The artificial worlds approach to emergent evolution

Artificial worlds models of evolutionary systems are computer models that map the essential logical structure of ecological systems, defined as self-sustaining biological organizations. The artificial world comprises an artificial environment, with mass components, energy input, and physical states. It also comprises artificial organisms, including a genome, a phenome, and a (developmental) map that connects the genome to the phenome. Mass components are cycled and space is limited. The evolution process results, as in nature, from genetic variation combined with natural selection imposed by the finiteness of the environment. The selection criteria (fitness values) are not imposed, but rather emerge from the interactions of the organisms with each other and with the environment. The dynamics at the population level also emerges from these basic interactions. In this paper we describe the comparative properties of the EVOLVE family of artificial worlds models.     

From computational quantum chemistry to computational biology: experiments and computations are (full) partners

Computations are being integrated into biological research at an increasingly fast pace. This has not only changed the way in which biological information is managed; it has also changed the way in which experiments are planned in order to obtain information from nature. Can experiments and computations be full partners? Computational chemistry has expanded over the years, proceeding from computations of a hydrogen molecule toward the challenging goal of systems biology, which attempts to handle the entire living cell. Applying theories from ab initio quantum mechanics to simplified models, the virtual worlds explored by computations provide replicas of real-world phenomena. At the same time, the virtual worlds can affect our perception of the real world. Computational biology targets a world of complex organization, for which a unified theory is unlikely to exist. A computational biology model, even if it has a clear physical or chemical basis, may not reduce to physics and chemistry. At the molecular level, computational biology and experimental biology have already been partners, mutually benefiting from each other. For the perception to become reality, computation and experiment should be united as full partners in biological research.     

"A disease of frozen feelings": ethically working on emotional worlds in a Russian Orthodox Church drug rehabilitation program

In a Russian Orthodox Church drug rehabilitation program in St. Petersburg, drug addiction was often described as a disease of frozen feelings. This image suggests that rehabilitation is a process of thawing emotional worlds and, thus, allows the emotions to flow once again. In this article I argue that "frozen feelings" is better understood as the unsocial emotional worlds many drug users experience, and that rehabilitation in this church-run program particularly focuses on the cultivation of an emotional world that supports sociality. This is done, I argue, by means of ethically training rehabilitants to learn how to control and manage their emotional worlds, and in so doing, rehabilitants become new moral persons better able to live in the social world.     

The sense of consciousness

I propose that consciousness might be understood as the property of a system that functions as a sense in the biological meaning of that term. The theory assumes that, as a complex system, the sense of consciousness is not a fixed structure but implies structure with variations and that it evolved, as many new functions do, through the integration of simpler systems. The recognized exteroceptive and enteroceptive senses provide information about the organism's environment and about the organism itself that are important to adaptation. The sense of consciousness provides information about the brain and thus about the organism and its environment. It senses other senses and processes in the brain, selecting and relating components into a form that "makes sense"-where making sense is defined as being useful to the organism in its adaptation to the environment. The theory argues that this highly adaptive organizing function evolved with the growing complexity of the brain and that it might have helped resolve discrepancies created at earlier stages. Neural energies in the brain that are the input to the sense of consciousness, along with the processing subsystem of which they are a part, constitute the base of consciousness. Consciousness itself is an emergent effect of an organizing process achieved through the sense of consciousness. The sense of consciousness thus serves an organizing function although it is not the only means of organization in the brain. Its uniqueness lies in the character of the organization it creates with consciousness as a property of that organization. The paper relates the theory to several general conceptions-interactionism, epiphenomenalism and identity theory-and illustrates a number of testable hypotheses. Viewing consciousness as a property of a sense provides a degree of conceptual integration. Much of what we know about the evolution and role of the conventionally recognized senses should help us understand the evolution and role of the sense of consciousness, and of consciousness itself.     

