Phyllis Illari

This book serves as the main reference for an undergraduate course on Philosophy of Information. The book is written to be accessible to the typical undergraduate student of Philosophy and does not require propaedeutic courses in Logic, Epistemology or Ethics. Each chapter includes a rich collection of references for the student interested in furthering her understanding of the topics reviewed in the book.

The book covers all the main topics of the Philosophy of Information and it should be considered an overview and not a comprehensive, in-depth analysis of a philosophical area. As a consequence, 'The Philosophy of Information: a Simple Introduction' does not contain research material as it is not aimed at graduate students or researchers.

The book is available for free in multiple formats and it is updated every twelve months by the team of the π Research Network: Patrick Allo, Bert Baumgaertner, Simon D'Alfonso, Penny Driscoll, Luciano Floridi, Nir Fresco, Carson Grubaugh, Phyllis Illari, Eric Kerr, Giuseppe Primiero, Federica Russo, Christoph Schulz, Mariarosaria Taddeo, Matteo Turilli, Orlin Vakarelov.

The proposed Handbook will be the first comprehensive presentation of the mechanisms literature, bringing readers up-to-date with the latest developments, but accessible to researchers and students with little background.  While our primary audience will be philosophers of science, we expect the book will also be valuable to researchers in the history and social studies of science, as well as to natural and social scientists who are looking for philosophical and historical lenses through which to gain insight into their practices.
 
The book will be organized into four parts.  The first part will contain chapters on the history of mechanistic thought.  The second part will be on the nature of mechanisms and will contain chapters exploring major philosophical debates about what mechanisms are, and how they are ontologically and conceptually related to other categories of things discussed by philosophers of science and metaphysicians – for instance, causes, laws, and levels of organization.  The third part covers mechanisms and philosophy of science, including issues about how scientists find, represent and explain mechanisms; in addition to general chapters on discovery, modelling and explanation, there will be chapters devoted to currently controversial topics, like the relationship between mechanisms and dynamical systems.  The fourth and final part will be on disciplinary perspectives on mechanisms and will contain chapters exploring philosophical questions surrounding mechanisms studied within particular scientific fields.

Part I Introduction 1

1 Phyllis McKay Illari, Federica Russo and Jon Williamson Why look at causality in the sciences? A manifesto


Part II Health sciences

2 R. Paul Thompson Causality, theories and medicine

3 Alex Broadbent Inferring causation in epidemiology: Mechanisms, black boxes, and contrasts

4 Harold Kincaid Causal modelling, mechanism, and probability in epidemiology

5 Bert Leuridan and Erik Weber The IARC and mechanistic evidence

6 Donald Gillies The Russo–Williamson thesis and the question of whether smoking causes heart disease


Part III Psychology

7 David Lagnado Causal thinking

8 Benjamin Rottman, Woo-kyoung Ahn and Christian Luhmann When and how do people reason about unobserved causes?

9 Clare R. Walsh and Steven A. Sloman Counterfactual and generative accounts of causal attribution

10 Ken Aizawa and Carl Gillett The autonomy of psychology in the age of neuroscience

11 Otto Lappi and Anna-Mari Rusanen Turing machines and causal mechanisms in cognitive science

12 Keith A. Markus Real causes and ideal manipulations: Pearl’s theory of causal inference from the point of view of psychological research methods


Part IV Social sciences

13 Daniel Little Causal mechanisms in the social realm

14 Ruth Groff Getting past Hume in the philosophy of social science

15 Michel Mouchart and Federica Russo Causal explanation: Recursive decompositions and mechanisms

16 Kevin D. Hoover Counterfactuals and causal structure

17 Damien Fennell The error term and its interpretation in structural models in econometrics

18 Hossein Hassani, Anatoly Zhigljavsky, Kerry Patterson, and Abdol S. Soofi A comprehensive causality test based on the singular spectrum analysis


Part V Natural sciences

19 Tudor M. Baetu Mechanism schemas and the relationship between biological theories

20 Roberta L. Millstein Chances and causes in evolutionary biology: How many chances become one chance

21 Sahotra Sarkar Drift and the causes of evolution

22 Garrett Pendergraft In defense of a causal requirement on explanation

23 Paolo Vineis, Aneire Khan and Flavio D’Abramo Epistemological issues raised by research on climate change

24 Giovanni Boniolo, Rossella Faraldo and Antonio Saggion Explicating the notion of ‘causation’: The role of extensive quantities

