Truthlikeness,Rationality And Scientific Method

I. A. Kieseppä's criticism of the methodological use of the theory of verisimilitude, and D. B. Resnik's arguments against the explanation of scientific method by appeal to scientific aims are critically considered. Since the notion of verisimilitude was introduced as an attempt to show that science can be seen as a rational enterprise in the pursuit of truth, defenders of the verisimilitude programme need to show that scientific norms can be interpreted (at least in principle) as rules that try to increase the degree of truthlikeness of scientific theories. This possibility is explored for several approaches to the problem of verisimilitude.     

Decision-theoretic epistemology

In this paper, I examine the possibility of accounting for the rationality of belief-formation by utilising decision-theoretic considerations. I consider the utilities to be used by such an approach, propose to employ verisimilitude as a measure of cognitive utility, and suggest a natural way of generalising any measure of verisimilitude defined on propositions to partial belief-systems, a generalisation which may enable us to incorporate Popper's insightful notion of verisimilitude within a Bayesian framework. I examine a dilemma generated by the decision-theoretic procedure and consider an adequacy condition (immodesty) designed to ameliorate one of its horns. Finally, I argue in a sceptical vein that no adequate verisimilitude measure can be used decision-theoretically.I am grateful to Philip Percival for his very useful comments and criticism on earlier drafts. This paper was written with the support of a Scheuer fellowship, for which I am most thankful.     

Metaphor and the discourse of virology: HIV as human being

Most analysis of the metaphors of AIDS has considered lay rather than scientific discourse. In general, that analysis has uncovered linguistic strategies that tend to distance the disease from people who are healthy. Moreover, it has criticized those strategies insofar as they categorize and dehumanize the AIDS patient. My own analysis, however, focuses on the writing of Robert Gallo, one of the virologists who claimed credit for isolating the HIV as the cause of AIDS. By examining Gallo's popular and scientific writing, I uncover the metaphor system that underlies his work — and perhaps the work of other virologists studying the HIV. I find in Gallo's writing a system of metaphor that personifies and humanizes the HIV (or the HTLV-III as he originally named it). This system differs significantly from lay systems that tend to objectify AIDS. I argue that the metaphor system that underlies Gallo's work is in fact productive, but that it might preclude other ways of conceptualizing the disease and its causes. The co-factor theory, for instance, entails a different metaphoric system entirely. I suggest that AIDS researchers might examine their own language in an effort to critique the metaphors they use and explore the possibility of using new metaphors.     

Moving Beyond Causes: Optimality Models and Scientific Explanation

A prominent approach to scientific explanation and modeling claims that for a model to provide an explanation it must accurately represent at least some of the actual causes in the event's causal history. In this paper, I argue that many optimality explanations present a serious challenge to this causal approach. I contend that many optimality models provide highly idealized equilibrium explanations that do not accurately represent the causes of their target system(s). Furthermore, in many contexts, it is in virtue of their independence of causes that optimality models are able to provide a better explanation than competing causal models. Consequently, our account of explanation and modeling must expand beyond the causal approach.     

Accuracy,Verisimilitude, and Scoring Rules

Suppose that beliefs come in degrees. How should we then measure the accuracy of these degrees of belief? Scoring rules are usually thought to be the mathematical tool appropriate for this job. But there are many scoring rules, which lead to different ordinal accuracy rankings. Recently, Fallis and Lewis [2016] have given an argument that, if sound, rules out many popular scoring rules, including the Brier score, as genuine measures of accuracy. I respond to this argument, in part by noting that the argument fails to account for verisimilitude—that certain false hypotheses might be closer to the truth than other false hypotheses are. Oddie [forthcoming], however, has argued that no member of a very wide class of scoring rules (the so-called proper scores) can appropriately handle verisimilitude. I explain how to respond to Oddie's argument, and I recommend a class of weighted scoring rules that, I argue, genuinely measure accuracy while escaping the arguments of Fallis and Lewis as well as Oddie.     

Modelling Beyond Application: Epistemic and Non-epistemic Values in Modern Science

In recent years, philosophers of science have begun to realize that the clear separation of the creation of models in academia and the application of models outside science is not possible. When these philosophers address hybrid contexts in which science is entwined with policy, business, and other realms of society, these often practically oriented realms no longer represent ‘the surroundings’ of science but rather are considered an essential part of it. I argue—and demonstrate empirically—that the judgement of a theory or model conducted by scientists in such hybrid contexts may contain two parts: one is truth supportive and the other is utility oriented. In relation to the debate on science and values, the article seeks to reinforce the argument in modern philosophy of science that the boundaries between epistemic and non-epistemic values are blurred. The article stresses that non-epistemic values may be also understood as values that contribute to the instrumental success of a theory or model and—in this way—influence scientific practice in the hybrid contexts.     

