The concept of emergence is widely used in both the philosophy of mind and in cognitive science. In the philosophy of mind it serves to refer to seemingly irreducible phenomena, in cognitive science it is often used to refer to phenomena not explicitly programmed. There is no unique concept of emergence available that serves both purposes.

A traditional view of perception and action makestwo assumptions: that the causal flow betweenperception and action is primarily linear or one-way,and that they are merely instrumentally related toeach other, so that each is a means to the other.Either or both of these assumptions can be rejected. Behaviorism rejects the instrumental but not theone-way aspect of the traditional view, thus leavingitself open to charges of verificationism. Ecologicalviews reject the one-way aspect but not theinstrumental aspect of the traditional view, so thatperception and (...) action are seen as instrumentallyinterdependent. It is argued here that a betteralternative is to reject both assumptions, resultingin a two-level interdependence view in whichperception and action co-depend on dynamicallycircular subpersonal relations and as a result may bemore than merely instrumentally interdependent. Thisis illustrated by reference to motor theories ofperception and control theories of action. (shrink)

Artificial life (also known as “ALife”) is a broad, interdisciplinary endeavor that studies life and life-like processes through simulation and synthesis. The goals of this activity include modelling and even creating life and life-like systems, as well as developing practical applications using intuitions and methods taken from living systems. Artificial life both illuminates traditional philosophical questions and raises new philosophical questions. Since both artificial life and philosophy investigate the essential nature of certain fundamental aspects of reality like life and adaptation, (...) artificial life offers philosophy a new perspective on these phenomena. This chapter provides an introduction to current research in artificial life and explains its philosophical implications. (shrink)

In this paper, I provide an argument for pannormism, the view according to which there are normative properties all the way down. In particular, I argue for what I call the trickling down principle, which says that if there is a metaphysically basic normative property, then, if whatever instantiates it has a ground, that ground instantiates it as well.

As non-biological machines come to be designed in ways which exhibit characteristics comparable to human mental states, the manner in which the law treats these entities will become increasingly important both to designers and to society at large. The direct question will become whether, given certain attributes, a non-biological machine could ever be viewed as a legal person. In order to begin to understand the ramifications of this question, this paper starts by exploring the distinction between the related concepts of (...) human, person, and property. Once it is understood that person in the legal sense can apply to a non-biological entity such as a corporation, the inquiry then goes on to examine the folk psychology view of intentionality and the concept of autonomy. The conclusion reached is that these two attributes can support the view that a non-biological machine, at least in theory, can be viewed as a legal person. (shrink)

Some recent cognitive-scientific research suggests that a considerable amount of intelligent action is generated not by the systematic activity of internal representations, but by complex interactions involving neural, bodily, and environmental factors. Following an analysis of this threat to representational explanation, we pursue an analogy between the role of genes in the production of biological form and the role of neural states in the production of behaviour, in order to develop a notion of genic representation. In both cases an appeal (...) to normal ecological context is used to balance multi-factoral, interactive causal determination against the intuition that certain aspects of the causal nexus play a special role in promoting adaptive success. Certain worries abut this vision help us to get a better grip on the concept of genic representation itself. We end with a puzzle concerning the relation between cognition and representation. (shrink)

Is it OK to lie to Siri? Is it bad to mistreat a robot for our own pleasure? Under what condition should we grant a moral status to an artificial intelligence (AI) system? This paper looks at different arguments for granting moral status to an AI system: the idea of indirect duties, the relational argument, the argument from intelligence, the arguments from life and information, and the argument from sentience. In each but the last case, we find unresolved issues with (...) the particular argument, which leads us to move to a different one. We leave the idea of indirect duties aside since these duties do not imply considering an AI system for its own sake. The paper rejects the relational argument and the argument from intelligence. The argument from life may lead us to grant a moral status to an AI system, but only in a weak sense. Sentience, by contrast, is a strong argument for the moral status of an AI system—based, among other things, on the Aristotelian principle of equality: that same cases should be treated in the same way. The paper points out, however, that no AI system is sentient given the current level of technological development. (shrink)

An important lesson that philosophy can learn from the Turing Test and computer science more generally concerns the careful use of the method of Levels of Abstraction (LoA). In this paper, the method is first briefly summarised. The constituents of the method are “observables”, collected together and moderated by predicates restraining their “behaviour”. The resulting collection of sets of observables is called a “gradient of abstractions” and it formalises the minimum consistency conditions that the chosen abstractions must satisfy. Two useful (...) kinds of gradient of abstraction – disjoint and nested – are identified. It is then argued that in any discrete (as distinct from analogue) domain of discourse, a complex phenomenon may be explicated in terms of simple approximations organised together in a gradient of abstractions. Thus, the method replaces, for discrete disciplines, the differential and integral calculus, which form the basis for understanding the complex analogue phenomena of science and engineering. The result formalises an approach that is rather common in computer science but has hitherto found little application in philosophy. So the philosophical value of the method is demonstrated by showing how making the LoA of discourse explicit can be fruitful for phenomenological and conceptual analysis. To this end, the method is applied to the Turing Test, the concept of agenthood, the definition of emergence, the notion of artificial life, quantum observation and decidable observation. It is hoped that this treatment will promote the use of the method in certain areas of the humanities and especially in philosophy. (shrink)

