Is it a "conceptual truth" or only a logically contingent fact that, in any given kind of case, an event x which asymmetrically causes ("produces") an event y likewise temporally precedes y or at least does not temporally succeed y? A bona fide physical example in which the cause retroproduces the effect would show that backward causation is no less conceptually possible than forward causation. And it has been claimed ([9], p. 151; [4], p. 41) that in Dirac's (...) classical electrodynamics (relativistic and non-relativistic), the preacceleration of charged particles before any forces are applied to them furnishes a genuine case of retrocausation by later forces. An exposition of the pertinent physics furnishes the basis for arguing the following: Whereas the non-zero acceleration of a neutral NEWTONIAN mass particle is, of course, causally connected as such to the simultaneously applied non-zero force, the non-zero acceleration of a DIRACIAN charged particle is not causally connected at all as such to the applied forces. A fortiori, in Dirac's electrodynamics, the applied forces do not qualify asymmetrically as the causes of a non-zero acceleration as such; nor does a non-zero acceleration as such qualify as an effect produced by forces. This is shown by means of two considerations as follows: (1) In Dirac's theory, no functional dependence of the value of a non-zero acceleration on the weighted time-average of the forces is vouchsafed as a matter of physical LAW alone without any value of a constant of integration, just as no Newtonian law(s) alone can guarantee a functional dependence of the non-zero value of the velocity of a Newtonian mass particle on the applied forces. Instead, both functional dependencies alike are vouchsafed only with the crucial aid of some de facto boundary condition pertaining to either the past or to the furute, so that (2) Non-zero preaccelerations of Diracian charged particles can no more be causally attributed as such to the retrocausal action of later forces than non-zero "prevelocities" of Newtonian mass particles can be held to be caused as such by later applied forces. The retrocausal interpretation of Dirac's preaccelerations is just as invalid as the retrocausal interpretation of Newtonian prevelocities. (shrink)

Top-down causation is often taken to be a metaphysically suspicious type of causation that is found in a few complex systems, such as in human mind-body relations. However, as Ellis and others have shown, top-down causation is ubiquitous in physics as well as in biology. Top-down causation occurs whenever specific dynamic behaviors are realized or selected among a broader set of possible lower-level states. Thus understood, the occurrence of dynamic and structural patterns in physical and biological (...) systems presents a problem for reductionist positions. We illustrate with examples of universality and functional equivalence classes how higher-level behaviors can be multiple realized by distinct lower-level systems or states. Multiple realizability in both contexts entails what Ellis calls “causal slack” between levels, or what others understand as relative explanatory autonomy. To clarify these notions further, we examine procedures for upscaling in multi-scale modeling. We argue that simple averaging strategies for upscaling only work for simplistic homogenous systems, because of the scale-dependency of characteristic behaviors in multi-scale systems. We suggest that this interpretation has implications for what Ellis calls mechanical top-down causation, as it presents a stronger challenge to reductionism than typically assumed. (shrink)

In what follows, I propose to evaluate Giere's analysis by applying it to a causal process considered in evolutionary theory, namely, natural selection. To say that there is selection for a given trait is to say that possessing that trait causes differential reproductive success. If there is selection for a trait and if no other evolutionary forces impinge and there is no "sampling error" due to random drift, individuals with the trait will on average have more offspring than individuals (...) without it. But sampling error can't be discounted for finite populations; so the combined effect of selection and drift will be a probability distribution of numbers of offspring. In this event, selection for a trait will imply that the mean value of the distribution associated with the individuals possessing the trait exceeds the mean value for individuals without the trait. Evolutionary theory assigns a probability distribution of possible reproductive outputs to each organism and, thereby, views organisms as stochastic systems. This implies no commitment to indeterminacy at the microlevel, of course. (shrink)

Causality in the abstract is a grand theme. We take it up when we want to penetrate to the bottom of things to understand general laws that govern the working at the world of the deepest and most detailed level.In this essay, I argue for a more situated understanding of causality. To counter our desire for ever greater generality, I suggest that causal relations, even those that hold only on average, require context. To counter our desire for ever greater (...) detail, I suggest that causal relations may exist only at a certain level of granularity. (shrink)

Causal inference is perhaps the most important form of reasoning in the sciences. A panoply of disciplines, ranging from epidemiology to biology, from econometrics to physics, make use of probability and statistics to infer causal relationships. The social and health sciences analyse population-level data using statistical methods to infer average causal relations. In diagnosis of disease, probabilistic statements are based on population-level causal knowledge combined with knowledge of a particular person’s symptoms. For the physical sciences, the Salmon-Dowe account develops (...) an analysis of causation based on the notion of process and interaction. In artificial intelligence, the development of graphical methods has leant impetus to a probabilistic analysis of causality. The biological sciences use probabilistic methods to look for evolutionary causes of the state of a current species and to look for genetic causal factors. This variegated situation raises at least two fundamental philosophical issues: about the relation between causality and probability, and about the interpretation of probability in causal analysis. In this book we bring philosophers and scientists together to discuss the relation between causality and probability, and the applications of these concepts within the sciences. (shrink)