SYSTEMATIC BIASES IN CODED SOCIAL INTERACTION DATA

Sequential techniques to analyze social interaction (lag-sequential analysis, Markov chains, semi-Markov models) are newcomers to the methodological arsenal of communication researchers. As with any new methodology, these have many as yet unvoiced presumptions whose violation can affect materially the outcome of their applications. I identify one such presumption—“the perfect measurement presumption”—and demonstrate that violations of it can lead to serious systematic bias in parameter estimates. In addition, I discuss briefly a set of palliatives for this source of systematic bias.     

Underdetermination,realism and empirical equivalence

Are theories ‘underdetermined by the evidence’ in any way that should worry the scientific realist? I argue that no convincing reason has been given for thinking so. A crucial distinction is drawn between data equivalence and empirical equivalence. Duhem showed that it is always possible to produce a data equivalent rival to any accepted scientific theory. But there is no reason to regard such a rival as equally well empirically supported and hence no threat to realism. Two theories are empirically equivalent if they share all consequences expressed in purely observational vocabulary. This is a much stronger requirement than has hitherto been recognised—two such ‘rival’ theories must in fact agree on many claims that are clearly theoretical in nature. Given this, it is unclear how much of an impact on realism a demonstration that there is always an empirically equivalent ‘rival’ to any accepted theory would have—even if such a demonstration could be produced. Certainly in the case of the version of realism that I defend—structural realism—such a demonstration would have precisely no impact: two empirically equivalent theories are, according to structural realism, cognitively indistinguishable.     

The content, consequence and likeness approaches to verisimilitude: compatibility, trivialization, and underdetermination

Theories of verisimilitude have routinely been classified into two rival camps—the content approach and the likeness approach—and these appear to be motivated by very different sets of data and principles. The question thus naturally arises as to whether these approaches can be fruitfully combined. Recently Zwart and Franssen (Synthese 158(1):75–92, 2007) have offered precise analyses of the content and likeness approaches, and shown that given these analyses any attempt to meld content and likeness orderings violates some basic desiderata. Unfortunately their characterizations of the approaches do not embrace the paradigm examples of those approaches. I offer somewhat different characterizations of these two approaches, as well as of the consequence approach (Schurz and Weingartner (Synthese 172(3):415–436, 2010) which happily embrace their respective paradigms. Finally I prove that the three approaches are indeed compatible, but only just, and that the cost of combining them is too high. Any account which combines the strictures of what I call the strong likeness approach with the demands of either the content or the consequence approach suffers from precisely the same defect as Popper’s—namely, it entails the trivialization of truthlikeness. The downside of eschewing the strong likeness constraints and embracing the content constraints alone is the underdetermination of the concept of truthlikeness.     

The Problem of Verisimilitude and Counting Partially Identical Properties

In this paper I propose a solution to the qualitative version of David Miller's verisimilitude reversal argument. Miller (1974) shows that verisimilitude rankings are relative to language choice and hence, are not objective. My solution stems from a reply to an earlier solution proposed by Eric Barnes (1991). Barnes argues that the verisimilitude reversal problem can be solved by revealing an epistemic dimension. I show that Miller's problem cannot be solved by side-stepping foundational metaphysical claims as his epistemic solution suggests. Rather, a substantive metaphysical basis grounds identity relations among properties. The problem of verisimilitude cannot be solved without embracing the fundamental metaphysical distinctions between basic and composite properties that ground the relationship of partial identity among properties.     

Semantic approaches in the philosophy of science

In this article I give an overview of some recent work in philosophy of science dedicated to analysing the scientific process in terms of (conceptual) mathematical models of theories and the various semantic relations between such models, scientific theories, and aspects of reality. In current philosophy of science, the most interesting questions centre around the ways in which writers distinguish between theories and the mathematical structures that interpret them and in which they are true, i.e. between scientific theories as linguistic systems and their non-linguistic models. In philosophy of science literature there are two main approaches to the structure of scientific theories, the statement or syntactic approach—advocated by Carnap, Hempel and Nagel—and the non-statement or semantic approach—advocated, among others, by Suppes, the structuralists, Beth, Van Fraassen, Giere, Wójcicki. In conclusion, I briefly review some of the usual realist inspired questions about the possibility and character of relations between scientific theories and reality as implied by the various approaches I discuss in the course of the article. The models of a scientific theory should indeed be adequate to the phenomena, but if the theory is ‘adequate’ to (true in) its conceptual (mathematical) models as well, we have a model-theoretic realism that addresses the possible meaning and reference of ‘theoretical entities’ without relapsing into the metaphysics typical of the usual scientific realist approaches.     

