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1.
Le Poidevin on the Reduction of Chemistry 总被引:1,自引:0,他引:1
Hendry Robin Findlay; Needham Paul 《The British journal for the philosophy of science》2007,58(2):339-353
In this article we critically evaluate Robin Le Poidevin's recentattempt to set out an argument for the ontological reductionof chemistry independently of intertheoretic reduction. We argue,firstly, that the argument he envisages applies only to a smallpart of chemistry, and that there is no obvious way to extendit. We argue, secondly, that the argument cannot establish thereduction of chemistry, properly so called.
- 1 Introduction
- 2The scope of the reductionist claim
- 3 The combinatorial argument
- 4 The strength of the reduction
- 5 Concludingremarks
- 2The scope of the reductionist claim
2.
In a recent issue of this journal, P.E. Vermaas ([2005]) claimsto have demonstrated that standard quantum mechanics is technologicallyinadequate in that it violates the technical functionscondition. We argue that this claim is false becausebased on a narrow interpretation of this technicalfunctions condition that Vermaas can only accept on pain ofcontradiction. We also argue that if, in order to avoid thiscontradiction, the technical functions condition is interpretedwidely rather than narrowly, thenVermaas, argument for his claim collapses. The conclusion isthat Vermaas' claim that standard quantum mechanics is technologicallyinadequate evaporates.
- 1 Introduction
- 2 The Narrow Interpretation
- 3 The Wide Interpretation
- 4 The Teleportation Scheme
- 5Conclusions
- 2 The Narrow Interpretation
3.
I argue in this article that there is a mistake in Searle'sChinese room argument that has not received sufficient attention.The mistake stems from Searle's use of the Church–Turingthesis. Searle assumes that the Church–Turing thesis licencesthe assumption that the Chinese room can run any program. Iargue that it does not, and that this assumption is false. Anumber of possible objections are considered and rejected. Myconclusion is that it is consistent with Searle's argument tohold onto the claim that understanding consists in the runningof a program.
- 1 Searle's Argument
- 1.1 The Church–Turingthesis
- 2 Criticism of Searle's Argument
- 3 Objectionsand Replies
- 3.1 The virtual brain machine objection
- 3.2The brain-basedobjection
- 3.3 The syntax/physics objection
- 3.4 The abstractionobjection
- 3.5 The same conclusionobjection
- 3.6 The unnecessary baggage objection
- 3.7The Chinese gym objection
- 3.8 The syntax/semantics objection
- 3.9 Turing's definition of algorithm
- 3.9.1 Consequences
- 3.9.2 Criticism of the defence
- 3.2The brain-basedobjection
- 4 Conclusion
4.
Fitelson Branden; Waterman Andrew 《The British journal for the philosophy of science》2007,58(2):333-338
By and large, we think (Strevens's [2005]) is a useful replyto our original critique (Fitelson and Waterman [2005]) of hisarticle on the QuineDuhem (QD) problem (Strevens [2001]).But, we remain unsatisfied with several aspects of his reply(and his original article). Ultimately, we do not think he properlyaddresses our most important worries. In this brief rejoinder,we explain our remaining worries, and we issue a revised challengefor Strevens's approach to QD.
- 1 Strevens's clarifications
- 2 Strevens's new-and-improved negligibility arguments
5.
What Are the New Implications of Chaos for Unpredictability? 总被引:1,自引:0,他引:1
From the beginning of chaos research until today, the unpredictabilityof chaos has been a central theme. It is widely believed andclaimed by philosophers, mathematicians and physicists alikethat chaos has a new implication for unpredictability, meaningthat chaotic systems are unpredictable in a way that other deterministicsystems are not. Hence, one might expect that the question Whatare the new implications of chaos for unpredictability?has already been answered in a satisfactory way. However, thisis not the case. I will critically evaluate the existing answersand argue that they do not fit the bill. Then I will approachthis question by showing that chaos can be defined via mixing,which has never before been explicitly argued for. Based onthis insight, I will propose that the sought-after new implicationof chaos for unpredictability is the following: for predictingany event, all sufficiently past events are approximately probabilisticallyirrelevant.
