Hans J. Briegel

We address the philosophical problem of agency via an explication of the notion of agency that defines a useful target for our modelling efforts. We start with an initial explication as offered in the groundbreaking and influential study A Metaphysics for Freedom by Steward (2012), which centres on animal agency and its basis in what our world is like. We agree with Steward’s metaphysical outlook and discuss the agency question not as one of epistemology, but as a question of ontology. The general ontological question is how nature can leave room for real higher-level ontological categories. Agency is just one such higher category. Life is another important one—but both have a somewhat contentious status in philosophy. Using the example of the sun as a real higher-level entity, we discuss the interdependence of levels of organisation and argue for a specific notion of ontologically emergent individuals. Such entities must be based on lower-level open possibilities. From that general basis we work our way towards a refined explication of agency, taking inspiration from our practices of agency attribution and tackling the possibility of artificial agents along the way. We characterise agency as free agency that requires open possibilities for the flexible configuration of the agent’s body. In our resulting explication, we also stress one of the most neglected, but crucially important aspects of agency: agents have to be able to learn and to adapt over time. To model agency is, therefore, to model a specific kind of flexible decision making and learning.

We discuss a phenomenological approach to agency based on Heidegger’s analysis of Dasein, the human way of being-in-the-world. Inspired by the works of Dreyfus, we understand the human way of being-in-the-world as acting in the world, and we seek to isolate general structures (existentiales) of agency by transposing the structures identified through the analysis of Dasein downwards. The most basic such structure is circumspect coping, through which an agent encounters its environment’s sensible possibilities in the form of equipment, or more generally, affordances. Our agency reading confronts the phenomenological analysis with two crucial questions. First: how to anchor the sensible agential possibilities that sustain an agent’s coping with the environment in the world as described by science? Second: how do development and learning find a place in the analysis? We claim that both issues can be successfully addressed in terms of our general account of agency (Chap. 2) and of the agency model of Projective Simulation (Chap. 5). We give three reasons for a phenomenological view on agency. First, it links to our individual experience as human agents. Second, Dreyfus’s critique of AI is a challenge for the conceptual possibility of artificial agency, which we defended by the project of agency modelling. Third, we believe that the phenomenological reading that we provide for the agency model of Projective Simulation strengthens our explication of agency from Chap. 2.

The introduction gives an overview of the philosophical problem of agency: How does agency fit into the natural world as described by the sciences? We introduce the general methodology of modelling and specify what that means in the case of agency, where an initial explication of the notion is required. We briefly introduce the agency model of Projective Simulation to be discussed later in the book. We mention that the notion of experiment takes on a special role in quantum mechanics, because the experimenter’s active role is ineliminable there. The introduction also includes a per-chapter overview of the five main chapters of the book.

The approach to quantum mechanics that we present in this book aligns fairly well with the QBist position in quantum foundations, which takes quantum mechanics as describing the interaction of an agent with the world: A theory mainly offers a way for the agent to manage her expectations about the outcomes of her interactions, not a picture of what nature is really like. We introduce QBism briefly and situate the framework within a range of options for integrating agency and quantum mechanics. We agree with QBism on many fundamental issues, but we do not subscribe to QBism wholesale. We agree with QBism’s pragmatic outlook that takes scientific theories to be tools for agents to use, and with the QBist goal of developing a participatory realism. But we are critical of the particular idealisations that QBism in its present form subscribes to. Most importantly, we are critical of the structural choice not to resolve the agent nor the world in any detail. In our view, scientific agents learn by modelling. Models are tools that agents can use and develop in dealing with the world. Guided by concrete examples, we describe a scientific modelling framework that allows for loops of three different degrees of difficulty and complexity. These range from quantum state updating, which neatly fits the QBist approach of working within a fixed probability space, via a broader class of updates including so-called Hamiltonian learning, up to the most difficult cases in which true scientific creativity is at play. All three levels of difficulty need to be acknowledged for a full picture of real scientific agency.At the end of the chapter, we describe the project of modelling scientific agency itself, which leads to the topics of Chaps. 5 and 6.

Projective Simulation (PS) is a model for agency that incorporates aspects of reinforcement learning, an indeterministic basic dynamics inspired by physical hopping processes as studied in quantum optics, and an overall orientation towards agency as a continuous process of interaction with the environment as described by phenomenology. We explain the formal structure of PS in detail and compare it to the more standard paradigm of reinforcement learning. PS stands out mostly due to its specific memory structure (Episodic and Compositional Memory, ECM) and its openness, which makes it apt as an agency model rather than just a learning model: PS does not start from a fixed environment model to be “solved”. The focus is, rather, on the internal dynamics of deliberation, which is resolved as a stochastic process within the ECM, as well as on learning and adaptation over a long time, allowing for significant and even disruptive changes in the environment to be incorporated. After discussing the model, we showcase several applications of PS, prominently in basic research in quantum mechanics and especially in quantum information. This strengthens our claim about the intimate connection between agency and quantum physics that we already defended in Chaps. 3 and 4. We also consider quantum versions of PS, which promise to show quantum advantages in machine learning while retaining a maximum of transparency. These applications motivate a separate discussion of transparency and traceability in the context of explainable (quantum) AI.