25 Miklós Rédei and Balázs Gyenis Causal completeness of probability theories – Results and open problems


Part VI Computer science, probability, and statistics

26 I. Guyon, C. Aliferis, G. Cooper, A. Elisseeff, J.-P. Pellet,
P. Spirtes and A. Statnikov Causality Workbench

27 Jan Lemeire, Kris Steenhaut and Abdellah Touhafi When are graphical causal models not good models?

28 Dawn E. Holmes Why making Bayesian networks objectively Bayesian makes sense

29 Branden Fitelson and Christopher Hitchcock Probabilistic measures of causal strength

30 Kevin B. Korb, Erik P. Nyberg and Lucas Hope A new causal power theory

31 Samantha Kleinberg and Bud Mishra Multiple testing of causal hypotheses

32 Ricardo Silva Measuring latent causal structure

33 Judea Pearl The structural theory of causation

34 S. Geneletti and A.P. Dawid Defining and identifying the effect of treatment on the treated

35 Nancy Cartwright Predicting ‘It will work for us’: (Way) beyond statistics


Part VII Causality and mechanisms

36 Stathis Psillos The idea of mechanism

37 Stuart Glennan Singular and general causal relations: A mechanist perspective

38 Phyllis McKay Illari and Jon Williamson Mechanisms are real and local

39 Jim Bogen and Peter Machamer Mechanistic information and causal continuity

40 Phil Dowe The causal-process-model theory of mechanisms

41 M. Kuhlmann Mechanisms in dynamically complex systems

42 Julian Reiss Third time’s a charm: Causation, science and Wittgensteinian pluralism

Example: Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat. Ut wisi enim ad minim veniam, quis nostrud exerci tation ullamcorper suscipit lobortis nisl ut aliquip ex ea commodo consequat.

In this paper, we examine what is to be said in defence of Machamer, Darden and Craver’s controversial dualism about activities and entities (MDC 2000). We explain why we believe the notion of an activity to be a novel, valuable one, and set about clearing away some initial objections that can lead to its being brushed aside unexamined.  We argue that substantive debate about ontology can only be effective when desiderata for an ontology are explicitly articulated.  We distinguish three such desiderata.  The first is a more permissive descriptive ontology of science, the second a more reductive ontology prioritising understanding, and the third a more reductive ontology prioritising minimalism.  We compare MDC’s entities-activities ontology to its closest rival, the entities-capacities ontology, and argue that the entities-activities ontology does better on all three desiderata.

This chapter is the introduction to the volume. The volume editors begin by setting out a manifesto that puts forward two theses: first, that the sciences are the best place to turn in order to understand causality; second, that scientifically-informed philosophical investigation can bring something to the sciences too. Next, the chapter goes through the various parts of the volume, drawing out relevant background to and themes of the chapters in those parts. Finally, the chapter discusses the progeny of the papers and identify some next steps for research into causality in the sciences.

After a decade of intense debate about mechanisms, there is still no consensus characterization. In this paper we argue for a characterization that applies widely to mechanisms across the sciences. We examine and defend our disagreements with the major current contenders for characterizations of mechanisms. Ultimately, we indicate that the major contenders can all sign up to our characterization.

According to current hierarchies of evidence for EBM, evidence of correlation (e.g., from RCTs) is always more important than evidence of mechanisms when evaluating and establishing causal claims. We argue that evidence of mechanisms needs to be treated alongside evidence of correlation. This is for three reasons. First, correlation is always a fallible indicator of causation, subject in particular to the problem of confounding; evidence of mechanisms can in some cases be more important than evidence of correlation when assessing a causal claim. Second, evidence of mechanisms is often required in order to obtain evidence of correlation (for example, in order to set up and evaluate RCTs). Third, evidence of mechanisms is often required in order to generalise and apply causal claims.

While the EBM movement has been enormously successful in making explicit and critically examining one aspect of our evidential practice, i.e., evidence of correlation, we wish to extend this line of work to make explicit and critically examine a second aspect of our evidential practices: evidence of mechanisms.

In this paper, we compare the mechanisms of protein synthesis and natural selection. We identify three core elements of mechanistic explanation: functional individuation, hierarchical nestedness or decomposition, and organization. These are now well understood elements of mechanistic explanation in fields such as protein synthesis, and widely accepted in the mechanisms literature. But Skipper and Millstein have argued (2005) that natural selection is neither decomposable nor organized. This would mean that much of the current mechanisms literature does not apply to the mechanism of natural selection. We take each element of mechanistic explanation in turn. Having appreciated the importance of functional individuation, we show how decomposition and organization should be better understood in these terms. We thereby show that mechanistic explanation by protein synthesis and natural selection are more closely analogous than they appear—both possess all three of these core elements of a mechanism widely recognized in the mechanisms literature.

The Recursive Bayesian Net (RBN) formalism was originally developed for modelling nested causal relationships. In this paper we argue that the formalism can also be applied to modelling the hierarchical structure of mechanisms. The resulting network contains quantitative information about probabilities, as well as qualitative information about mechanistic structure and causal relations. Since information about probabilities, mechanisms and causal relations is vital for prediction, explanation and control respectively, an RBN can be applied to all these tasks. We show in particular how a simple two-level RBN can be used to model a mechanism in cancer science. The higher level of our model contains variables at the clinical level, while the lower level maps the structure of the cell's mechanism for apoptosis.

Mechanisms have become much-discussed, yet there is still no consensus on how to characterise them.  In this paper, we start with something everyone is agreed on – that mechanisms explain – and investigate what constraints this imposes on our metaphysics of mechanisms.  We examine two widely shared premises about how to understand mechanistic explanation: (1) that mechanistic explanation offers a welcome alternative to traditional laws-based explanation and (2) that there are two senses of mechanistic explanation that we call ‘epistemic explanation’ and ‘physical explanation’.  We argue that mechanistic explanation requires that mechanisms are both real and local. We then go on to argue that real, local mechanisms require a broadly active metaphysics for mechanisms, such as a capacities metaphysics.