A number of thinkers have been wondering about the moral obligations humans have, or will have, to intelligent technologies. An underlying assumption is that “moral machines” are decades in the offing, and thus we have no pressing obligations now. But, in the context of technology, we are yet to consider that we might owe moral consideration to something that is not a member of the moral community but eventually will be as an outcome of human action. Do we have current (...) actual obligations to technologies that do not currently exist? If there are obligations to currently non-existing technologies, we must confront what might be called the Non-Identical Machines Problem. Can we harm or benefit an entity by making it one way rather than another? This paper presents the problem and argues that it is more challenging than the standard Non-Identity Problem. (shrink)

Is it OK to lie to Siri? Is it bad to mistreat a robot for our own pleasure? Under what condition should we grant a moral status to an artificial intelligence system? This paper looks at different arguments for granting moral status to an AI system: the idea of indirect duties, the relational argument, the argument from intelligence, the arguments from life and information, and the argument from sentience. In each but the last case, we find unresolved issues with the (...) particular argument, which leads us to move to a different one. We leave the idea of indirect duties aside since these duties do not imply considering an AI system for its own sake. The paper rejects the relational argument and the argument from intelligence. The argument from life may lead us to grant a moral status to an AI system, but only in a weak sense. Sentience, by contrast, is a strong argument for the moral status of an AI system—based, among other things, on the Aristotelian principle of equality: that same cases should be treated in the same way. The paper points out, however, that no AI system is sentient given the current level of technological development. (shrink)

Since their inception in the 1980s, complexity sciences have been described as a revolutionary new domain of research. By describing some of the practices and assumptions of its representatives, the present article shows that this field is an association of subdisciplines laying on existing disciplinary footholds. The general question guiding us here is: On what basis do complexity scientists consider their inquiry methods and results as valuable? To answer it, I describe five “epistemic argumentative regimes,” namely the ways in which (...) complexity scientists argue the credibility of their research, and five “ontological views,” that is the ways in which they interpret the material and formal causes of their study objects and models. Finally, the article proposes the term of “regime of evidence” to designate the specific combination of one ontological view with one or more epistemic argumentative regimes. (shrink)

During the last decades the question of defining life has gained increased interest but, at the same time, the difficulty in reaching consensus on a possible answer has led many to skeptical positions. This, in turn, has raised a wider debate about why defining life is so hard and controversial. Such a debate introduces additional aspects to be considered, like the role and nature of a definition of life itself. In this paper, we will focus on those aspects, arguing that (...) progress can be made if we conceive definitions of life as open heuristic tools that contribute to develop specific research strategies in the biological sciences and, more generally, to increase our understanding of life’s complexity. In contrast with pragmatic or operationalist approaches, we will defend that definitions of life comprise a set of ontological assumptions, together with an inherent unifying vocation, so they should be subject to comparison and critical assessment, closely related to the success or failure of the corresponding research programs, but also to the success or failure in establishing well-grounded interconnections among the latter. We consider that the search for a more coherent, integrated and generalized theory of biology cannot be pursued without keeping an empirical standpoint, and the exercise of defining life should not be taken as an obstacle but as a valuable instrument to achieve that goal. (shrink)

This chapter draws on insights from non-equilibrium thermodynamics to demonstrate the ontological inadequacy of the machine conception of the organism. The thermodynamic character of living systems underlies the importance of metabolism and calls for the adoption of a processual view, exemplified by the Heraclitean metaphor of the stream of life. This alternative conception is explored in its various historical formulations and the extent to which it captures the nature of living systems is examined. Following this, the chapter considers the metaphysical (...) implications of reconceptualizing the organism from complex machine to flowing stream. What do we learn when we reject the mechanical and embrace the processual? Three key lessons for biological ontology are identified. The first is that activity is a necessary condition for existence. The second is that persistence is grounded in the continuous self-maintenance of form. And the third is that order does not entail design. (shrink)

