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Based on Kolchinsky and Wolpert’s work on the semantics of autonomous agents, I propose an application of Mathematical Logic and Probability to model cognitive processes. In this work, I will follow Bateson’s insights on the hierarchy of learning in complex organisms and formalize his idea of applying Russell’s Type Theory. Following Weaver’s three levels for the communication problem, I link the Kolchinsky–Wolpert model to Bateson’s insights, and I reach a semantic and conceptual hierarchy in living systems as an (...) explicative model of some adaptive constraints. Due to the generality of Kolchinsky and Wolpert’s hypotheses, I highlight some fundamental gaps between the results in current Artificial Intelligence and the semantic structures in human beings. In light of the consequences of my model, I conclude the paper by proposing a general definition of knowledge in probabilistic terms, overturning de Finetti’s Subjectivist Definition of Probability. (shrink)

Formal and social epistemologists have devoted significant attention to the question of how to aggregate the credences of a group of agents who disagree about the probabilities of events. Moss and Pettigrew argue that group credences can be a linear mean of the credences of each individual in the group. By contrast, I argue that if the epistemic value of a credence function is determined solely by its accuracy, then we should, where possible, aggregate the underlying statistical models that individuals (...) use to generate their credence functions, using “stacking” techniques from statistics and machine learning first developed by Wolpert. (shrink)

As scientists start to adopt machine learning (ML) as one research tool, the security of ML and the knowledge generated become a concern. In this paper, I explain how supervised ML can be improved with better data ontology, or the way we make categories and turn information into data. More specifically, we should design data ontology in such a way that is consistent with the knowledge that we have about the target phenomenon so that such ontology can help us make (...) the inductive leap. I do so by thinking through a thought experiment, Goodman’s New Riddle of Induction (Fact, fiction, and forecast, Harvard University Press, 1955). Goodman’s riddle helps flesh out three problems of induction: (1) the problem of equal goodies, that there are often too many equally good inductive results given the same data; (2) the problem of diverging performance, that these equally good results can give opposite predictions in the future; and (3) the problem of mediocrity, that when averaged across all equally possible datasets and tasks, no inductive algorithm outperforms any other. I show that all these three problems are manifested as real obstacles in ML practice, namely, the Rashomon effect (Breiman in Stat Sci 16(3):199–231, 2001), the problem of underspecification (D’Amour et al. in J Mach Learn Res, 2020, https://doi.org/10.48550/arXiv.2011.03395 ), and the No Free Lunch theorem (Wolpert in Neural Comput 8(7):1341–90, 1996, https://doi.org/10.1162/neco.1996.8.7.1341 ). Lastly, I argue that proper data ontology can help mitigate these problems and I demonstrate how using concrete examples from climate science. This research highlights the links between philosophers’ discussions of induction and implications in ML practice. (shrink)

Recent debates about the values and virtues of the sciences have been marked by philosophical errors and misunderstandings among both the supporters and the critics of the value of science. Some authors, such as Wilson defending the ultimate value of science and Appleyard decrying the influence of scientific modes of thinking, both assume the positivistic stance to understanding science. Others, such as Dawkins, Maddox and Wolpert, come through as scientific realists, celebrating the power of science to reach beyond what (...) can be perceived. Yet all three neglect the role of instruments and apparatus and miss the importance of the part that social forces play in the creation of belief. Finally Maddox slips into assuming that the only truly scientific approach to understanding human life scientifically is to follow the pattern of realism and model making as it is exemplified in the physical sciences. Psychology can be scientific without being reductionist. (shrink)

How and why did we become who we are? In "What Makes Us Human?" some of theorld's most brilliant thinkers offer their answers to this perennial puzzle,ncluding Susan Blackmore, Robin Dunbar, Susan Greenfield, Richard Harries,enan Malik, Richard Wrangham, Ian Tattersall, and Lewis Wolpert. Together,hey draw on a broad spectrum of disciplines, from anthropology, biochemistry,edicine, and neuroscience, to philosophy, psychology, and religion, to askhat makes us distinctively human. Is it our cognitive abilities, or our usef tools, our story-telling, our beliefs, (...) our curiosity, our ability to cook,r our culture? Are we half-ape or half-angel? "What Makes Us Human?"xplains how and why our ancestors adapted to their surroundings to produceuch clever, talented, and unlikely progeny. It is for all to enjoy. (shrink)

In this paper, the contributions to the account of meta-induction (Schurz 2019) collected in this volume are critically discussed and thereby, new insights are developed. How broad and expandable the program of meta-induction is can be learned from Ortner’s contribution. New insights about the transition from the a priori justification of meta-induction to the a posteriori justification of object-induction emerge from the reflection of Shogenji’s paper. How meta-induction may be applied also to religious prophecies and that their meta-inductive justification does (...) not fail for a priori reasons but because of missing evidence for predictive success is learned from the discussion of Pitts’ contribution. That meta-induction does not rely on a particular prior distribution, while the no free lunch theorem depends implicitly on a uniform prior, is the major conclusion drawn from the discussion of Wolpert’s article. How the problem of induction is treated in different versions of the Bayesian account is learned from the discussion of Willliamson’s paper. That meta-induction can also be employed for abduction, and that abductive theory-revision can offer meta-inductive aggregation methods is a new insight emerging from the reflection of Aliseda’s contribution. (shrink)

Notions like meaning, signal, intentionality, are difficult to relate to a physical word. I study a purely physical definition of "meaningful information", from which these notions can be derived. It is inspired by a model recently illustrated by Kolchinsky and Wolpert, and improves on Dretske classic work on the relation between knowledge and information. I discuss what makes a physical process into a signal.