The RNA worlds in context

There are two RNA worlds. The first is the primordial RNA world, a hypothetical era when RNA served as both information and function, both genotype and phenotype. The second RNA world is that of today's biological systems, where RNA plays active roles in catalyzing biochemical reactions, in translating mRNA into proteins, in regulating gene expression, and in the constant battle between infectious agents trying to subvert host defense systems and host cells protecting themselves from infection. This second RNA world is not at all hypothetical, and although we do not have all the answers about how it works, we have the tools to continue our interrogation of this world and refine our understanding. The fun comes when we try to use our secure knowledge of the modern RNA world to infer what the primordial RNA world might have looked like.     

Experimental simulation in behavioral ecology: a multimedia approach with the spatial searching simulation RattleSnake?

Searchers in nature often have accurate knowledge of the spatial location of the resource targets they seek, though in many other cases they have none. For example, the spatial distribution of targets such as food patches or potential mates may shift or change unpredictably from season to season. Searchers encountering circumstances of these sorts may be said to be “naive”. This problem is compounded by the fact that spatial distributions of targets may vary statistically as well: they may be distributed randomly, uniformly, or they may be clustered. Accordingly, since we study an animal system in nature that encounters such challenges (i.e., free-ranging rattlesnakes in many parts of their range), we wrote a comprehensive spatial searching program for Macintosh systems that simulates this problem thoroughly, RattleSnake^©. In a large series of experimental simulations using this software, we found that search paths of high vector magnitude (approaching 1.0), or those that approached straight lines, generated large numbers of collisions in large, clustered worlds. No search path was any better than any other in large, randomly or uniformly distributed worlds. Zig-zag paths of low vector magnitude (approaching zero) in small worlds of all types and of all densities were efficacious, due to continuous turning which prevented searchers from moving out of or exiting patches. Thus it appears that there are design rules in nature governing target collision probabilities in some but not all two-dimensional spatial worlds. Search paths of high vector magnitude, or those approaching straight lines, generate high collision frequencies in statistically clustered spatial worlds, for example. RattleSnake^© thus may be useful in programs of basic and/or applied behavioral ecology, including conservation, as well as in laboratory and multimedia classroom education.Electronic supplementary material Electronic supplementary material is available for this article at and accessible for authorised users.     

Explanations in search of observations

The paper explores how, in economics and biology, theoretical models are used as explanatory devices. It focuses on a modelling strategy by which, instead of starting with an unexplained regularity in the world, the modeller begins by creating a credible model world. The model world exhibits a regularity, induced by a mechanism in that world. The modeller concludes that there may be a part of the real world in which a similar regularity occurs and that, were that the case, the model would offer an explanation. Little concrete guidance is given about where such a regularity might be found. Three modelling exercises in evolutionary game theory—one from economics and two from biology—are used as case studies. Two of these (one from each discipline) exemplify ‘explanation in search of observation’. The third goes a step further, analysing a regularity in a model world and treating it as informative about the real world, but without saying anything about real phenomena. The paper argues that if the relation between the model and real worlds is understood in terms of similarity, and if modelling is understood as an ongoing discovery process rather than as the demonstration of empirical truths, there can be value in creating explanations before finding the regularities that are to be explained.     

Peculiarities of interhemisphere interaction at different levels of consciousness

Transition from wakefulness to drowsiness was used as a model of a gradual decrease in the consciousness level. Subjects' self-reported changes in the contents of consciousness, namely: the intensity and quality of involuntary mental processes, served as an indicator of the consciousness level. Each observed level of consciousness was shown to be characterised by a certain EEG profile. It was also shown that the state in which the subject signals of a slowdown of the "internal speech" or the "lapse of thoughts" corresponds to the lowest level of activation. In this case, the interhemisphere connections involving the main mechanism of cognitive information transmission, i.e. the EEG high-frequency rhythms, becomes suppressed.     

Metabolic footprinting and systems biology: the medium is the message

One element of classical systems analysis treats a system as a black or grey box, the inner structure and behaviour of which can be analysed and modelled by varying an internal or external condition, probing it from outside and studying the effect of the variation on the external observables. The result is an understanding of the inner make-up and workings of the system. The equivalent of this in biology is to observe what a cell or system excretes under controlled conditions - the 'metabolic footprint' or exometabolome - as this is readily and accurately measurable. Here, we review the principles, experimental approaches and scientific outcomes that have been obtained with this useful and convenient strategy.     