Models of Machines and Models of Phenomena

Experimental engineering models have been used both to model general phenomena, such as the onset of turbulence in fluid flow, and to predict the performance of machines of particular size and configuration in particular contexts. Various sorts of knowledge are involved in the method—logical consistency, general scientific principles, laws of specific sciences, and experience. I critically examine three different accounts of the foundations of the method of experimental engineering models (scale models), and examine how theory, practice, and experience are involved in employing the method to obtain practical results. Models of machines and mechanisms can be (and generally are) involved in establishing criteria for similar phenomena, which provide guidance in using events to model other events. Conversely, models of phenomena such as events that model other events can be (and generally are) involved in experimentation on models of machines. I conclude that often it is not more detailed models or the more precise equations they engender that leads to better understanding, but rather an insightful use of knowledge at hand to determine which similarity principles are appropriate in allowing us to infer what we do not know from what we are able to observe.     

Technology,gender, and history: Toward a nonlinear model of social evolution

This paper presents a new nonlinear cognitive map of social evolution— sharply departing from the conventional 19th century‐based theories of linear stages from barbarism to civilization. It draws from a data base that includes the whole of humanity (both its female and male halves). And it takes into account new data from archaeology indicating that civilization is not only thousands of years older than previously thought, but originally oriented more closely to what the author calls a Partnership rather than a Dominator model of social organization. Covering over 25,000 years of social history, this article charts major technological phase changes—from the agricultural revolution circa 10,000 years ago to the industrial and more recently electronic revolutions or modern times—from the perspective of the tension between the Dominator and Partnership models. Most critically, it shows how the present period of social disequilibrium leads us to either an evolutionary breakdown or breakthrough, with the determining factor not technology per se, but rather how effectively and quickly we shift to a social organization orienting primarily to partnership rather than domination.     

Non‐competitor Conditions in the Scientific Realism Debate

A general insight of 20th‐century philosophy of science is that the acceptance of a scientific theory is grounded, not merely on a theory’s relation to data, but on its status as having no, or being superior to its, competitors. I explore the ways in which scientific realists might be thought to utilise this insight, have in fact utilised it, and can legitimately utilise it. In more detail, I point out that, barring a natural but mistaken characterisation of scientific realism, traditional realism has not utilised that insight regarding scientific theories, i.e., has not explicitly factored that insight into, and invoked it as justification for, what realists believe. Nonetheless, a new form of realism has. In response to a key historical threat, two of the most thoroughly developed contemporary versions of realism—one put forward by Jarrett Leplin, another by Stathis Psillos—are anchored on the sensible tactic of requiring that the theories to which realists commit themselves have no competitors. I argue, however, that the particular kind of non‐competitor condition they invoke is illegitimate in the context of the realism debate. I contend further that invoking a non‐competitor condition that is legitimate, sensible, and even, as it turns out, required in the context of the debate threatens to eliminate the possibility of scientific realism altogether.     

A Dao of Technology?

Scholars have detected hostility to technology in Daoist thought. But is this a problem with any machine or only some applications of some machines by some people? I show that the problem is not with machines per se but with the people who introduce them, or more exactly with their knowledge. It is not knowledge as such that causes the disorder Laozi and Zhuangzi associate with machines; it is confused, disordered knowledge—superficial, inadequate, unsubtle, and artless. In other words the problem is not with machines but with the ethics of engineering and the government of technology. The Daoist argument does not devalue machines or knowledge of them; instead, it sets a new goal, defining an alternative ideal—alternative to techniques held hostage to despotic ideas about efficiency and profit.     

INTERPRETATION NEUTRALITY FOR QUANTUM THEOLOGY

Within contemporary scientific and science-adjacent communities, it is generally accepted that quantum physics is our best theory. For this reason, it is understandable—and laudable—that scholars interested in questions at the intersection of science and theology wish to meaningfully engage with this physics. Recent work in foundations of physics has, however, importantly altered the landscape of quantum theory; in this article, my goal is to introduce these advances, then make an argument within this new landscape that I hope will be useful for certain theological inquiries. Specifically, I shall argue from grounds of the physics itself that one may, with clear philosophical conscience, access the majority of quantum theory's tools, models, and explanations while maintaining an interpretation-neutral yet realist stance toward this physics.     

Belief Revision and Verisimilitude Based on Preference and Truth Orderings

In this rather technical paper we establish a useful combination of belief revision and verisimilitude according to which better theories provide better predictions, and revising with more verisimilar data results in theories that are closer to the truth. Moreover, this paper presents two alternative definitions of refined verisimilitude, which are more perspicuous than the algebraic version used in previous publications.     

Briggs on antirealist accounts of scientific law

Rachel Briggs’ critique of “antirealist” accounts of scientific law— including my own perspectivalist best-system account—is part of a project meant to show that Humean conceptions of scientific law are more problematic than has been commonly realized. Indeed, her argument provides a new challenge to the Humean, a thoroughly epistemic version of David Lewis’ “big, bad bug” for Humeanism. Still, I will argue, the antirealist (perspectivalist and expressivist) accounts she criticizes have the resources to withstand the challenge and come out stronger for it. Attention to epistemic possibilities, I argue, shows a number of advantages to a perspectivalist account of scientific law.