- Introduction
- Dynamical Systems and Unpredictability
- 2.1 Dynamical systems
- 2.2 Natural invariant measures
- 2.3Unpredictability
- 2.2 Natural invariant measures
- 2.1 Dynamical systems
- Chaos
- 3.1 Defining chaos
- 3.2 Definingchaos via mixing
- 3.1 Defining chaos
- Criticism of Answers in the Literature
- 4.1 Asymptotic unpredictability?
- 4.2 Unpredictability dueto rapid or exponential divergence?
- 4.3 Macro-predictabilityand Micro-unpredictability?
- 4.2 Unpredictability dueto rapid or exponential divergence?
- 4.1 Asymptotic unpredictability?
- A General New Implication ofChaos for Unpredictability
- 5.1Approximate probabilistic irrelevance
- 5.2 Sufficiently pastevents are approximately probabilisticallyirrelevant for predictions
- 5.1Approximate probabilistic irrelevance
- Conclusion
6.
The one world, one science argument (so named by Rescher) isadvanced by Carl Sagan and others to support the thesis thatwe will be able to learn to converse with intelligent extraterrestrialsif and when we encounter them. The prima facie obstacle to extraterrestrialcommunication is that the aliens culture and geographyare bound to be so different from ours that we would find itextremely difficult, if not practically impossible, to finda common topic on which we can both converse. Sagan's rebuttalis that we will share mathematics and the laws of physics, thesebeing the same for all intelligent beings regardless of localcultural and geographical variations. I show that this argumentfails even if its contentious assumptions about science andthe world are granted—that is to say, it fails on uncontentiousgrounds.
- 1 OWOS
- 2 OWOS and Social Constructivism
- 3 OWOSand Conceptual Relativism
- 4 OWOS and the Selection Problem
- 5 The Fundamental Laws Solution
- 6 The Mathematics Solution
- 7 The Radio Solution
- 8 The Common Conditions Solution
- 9The Intractability of the Selection Problem
- 10 The Superfluityof OWOS
- 2 OWOS and Social Constructivism
7.
Many have found attractive views according to which the veracityof specific causal judgements is underwritten by general causallaws. This paper describes various variants of that view andexplores complications that appear when one looks at a certainsimple type of example from physics. To capture certain causaldependencies, physics is driven to look at equations which,I argue, are not causal laws. One place where physics is forcedto look at such equations (and not the only place) is in itshandling of Green's functions which reveal point-wise causaldependencies. Thus, I claim that there is no simple relationshipbetween causal dependence and causal laws of the sort oftenpictured. Rather, this paper explores the complexity of therelationship in a certain well-understood case.
- 1 Introduction
- 2 The Causal Covering-Law Thesis
- 3 The Laws of String Motion
- 4 Green's Functions and Causation
- 5 Green's Functions andBoundary Conditions
- 6 The Green's Function as a Violationof the Wave Equation
- 6.1The Green's Function and other Sensesof Causal Law:Temporal Propagation and LocalPropagation
- 7 The Distributional Wave Equation
- 8 Whyis not the Green's Function a Causal Law?
- 9Conclusion
- 2 The Causal Covering-Law Thesis
8.
The evidence from randomized controlled trials (RCTs) is widelyregarded as supplying the gold standard in medicine—wemay sometimes have to settle for other forms of evidence, butthis is always epistemically second-best. But how well justifiedis the epistemic claim about the superiority of RCTs? This paperadds to my earlier (predominantly negative) analyses of theclaims produced in favour of the idea that randomization playsa uniquely privileged epistemic role, by closely inspectingthree related arguments from leading contributors to the burgeoningfield of probabilistic causality—Papineau, Cartwrightand Pearl. It concludes that none of these further argumentssupplies any practical reason for thinking of randomizationas having unique epistemic power.
- 1 Introduction
- 2 Why theissue is of great practical importance—the ECMOcase
- 3Papineau on the virtues of randomization
- 4 Cartwrighton causality and the ideal randomizedexperiment
- 5 Pearl on randomization, nets and causes
- 6 Conclusion
- 2 Why theissue is of great practical importance—the ECMOcase
9.