Cultural evolution: implications for understanding the human language faculty and its evolution

Human language is unique among the communication systems of the natural world: it is socially learned and, as a consequence of its recursively compositional structure, offers open-ended communicative potential. The structure of this communication system can be explained as a consequence of the evolution of the human biological capacity for language or the cultural evolution of language itself. We argue, supported by a formal model, that an explanatory account that involves some role for cultural evolution has profound implications for our understanding of the biological evolution of the language faculty: under a number of reasonable scenarios, cultural evolution can shield the language faculty from selection, such that strongly constraining language-specific learning biases are unlikely to evolve. We therefore argue that language is best seen as a consequence of cultural evolution in populations with a weak and/or domain-general language faculty.     

Globality and Constructions of World Health

Explorations of the social construction of health and illness reveal that popular and professional discourses on health and disease implicitly contain underlying images of how societies can or should be structured. Globality, which is a notion that refers to the consciousness of the world as a single place, suggests that these images of world structure are now salient. This article describes four current discourses of "world health" and discusses them in terms of their underlying ideal images of world order. The four images of the world are defined in terms of four elemental points of reference: individuals, societies, the system of societies, and humankind. These ideal images have either a gemeinschaft (community) or gesellschaft (society) orientation. An anthropology of globality can refine and extend our understandings of discourses of world health. Sensitivity to these discourses and their world-oriented ideological roots should help to demystify the notion of world health. [world health, globality, global culture]     

Do we have an internal model of the outside world?

Our phenomenal world remains stationary in spite of movements of the eyes, head and body. In addition, we can point or turn to objects in the surroundings whether or not they are in the field of view. In this review, I argue that these two features of experience and behaviour are related. The ability to interact with objects we cannot see implies an internal memory model of the surroundings, available to the motor system. And, because we maintain this ability when we move around, the model must be updated, so that the locations of object memories change continuously to provide accurate directional information. The model thus contains an internal representation of both the surroundings and the motions of the head and body: in other words, a stable representation of space. Recent functional MRI studies have provided strong evidence that this egocentric representation has a location in the precuneus, on the medial surface of the superior parietal cortex. This is a region previously identified with ‘self-centred mental imagery’, so it seems likely that the stable egocentric representation, required by the motor system, is also the source of our conscious percept of a stable world.     

From interesting details to dynamical relevance: toward more effective use of empirical insights in theory construction

A perennial challenge in ecology is to develop dynamical systems models that appropriately abstract and characterize the dynamics of natural systems. Deriving an appropriate model of system dynamics can be a long and iterative process whose outcome depends critically on the quality of empirical data describing the long‐term behavior of a natural system. Most ecological time series are insufficient to offer insight into the way organizational hierarchies and spatial scales are causally linked to natural system dynamics. Moreover, the classic tradition of hypothesis testing in ecology is not likely to lead to those key insights. This because empirical research is geared almost exclusively toward testing model predictions based on underlying causal relationships assumed by theorists. So, empirical research relies heavily on theory for guidance on what is or is not dynamically relevant. I argue here that it is entirely possible to reduce much of this guesswork involved with deciding on causal structure by giving empirical research a new role in theory development. In this role, natural history and field observations are used to develop stochastic, individual‐based and spatially explicit computational models or IBMs that can explore the range of contingency and complexity inherent in real‐world systems.
IBMs can be used to run simulations allowing deductions to be made about the causal linkages between organizational hierarchies, spatial scales, and dynamics. These deductions can be tested under field conditions using experiments that manipulate the putative causal structure and evaluate the dynamical consequences. The emerging insights from this stage can then be used to inspire an analytical construct that embodies the dynamically relevant scales and mechanisms. In essence, computational modeling serves as an intermediate step in theory development in that a wide range of possibly important biological details are considered and then reduced to a subset that is dynamically relevant.     