The learning of immediate causation within a dynamic environment was examined. Participants encountered seven decision points in which they needed to choose which of three possible candidates was the cause of explosions in the environment. Each candidate was firing a weapon at random every few seconds, but only one of them produced an immediate effect. Some participants showed little learning, but most demonstrated increases in accuracy across time. On average, men showed higher accuracy and shorter latencies that were (...) not explained by differences in self-reported prior video game experience. This result suggests that prior reports of sex differences in causal choice in the game are not specific to situations involving delayed or probabilistic causal relations. (shrink)

Recent work by Brian Skyrms offers a very general way to think about how information flows and evolves in biological networks—from the way monkeys in a troop communicate to the way cells in a body coordinate their actions. A central feature of his account is a way to formally measure the quantity of information contained in the signals in these networks. In this article, we argue there is a tension between how Skyrms talks of signalling networks and his formal measure (...) of information. Although Skyrms refers to both how information flows through networks and that signals carry information, we show that his formal measure only captures the latter. We then suggest that to capture the notion of flow in signalling networks, we need to treat them as causal networks. This provides the formal tools to define a measure that does capture flow, and we do so by drawing on recent work defining causal specificity. Finally, we suggest that this new measure is crucial if we wish to explain how evolution creates information. For signals to play a role in explaining their own origins and stability, they can’t just carry information about acts; they must be difference-makers for acts. _1_ Signalling, Evolution, and Information _2_ Skyrms’s Measure of Information _3_ Carrying Information versus Information Flow _3.1_ Example 1 _3.2_ Example 2 _3.3_ Example 3 _4_ Signalling Networks Are Causal Networks _4.1_ Causal specificity _4.2_ Formalizing causal specificity _5_ Information Flow as Causal Control _5.1_ Example 1 _5.2_ Examples 2 and 3 _5.3_ Average control implicitly ‘holds fixed’ other pathways _6_ How Does Evolution Create Information? _7_ Conclusion Appendix >. (shrink)

Despite the urgency of this issue, AI still struggles to represent social life. This article presents a comprehensive agent-based model that investigates status-power dynamics in groups. Kemper’s sociological status–power theory of social relationships, and a literature review on school children in middle youth, is its basis. The model allows us to investigate causation of the near-ubiquitous phenomenon that females have lower social status on average than males. Possible causes included in the model are children’s dispositional traits (kindness, beauty, (...) and physical power), schoolyard culture (social acceptability of fighting), behavioural strategy (amount of rough-and-tumble play) and the balance between public and dyadic sources of status. An agent-based model of a virtual schoolyard was created in which the children assemble in changing groups and mutually confer status. The status conferred upon a child modifies the status it holds. Rough-and-tumble is modelled as ambiguous: it is intended as a status conferral, but may be perceived as a power move. Running many trials of the model we found that in time, depending on the parameter settings, a gender-based status gap emerged. Rough-and-tumble play had more impact on emergent status differences than did physical power differences. Social acceptability of fighting also strongly moderated the resulting status gap. Placing more weight on dyadic relationship could alleviate status loss. All these model behaviours are in line with empirical findings of child behaviour studies at schools. They have face validity for social status issues in the adult world. We conclude from this that this kind of agent-based model merits use in studying the status–power dynamics of other issues in child behaviour, or indeed in social behaviour in general. (shrink)

The goal of synthetic biology is to create artificial organisms. To achieve this it is essential to understand what life is. Metabolism-replacement systems, or (M, R)-systems, constitute a theory of life developed by Robert Rosen, characterized in the statement that organisms are closed to efficient causation, which means that they must themselves produce all the catalysts they need. This theory overlaps in part with other current theories, including autopoiesis, the chemoton, and autocatalytic sets, all of them invoking some idea (...) of closure. A simple model of an (M, R)-system has been implemented in the computer, and behaves in ways that may shed light on the requirements for a prebiotic self-organizing system. In addition to a trivial steady state in which nothing happens, it can establish a non-trivial steady state in which all intermediates have finite concentrations, with their rates of degradation balanced by their rates of synthesis. The system can be regenerated from the set of food components plus a single intermediate, and maintain itself in that state indefinitely, despite continuous degradation. At the very low compartment volumes that may have existed in prebiotic conditions, for example in cavities in minerals, or in micelles formed by simple amphiphiles, statistical fluctuations in the numbers of molecules need to be taken into account. With the stochastic approach there is no non-trivial steady state in strict mathematical terms, because the system will always collapse to the trivial state after sufficient time. However, the average time before collapse is so long for volumes greater than $10^{-19}\;{\textsc{l}}$ (much smaller than the volume of the order of $10^{-15}\;{\textsc{l}}$ for a typical bacterial cell) that for practical purposes the self-maintaining state of non-null concentrations becomes significant, recalling the situation of bistability that is observed in deterministic analysis. In turn, there exists a minimum size below which the self-organizing system cannot maintain itself on chemically relevant time scales. The value of the critical volume depends on the particular concentrations and rate constants assumed, but the principle could apply generally. (shrink)