This open access monograph presents an in-depth study of the problem of how agency fits into the physical world. In particular, the authors focus on agency as a precondition of free will. They present a detailed and physically well motivated formal model to anchor their philosophical discussion. Coverage brings together perspectives from physics, computer science, and different branches of philosophy. The book describes the agency model of Projective Simulation, its physical realisability and its quantum extensions. It situates this model within the discussion of agency in philosophy and in Artificial Intelligence. In addition, the authors highlight the role of agency in Quantum Mechanics itself, recently stressed by the Bayesian-inspired interpretation of Quantum Mechanics, QBism. They provide a comprehensive exposition of Quantum Mechanics and a reflection on the embodied nature of agents. (Quantum) indeterminism turns out to be a key resource for Projective Simulation, and for agency in general. This establishes a novel connection between agency and phenomenology. Overall, the book provides a coherent picture of agents as persisting physical entities endowed with active capacities. Such an explanation does not necessarily settle the question of the actual empirical basis of our human agency. It does, however, show that a coherent notion of agency is possible within a modern scientific world-view.

According to a mainstream position in contemporary cognitive science and philosophy, the use of abstract compositional concepts is amongst the most characteristic indicators of meaningful deliberative thought in an organism or agent. In this article, we show how the ability to develop and utilise abstract conceptual structures can be achieved by a particular kind of learning agent. More specifically, we provide and motivate a concrete operational definition of what it means for these agents to be in possession of abstract concepts, before presenting an explicit example of a minimal architecture that supports this capability. We then proceed to demonstrate how the existence of abstract conceptual structures can be operationally useful in the process of employing previously acquired knowledge in the face of new experiences, thereby vindicating the natural conjecture that the cognitive functions of abstraction and generalisation are closely related.

Theories of free agency based on indeterminism -- that is, libertarian theories -- are often accused of undermining an agent's integrity: If an action is due to indeterministic happenings, how can it be called the agent's action to begin with? Isn't a deterministic connection between an agent's circumstances and her action needed to maintain her integrity? We claim that a meaningful notion of agency does not need determinism. In this paper we introduce stochastic libertarianism, a novel theory of free agency under indeterminism. Based on a physically motivated, stochastic model of the temporal evolution of a deliberation process, stochastic libertarianism views indeterminism as a core resource for meaningful agency rather than as a threat. We counter the supposed threat by explicitly discussing Van Inwagen's replay argument, exposing a flaw in the argument that is due to insufficient attention to temporal details. Our approach can also explain how a stochastically libertarian agent developing over time can exhibit highly realiable behavior. We claim, therefore, that integrity in action does not need determinism.

The aim of this paper is to establish that free agency, which is a capacity of many animals including human beings, is compatible with indeterminism: an indeterministic world allows for the existence of free agency. The question of the compatibility of free agency and indeterminism is less discussed than its mirror image, the question of the compatibility of free agency and determinism. It is, however, of great importance for our self-conception as free agents in our (arguably) indeterministic world. We begin by explicating the notions of indeterminism and free agency and by clarifying the interrelation of free agency and the human-specific notion of free will. We then situate our claim of the compatibility of free agency and indeterminism precisely in the landscape of the current debate on freedom and determinism, exposing an unhappy asymmetry in that debate. Then we proceed to make our case by describing the mathematically precise, physically motivated model of projective simulation, which employs indeterminism as a central resource for agency modeling. We argue that an indeterministic process of deliberation modeled by the dynamics of projective simulation can exemplify free agency under indeterminism, thereby establishing our compatibility claim: Free agency can develop and thrive in an indeterministic world.

Can we sensibly attribute some of the happenings in our world to the agency of some of the things around us? We do this all the time, but there are conceptual challenges purporting to show that attributable agency, and specifically one of its most important subspecies, human free agency, is incoherent. We address these challenges in a novel way: rather than merely rebutting specific arguments, we discuss a concrete model that we claim positively illustrates attributable agency in an indeterministic setting. The model, recently introduced by one of the authors in the context of artificial intelligence, shows that an agent with a sufficiently complex memory organization can employ indeterministic happenings in a meaningful way. We claim that these considerations successfully counter arguments against the coherence of libertarian free will.

We establish a connection between measurement-based quantum computation and the field of mathematical logic. We show that the computational power of an important class of quantum states called graph states, representing resources for measurement-based quantum computation, is reflected in the expressive power of (classical) formal logic languages defined on the underlying mathematical graphs. In particular, we show that for all graph state resources which can yield a computational speed-up with respect to classical computation, the underlying graphs—describing the quantum correlations of the states—are associated with undecidable logic theories. Here undecidability is to be interpreted in a sense similar to Gödel’s incompleteness results, meaning that there exist propositions, expressible in the above classical formal logic, which cannot be proven or disproven

Quantum entanglement has been focus of fundamental debate since the original paper of Einstein, Podolsky and Rosen [1] and the work of Bell [2], discussing its implications on fundamental issues related to the concepts of physical reality and locality. It has only been during the last few years when it has been recognized that this feature of Quantum Mechanics may also have important applications in the fields of communication and computation. In particular, it has been shown that using entanglement would allow to perform certain computational tasks, such as factoring, much faster. On the other hand, it provides the basis for provably secure secret communication and cryptography.