities of a special individual is not relevant. For example, when asked to provide an assessment of the energy output of 1 h of human labor, we would be totally uninterested in assessing the special performance of Hercules during one of his mythical achievements or a world record established during the Olympic games. In science, miracles and unique events do not count. Coming to the assessment of the energy equivalent of 1 h of labor, we want to know average values (obtained through sound measurement schemes) referring to the energy output of 1-h of effort performed by a given typology of human worker (e.g., man, woman, average adult). This is why we need an adequate sample of human beings to be used in the test. Scientific assessments must come with appropriate error bars. Error bars and other quality checks based on statistical tests are required to guarantee that what is measured are observable qualities of an equivalence class (belonging to a given type, i.e., average adult human worker) and not characteristics of any of the particular individuals included in the sample. Put another way, when doing experimental analyses we do not want to measure the characteristics of any real individual entity belonging to the class (of those included in the sample). We want to measure only the characteristics of simplified models of objects sharing a given template (which are describable using an identity). That is, we want to measure the characteristics of the type used to identify an equivalence class (the class to which the sampled entity belongs). This is why care is taken to eliminate the possibility that our measurement will be affected by special characteristics of individual objects (individual, special natural systems) interacting with the meter. The previous paragraph points to a major paradox implied by science: (1) science has to be able to make a distinction between types and individuals belonging to the same typology (or between roles and incumbents, using sociological jargon, or essences and realization of essences, using philosophical jargon) when coming to the measurement step, but, at the same time, (2) science has to confuse individuals belonging to the same type when coming to the making of models, to gain predictive power and compression. This paradox will be discussed in detail in the rest of the book. This requires, however, the rediscovery of new concepts and ideas that have been developed for centuries in philosophy (for an overview, see Hospers (1997)) or in disciplines related to the process through which humans organize their perceptions to make sense of them—e.g., semiotic (for an overview, see Barthes (1964) or the work of Polany (1958, 1977) and Popper (1993)). This issue has been explored recently within the field of FIGURE 2.2 The open universe of semantic identities for a fish determined by goals and contexts. (Courtesy FAO Photo Library.) © 2004 by CRC Press LLC Multi-Scale Integrated Analysis of Agroecosystems22 FIGURE 2.3 The open nature of the set of attributes making up the semantic identity of a fish. (After Gomiero, T. 2003 Multi-Objective Integrated Representation (Moir) As a Tool To Study and Monitor Farming System Development and Management. Ph.D. Thesis to be submitted in Environmental Science, Universitat Autonoma de Barcelona, Spain.) © 2004 by CRC Press LLC The Epistemological Predicament Entailed by Complexity 23 complex systems theory, especially in relation to the epistemological implications of hierarchy theory— Koestler (1968, 1978), Simon (1962), Allen and Starr (1982), Allen and Hoekstra (1992), O’Neill (1989), Ahl and Allen (1996). In the rest of this chapter I will just provide an introductory overview of these themes. The reader should not feel uncomfortable with the high density of concepts and terms found in this chapter. These concepts will be discussed again, in more detail, later on. The main goal now is to induce a first familiarization with new terms, especially for those who are seeing them for the first time. 2.1.2 The Take of Complex Systems Thinking on Science and Reality The idea that the preanalytical selection of a set of encoding variables (deciding the formal identity that will be used as a model of the natural system) does affect what the observer will measure has huge theoretical implications. When using the equation of perfect gas (PV=nRT) we are adopting a model (a formal identity for the gas) that perceives and describes a gas only in terms of changes in pressure, volume, number of molecules and temperature, with R as a gas constant. Characteristics such as smell or color are not considered by this equation as relevant qualities of a gas to be mapped in such a formal identity. Therefore, this particular selection of relevant qualities of a gas has nothing to do with the intrinsic real characteristics of the system under investigation (a given gas in a given container). This does not mean, however, that a modeling relation based on this equation is not reflecting intrinsic characteristics of that particular gas kept in the container, and therefore that our model is wrong or not useful. It means only that what we are describing and measuring with that model, after having selected one of the possible formal identities for the investigated system (a perfect gas), is a simplified version of the real system (a real amount of molecules in a gaseous state). Any numerical assessment coming out from a process of scientific modeling and then measurement is coming out of a process of abstraction from the reality. “The model shares certain properties with the original system [those belonging to the type], but other properties have been abstracted away [those that make the individual member special within that typology]” (Rosen, 1977, p. 230). The very concept of selecting a finite set of encoding variables to define a formal identity for the system (defining a state space to describe changes) means “replacing the thing measured [e.g., the natural system] by a limited set of numbers” (e.g., the values obtained through measurement