Michael Bishop

Theory and evidence are mutually reinforcing. Evidence shapes our theories. And good theories repay this debt by helping to clarify and resolve methodological and conceptual issues that arise in scientific practice. This chapter aims to show that network theory (NT) can help to clarify and sometimes resolve three open issues that arise in Positive Psychology. First, Positive Psychology is full of studies about happiness. The placeholder view of happiness holds that happiness is positive-states-of-mind-and-the-mechanisms-responsible-for-them-whatever-they-may-be. The placeholder view is implicit in the empirical study of happiness, and it allows the psychologist to elude tough philosophical questions about the nature of happiness. Second, NT provides a natural way to understand Daniel Kahneman’s notion of objective happiness. Third, NT strikes a plausible balance between optimism and pessimism about the status of Subjective Well-Being instruments in Positive Psychology. This chapter also takes a critical look at the evidence for a happiness “set point” that is significantly genetically determined.

The traditional approach to the study of well-being holds that a theory of well-being must capture our commonsense judgments. But because different philosophers have different commonsense judgments, the traditional approach has led to dissensus. The inclusive approach breaks this logjam by introducing a new source of evidence—science. Start with an assumption of basic respect. When philosophers theorize about well-being and when psychologists investigate well-being, they’re both reasonably close to the truth. This means that well-being—the real thing, whatever it is—will express itself in their labors. The correct theory of well-being will make it true that when philosophers theorize about well-being, they’re theorizing about _well-being_, even if they have some of the details wrong. And it will make it true that when psychologists investigate well-being, they’re investigating _well-being_, even if they have some of the details wrong. The trick is to figure out what they’re _all_roughly right about.

This chapter presents a provisional account of Positive Causal Networks (PCNs). There exist networks of causally interrelated feelings, emotions, attitudes, traits, and accomplishments. Such a causal network is positive if it consists of a relatively high number of valued states and states with a positive hedonic tone. PCNs are homeostatic systems, like living organisms or running engines. Changes strengthen the system when they make it more robust—tougher, more durable. Changes weaken it when they make it less robust—more delicate, less durable. The _causal drivers_of a PCN are those states that tend to establish, maintain, or strengthen it. A state or set of states is a PCN fragment just in case it is or could be a causal driver in a PCN for that person.

On the inclusive approach, the case for a theory of well-being will be an inference to the best explanation. This chapter argues that the network theory (NT) explains the scientific and commonsense evidence better than four well-known alternatives—hedonist, informed desire, authentic happiness, and Aristotelian theories. All five competitor theories explain the commonsense evidence reasonably well. But NT is the only theory that organizes and makes sense of the science, Positive Psychology. The other four theories fall victim to either the fitting problem (the challenge of fitting the central construct of a theory to the scientific literature) or the privileging problem (the challenge of organizing and making sense of the startling diversity of research that flies under the banner of Positive Psychology). NT is the best explanation of the totality of the evidence.

This chapter considers three objections to the network theory (NT). The first is that it has counterintuitive implications. For example, a sad person might instantiate a PCN and so, according to NT, have well-being. While NT has implications some people will find counterintuitive, the inclusive approach recommends the theory that best explains the totality of the evidence, and that theory is NT. The second objection springs from Thomas Scanlon’s argument that the concept of well-being is useless for first-person deliberation. NT explains the power and the flaw in Scanlon’s argument. The third objection is that NT cannot account for the _normativity_of well-being, for why it is valuable. There are many different views about the nature of value. On some plausible views about the nature of normativity, NT can explain the value of well-being. But on other views of normativity, NT cannot explain the value of well-being. But these conceptions of normativity are too controversial to seriously threaten NT.

This chapter presents a simple and succinct introduction to the network theory of well-being. If you were to describe a person with well-being, you would describe a host of objective and subjective facts about the person, including (1) positive feelings, moods, emotions (e.g., joy, contentment), (2) positive attitudes (e.g., optimism, hope, openness to new experiences), (3) positive traits (e.g., friendliness, curiosity, perseverance), and (4) successful interactions with the world (e.g., strong relationships, professional accomplishment, fulfilling hobbies or projects). These elements are nodes in a causal network. Each node is causally connected to some of the other nodes—it fosters some and is fostered by others. The network theory holds that to have well-being is to instantiate a positive causal network or fragments of a positive causal network.

