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Robots have been often used as models to theorise on the behaviour and cognition of living systems. Quite recently, a novel approach using robots for epistemic purposes has emerged. In what is called here interactive humanoid robotics, humanoid robots are not used as simulations of human beings (as in classical cybernetics and biorobotics), but as tools to provide predictable, manipulable, and controllable “social stimuli” to humans—i.e., as tools to stimulate, rather than simulate, living systems. By analysing how humans react to the stimuli delivered by humanoid robots in various conditions and having various characteristics, one obtains empirical evidence to theorise about the dynamics of human-human interaction. The structure and validity of this experimental approach has received little attention by philosophers of science so far. This article aims at paving the way for a methodological analysis of it, by (1) circumscribing the class of research questions that can be sensibly addressed using interactive humanoid robots, (2) outlining the structure of the experimental procedures adopted in the field, and (3) reflecting about the validity of the approach, with a particular focus on the impact of biomimicry and of the (folk-psychological) theories that humans, perhaps implicitly, formulate and use during interaction with humanoid robots.

Under what circumstances do we attribute a mind to AI systems? And, in this case, how do we think their mind works? Answering these questions is crucial to inform the design of safe and trustable AI, to inform research on the ethical, social and legal issues raised by the increasing presence of AI systems in everyday life and to investigate how they can be used as tools to study human and social cognition. This work proposes a philosophical reflection on the possible structure of people’s mental models of AI systems. We distinguish between two possible styles of modeling that people may adopt in everyday contexts. Both involve the attribution of mental states and cognitive abilities to the AI system, even though they differ from one another in some relevant aspects. One modeling style is akin to folk psychology and relies on the attribution of beliefs, desires, and other propositional attitudes to the system. The other, which we will refer to as folk-cognitivist, is more akin to the account of the structure of the mind that characterizes classical cognitive science. These modeling styles correspond to different classes of mentalistic stances that people may adopt when they interact with AI systems in ordinary contexts.

An issue that is usually neglected in the literature on the attribution of mental states in human-robot interaction is the ontological commitment of users to these states. In this context, we argue the need to decouple the concept of attributing mental states from the act of uttering sentences with mentalistic content. We will perform an analysis of the different types of ontological commitment underlying the user’s utterance of a sentence with mentalistic content, in order to highlight the importance of estimating the user’s belief set for understanding the psychological dynamics of human-robot interaction. The different attitudes range from realism to eliminativism or the agnostic view, within which two positions can be distinguished: the reductionist position, which traces mentalistic statements back to beliefs about the physical state of the system, and the fictional position, according to which the user’s beliefs are true in the context of a make-believe play. In relation to the latter position, we consider a recent proposal by Clark and Fischer (2022), the so-called ‘depiction theory’, which was created to solve the social artefact puzzle, whereby, during human-robot interaction, users interact with the robot as if it were a real social agent, even though they are aware of its artefact nature. By reading the robot as a superimposition of several scenes, including the raw artefact and the depicted character, the theory not only explains the aforementioned paradox, but also presents itself as a fictional interpretation of human-robot interaction, since in this framework beliefs about the robot’s mind can be interpreted as beliefs about the depicted character’s mind. Nevertheless, other ontological attitudes can explain the social artefact puzzle, and future work is needed to determine user’s beliefs about robots’ beliefs from experimental data.

Computer simulations are widely used for surrogative reasoning in scientific research. They also play a crucial role in engineering, more specifically in the design of new robotic systems, yet the nature of this role has been little discussed so far in the philosophy of technology literature. The main claim made in this article is that the notion of surrogative reasoning is central to understanding how computer simulations can serve the purpose of designing new robots. More specifically, it is argued that computer simulations can support two forms of surrogative reasoning, which are called model-oriented and prediction-oriented, whose inferential structure is reconstructed to some extent. And it is argued that, when computer simulations are used to design new robots, they are distinctively used in the model-oriented way. By unravelling the structure of the computer simulation-supported methods adopted in robotic design, this article may contribute to a finer-grained understanding of the epistemic processes involved in technological research.