This history of a statement attributed to the developmental biologist Lewis Wolpert exemplifies the making and uses of quotations in recent science. Wolpert's dictum, ‘It is not birth, marriage or death, but gastrulation which is truly the most important time in your life’, was produced in a series of international shifts of medium and scale. It originated in his vivid declaration in conversation with a non-specialist at a workshop dinner, gained its canonical form in a colleague's monograph, and (...) was amplified as a quotation on a poster derived from an undergraduate project. Although it drew on Wolpert's authority and he accepted his authorship, it thus represents a collective sifting of earlier claims for the significance of prenatal existence through the values of 1980s developmental biology. Juxtaposing a technical term with major life events has let teachers engage students, and researchers entice journalists, while sharing an in-joke that came to mark community identity. Serious applications include arguing for an extension of the fourteen-day limit on human-embryo research. On this evidence, quotations have been kept busy addressing every audience of specialized knowledge. (shrink)

A critique of dubious contrasts between "science" and "religion" drawn on the basis of cognitive-evolutionary accounts of human psychology, e.g.,. the claim that religious concepts are “likely” and “natural” for the human mind whereas scientific thinking is “rare” and “unnatural.” Initially made by biologist Lewis Wolpert in *The Unnatural Nature of Science* (1993) and anthropologist Pascal Boyer in *Religion Explained: The Evolutionary Origins of Religious Thought* (2001), they are developed at length by philosopher R. N. McCauley in a*Why Religion (...) is Natural and Science is Not* (2011). These contrasts involve tendentiously narrow or broad definitions of the terms “science” and “religion” and the obliteration of the complex historical and intellectual relations between the ideas and practices currently identified by those terms. (shrink)

Despite intensive debates regarding action imitation and sentence imitation, few studies have examined their relationship. In this paper, we argue that the mechanism of action imitation is necessary and in some cases sufficient to describe sentence imitation. We first develop a framework for action imitation in which key ideas of Hurley’s shared circuits model are integrated with Wolpert et al.’s motor selection mechanism and its extensions. We then explain how this action-based framework clarifies sentence imitation without a language-specific faculty. (...) Finally, we discuss the empirical support for and philosophical significance of this perspective. (shrink)

This paper argues that the consensus physicalist antireductionism in the philosophy of biology cannot accommodate the research strategy or indeed the recent findings of molecular developmental biology. After describing Wolperts programmatic claims on its behalf, and recent work by Gehring and others to identify the molecular determinants of development, the paper attempts to identify the relationship between evolutionary and developmental biology by reconciling two apparently conflicting accounts of bio-function – Wrights and Nagels (as elaborated by Cummins). Finally, the paper seeks (...) a way of defending the two central theses of physicalist antireductionism in the light of the research program of molecular developmental biology, by sharply reducing their metaphysical force. (shrink)

Newcomb’s problem involves a decision-maker faced with a choice and a predictor forecasting this choice. The agents’ interaction seems to generate a choice dilemma once the decision-maker seeks to apply two basic principles of rational choice theory : maximize expected utility ; adopt the dominant strategy. We review unsuccessful attempts at pacifying the dilemma by excluding Newcomb’s problem as an RCT-application, by restricting MEU and ADS, and by allowing for backward causation. A probability approach shows that Newcomb’s original problem-formulation lacks (...) causal information. This makes it impossible to specify the probability structure of Newcomb’s univocally. Once missing information is added, Newcomb’s problem and RCT re-align, thus explaining Newcomb’s problem as a seeming dilemma. Building on Wolpert and Benford :1637–1646, 2013), we supply additional details and offer a crucial correction to their formal proof. (shrink)

The leading Intelligent Design theorist William Dembski (Rowman & Littlefield, Lanham MD, 2002) argued that the first No Free Lunch theorem, first formulated by Wolpert and Macready (IEEE Trans Evol Comput 1: 67–82, 1997), renders Darwinian evolution impossible. In response, Dembski’s critics pointed out that the theorem is irrelevant to biological evolution. Meester (Biol Phil 24: 461–472, 2009) agrees with this conclusion, but still thinks that the theorem does apply to simulations of evolutionary processes. According to Meester, the theorem (...) shows that simulations of Darwinian evolution, as these are typically set in advance by the programmer, are teleological and therefore non-Darwinian. Therefore, Meester argues, they are useless in showing how complex adaptations arise in the universe. Meester uses the term teleological inconsistently, however, and we argue that, no matter how we interpret the term, a Darwinian algorithm does not become non-Darwinian by simulation. We show that the NFL theorem is entirely irrelevant to this argument, and conclude that it does not pose a threat to the relevance of simulations of biological evolution. (shrink)

The upshot of the No Free Lunch theorems is that averaged over all fitness functions, evolutionary computation does no better than blind search (see Dembski 2002, ch 4 as well as Dembski 2005 for an overview). But this raises a question: How does evolutionary computation obtain its power since, clearly, it is capable of doing better than blind search? One approach is to limit the fitness functions (see Igel and Toussaint 2001). Another, illustrated in David Fogel’s work on automated checker (...) and chess playing (see, for instance, Chellapilla and Fogel 1999 and Fogel et al. 2004) and, more recently, given a theoretical underpinning by David Wolpert and William Macready (2005), is to limit optimization problems to search spaces consisting of agents that play competitively against one another. In this brief note, I focus on the latter attempt to get around the force of No Free Lunch. (shrink)