This paper assesses Sarkar's ([2003]) deflationary account ofgenetic information. On Sarkar's account, genes carry informationabout proteins because protein synthesis exemplifies what Sarkarcalls a formal information system. Furthermore,genes are informationally privileged over non-genetic factorsof development because only genes enter into arbitrary relationsto their products (in virtue of the alleged arbitrariness ofthe genetic code). I argue that the deflationary theory doesnot capture four essential features of the ordinary conceptof genetic information: intentionality, exclusiveness, asymmetry,and causal relevance. It is therefore further removed from whatis customarily meant by genetic information than Sarkar admits.Moreover, I argue that it is questionable whether the accountsucceeds in demonstrating that information is theoreticallyuseful in molecular genetics.
- Introduction
- Sarkar's InformationSystem
- The Pre-theoretic Features of Genetic Information
- 3.1Intentionality
- 3.2 Exclusiveness
- 3.3 Asymmetry
- 3.4 Causalrelevance
- 3.2 Exclusiveness
- 3.1Intentionality
- Theoretical Usefulness
- Conclusion
10.
An assessment is offered of the recent debate on informationin the philosophy of biology, and an analysis is provided ofthe notion of information as applied in scientific practicein molecular genetics. In particular, this paper deals withthe dependence of basic generalizations of molecular biology,above all the central dogma, on the so-calledinformational talk (Maynard Smith [2000a]). Itis argued that talk of information in the central dogmacan be reduced to causal claims. In that respect, the primaryaim of the paper is to consider a solution to the major difficultyof the causal interpretation of genetic information: how todistinguish the privileged causal role assigned to nucleic acids,DNA in particular, in the processes of replication and proteinproduction. A close reading is proposed of Francis H. C. Crick'sOn Protein Synthesis (1958) and related works, to which we owethe first explicit definition of information within the scientificpractice of molecular biology.
- Introduction
- 1.1 The basicquestions of the information debate
- 1.2 Thecausal interpretation(CI) of biological informationand Crick'scentral dogma
- 1.1 The basicquestions of the information debate
- Crick's definitions of genetic information
- The main requirementfor (CI)
- Types of causation in molecular biology
- 4.1 Structuralcausation in molecular biology
- 4.2 Nucleicacids as correlativecausal factors
- 4.1 Structuralcausation in molecular biology
- The central dogma withoutthe notion of information
- Concluding remarks
11.
Internal laws of probability, generalized likelihoods and Lewis' infinitesimal chances-A response to Adam Elga 总被引:1,自引:0,他引:1
The rejection of an infinitesimal solution to the zero-fit problemby A. Elga ([2004]) does not seem to appreciate the opportunitiesprovided by the use of internal finitely-additive probabilitymeasures. Indeed, internal laws of probability can be used tofind a satisfactory infinitesimal answer to many zero-fit problems,not only to the one suggested by Elga, but also to the Markovchain (that is, discrete and memory-less) models of reality.Moreover, the generalization of likelihoods that Elga has inmind is not as hopeless as it appears to be in his article.In fact, for many practically important examples, through theuse of likelihoods one can succeed in circumventing the zero-fitproblem.
- 1 The Zero-fit Problem on Infinite State Spaces
- 2Elga's Critique of the Infinitesimal Approach to the Zero-fitProblem
- 3 Two Examples for Infinitesimal Solutions to theZero-fit Problem
- 4 Mathematical Modelling in Nonstandard Universes?
- 5 Are Nonstandard Models Unnatural?
- 6 Likelihoods and Densities
- A Internal Probability Measures and the Loeb Measure Construction
- B The (Countable) Coin Tossing Sequence Revisited
- C Solutionto the Zero-fit Problem for a Finite-state Modelwithout Memory
- D An Additional Note on Integrating over Densities
- E Well-defined Continuous Versions of Density Functions
- 2Elga's Critique of the Infinitesimal Approach to the Zero-fitProblem
12.