for the selected variables used as encoding] (Rosen, 1991, p. 60). According to Rosen, experimentalists should be defined as those scientists who base their assessments on procedures aiming at generating abstractions from reality. The ultimate goal of a measurement scheme is, in fact, to keep the set of qualities of the natural system, which are not included in the formal definition of system identity, from affecting the reading of the meters. Actually, when this happens, we describe the result of this event as a noise that is affecting the numerical assessment of the selected variable. When assuming the existence of simple systems (e.g., elementary particles) that can be usefully characterized with a very simple definition of identity (e.g., position and speed), one can be easily fouled by the neutral role of the observer. In this situation one can come up with the idea that the only possible interference that an observer can induce on the observed system is due to the interaction associated to the measurement process. But this limited interference of the observer is simply due to the fact that simple systems and simple identities that are applicable to all types of natural systems are not very relevant when dealing with the learning of interacting nonequivalent observers (e.g., when dealing with life and complex adaptive systems). Simple systems, in fact, can be defined as those systems in which there is a full overlapping of semantic identity (the open set of potential relevant system qualities associated with the perception of the system) with formal identity (representation of the system based on a finite set of encoding variables). This assumes also that with the formal identity we are able to deal with all system qualities that are considered relevant by the population of nonequivalent observers: the potential users of the model. This means that simple systems such as ideal particles and frictionless or adiabatic processes do not exist; rather, they are artifacts generated by the simplifications associated with a particular relationship between perception and representation of the reality. This particular forced full overlapping of formal and semantic identities of the investigated system has been imposed on scientists operating in these fields by the basic epistemological assumption of elementary mechanics. This explains why simple © 2004 by CRC Press LLC Multi-Scale Integrated Analysis of Agroecosystems24 models of the behavior of simple systems are very useful when applied to real situations (e.g., movements of planets). In these models the typologies of mechanical systems are viewed as not becoming in time. Unfortunately, when this is true, the relative behaviors are not relevant to the issue of sustainability. Whenever the preanalytical choices made by the observer when establishing a relation between the set of potential perceptions (the semantic identity) and the chosen representation (the formal identity used in the model) of a natural system cannot be ignored, we are dealing with complexity. Imagine, for example, that the task of the scientist is to perceive and represent her mother (which I hope reductionist scientists will accept to be a natural entity worth of attention). In any scientific representation of the behavior of someone’s mother, the bias introduced by the process of measurement would be quite negligible when compared with the bias generated by the decision of what relevant characteristics and observable qualities of a mother should be included in the finite and limited set of variables adopted in the formal identity. Dealing with 1000 persons, it is much more difficult to reach an agreement about the right choice of the set of relevant qualities that have to be used in the definition of a mother, to describe with a model her changes in time, rather than to reach an agreement on the protocols to be used for measuring any set of agreed-upon encoding variables. On the other hand, without an initial definition of what are the relevant characteristics associated with the study of a mother, it would be impossible to work out a set of observable qualities used for numerical characterizations (no hard science is possible). This problem becomes even more important when the future behavior of the observer toward the observed system is guided by the model that the observer used. The problem of self-fulfilling prophecies is in fact a standard predicament when discussing policy in reflexive systems (see Chapter 4 on postnormal science). These basic epistemological issues, which have been systematically ignored by reductionist scientists, are finally being addressed by the emerging scientific paradigm associated with complex systems thinking (and not even by all those working in complexity). In fact, an intriguing definition of complexity, given by Rosen (1977, p. 229), can be used to introduce the topic of the rest of this chapter: “a complex system is one which allows us to discern many subsystems [a subsystem is the description of the system determined by a particular choice of mapping only a certain set of its qualities/properties] depending entirely on how we choose to interact with the system.” The relation of this statement to the example of Figure 2.1 is evident. Two important points in this quote are: (1) The concept of complexity is a property of the appraisal process rather than a property inherent to the system itself. That is, Rosen points at an epistemological dimension of the concept of complexity, which is related to the unavoidable existence of different relevant perspectives (choices of relevant attributes in the language of integrated assessment) that cannot all be mapped at the same time by a unique modeling relation. (2) Models can see only a part of the reality— the part the modeler is interested in. Put another way, any scientific representation of a complex system is reflecting only a subset of our possible relations (potential interactions) with it. “A stone can be a simple system for a person kicking it when walking in the road, but at the same time be an extremely complex system for a geologist examining it during an investigation of a mineral site” (Rosen, 1977, p. 229). Going back to the example of the equation of perfect gas (PV=nRT), as noted earlier it does not say anything about how it smells. Smell can be a nonrelevant system quality (attribute) for an engineer calculating the range of stability of a container under pressure. On the other hand, it can be a very relevant system quality for a chemist doing an analysis or a household living close to a chemical plant. The unavoidable existence of nonequivalent views about what should be the set of relevant qualities to be considered when modeling a natural system is a crucial point in the discussion of science for sustainability. 