A crucial question for a process view of life is how to identify a process and how to follow it through time. The genidentity view can contribute decisively to this project. It says that the identity through time of an entity X is given by a well-identified series of continuous states of affairs. Genidentity helps address the problem of diachronic identity in the living world. This chapter describes the centrality of the concept of genidentity for David Hull and proposes an (...) extension of Hull’s view to the ubiquitous phenomenon of symbiosis. Finally, using immunology as a key example, it shows that the genidentity view suggests that the main interest of a process approach is epistemological rather than ontological and that its principal claim is one of priority, namely that processes precede and define things, and not vice versa. (shrink)

One of the central problems in philosophy of artificial life (AL) is whether the artificial life entities we create can be genuine life. Proponents of strong AL believe that the artificial life entities exhibiting characteristics of natural life in a physical or a virtual environment can be real life. Opponents of strong artificial life, however, think that artificial life entities are not real life or just simulation of natural life. The aim of this paper is to demonstrate which view of (...) strong artificial life is valid. The method is to use philosophical theory and logics to analyze the opponents’ arguments. The conclusion is that the opponents’ arguments for denying strong AL are insufficient to exclude the possibility of strong AL. (shrink)

I develop and defend a version of panpsychism that avoids the combination problem by appealing to plural collective properties.

The invention of the computer has revolutionized science. With respect to finding the essential structures of life, for example, it has enabled scientists not only to investigate empirical examples, but also to create and study novel hypothetical variations by means of simulation: ‘life as it could be’. We argue that this kind of research in the field of artificial life, namely the specification, implementation and evaluation of artificial systems, is akin to Husserl’s method of free imaginative variation as applied to (...) the specific regional ontology of biology. Thus, at a time when the clarification of the essence of our biological embodiment is of growing interest for phenomenology, we suggest that artificial life should be seen as a method of externalizing some of the insurmountable complexity of imaginatively varying the phenomenon of life. (shrink)

What is artificial life? Much has been said about this interesting collection of efforts to artificially simulate and synthesize lifelike behavior and processes, yet we are far from having a robust philosophical understanding of just what Alifers are doing and why it ought to interest philosophers of science, and philosophers of biology in particular. In this paper, I first provide three introductory examples from the particular subset of artificial life I focus on, known as ‘soft Alife’ (s-Alife), and follow up (...) with a more in-depth review of the Avida program, which serves as my case study of s-Alife. Next, I review three well-known accounts of thought experiments, and then offer my own synthesized account, to make the argument that s-Alife functions as thought experimentation in biology. I draw a comparison between the methodology of the thought-experimental world that yields real-world results, and the s-Alife research that informs our understanding of natural life. I conclude that the insights provided by s-Alife research have the potential to fundamentally alter our understanding of the nature of organic life and thus deserve the attention of both philosophers and natural scientists. (shrink)

Recent work in biology and cognitive science depicts a variety of target phenomena as the products of a tangled web of causal influences. Such influences may include both internal and external factors as well as complex patterns of reciprocal causal interaction. Such twisted tales are sometimes seen as a threat to explanatory strategies that invoke notions such as inner programs, genes for and sometimes even internal representations. But the threat, I shall argue, is more apparent than real. Complex causal influence, (...) in and of itself, provides no good reason to reject these familiar explanatory notions. To believe otherwise, I suggest, is generally to commit (at least) one of two seductive errors. The first error is to think that the general notion of a state x coding for an outcome y involves the state's constituting a full description of y. This is what I call the myth of the self-contained code. The second error is to think that the practice of treating certain factors as special (e.g., seeing genes as coding for outcomes in a way environmental factors do not) depends on the (often mistaken) belief that the singled out factor is somehow doing the most real work. Where the amounts of causal influence are evenly spread, it is assumed there can be no reason to treat one factor in a special way. This is what I term the Myth of Explanatory Equality. Avoiding these errors involves reminding ourselves of (1) the rich context-dependence of even standard, unproblematic uses of the notions of code, program and information content (all three make sense only relative to an assumed ecological backdrop) and (2) the difference between explaining why an event occurred and displaying the full workings of a complex causal system. (shrink)

We confront the following popular views: that mind or life are algorithms; that thinking, or more generally any process other than computation, is computation; that anything other than a working brain can have thoughts; that anything other than a biological organism can be alive; that form and function are independent of matter; that sufficiently accurate simulations are just as genuine as the real things they imitate; and that the Turing test is either a necessary or sufficient or scientific procedure for (...) evaluating whether or not an entity is intelligent. Drawing on the distinction between activities and tasks, and the fundamental scientific principles of ontological lawfulness, epistemological realism, and methodological skepticism, we argue for traditional scientific materialism of the emergentist kind in opposition to the functionalism, behaviourism, tacit idealism, and merely decorative materialism of the artificial intelligence and artificial life communities. (shrink)