This chapter explores the role of uncertainty in the entrepreneurial process and how entrepreneurs may navigate it. We begin by outlining the different types of uncertainty in the entrepreneurial process, followed by a discussion of various approaches that entrepreneurial ventures can adopt to manage uncertainty. These include design thinking, the lean startup method, effectuation, and entrepreneurial bricolage. The chapter concludes by considering the importance of appreciating business model design from both a static and dynamic perspective.

Chapter 6 takes a detailed look at the concept of negligence as it applies to health care, and how it is tied to the nurse’s duty of care towards their patients. It explains how a failure to meet that duty of care can lead to a legal finding of negligence against a nurse. It describes how a nurse’s required standard of care is determined in law, and how negligence is proven (or defended). It also explains a nurse’s legal liability and professional indemnity, as well as the employer’s vicarious liability for the actions of nurses. Finally, it discusses the role of apology and when an apology to a patient is appropriate. It also introduces the related concept of a ‘statutory duty of candour’, recently introduced into law in Victoria.

This chapter reframes veneficium not as a form of magic but as a Roman category for theorizing causation at a distance, tracing how philosophers, jurists, and poets grappled with the conditions under which harm could occur without observable contact. By treating “witchcraft” as a comparative prompt rather than an emic term, the chapter shows that debates over veneficium reveal Roman efforts to manage uncertainty about agency, efficacy, and the mechanics of harm.

This chapter focusses on Aquinas’s philosophical arguments for the existence of God. It begins by noting where he offers such arguments. Then it notes his general strategy in presenting them. Unlike many philosophers, Aquinas argues that the claim that we can only know that God exists by means of demonstrative arguments from certain facts about the universe to the conclusion that God must exist as cause of these facts. This chapter explains what that view amounts to for Aquinas and compares and contrasts it with other philosophical approaches to the question ‘How can we know that God exists’. The chapter then turns in detail to Aquinas’s arguments for God’s existence in his De Ente et Essentia (4) and his Summa Theologiae, 1a, 2, 3. The chapter explains how these arguments run and comments on what can be said for and against them.

This chapter explores Aquinas’s account of God as Creator, both philosophically and theologically. Aquinas argues that everything other than God depends on divine causality not only for its origin but for its continued existence. Central here is the distinction between essence and existence: creatures have esse only by receiving it directly from God, whereas God is ipsum esse subsistens. Creation, therefore, is not the modification of pre-existing matter but the bringing into being from nothing (ex nihilo). The chapter examines how Aquinas differentiates divine causality from creaturely causality, allowing for genuine secondary causes while insisting that only God creates. It also considers Aquinas’s nuanced position on the eternity of the world, maintaining that reason cannot prove whether creation had a temporal beginning, though revelation affirms it. Finally, the chapter engages modern objections – such as existential inertia and the coherence of ‘creation from nothing’ – and shows how Aquinas’s metaphysics continues to challenge such critiques.

This chapter examines the collision between generative AI (GenAI) and traditional tort law, specifically with respect to negligence and product liability. It presents the legal dilemma of who is accountable when probabilistic, “black-box” algorithms cause harm. Courts struggle to apply concepts like duty of care and causation to nondeterministic systems. The discussion contrasts negligence, which focuses on a “reasonably prudent developer,” with strict liability, which is limited by whether GenAI is defined as a “product” or a “service.” Key legal frameworks explored include the Learned Hand formula (B < PL) and the “cheapest cost avoider” approach. Ultimately, the chapter argues that courts must evolve past rigid tests, prioritizing functional analysis and expanded developer accountability to deter wrongdoing and prevent catastrophic risks.

In one view, scientific explanation depends on laws, and generalizations in psychiatry let us explain the symptoms of individual patients. These generalizations might themselves be explained by being subsumed under further generalizations. Insofar as individual, idiosyncratic aspects of the patient’s behavior cannot be subsumed under laws, they cannot be scientifically explained. In contrast, though, the psychiatric interview, one of the basic data points for psychiatry, may include the therapist using idiosyncratic details of the patient’s delusions and working through the way in which one stage in the patient’s psychological history generated another. This involves understanding how one thing caused another, but there seems to be no subsumption under laws. Campbell proposes we think of causal explanation in psychiatry as not solely law based but as giving weight to the idea of the patient’s illness over time as a single construct, unfolding in accordance with general patterns. The role of generalizations in psychiatry is to define those constructs that develop over time. Nonetheless, the idiosyncratic detailed causal development of those progressions can be understood at a subjective level by the therapist following the patient’s line of thought and feeling as the disorder develops.