This article aims to show that there is an alternative way to explain human action with respect to the bottlenecks of the psychology of decision making. The empirical study of human behaviour from mid-20th century to date has mainly developed by looking at a normative model of decision making. In particular Subjective Expected Utility (SEU) decision making, which stems from the subjective expected utility theory of Savage (1954) that itself extended the analysis by Von Neumann and Morgenstern (1944). On this view, the cognitive psychology of decision making precisely reflects the conceptual structure of formal decision theory. This article shows that there is an alternative way to understand decision making by recovering Newell and Simon’s account of problem solving, developed in the framework of bounded rationality, and inserting it into the more recent research program of embodied cognition. Herbert Simon emphasized the importance of problem solving and differentiated it from decision making, which he considered a phase downstream of the former. Moreover according to Simon the centre of gravity of the rationality of the action lies in the ability to adapt. And the centre of gravity of adaptation is not so much in the internal environment of the actor as in the pragmatic external environment. The behaviour adapts to external purposes and reveals those characteristics of the system that limit its adaptation. According to Simon (1981), in fact, environmental feedback is the most effective factor in modelling human actions in solving a problem. In addition, his notion of problem space signifies the possible situations to be searched in order to find that situation which corresponds to the solution. Using the language of embodied cognition, the notion of problem space is about the possible solutions that are enacted in relation to environmental affordances. The correspondence between action and the solution of a problem conceptually bypasses the analytic phase of the d

In this paper, we ask one fairly simple question: to what extent can biorobotics be sensibly qualified as science? The answer clearly depends on what ‘science’ means and whether what is actually done in biorobotics corresponds to this meaning. To respond to this question, we will deploy the distinction between science and so-called technoscience, and isolate different kinds of objects of inquiry in biorobotics research. Capitalising on the distinction between ‘proximal’ and ‘distal’ biorobotic hypotheses, we will argue that technoscientific biorobotic studies address proximal hypotheses, whilst scientific biorobotic studies address distal hypotheses. As a result, we argue that bioroboticians can be both considered as scientists and technoscientists and that this is one of the main payoffs of biorobotics. Indeed, technoscientists play an extremely important role in 21st-century culture and in the current critical production of knowledge. Today’s world is increasingly technological, or rather, it is a bio-hybrid system in which the biological and the technological are mixed. Therefore, studying the behaviour of robotic systems and the phenomena of animal-robot interaction means analysing, understanding, and shaping our world. Indeed, in the conclusion of the paper, we broadly reflect on the philosophical and disciplinary payoff of seeing biorobotics as a science and/or technoscience for the increasingly bio-hybrid and technical world of the 21st century.

Heavily influenced by Galvano Della Volpe’s original interpretation of Marx’s analysis of capitalism as an extension of Galileo’s scientific revolution to the social realm, throughout the 1950s and 1960s Lucio Colletti defended a scientific approach to Marxism as opposed to the “Historicism” prevalent in Italian Marxism at the time. Unlike Della Volpe, however, Colletti considered scientific Marxism to be incompatible with “the Italian road to socialism” promoted by the Italian communist party. Scientific method, Colletti argued, demands all capitalist economies to be understood by referring to the “general” form of capitalism, irrespective of the alleged “backwardness” of Italian capitalism. Accordingly, the transition to socialism implies the revolutionary socialisation of the means of production in “backward” and “advanced” countries alike. Towards the end of the 1960s, Colletti reinforced this view by showing that Marx’s labour theory of value had to be interpreted as a theory of exploitation. However, the crisis of global communism and of Marxism in the 1970s led Colletti to conclude that Marx’s labour theory of value is in fact non-scientific, and that Marxism as a whole violates rather than fully instantiating the principles of scientific rationality.

In so-called ethorobotics and robot-supported social cognitive neurosciences, robots are used as scientific tools to study animal behavior and cognition. Building on previous epistemological analyses of biorobotics, in this article it is argued that these two research fields, widely differing from one another in the kinds of robots involved and in the research questions addressed, share a common methodology, which significantly differs from the “synthetic method” that, until recently, dominated biorobotics. The methodological novelty of this strategy, the research opportunities that it opens, and the theoretical and technological challenges that it gives rise to, will be discussed with reference to the peculiarities of the two research fields. Some broad methodological issues related to the generalization of results concerning robot-animal interaction to theoretical conclusions on animal-animal interaction will be identified and discussed.