Going back at least to Duhem, there is a tradition of thinkingthat crucial experiments are impossible in science. I analyseDuhem's arguments and show that they are based on the excessivelystrong assumption that only deductive reasoning is permissiblein experimental science. This opens the possibility that someprinciple of inductive inference could provide a sufficientreason for preferring one among a group of hypotheses on thebasis of an appropriately controlled experiment. To be sure,there are analogues to Duhem's problems that pertain to inductiveinference. Using a famous experiment from the history of molecularbiology as an example, I show that an experimentalist versionof inference to the best explanation (IBE) does a better jobin handling these problems than other accounts of scientificinference. Furthermore, I introduce a concept of experimentalmechanism and show that it can guide inferences from data withinan IBE-based framework for induction.
- Introduction
- Duhem onthe Logic of Crucial Experiments
- The Most BeautifulExperiment in Biology
- Why Not Simple Elimination?
- SevereTesting
- An Experimentalist Version of IBE
- 6.1 Physiologicaland experimentalmechanisms
- 6.2 Explaining the data
- 6.3IBE and the problemof untested auxiliaries
- 6.4 IBE-turtlesall the way down
- 6.2 Explaining the data
- 6.1 Physiologicaland experimentalmechanisms
- Van Fraassen's Bad Lot Argument
- IBE and Bayesianism
- Conclusions
13.
While there is no universal logic of induction, the probabilitycalculus succeeds as a logic of induction in many contexts throughits use of several notions concerning inductive inference. Theyinclude Addition, through which low probabilities representdisbelief as opposed to ignorance; and Bayes property, whichcommits the calculus to a refute and rescale dynamicsfor incorporating new evidence. These notions are independentand it is urged that they be employed selectively accordingto needs of the problem at hand. It is shown that neither isadapted to inductive inference concerning some indeterministicsystems.
- 1 Introduction
- 2 Failure of demonstrations of universality
- 2.1 Working backwards
- 2.2 The surface logic
- 3 Framework
- 3.1 The properties
- 3.2 Boundaries
- 3.2.1 Universalcomparability
- 3.2.2 Transitivity
- 3.2.3 Monotonicity
- 3.2.2 Transitivity
- 3.2 Boundaries
- 4 Addition
- 4.1 The property: disbelief versus ignorance
- 4.2Boundaries
- 5 Bayes property
- 5.1 The property
- 5.2 Bayes' theorem
- 5.3Boundaries
- 5.3.1 Dogmatism of the priors
- 5.3.2 Impossibilityof prior ignorance
- 5.3.3 Accommodation of virtues
- 5.3.2 Impossibilityof prior ignorance
- 5.2 Bayes' theorem
- 6Real values
- 7 Sufficiency and independence
- 8 Illustrations
- 8.1 All properties retained
- 8.2 Bayes propertyonly retained
- 8.3 Induction without additivity and Bayes property
- 8.2 Bayes propertyonly retained
- 9Conclusion
- 2 Failure of demonstrations of universality
14.
Maddy and Mathematics: Naturalism or Not 总被引:1,自引:0,他引:1
Penelope Maddy advances a purportedly naturalistic account ofmathematical methodology which might be taken to answer thequestion `What justifies axioms of set theory?' I argue thather account fails both to adequately answer this question andto be naturalistic. Further, the way in which it fails to answerthe question deprives it of an analog to one of the chief attractionsof naturalism. Naturalism is attractive to naturalists andnonnaturalists alike because it explains the reliability ofscientific practice. Maddy's account, on the other hand, appearsto be unable to similarly explain the reliability of mathematicalpractice without violating one of its central tenets.
- 1 Introduction
- 2 Mathematical Naturalism
- 3 Desiderata and the attractionof naturalism
- 4 Assessment: Naturalism and names
- 4.1 Takingnaturalismseriously
- 4.2 Second philosophy (orwhat's in a name)
- 5 A way out?
- 6 Or out of the way?
- 2 Mathematical Naturalism
15.
