e-book Foundations of complex systems : emergence, information and predicition

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All in-person attendees must be pre-registered to gain entry to the NIST campus. Photo identification must be presented at the main gate to be admitted to the conference. International attendees are required to present a passport. Attendees must wear their conference badge at all times while on the campus. There is no on-site registration for meetings held at NIST. The objective of this Workshop is to bring together scientists and engineers from industry, government, and academia to describe the state-of-the-art in the titular areas and to identify their current roles and potential in the chemical, manufacturing, energy, pharmaceutical, and related sectors.

The Workshop will consist of a number of lectures by leaders in the field, followed by breakout sessions in several major areas. These latter will include the role of emergence, information theory, etc. With these kinds of interventions we can quantify how the original system is covered in excess when an intervention takes place. For simplicity, suppose that each component i is fully characterized by a single variable x i.

This quantity takes a value of zero when the states recovered are the same as the original ones, and it is equal to one when the whole phase space is recovered. To consider a single value for the coverage excess we will average the result of the intervention over all variables: 9. If the system is very large an exhaustive computation may be unfeasible, and a random sampling of the different components or more complex interventions such as the removal of several variables would be needed. The coverage excess reflects the vulnerability of the system to this intervention and, as the effects link the interventions with the number and scope of the constraints present in the system, it provides a mechanism to differentiate between upward and downward causation [ 36 ].

The system S1 is very vulnerable if x 1 is neglected, but it is not affected at all if any other variable is neglected, and thus there is upward causation from the first variable to the whole system. On the other hand, the system S3 is very vulnerable as the coverage excess is maximum irrespective of the variable over which we intervene, thus highlighting that there is a global constraint affecting the system downwards.

Finally, we consider the particular case in which we are already dealing with a subset of microscopic concepts U such that an emergent process described by the macroscopic concept is perfectly covered, i. In this case, we will say that the Eq 9 provides the coverage excess of the emergent property , that we denote as. The sensitivity of the model when an intervention over the system takes place suggests that systems with higher coverage excess will be more difficult to analyse. This difficulty can be combined with the notion of traceability we proposed above.

If we have a perfectly traceable system, we can quantify how much traceability we lose after intervention with the loss of traceability With this quantity, we can express those systems that are easily covered in excess have low traceability and the other way around. We haven taken the logarithm of the traceability to represent the emergence strength as a dissimilarity between a state of perfect knowledge of the system complete traceability and the state after intervention.


If, after intervention, we still remain in a situation of perfect traceability, this dissimilarity will be zero. On the other hand, if we completely lose all the information about the system in a way that we recover an unconstrained phase space, this dissimilarity will be infinite, thus reflecting some epistemological gap for these systems. With these definitions we expect to reconcile different positions on whether the origin of emergence is epistemological or ontological: even if we deal with a perfectly traceable system, which is therefore epistemologically accessible, we can still see that there are systems that are more inaccessible than others, and there are ontological reasons for that: the type of constraints involved in the system.

For these systems, until perfect traceability is attained, we will probably be tempted to say that they are epistemologically inaccessible, and that there is a strictly ontological and not epistemological reason for that. Of course, we should keep in mind that achieving a compact description does not mean that we have a full mechanistic understanding of the process: we still need to develop a model in the sense introduced here, that would interpret the constraints and generate the microcopic phase space.

If the constraints are very complex the implementation of constraints may never be feasible. Furthermore, even if a satisfactory generative model is developed, it does not mean that we are able to generate the macroscopic emergent pattern with a model. This would require a final process to decode the formal model, sensu Rosen [ 28 ], which may further require a complex experimental setup the decoding step shown in Fig 1. Given that during the process of researching an emergent property there are different steps we could use to evaluate the accessibility of the underlying process, we propose to focus upon the analysis of the patterns obtained from the experimental data as the starting step.

Complexity as a systems concept

This may help to reconcile the definition of weak and strong emergent properties [ 14 ] using the emergent strength: if the emergence strength is infinite we deal with a strongly emergent pattern, whereas if the value is finite we deal with a weak emergent pattern with the associated strength as an indicator of how difficult it is to achieve traceability. Of course, we cannot discount the possibility that there exist systems which are epistemologically inaccessible. This may be the case for quantum or computational systems—for which some of the definitions of weak and strong emergence where originally proposed—but not for many systems of scientific interest, where we believe that the situation is the one that we examined here: these systems are very large and they are under constraints large in scope.

Note that it may be argued that, with the above definitions, the emergence strength cannot be determined unless we achieve perfect traceability. However, this is only true for strongly emergent properties because Eq 10 can be easily modified to consider an intermediate incomplete model, using only the expression in Eq 9 and not , which will give us an estimate of the emergence strength. We expect that, for natural systems, there is a complex structure of constraints with different scopes, and we will be able to progressively discover this structure —possibly from low to high scopes— and so provide an estimate at any time.