Quantum-like formalism for cognitive measurements

We develop a quantum formalism (Hilbert space probabilistic calculus) for measurements performed over cognitive systems. In particular, this formalism is used for mathematical modelling of the functioning of consciousness as a self-measuring quantum-like system. By using this formalism, we could predict averages of cognitive observables. Reflecting the basic idea of neurophysiological and psychological studies on a hierarchic structure of cognitive processes, we use p-adic hierarchic trees as a mathematical model of a mental space. We also briefly discuss the general problem of the choice of an adequate mental geometry.     

The mid-domain effect and diversity gradients: is there anything to learn?

The mid-domain effect (MDE) has been proposed as a null model for diversity gradients and an explanation for observed patterns. Here we respond to a recent defense of the concept, explaining that it cannot represent a viable model in either real or null worlds. First, the MDE misrepresents the nature of species ranges. There is also an internal logical inconsistency underlying the MDE because the range size frequency distribution, necessary to generate a hump-shaped pattern under randomization, cannot exist in the absence of environmental gradients and is generated by the ecological and historical processes that the MDE claims to exclude.     

Role of connectivity in immune and neural network models: memory development and aging.

In this paper we analyzed how connectivity (defined as number of connections between network elements) can affect the memory capacity of a network-based model of the Immune System (IS) and of a model of the Nervous System (NS) synaptic plasticity (BCM model). The key point is the concept of competition between the characteristic variables that represent the response of such systems to environmental stimuli: the clonal concentrations for the IS, and the neuron responses for the BCM model. The memory states of both systems are characterized by a high selectivity to specific input patterns, reflecting a similar behaviour of their development rules. This selectivity property of memory states can be controlled by changing the degree of the internal connectivity in each system. We can explain the changes occurring in IS memory states during lifespan as due to a reshaping of its internal connectivity. This assumption is in agreement with experimental observations, reporting an increase of IS memory cells during lifespan. The change of connectivity in the BCM model leads to the introduction of quasilocal variables governing the plasticity of groups of synaptic junctions. This could be interpreted as the result of a refinement of neuron internal mechanisms during development, or it could be seen as a different learning rule deriving from the original BCM theory. We argue that connectivity seems to play an important role in a large class of biological systems controlled by competition mechanisms. Moreover, changes in connectivity may lead to changes in memory properties during development and aging.     

Knowledge-based design of artificial worlds

We review the concepts of knowledge representation and modelling and simulation methodology which facilitate computer exploration of alternative artificial worlds, such as self-sufficient human habitats. An object-oriented computer environment which supports such simulation studies is briefly described. A simplified example of an artificial world model is given to demonstrate the power of the approach.     

Thermodynamics of the Human Brain

The human brain may be regarded as an irreversible system which is constrained by a fixed inflow of free energy in the form of chemical nourishment from within the body and information from the environment. The evolution of the internal spontaneous process may be described as a path on the saddle surface of entropy production rate in the configuration space of the process variables. From any initial state of high entropy production the system evolves towards the saddle point by a series of regessions to temporary minima alternating with fluctuations which introduce new internal constraints and open new channels for regression. The spontaneous regression steps of the process are to be associated with the learning process and the deductive processes of thought since information is necessarily being stored, whereas the fluctuation or nucleation steps are to be associated with the inductive or creative part of the thought process. The state of consciousness is to be associated with the system undergoing regression. If, under certain boundary conditions, the system attains the stable saddle point, a state of unconsciousness is attained in which no change of state variables occurs in time. The stability of this state is indicated by its resistance to perturbation.     

Quantum monadology: a consistent world model for consciousness and physics

The NL world model presented in the previous paper is embodied by use of relativistic quantum mechanics, which reveals the significance of the reduction of quantum states and the relativity principle, and locates consciousness and the concept of flowing time consistently in physics. This model provides a consistent framework to solve apparent incompatibilities between consciousness (as our interior experience) and matter (as described by quantum mechanics and relativity theory). Does matter have an inside? What is the flowing time now? Does physics allow the indeterminism by volition? The problem of quantum measurement is also resolved in this model.