A consensus exists among contemporary philosophers of biologyabout the history of their field. According to the receivedview, mainstream philosophy of science in the 1930s, 40s, and50s focused on physics and general epistemology, neglectinganalyses of the special sciences, including biology.The subdiscipline of philosophy of biology emerged (and couldonly have emerged) after the decline of logical positivism inthe 1960s and 70s. In this article, I present bibliometric datafrom four major philosophy of science journals (Erkenntnis,Philosophy of Science, Synthese, and the British Journal forthe Philosophy of Science), covering 1930–59, which challengethis view.
- 1 Introduction
- 2 Methods
- 3 Results
- 4 Conclusions
- 2 Methods
16.
This essay presents results about a deviation from independencemeasure called focused correlation. This measure explicatesthe formal relationship between probabilistic dependence ofan evidence set and the incremental confirmation of a hypothesis,resolves a basic question underlying Peter Klein and Ted Warfield'struth-conduciveness problem for Bayesian coherentism,and provides a qualified rebuttal to Erik Olsson's claim thatthere is no informative link between correlation and confirmation.The generality of the result is compared to recent programsin Bayesian epistemology that attempt to link correlation andconfirmation by utilizing a conditional evidential independencecondition. Several properties of focused correlation are alsohighlighted.
- Introduction
- Correlation Measures
- 2.1 Standard covarianceand correlation measures
- 2.2 The Wayne–Shogenjimeasure
- 2.3 Interpreting correlation measures
- 2.4 Correlationandevidential independence
- 2.2 The Wayne–Shogenjimeasure
- 2.1 Standard covarianceand correlation measures
- Focused Correlation
- Conclusion
17.
In this paper I argueagainst van Fraassen's constructiveempiricismthat the practice of saving phenomena is muchbroader than usually thought, and includes unobservable phenomenaas well as observable ones. My argument turns on the distinctionbetween data and phenomena: I discuss how unobservable phenomenamanifest themselves in data models and how theoretical modelsable to save them are chosen. I present a paradigmatic casestudy taken from the history of particle physics to illustratemy argument. The first aim of this paper is to draw attentionto the experimental practice of saving unobservable phenomena,which philosophers have overlooked for too long. The secondaim is to explore some far-reaching implications this practicemay have for the debate on scientific realism and constructiveempiricism.
- 1 Introduction
- 2 Unobservable Phenomena
- 2.1 Dataand phenomena
- 2.2 What isa data model?
- 2.3 Data modelsand unobservable phenomena
- 2.2 What isa data model?
- 3 Saving Unobservable Phenomena:An Exemplar
- 4 The October Revolution of 1974: From the J/to Charmonium
- 4.1 A new unobservable phenomenon at 3.1 Ge V
- 4.2 How thecharmonium model saved the new unobservable phenomenon
- 4.2.1The J/ as a baryonantibaryon bound state
- 4.2.2TheJ/ as the spin-1 meson of a model with three charmedquarks
- 4.2.3 The J/ as a charmonium state
- 4.2.2TheJ/ as the spin-1 meson of a model with three charmedquarks
- 4.2 How thecharmonium model saved the new unobservable phenomenon
- 5 Concluding Remarks
- 2 Unobservable Phenomena
18.
The traditional Bayesian qualitative account of evidential support(TB) takes assertions of the form E evidentially supportsH to affirm the existence of a two-place relation ofevidential support between E and H. The analysans given forthis relation is C(H,E) =def Pr(H|E) > Pr(H). Now it is wellknown that when a hypothesis H entails evidence E, not onlyis it the case that C(H,E), but it is also the case that C(H&X,E)for any arbitrary X. There is a widespread feeling that thisis a problematic result for TB. Indeed, there are a number ofcases in which many feel it is false to assert E evidentiallysupports H&X, despite H entailing E. This is known,by those who share that feeling, as the tacking problemfor Bayesian confirmation theory. After outlining a generalizationof the problem, I argue that the Bayesian response has so farbeen unsatisfactory. I then argue the following: (i) There exists,either instead of, or in addition to, a two-place relation ofconfirmation, a three-place, contrastive relationof confirmation, holding between an item of evidence E and twocompeting hypotheses H1 and H2. (ii) The correct analysans ofthe relation is a particular probabilistic inequality, abbreviatedC(H1, H2, E). (iii) Those who take the putative counterexamplesto TB discussed to indeed be counterexamples are interpretingthe relevant utterances as implicitly contrastive, contrastingthe relevant hypothesis H1 with a particular competitor H2.(iv) The probabilistic structure of these cases is such thatC(H1, H2, E). This solves my generalization of the tacking problem.I then conclude with some thoughts about the relationship betweenthe traditional Bayesian account of evidential support and myproposed account of the three-place relation of confirmation.