In this article, we presented a novel approach to investigate the concept of emergence in complex systems. We tackled the problem through a constructive logical system that permits the investigation of the relationship between concepts and objects of observation [ 25 ]. In doing so, we focused upon a particular kind of system, which we believe are of much interest to the current discussion of emergence.

We start by supposing that we are analysing a naturally occurring macroscopic emergent property, and not a purely computational one. In addition, we neglect any vitalism, which means simply accepting explanatory physicalism; in the words of Mitchell, what else could there be?

We then assume that we are able to describe microstates of the system through experimental measurements. This implicitly assumes that we are able to differentiate the system from its background [ 21 ] and to provide a bottom-up characterization in terms of concepts associated with the elements that constitute the system [ 16 ], thus justifying the constructive approach. Nevertheless, we allowed for the possibility that we have no clue about the mechanistic processes underlying these observations, as often happens when a research program is in its infancy.

This fact differentiates this work from other theoretical approximations aiming to understand which features belong to systems exhibiting emergent properties, but that already assume that sufficent knowledge about the system exists so as to test its computational compressibility Bedau [ 8 ] , to postulate the existence of a closure of efficient causation Rosen, which requires determining the causal relationships [ 28 ] or to intervene over a system for which we already have a mechanistic model see for instance Hoel et al.

However, this is typically not the case when in the early stages of research, and this is why believe our proposal may be helpful for a wide variety of scientists. Invoking a mild condition relating the macroscopic observation of an emergent property and the constrained walk of the system in a certain region of the phase space, we focus on systems from which we expect to find sufficient regularities in the analysis of their microstates so as to be able to build explanatory models, i. We showed that building a microscopic model aimed to explain an emergent macroscopic observation requires identifying constraints in the viable values of the microscopic variables.

Interestingly, we identified concept disjunction as the basic logic operation to find constraints. The search for similarity measures, dissimilarity measures or distances is an essential task in Biology and Ecology [ 38 ] aiming to understand, following a top-down approach, the information shared between the different observations. This probably explains the success of complex networks theory and its philosophical interest, or why methods comparing objects of observation, such as protein sequence alignments like BLAST [ 39 ], are among the most cited ever in the scientific literature [ 40 ].

In general, disjunction underlies dimensionality reduction techniques such as principal components analysis [ 41 ]. From the perspective of our framework, these are techniques aiming at obtaining a representation with the minimum number of concepts whose extension explains the full variability of the microstates.

Identifying Emergent Behaviors of Complex Systems — In Nature and Computers - The New Stack

In this way, we are able to talk about the set of objects using a subset of concepts, which is essentially the task addressed by dimensionality reduction techniques, and that we defined here with the notion of compact description. We applied these tools to three different ensembles of microstates of a 3-bit synthetic system. We observed that the scope of the constraints is the main difficulty in identifying them: the larger the scope of the constraint, the more difficult is to assess it.

In particular, our method was unable to find a compact representation when the scope of the constraint has the same size as the system, which directly links the epistemological limitation of our framework with an ontological property of the system. We briefly considered other approximations, and were able to show that the number and type of constraints heavily influence the consequences that either an increase in system size or a loss of components may have upon our ability to identify them.

This observation seems to be independent of the formalism used, and so will also be independent of any subjectivity induced by the formalism we chose here. Notably, we were able to express this observation in the concrete space; thus further research would be needed to find equivalent definitions in the formal space. We also proposed a procedure based on the intervention of the observer on the system, thus compatible with the scientific method, to compute the loss of information experienced when we neglect components in the system.

Given that the loss of information depends on the type of constraints present, we can quantify how difficult it is to achieve traceability between the microscopic and macroscopic description. The loss of traceability was then used as a quantity to establish a distance between perfect traceability and our knowledge of the system after systematic interventions, which was what we called the emergence strength.

We believe that, for the kind of systems we are interested in, the emergence strength paves the way for us to reconcile different notions of emergence. For natural systems, we aim to develop computational models to reproduce experimentally measured data and to then simulate the emergent process, and thus it is compatible with weak emergence.

Nevertheless, we propose to combine the ability to build a computational model with the identification of constraints from experimental data, since the identification of constraints is where we start learning about the natural process we face. In this way, we focus on disentangling the number and scope of the constraints, whose complexity will determine its emergence strength. We conjecture that, for systems with different types of constraints, those with smaller scope are identified first. Accordingly, if a system has only constraints with a large scope or there is a big gap with respect to those with smaller scope, it may be simply impossible at a certain state of knowledge to assess them, an example of which may be our current knowledge of consciousness.