Philosophers who study well-being appear stuck in a never-ending stalemate. And psychologists who study well-being appear incapable of providing a clear account of their discipline, Positive Psychology. This introduction describes the overall plan of this book as an attempt to use some conventional philosophy of science to stitch together the methods of psychology and philosophy to form an inclusive approach to the study of well-being. This approach can then be used to resolve the stalemate problem for philosophers and the foundation problem for psychologists.

This chapter argues that Positive Psychology is the study of the structure and dynamics of positive causal networks (PCNs). There are six lines of evidence for this hypothesis. First, many psychologists have identified PCNs (though not under that description). Second, the many definitions psychologists have offered of Positive Psychology can be plausibly interpreted as expressing the basic idea that it is the study of PCNs. Third, the study of group well-being can be understood as the study of interpersonal PCNs. Fourth, because PCNs are complex and multiply realizable states, the hypothesis organizes and makes sense of the various methods and approaches different researchers adopt to the study of well-being. Fifth, Positive Psychology studies the structure of PCNs—correlations and causal connections between positive feelings, moods, emotions, attitudes, traits, and objective factors (e.g., income, longevity, health). Sixth, Positive Psychology studies the dynamics of PCNs—states that establish, maintain, strengthen, and extinguish PCNs.

A strategy called “the flight to reference” is commonly used by contemporary philosophers to resolve issues in the philosophy of science. The strategy involves making philosophical issues turn on questions of reference. This chapter defends the thesis that, whenever the flight to reference strategy is invoked there is a crucial step that is left undefended; and without a defense of this step, the flight to reference is a fatally flawed strategy for resolving philosophical issues. The chapter deals with arguments in two domains. The first is the debate about eliminative materialism, where the flight to reference is invoked by writers on both sides of the issue. The second is the debate over scientific realism, focusing on Philip Kitcher's attempt to use the flight to reference to defend a version of scientific realism.

This chapter attempts to consolidate some of the lessons of Ameliorative Psychology with some handy heuristics and illustrative injunctions. It explores the empirical research that shows how to enhance the accuracy of diagnostic reasoning, reduce overconfidence, avoid the regression fallacy, improve policy assessments, and restrain the unbridled story-telling surrounding rare or unusual events. For problems tractable to voluntary reasoning strategies, the simple strategies recommended in the chapter can improve reasoning at low cost and high fidelity.

This chapter compares the authors' naturalistic approach to epistemology to that of SAE. It is argued that the theories of SAE are structurally analogous to the naturalistic approach — they have at their core a descriptive theory, and from that descriptive theory, proponents of SAE draw normative, epistemological prescriptions. The prospects for the theories of SAE overcoming the is-ought gap are not good. The chapter also argues for the superiority of Strategic Reliabilism over any extant theory of Standard Analytic Epistemology.

This chapter offers a framework for understanding significance that tolerates our incomplete knowledge of the conditions for human well-being. Topics discussed include the role of significance in Strategic Reliabilism, a reason-based approach to significance, and the potential unavailability of objective reasons. It is argued that a proper understanding of the notion of epistemic significance is a core problem for any epistemological theory that claims to be able to guide reason, and that any epistemological view that gives a central place to the notion of significance is bound to be deeply empirical.

Strategic Reliabilism addresses resource allocation considerations within a cost-benefit framework. However, there are serious reasons to worry about the feasibility of a cost-benefit approach to epistemology. First, there are serious general objections to cost-benefit analyses; and second, it is not clear how we can identify the costs and benefits of reasoning. This chapter addresses these two concerns. It is argued that although many of the deep general concerns about cost-benefit analysis are legitimate, flawed cost-benefit analyses can be very useful, especially if we are clear about the ways in which such analyses are flawed. It is further argued that there are measurable proxies for the costs and benefits of reasoning we can employ in a cost-benefit approach to epistemology.

