Measuring autonomy and emergence via Granger causality

Concepts of emergence and autonomy are central to artificial life and related cognitive and behavioral sciences. However, quantitative and easy-to-apply measures of these phenomena are mostly lacking. Here, I describe quantitative and practicable measures for both autonomy and emergence, based on the framework of multivariate autoregression and specifically Granger causality. G-autonomy measures the extent to which the knowing the past of a variable helps predict its future, as compared to predictions based on past states of external (environmental) variables. G-emergence measures the extent to which a process is both dependent upon and autonomous from its underlying causal factors. These measures are validated by application to agent-based models of predation (for autonomy) and flocking (for emergence). In the former, evolutionary adaptation enhances autonomy; the latter model illustrates not only emergence but also downward causation. I end with a discussion of relations among autonomy, emergence, and consciousness

A Functional Naturalism

I provide two arguments against value-free naturalism. Both are based on considerations concerning biological teleology. Value-free naturalism is the thesis that both (1) everything is, at least in principle, under the purview of the sciences and (2) all scientific facts are purely non-evaluative. First, I advance a counterexample to any analysis on which natural selection is necessary to biological teleology. This should concern the value-free naturalist, since most value-free analyses of biological teleology appeal to natural selection. My counterexample is unique in that it is likely to actually occur. It concerns the creation of synthetic life. Recent developments in synthetic biology suggest scientists will eventually be able to develop synthetic life. Such life, however, would not have any of its traits naturally selected for. Second, I develop a simple argument that biological teleology is a scientific but value-laden notion. Consequently, value-free naturalism is false. I end with some concluding remarks on the implications for naturalism, the thesis that (1). Naturalism may be salvaged only if we reject (2). (2) is a dogma that unnecessarily constrains our conception of the sciences. Only a naturalism that recognizes value-laden notions as scientifically respectable can be true. Such a naturalism is a functional naturalism

Multivariate Granger Causality and Generalized Variance

Granger causality analysis is a popular method for inference on directed interactions in complex systems of many variables. A shortcoming of the standard framework for Granger causality is that it only allows for examination of interactions between single (univariate) variables within a system, perhaps conditioned on other variables. However, interactions do not necessarily take place between single variables, but may occur among groups, or "ensembles", of variables. In this study we establish a principled framework for Granger causality in the context of causal interactions among two or more multivariate sets of variables. Building on Geweke's seminal 1982 work, we offer new justifications for one particular form of multivariate Granger causality based on the generalized variances of residual errors. Taken together, our results support a comprehensive and theoretically consistent extension of Granger causality to the multivariate case. Treated individually, they highlight several specific advantages of the generalized variance measure, which we illustrate using applications in neuroscience as an example. We further show how the measure can be used to define "partial" Granger causality in the multivariate context and we also motivate reformulations of "causal density" and "Granger autonomy". Our results are directly applicable to experimental data and promise to reveal new types of functional relations in complex systems, neural and otherwise.Comment: added 1 reference, minor change to discussion, typos corrected; 28 pages, 3 figures, 1 table, LaTe

Social and ethical checkpoints for bottom-up synthetic biology, or protocells

An alternative to creating novel organisms through the traditional “top-down” approach to synthetic biology involves creating them from the “bottom up” by assembling them from non-living components; the products of this approach are called “protocells.” In this paper we describe how bottom-up and top-down synthetic biology differ, review the current state of protocell research and development, and examine the unique ethical, social, and regulatory issues raised by bottom-up synthetic biology. Protocells have not yet been developed, but many expect this to happen within the next five to ten years. Accordingly, we identify six key checkpoints in protocell development at which particular attention should be given to specific ethical, social and regulatory issues concerning bottom-up synthetic biology, and make ten recommendations for responsible protocell science that are tied to the achievement of these checkpoints

Felony Murder and Capital Punishment: an Examination of the Deterrence Question

A proper test of the deterrent effect of the death penalty must consider capital homicides. However, the criterion variable in most investigations has been total homicides—most of which bear no legal or theoretical relationship to capital punishment. To address this fundamental data problem, this investigation used Federal Bureau of Investigation data for 1976–1987 to examine the relationship between capital punishment and felony murder, the most common type of capital homicide. We conducted time series analyses of monthly felony murder rates, the frequency of executions, and the amount and type of television coverage of executions over the period. The analyses revealed occasional departures (for vehicle theft and narcotics killings) from the null hypotheses. However, on balance, and in line with the vast majority of capital punishment studies, this investigation found no consistent evidence that executions and the television coverage they receive are associated significantly with rates for total, index, or different types of felony murder

Engineers of Life? A Critical Examination of the Concept of Life in the Debate on Synthetic Biology

The concept of life plays a crucial role in the debate on synthetic biology. The first part of this chapter outlines the controversial debate on the status of the concept of life in current science and philosophy. Against this background, synthetic biology and the discourse on its scientific and societal consequences is revealed as an exception. Here, the concept of life is not only used as buzzword but also discussed theoretically and links the ethical aspects with the epistemological prerequisites and the ontological consequences of synthetic biology. The second part examines this point of intersection and analyses some of the issues which are discussed in terms of the concept of life. The third part turns to the history of the concept of life. It offers an examination of scientific and philosophical discourses on life at the turn of the 20th century and suggests a surprising result: In the light of this history, synthetic biology leads to well-known debates, arguments, notions and questions. But it is concluded that the concept of life is too ambiguous and controversial to be useful for capturing the actual practice of synthetic biology. In the fourth part I argue that with regard to the ethical evaluation of synthetic biology, the ambiguity of the concept of life is not as problematic as sometimes held because other challenges are more important. The question whether the activity of synthetic biological systems should be conceived as life or not is primarily theoretical

Defining and simulating open-ended novelty: requirements, guidelines, and challenges

The open-endedness of a system is often defined as a continual production of novelty. Here we pin down this concept more fully by defining several types of novelty that a system may exhibit, classified as variation, innovation, and emergence. We then provide a meta-model for including levels of structure in a system’s model. From there, we define an architecture suitable for building simulations of open-ended novelty-generating systems and discuss how previously proposed systems fit into this framework. We discuss the design principles applicable to those systems and close with some challenges for the community