For these processes, the emergence strength may be so high that we would be justified in calling them strongly emergent processes. For such processes, it may be the case that not only is it impossible to decipher the constraints, but even if the microscopic constraints are deciphered, it may still not be possible to build an experimental setup for a model to recreate the observed emergent pattern, given the complexity of the environment in which the system should be embedded to reproduce such a constraint.

Our definition seems to also be compatible with the classification proposed by de Haan [ 7 ], as the existence of a microscopic emergent conjugated causally affecting the macroscopic pattern in the strongest version, consciously , can be understood in terms of a global constraint as he suggests in the relationship between this type of emergence and downward causation. This is the case for living systems, where we believe strong emergence may be pervasive.

Our findings might be criticized as saying that describing emergence in terms of constraints provides a static description for systems that are intrinsically dynamic; an approach which may be thought of as a kind of ontological reductionism [ 17 ]. Note, however, that constraints may themselves be dynamic and, either their variation occurs on a longer timescale, or the constraints dynamics is itself sufficiently constrained, e. This would also address potential criticisms regarding multiple realizability: similar microscopic patterns can be found for systems under similar constraints even if the particular realizations of the microstates are substantially different for each system.

Multiple examples of this can be found in the literature. For example, the evolutionary process allow us to classify protein structures in clusters if they have global structural similarity that result from similar physico-chemical constraints even if they perform different functions [ 42 ]. Similarly, ecological patterns such as the nestedness found in ecological networks describe complex constraints in the way in which species interact, even if they are found in different ecosystems, from plant-pollinators [ 43 ] to host-virus systems [ 44 ].

If patterns observed in organisms are the consequence of natural selection, and global constraints are acting on individuals in the selection process, natural selection itself can be thought of as an expression of downward causation [ 45 ]. Interestingly, adaptation takes place when the organisms are able to predict and overcome environmental changes, and predictability is a consequence of the amount of structured information that exists in the environment [ 46 ].

But, organisms share their environment with other organisms, and thus ecological interactions are fundamental to the adaptive process.

Mark Newman - The Physics of Complex Systems - 02/10/18

This picture, in its stronger version, in which the influence of ecological interactions is so important that the notion of an individual as object of selection is challenged, becomes increasingly important in current research, particularly in the microbial world see for instance [ 47 ]. To illustrate this point, consider the following example proposed in [ 48 ], in which we consider one individual for which its fitness f i can be decomposed into two components, where the first component reflects the fitness of the individual as a consequence of its ecological interactions with other species j , and the second its fitness due to any other process, i.

Now consider a particular example, in which two individuals belong to two different species, a and b , interacting mutualistically. The effect of the interaction on the fitness f i would be positive through an increase reflected in the term. Finally, think of an evolutionary event which becomes fixed in the population of species a affecting its fitness, , in such a way that the new fitness and, in particular, but.

This means that the fitness of species b due to the interaction with species a will also be affected after the evolutionary event and thus there will be a change in the selection pressure on the regions of the genomes of both species codifying the traits needed for the interaction. Furthermore, if we consider an extreme scenario in which and —that may be the case for auxotrophs see a synthetic ecological experiment in [ 49 ] — the relevance of these coevolving regions in the evolutionary process would be so important that the concept of an object of selection should be revisited [ 50 ].

In particular, it might be more appropriate to frame the evolution of both species by considering them as some sort of multicellular species. In this sense, even if the individual is still the main object of selection, it becomes entangled with an object of selection determined on a larger scale, which is the consortia of species. Furthermore, if this consortia acquires new functionality that make its members selectively advantageous in line with Kim [ 5 ], we may say that a new object of selection emerges.

In the same way that we admit the existence of different levels of organization of living beings that depend on a hierarchy of constraints, we should also consider the possibility that individuals belong to different objects of selection influencing their fitness to different degrees. In this way, the term upwards downwards causation would be used in this context as the effect of constraints acting on a given level has ramifications for objects of selection on upper lower levels. This perspective probably reflects the interest of modern science on mutualistic interactions and its relation with emergent processes [ 4 ], although mutualism may not be necessary to derive a measure of community-level fitness [ 51 ].

The determination of fitness above the individual level may be seen as a form of self-determination that would engage with the concept of closure of efficient causation. And in the same way that it is possible to argue that complete or perfect closure exists for any living being, it is also possible to argue for the concept of the object of selection to be a closed concept and reality.

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This is probably why it is aimed at extending the concept of closure to an ecological context [ 52 ]. A rigid definition of closure or of an object of selection might be an acceptable concept in an ideal thermodynamical scenario of natural selection [ 53 ] —and thus theoretically interesting— but it may perhaps be a good idealization of the limits of biological organization, like simple cells or the whole biosphere.