What relationship holds between robotics and philosophy of science? Can robotics research contribute to research in philosophy of science? Conversely, can the results achieved by philosophers of science contribute to the progress of research in robotics? This article will deal with both questions based on the distinction between the so-called philosophy of general science and the philosophy of the particular sciences. It will draw relatively pessimistic conclusions about the possibility that robotics research shed light on questions pertaining to the philosophy of general science, and more optimistic conclusions about the potential contribution of robotics research to the philosophy of particular sciences. The normative aspect of philosophy of science is consistent with the possibility that this field of research, taking robotics as its object of study, provides guidelines and regulative methodological principles for engineers and roboticists. Through these considerations, this article attempts to explore a possible field of interaction between philosophy and scientific-technological research, with reference to a discipline that is assuming an increasingly predominant role in many aspects of daily life.

Today, diagnostic reasoning combines common and specialized knowledge, elements of numeracy, some facility with the basics of probability theory and, last but not least, ease in interactions with AI tools. We present procedures and tools for constructing trees that lead to understandable, transparent, simple, and robust classifications and decisions. These tools are more heuristic than optimal models, inspired by the perspective of Bounded Rationality. We describe how the tenets of Bounded Rationality provide a framework for the human-machine interaction this paper is devoted to. We claim that, because of this rationality, our proposed tools facilitate machine-aided decision making that is smooth, transparent and successful.

«L’uomo ha sempre cercato di dominare la natura. Gradualmente ha provato a comprenderla. Molto tempo dopo, ha imparato a combinare i due desideri: fu allora che la scienza moderna prese forma. Lo sviluppo della scienza moderna poggia però sulla curiosità e l’interesse di molti secoli, nei quali le tecniche per l’esplorazione della natura furono sviluppate lentamente, così come lentamente fu accumulata la sua conoscenza. Le radici della scienza moderna affondano nel passato e non vi è un istante di cui potremmo dire: “Ecco, qui realmente comincia la scienza”. A ogni stadio di sviluppo vi sono sia residui del passato che anticipazioni del futuro». Così inizia l’affascinante e documentata storia della scienza occidentale che Alfred Rupert Hall e Marie Boas Hall propongono, con un linguaggio chiaro e sintetico, in questo volume. Dal mondo greco-antico all’Europa medievale, dalla rivoluzione scientifica alle più rilevanti conquiste della matematica, della fisica e della biologia novecentesche, gli autori ricostruiscono qui le principali tappe dello sviluppo del pensiero scientifico attraverso le opere dei loro principali protagonisti, guidando il lettore nel complesso labirinto dei presupposti anche molto lontani delle scoperte scientifiche di cui è piena la nostra vita quotidiana.

In so-called interactive biorobotics, robotic models of living systems interact with animals in controlled experimental settings. By observing how the focal animal reacts to the stimuli delivered by the robot, one tests hypotheses concerning the determinants of animal behaviour in social contexts. Building on previous methodological reconstructions of interactive biorobotics, this article reflects on the claim, made by several authors in the field, that this strategy may enable one to explain social phenomena in animals. The answer offered here will be negative: interactive biorobotics does not contribute to the explanation of social phenomena. However, it may greatly contribute to the study of animal behaviour by creating social phenomena in the sense discussed by Ian Hacking, i.e. by precisely defining new phenomena to be explained. It will be also suggested that interactive biorobotics can be combined with more classical robot-based approaches to the study of living systems, leading to a so-called simulation-interactive strategy for the mechanistic explanation of social behaviour in animals.

Biorobotics is a form of ‘hard’ Artificial Life research, as it involves experimentation on robotic models of living organisms. Rational justification of biorobotic experiments requires a careful analysis of their methodological structure. Philosophy of science has much to offer for this purpose. Here it is suggested that interactive branches of biorobotics adopt a methodology which radically departs from the “understanding by building” approach as traditionally conceived. It is also observed that some biorobotic studies aim neither at explaining nor at predicting phenomena, but at creating them in Hacking's sense. Finally, it is suggested that biorobotic studies vary in their scope, some of them being limited to the analysis of animal-robot interaction, others reaching conclusion on animal-animal interaction. These considerations are brought to bear on the complexities involved in the justification of biorobotics.