This chapter considers the concept of proximal and distal causes in various health science fields and common reference to the “proximal–distal model” of disease causation. In many cases, this model is used to capture the interaction of social and biological factors, with social factors as distal causes and biological factors as proximal causes. The authors examine Krieger’s (2008) criticism of this framework and suggestion that proximal–distal language confuses causal thinking, reasoning, and distinctions in health science domains. While Krieger (2008) has recommended the elimination of language of proximal and distal causes, Ross and Kendler argue for another alternative, specifically that while the proximal–distal model captures some etiological scenarios, it should not be applied to all cases. They examine three ways in which social and biological causes lead to health outcomes and argue for the value (and possibility) of clear notions of proximal and distal causes.

Many of the more lurid predictions of the course of the French Revolution, such as the forecast of ‘torrents of blood’ made by Mably’s executors to Thomas Jefferson in 1791, or the warning about ‘fire and blood’ famously issued by Edmund Burke in his Reflections on the Revolution in France of 1790, or the scenario set out much earlier by the comte de Guibert in 1772, now look quite prescient. Other predictions of revolution, however, simply failed to materialise. Two, in particular, slowly faded from the sequence of events that began to unfold in France between 1787 and 1789. One set of predictions was centred on opposition to the royal government by the French Parlements, while the other focussed on the idea of a patriot king. Despite their disappearance, however, both are still relevant to the sequence of events that gave rise to the French Revolution partly because they were canvassed very widely at its beginning, but mainly because they also throw real light on the tangle of different and often competing assessments or evaluations of social, economic, moral and political conditions in late-eighteenth-century France. To some, like Voltaire, France was significantly backward, or not modern enough. To others, however, France was far too modern and possibly at a tipping point. One looked forward to the future, while the other looked backwards to the past. Both, however, converged on finding fault with the present.

So far, we have focused on predicting an outcome Y from a set of features X. In this chapter, we turn to causal inference where we ask: What would Y be if we set X to a particular value x? This question concerns the distribution of the outcome Y after some hypothetical intervention. Answering such causal questions requires new tools and stronger assumptions than prediction.

Causes must occur before effects. So causal relations are irreflexive and asymmetric. Causes are also transitive-along-causal-pathways in that along a given causal pathway, to causally contribute to something that causally contributes to an effect is to causally contribute to that effect. This may generate counterintuitive judgement where a feature causally contributes both to an effect occurring and to it not occurring along different pathways. But these results should be accepted, we argue, because it is often important to know both are occurring (even though at the cost of complicating relations between causal contributions and legal or moral responsibility). Notational conventions are outlined.

Causation is arguably the most complex of the elements of negligence. It is covered in three sections in this chapter. Section 13.1 covers the criteria for identifying a recognisable harm and the nature of causation in tort law – what type of explanation does it look for? Section 13.2 covers the evolution of the methods adopted by Australian courts to identify factual causation. Section 13.3 discusses the difficult normative challenge of putting a limit on the extent (or ‘scope’) of a defendant’s liability.

Because this area of the law of negligence is extremely complex, the chapter includes two sets of ‘Summary points’ and two ‘Test your understanding’ features. At the end of the chapter, you will find an appendix with the provisions on causation adopted in the civil liability legislation in each state and territory.

This book shows how to warrant claims about causation in a particular place at a particular time, ‘here and now’, ‘there and then’ – ‘singular’ causation. Good warrant matters if your efforts to affect change are to work. But you cannot properly warrant that a relation obtains without understanding what that relation is. To this end, Part 1 offers a set of features that characterise singular causal relations. These make up a ‘thick’ theory of singular causation, offering far more information than the usual ‘thin’ definitions, like those based on counterfactual or probabilistic dependence, difference-making or production. Details about how causal processes work play a central role here. This theory then provides the grounds for, in Part 2, identifying and systemising what kinds of evidence can warrant singular causal claims. Part 3 shows how this account may be used in practice, using examples from child protection.

Among the key constructs of biomedical research (random error [chance], risk, and bias in the search for causation), bias (or systemic error) is the most formidable source of inefficient and wasteful research, leading to incorrect or exaggerated results. The cause of most disease is complex, owed to many inherent (genetic) and environmental risk factors. It is in studying the interplay of these, each incurring modest risk, that many biases come into play.