This chapter discusses Strategic Reliabilism. The epistemological theory underlying Ameliorative Psychology is a view called Strategic Reliabilism: Epistemic excellence involves the efficient allocation of cognitive resources to robustly reliable reasoning strategies applied to significant problems. Strategic Reliabilism gives a systematic voice and a theoretical foundation to the long-standing success of SPRs while at the same time avoiding the most serious objections to traditional process reliabilism.

This chapter discusses Statistical Prediction Rules (SPRs) and provides an explanation for their success. SPRs are simple, formal rules that have been shown to be typically more reliable, than the predictions of human experts on a wide variety of problems. Based on testable results, psychology can make normative recommendations about how we ought to reason. The branches of psychology that provide normative recommendations are dubbed as ‘Ameliorative Psychology’. Two central lessons of Ameliorative Psychology are that when it comes to social judgment, (a) proper unit weight models outperform humans in terms of reliability and (b) improper unit weight models often perform nearly as well as proper models and therefore better than humans. It is argued that the reason people resist this ‘practical conclusion’ is partly because the SPR results are noxious to our conception of ourselves as good reasoners. Further, they undermine our hope — so evident in the a priorism of so much contemporary epistemology — that we can be experts at recognizing good reasoning without massive empirical aid.

This chapter begins by giving two reasons as to why epistemology is important: epistemology guides reasoning, and people don't fully appreciate the risks and dangers of poor reasoning the importance of epistemology. It then introduces the basic motives and methods of the epistemology developed in the book. Topics covered include the standard analytic approach to epistemology, the philosophy of science approach to epistemology, the theories generated by the two approaches, and whether scientific investigation into normative epistemology possible.

This chapter focuses on the two opposing sides of the current rationality wars with the “heuristics and biases” researchers on the one hand, and the evolutionary psychologists on the other. The former group cites decades of evidence that people have systematic deviations from rationality, as evidenced by their performance on certain types of formal reasoning tasks. The latter group asserts the implausibility of the human architecture evolving with an inaccurate sense of probability and offers evidence that a representation of various formal problems in more ecologically valid forms makes the so-called irrational response patterns disappear. It is argued that the illusion that evolutionary psychology and the heuristics and biases tradition have a deep disagreement about how rational human beings are should disappear. This is not to say, however, that there are _no_ genuine disagreements between these two research programs.

Critical thinking classes are ubiquitous in U.S. college curricula. One of their aims is to teach good reasoning skills. To date, there is little systematic evidence that they do this. We report the results of a field experiment (N =397) that compared undergraduate critical thinking classes taught in a philosophy department to other undergraduate philosophy classes. The results suggest that an appropriately designed critical thinking class can dramatically reduce 4 common biases in judgment and decision making: honoring sunk costs, inferring causation from correlation, ignoring regression to the mean, and overlooking opportunity costs. The size of the debiasing effects were substantial (Cohen’s d > 0.80) and persisted at least 16 months after the class ended.

The repugnant conclusion says that for any population where everyone’s quality of life is high (A), there’s a possible population where everyone’s life is barely worth living (Z) that’s better. Derek Parfit advanced an argument for the repugnant conclusion that seems rationally compelling: It’s valid and its premises seem obviously true. We argue that Parfit’s argument might show that Z is better than A with respect to welfare, but this doesn’t mean it’s better. This criticism, however, leaves intact the intuition behind Parfit’s argument: A’s value is finite, Z’s value can grow as its population grows, and so there must be a possible Z whose total value can eclipse (equal and surpass as it grows) the total value of any A. We propose a framework for understanding eclipse arguments, and we argue that they make substantive, non-obvious assumptions. Therefore, there is currently no rationally compelling argument for the repugnant conclusion.

Philosophers investigate the nature of morality. And scientists study the moral judgments people make, the moral norms people enforce, and the systems of moral rules people embrace. What is the relationship between these investigations? The traditional philosophical view is that an unbreachable wall divides these activities. Philosophy and science investigate entirely separate domains. Philosophy investigates the normative realm – the true nature of morality. Science investigates the descriptive realm – how different people or groups of people think about morality and how they deploy moral norms and rules. Just as it would be absurd to investigate the nature of the heavens by exploring how different people think about the heavens, it would be absurd to investigate the nature of morality by exploring how different people think about morality.