Che cos’è la robotica educativa? In che modo si inserisce nei curricoli e nel ventaglio degli interventi psico-educativi e sociali dell’educazione formale e non formale, e nel contesto ospedaliero? Come formare “con” e “alle” tecnologie robotiche e per quali competenze specifiche? Quale ruolo hanno i robot nel migliorare e facilitare apprendimenti in età evolutiva? Il testo è destinato a ricercatori, insegnanti, educatori, operatori socio-sanitari e formatori impegnati in differenti ambiti di ricerca e presenta lo stato dell’arte su concetti chiave e risultati di ricerche ed esperienze realizzate in questi ultimi anni con bambine/i, ragazze/i e insegnanti dalla scuola dell’infanzia alla scuola secondaria di secondo grado. Le sezioni II e III del volume ospitano gli interventi di insegnanti presentati al Convegno “Interazione bambino-robot 2019” (IBR19), organizzato dal RobotiCSS Lab – Laboratorio di Robotica per le Scienze Cognitive e Sociali del Dipartimento di Scienze Umane per la Formazione “R. Massa”, Università degli Studi di Milano-Bicocca, e dall’Associazione Yunik e svoltosi il 12-13 giugno 2019.

Since the early decades of the XX century, robots have been often used as experimental platforms for the study of animal and human behaviour. This article offers an overview of these epistemic uses with a special focus on contemporary biorobotics and bionics. Some experimental procedures for the study of the behaviour of animals and humans, involving fully artificial and hybrid bionic systems, are reconstructed on the basis of examples drawn from the scientific literature. Some conditions under which they can produce genuine knowledge about living systems are identified and discussed.

The concept of “trial and error strategy” is pervasively used in the literature on robot programming: in particular, it is often claimed that some novices use a trial and error strategy, while others use a more rational and reasoned strategy. Here it is argued that the concept of “trial and error” is of little use for the analysis of the strategies adopted by novices and experts in programming robots. Indeed, in a certain sense, all problems and sub-problems faced by programmers are addressed in a trial and error and rational fashion. Moreover, that very concept does not play any meaningful role in revealing the richness of the criteria used by novices and experts to orient themselves in the vast space of the possible solutions to their problems. These claims will rely on an artificial-intelligence-inspired construal of the concepts of “problem” and “strategy” and will be substantiated by a preliminary analysis of the various sub-problems involved in robot programming.

Initially, the rationalist legacy of 18th century Enlightenment resulted in a very clear separation of reason from madness. The madman was characterized by a failure of deductive and inductive logical thinking. In the 1950s, however, this distinction disappeared when cognitive science brought to light a whole series of supposed failures of rational behavior in the normal individual as well. What dealt a serious blow to the theories of human decision-making and judgment formation was the fact that several reasoning flaws that had been attributed solely to the psychiatric patient were, in fact, also found in the normal individual. Over the past few years, the theory that started with the Enlightenment seems to have reversed in a circular way. If we consider rationality as based on the logical coherence adopted in economic theory, then rationality is more apparent in individuals suffering from certain neurological and psychiatric disorders than among people without a disease. Paradoxically, normality seems characterized by irrationality, and abnormality by rationality. Why this paradox? There is a anti-ecological bias at work here: behavioral irrationality is not assessed in its capacity to adapt to the real environment and to solve environmental problems. Instead, it is tested in abstract and artificial situations rather than the real world. The world to which the tests pertain is characterized by risk, but not uncertainty. The paradox lies in the fact that we do not consider the lesson from the theory of bounded rationality, expressed aptly by the Herbert Simon’s metaphor (1990). Often the real environments have an unlimited space of possibilities because they are characterized by uncertainty or they have a limited space but they are computationally intractable. Therefore the classical model of rationality, characterized by coherence with formal norms of logic, probability and utility cannot be applied (Simon, 1978). Judgment of the rationality of human behavior does