2.1.3 Conclusion Before closing this introductory section, I would like to explain why I embarked on such a deep epistemological discussion about the scientific process in the first place. There are subjects that are taboo in the scientific arena, especially for modelers operating in the so-called field of hard sciences. Examples of these taboos include avoiding acknowledging: 1. The existence of impredicative loops—Chicken-egg processes defining the identity of living systems require the consideration of self-entailing processes across levels and scales © 2004 by CRC Press LLC [...]... Giampietro and Pastore, 20 01) The definition of hierarchy theory suggested by Ahl and Allen is perfect for closing this section: “Hierarchy theory is a theory of the observer’s role in any formal study of complex sysems” (Ahl and Allen, 1996, p 29 ) © 20 04 by CRC Press LLC 32 Multi- Scale Integrated Analysis of Agroecosystems 2. 3 .2 Holons and Holarchies Holons and holarchies are a new class of hierarchical systems... loss of one-to-one mapping between representation and direct perception (this implies confusing the identities of the individual members of equivalence classes) © 20 04 by CRC Press LLC 34 Multi- Scale Integrated Analysis of Agroecosystems 2. 3.3 Near Decomposability of the Hierarchical System: Triadic Reading To better understand the nature of the epistemological predicament faced when making models of. .. the choice of a space-time window of observation by which the qualities of interest of the particular holon (expressed in the formal identity) can be defined © 20 04 by CRC Press LLC 36 Multi- Scale Integrated Analysis of Agroecosystems FIGURE 2. 5 Triadic reading and the need for five contiguous levels 2 3 and studied by using a set of observables (encoding variables assumed to be proxies of changes... agreement on how to compress this semantic identity into a finite, limited, closed formal identity © 20 04 by CRC Press LLC 38 2. 3.4 Multi- Scale Integrated Analysis of Agroecosystems Types Are out of Scale and out of Time, Realizations Are Scaled and Getting Old A type is a given set of relations of qualities of a system associated with the ability to express some emergent property in a given associative... quasi-steady state) That is, one has to choose a space-time window at which it is possible to define a clear identity for the system of interest (the triadic reading is often © 20 04 by CRC Press LLC 40 Multi- Scale Integrated Analysis of Agroecosystems expressed using the more familiar term of ceteris paribus assumption) However, as soon as one obtains the possibility of quantifying characteristics of. .. validity of an identity requires two quality checks in parallel, as illustrated in Figure 2. 4: © 20 04 by CRC Press LLC 30 Multi- Scale Integrated Analysis of Agroecosystems 1 2 A congruence check (in relation to an external referent) over a small scale This check is about the validity of the correspondence between the mental object (semantic identity associated with an epistemic category in the mind of the... if all the observers perceiving the characteristics of a dog can agree on the usefulness and validity of the identity associated with such a label, we can infer that something “real” is responsible for the coherence of the validity of such a label Such a real thing obviously is not an © 20 04 by CRC Press LLC 28 Multi- Scale Integrated Analysis of Agroecosystems organism belonging to the species Canis... of biological evolution (e.g., the becoming of ecological holons) requires the use of relevant time differentials of thousands of years The process of evolution of institutional settings of human societies requires the use of relevant time differentials of centuries The process of evolution of human technology requires the use of relevant time differentials of decades When dealing with price formation,... are discussed in Chapter 5 Alternative scientific approaches that can be developed by adopting complex systems thinking are discussed in Chapters 6, 7 and 8, and applications to the issue of multi- scale integrated analysis of agroecosystems are given in Chapters 9, 10 and 11 However, facing these challenges requires being serious about changing paradigms This is why, before discussing potential solutions... Hoekstra (19 92) , the definition of a type per se does not carry a scale tag A given ratio between the relative size of the head, the body and the legs of a given shape of organism can be realized at different scales (this is the basis of modeling) It is only when a particular typology is realized that the issue of scale enters into play At that point, scale matters in relation to (1) the definition of the . (Ahl and Allen, 1996, p. 29 ). © 20 04 by CRC Press LLC Multi- Scale Integrated Analysis of Agroecosystems3 2 2.3 .2 Holons and Holarchies Holons and holarchies are a new class of hierarchical systems. universe of semantic identities for a fish determined by goals and contexts. (Courtesy FAO Photo Library.) © 20 04 by CRC Press LLC Multi- Scale Integrated Analysis of Agroecosystems2 2 FIGURE 2. 3 The. epistemological assumption of elementary mechanics. This explains why simple © 20 04 by CRC Press LLC Multi- Scale Integrated Analysis of Agroecosystems2 4 models of the behavior of simple systems are