- 1 The tacking problem and the traditional Bayesianresponse
- 2 Contrastive support
- 3 Concluding comments
- 2 Contrastive support
19.
There is a recurring line of argument in the literature to theeffect that Bohm's theory fails to solve the measurement problem.I show that this argument fails in all its variants. Hence Bohm'stheory, whatever its drawbacks, at least succeeds in solvingthe measurement problem. I briefly discuss a similar argumentthat has been raised against the GRW theory.
- 1 Introduction
- 2 Three Theories
- 3 The Basic Argument
- 4 Branches and Worlds
- 5 Theory and Interpretation
- 6 Size Matters
- 7 Conclusion
- 2 Three Theories
20.
Stochastic Einstein Locality Revisited 总被引:1,自引:0,他引:1
I discuss various formulations of stochastic Einstein locality(SEL), which is a version of the idea of relativistic causality,that is, the idea that influences propagate at most as fastas light. SEL is similar to Reichenbach's Principle of the CommonCause (PCC), and Bell's Local Causality. My main aim is to discuss formulations of SEL for a fixed backgroundspacetime. I previously argued that SEL is violated by the outcomedependence shown by Bell correlations, both in quantum mechanicsand in quantum field theory. Here I reassess those verdictsin the light of some recent literature which argues that outcomedependence does not violate the PCC. I argue that the verdictsabout SEL still stand. Finally, I briefly discuss how to formulate relativistic causalityif there is no fixed background spacetime.
- 1 Introduction
- 2Formulating Stochastic Einstein Locality
- 2.1 Events and regions
- 2.2 The idea of SEL
- 2.3 Three formulations of SEL
- 2.3.1The formulations
- 2.3.2Comparisons
- 2.4 Implications betweenthe formulations
- 2.4.1 Conditions forthe equivalence of SELD1and SELD2
- 2.4.2 Conditions for theequivalence of SELS andSELD2
- 2.2 The idea of SEL
- 3 Relativistic Causality in the Bell Experiment
- 3.1 The background
- 3.1.1 The Bell experiment reviewed
- 3.1.2My previous position
- 3.2 A common common cause? The Budapestschool
- 3.2.1 Resuscitatingthe PCC
- 3.2.2 Known proofs of aBell inequality need a strongPCC
- 3.2.3 Two distinctions
- 3.2.4Szabó's model
- 3.2.5A common common cause is plausible
- 3.2.6 Bell inequalitiesfrom a weak PCC: the Bern school
- 3.2.2 Known proofs of aBell inequality need a strongPCC
- 3.3 SEL in the Bell experiment
- 3.3.1 PCC and SEL are connectedby PPSI
- 3.3.2 The need forother judgments
- 3.3.3 Weak vs.strong SELD
- 3.3.2 The need forother judgments
- 3.1.1 The Bell experiment reviewed
- 4 SEL in Algebraic Quantum Field Theory
- 4.1 The story so far
- 4.2 Questions
- 4.2.1 Our formulations
- 4.2.2 The BudapestandBern schools
- 4.2 Questions
- 5 SEL in DynamicalSpacetimes
- 5.1 SEL for metric structure?
- 5.2 SEL for causalsets?
- 5.2.1 The causal set approach
- 5.2.2Labelled causalsets; general covariance
- 5.2.3 Deducing thedynamics
- 5.2.4The fate of SEL
- 5.2.2Labelled causalsets; general covariance
- 5.2 SEL for causalsets?
- 2Formulating Stochastic Einstein Locality