There is little doubt that one of Simon’s key contributions throughout his scientific career - if not the main one - was rooting the notion of bounded rationality in cognitive psychology (Simon, 1976). He called his notion of bounded rationality in the cognitive psychology realm ‘cognitivism’ (Haugeland, 1978), an approach, also known as the ‘information-processing’ approach, which he contributed to affirming together with his colleague Allen Newell starting in the mid-1950s. According to cognitivism, cognition works through the internal (i.e. mental) manipulation of representations of the external environment accomplished through referential ‘symbols’ (e.g., Newell & Simon, 1972). Connecting Simon’s theory of cognition to his theory of rationality is the notion that cognition works in a way that is necessary and sufficient for intelligent behavior (what is known as the ‘physical symbol system hypothesis’, see Newell & Simon, 1976). The idea that results from integrating Simon’s view of cognition with his view of bounded rationality is that rationality is a “process and product of thought” (Simon, 1978) in which the internal bounds of reason (Simon, 1955) adapt to the external bounds of the environment (Simon, 1956) in a disembodied fashion. In this picture, in fact, there is no place for flesh and blood. Patokorpi (2008) has emphasized that there is an inner tension in Simon’s thought, as on the one hand he was a strong advocate for a realistic approach to the bounds of rationality while, on the other hand, he represented them through the ‘unbounded’ power of digital computation and the metaphor of computers. This inner tension, which did not simply concern Simon’s thought but an entire generation of cognitive scientists, would have huge consequences in the history of cognitive psychology. In the early 1990s, Newell and Simon’s physical symbol system hypothesis was questioned when the ‘embodied robots’ designed by Rodney Brooks proved able to simulate simpl

The brief article deals with the following questions: Was the adaptive toolbox of heuristics ecologically rational and specifically accurate in the initial stages of COVID-19, which was characterized by epistemic uncertainty? In other words, in dealing with COVID-19 did the environmental structural variables allow the success of a given heuristic strategy?

What can interactive robots offer to the study of social behaviour? Philosophical reflections about the use of robotic models in animal research have focused so far on methods (including the so-called synthetic method) involving robots which do not interact with the target system. Yet, leading researchers have claimed that interactive robots may constitute powerful experimental tools to study collective behaviour. Can they live up to these epistemic expectations? This question is addressed here by focusing on a particular experimental methodology involving interactive robots which has been often adopted in animal research. This methodology is shown to differ from other robot-supported methods for the study of animal behaviour analysed in the philosophical literature, chiefly including the synthetic method. It is also discussed whether biomimicry (i.e., similarity between the robot and the target animal in behaviour, appearance, and internal mechanisms) and acceptability (i.e., whether or not the robot is accepted as a conspecific by the animal) are necessary for an interactive robot to be sensibly used in animal research according to this method.

The Routledge Handbook of Bounded Rationality draws together an international team of leading experts to survey the recent literature and latest developments regarding Herbert Simon’s renowned theory of bounded rationality. Chapters feature entries on key behavioural phenomena including reasoning, judgement, decision making, uncertainty, risk, heuristics and biases, and smart and frugal heuristics. The text also examines current ideas such as fast and slow thinking, nudge, ecological rationality, evolutionary psychology, and embodied cognition. This book is essential reading for students and scholars of economics, psychology, neurocognitive sciences, and political science.

In this article, we discuss the issue of context-dependence of mechanism-based explanation in the social sciences. The different ways in which the context-dependence and context-independence of mechanism-based explanation have been understood in the social sciences are often motivated by different and apparently incompatible understandings of what explanatory mechanisms are. Instead, we suggest that the different varieties of context-dependence are best seen as corresponding to different research goals. Rather than conflicting with one another, these goals are complementary to each other and therefore pave the way to a methodologically more cooperative approach to mechanism-based explanation in the social sciences.

Compared to other philosophies of special sciences, the scope, object, and definition of the philosophy of political science remain vague. This article traces this vagueness to the changing subject matter of political science throughout its history, but argues that all social sciences are subject to radical changes in what count as their defining characteristics. Accordingly, the only legitimate definition of “philosophy of political science” is “the philosophical study of whatever happens to conventionally fall within the scope of political science at a given moment.” Moving from this assumption, this article makes the case for a unified philosophy of social science.