But these concepts become blurred in complex ecosystems in natural environments. We believe that there is no need to invoke any teleological principle [ 54 ]. Only in an ideal scenario in which life evolves spontaneously following thermodynamical principles may one envisage a teleological ideal in which biological organization maximizes any entropic or energetic principle [ 55 ], or arbitrary fitness.

It can be argued that this scenario is true in general. A simple counterexample may be found in the way in which human beings not only do not optimize any spontaneous physico-chemical principle from which life may have emerged, but rather generate a process that may eventually lead to extinction. In summary, we believe that the formalism introduced here improves our ability to synthetically understand complex systems. We believe that it could also be used to tackle other challenging questions, and thus we hope that our effort will stimulate both scientific and philosophical discussion.

Looking for fresh formal approaches to talk about philosophical questions is particularly important because formally shaping our philosophical knowledge is a way to create new bridges between science and philosophy. This would be probably good news for science, as the benefits of philosophy seem to be, for current scientists, left behind.

I am particularly in debt with Silvio Valentini for his continuous and patient feedback. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract Emergent patterns in complex systems are related with many intriguing phenomena in modern science. Data Availability: All relevant data are within the paper.

Download: PPT. Fig 1. Scheme of the epistemological approximation of an observer in the analysis of emergent properties. Methods Main definitions and operations System definition. Measurable properties and the definition of concepts. Logical operations. It follows from the previous definitions the operations below to build new concepts.

The space of objects of observation. Epistemological approximation Microscopic and macroscopic descriptions. Traceability, compact descriptions and models. Results Identification of constraints: Focusing on disjunction As we anticipated, when an emergent property is observed the probability distribution of the values of one or several variables depart from the distribution observed when the system is free of constraints, thus losing ergodicity [ 29 ] p.

Fig 2. Illustration of conjunction and disjunction of concepts. A synthetic example: The three bits system We are already equipped with the necessary tools to analyse a synthetic example in detail. System with a single constraint of scope one S1. Table 2. Fig 4. Representations of a three bits system with a single constraint of scope one. System with two constraints of scope two S2. Table 3.

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Fig 5. Representations of a three bits system with two constraints of scope two. System with a single constraint of scope three S3; the parity bit system. Table 4. Fig 6. Representations of a three bits system with one constraint of scope three. Quantification of emergence According to the above results, the number and scope of the constraints are ontological properties that determine our epistemological ability to achieve a compact description, which we raised as a necessary condition to delineate a model aimed at explaining an emergent macroscopic property.

Coverage excess. Loss of traceability and emergence strength. If we have a perfectly traceable system, we can quantify how much traceability we lose after intervention with the loss of traceability 10 With this quantity, we can express those systems that are easily covered in excess have low traceability and the other way around.

Discussion In this article, we presented a novel approach to investigate the concept of emergence in complex systems. Acknowledgments I am particularly in debt with Silvio Valentini for his continuous and patient feedback. References 1.


Nietzsche F. Thus spoke Zarathustra. Manis J, editor. Hazleton: Pennsylvania State Univerity; Brenner S. Sequences and consequences. View Article Google Scholar 3. Explaining emergence: towards an ontology of levels. Journal for General Philosophy of Science. View Article Google Scholar 4. Corning PA. The re-emergence of emergence, and the causal role of synergy in emergent evolution. View Article Google Scholar 5. Kim J. Making sense of emergence. Philosophical studies. View Article Google Scholar 6. Crick F, Clark J. The astonishing hypothesis. Journal of Consciousness Studies.

View Article Google Scholar 7. How emergence arises. Ecological Complexity. View Article Google Scholar 8. Bedau MA. Weak emergence. View Article Google Scholar 9. Crutchfield JP. When evolution is revolution—Origins of innovation. Evolutionary dynamics: exploring the interplay of selection, neutrality, accident, and function. Oxford: Oxford University Press; ; p. Bedau M. Downward causation and the autonomy of weak emergence.

View Article Google Scholar Holland JH. Emergence: From chaos to order. Huneman P, Humphreys P. Dynamical emergence and computation: an introduction. Minds and Machines. Huneman P. Determinism, predictability and open-ended evolution: lessons from computational emergence. Bar-Yam Y. A mathematical theory of strong emergence using multiscale variety.

Chalmers DJ. Strong and weak emergence. In: Clayton P, and Davies P, editors. Bich L. Complex emergence and the living organization: an epistemological framework for biology. The book can be used as a textbook by graduate students, researchers and teachers in science, as well as non-experts who wish to have an overview of the field. Convert currency. Add to Basket. Book Description World Scientific, Condition: New.

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