This chapter examines some of the social science data concerning the relationship between happiness and achievement. The chapter explores further the idea that there are causal links between happiness and achievement, focusing on what common sense has to say about these matters. Reflective common sense supports a bidirectional model of causation: good moods often cause achievement and achievement often causes good moods. Before looking at the studies in support of the claim that happiness promotes achievement, the chapter considers the (...) notion of ‘effect size’. The question of whether positive affect causes achievement can be addressed by two sorts of studies. One type is longitudinal studies; the other is laboratory studies. Correlation, longitudinal, and experimental studies converge to show that positive moods and emotions cause achievement. Social science research suggests that those who are happier than average will be doubly blessed: they are happier and, on average, they will achieve more. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:_Russell_ journal (home office): E:CPBRRUSSJOURTYPE2402\INDEXISS.242 : 2005-05-19 13:34
ibliographies, rchival nventories, ndexes INDEX TO RUSSELL’S THE IMPACT OF SCIENCE ON SOCIETY R H Summerfields, The Glade Escrick, York  , .. @.. he edition of the richly allusioned The Impact of Science on Society Tindexed here is that of George Allen and Unwin, published in London in . The pagination of Simon and Schuster’s edition (New York, ) is (...) different and spans pages – instead of the first British edition’s –. The very first edition of the book (New York: Columbia U. P., ) contained two paragraphs comparing Catholicism and Communism that were overlooked for the British and all subsequent editions. Russell regarded them as important, and Sir Stanley Unwin wrote him that they would be incorporated in the next printing. The next printing was not until , and the paragraphs were again overlooked. They belong just before the paragraph on page  beginning “Every fanatical creed” in Chapter , “Science and Values”. These paragraphs are not indexed.—Ed. I meet many people who think that the evils of Communism can be combated by Catholicism. This seems to me a dangerous error. What are the evils of Communism as it has developed? First, adherence to a rigid and static system of doctrine, of which part is doubtful and part demonstrably false. Second, persecution as a means of enforcing orthodoxy. Third, a belief that salvation is only to be found within the Church and that the True Faith must be spread throughout the world, by force if necessary. (These are the beliefs of Communists.) Fourth, that the priesthood, which alone has the right to interpret the Scriptures, has enormous power, physical east of the Iron Curtain and spiritual over the faithful in partibus. Fifth, that this power is used to secure an undue share of wealth for the priesthood at the expense of the rest of the population. Sixth, that bigotry, and the hostility that it engenders, is a potent source of war. Every one of these evils was exhibited by the Catholic Church when it had power and would probably be exhibited again if it recovered the position it had in the Middle Ages. It is therefore irrational to suppose that much would be gained if, in the defeat of Communism, Catholicism were enthroned in its place. (Pp. –) russell: the Journal of Bertrand Russell Studies n.s.  (winter –): – The Bertrand Russell Research Centre, McMaster U.  - _Russell_ journal (home office): E:CPBRRUSSJOURTYPE2402\INDEXISS.242 : 2005-05-19 13:34    Abraham  absolute monarchy  Adler, Alfred  adventure, impulse to ,  vs. rashness  aeroplanes  Africa cotton market  North, and Romans  age, average, in West – agriculture effects of  in history of man  integration into larger economy – wasteful of natural resources – see also soil conservation air power and governments  Alexander the Great and superstition  ambassadors, independence of  America see United States anaesthetics  anarchy  Anaxagoras ,  animals, and man  anthropology  apathy  Arab-Israeli conflict – Arabian Nights slaves of lamp in  Arabs and scientific technique  Archimedes , ,  Argentina agriculture  Arians  Aristotle  and causation – movement of heavenly bodies  armed forces and civil authorities  international, as concentration of power ,  perceptions of own/other nations’  in Russia –,  artillery  arts: authors and artists independence of –, –,  ascetic mentality  Asia: population and prosperity  astrology – astronomers Greek  prospects for life on earth  astronomical events indicating disaster  Atlantic Pact  atom bomb , , ,  atomic energy –, ,  Attica, history  Attlee, Clement  Augustine, Saint scientific argument against astrology – Auschwitz  Australia aborigines ,  agriculture  federal government  Austria: war with Prussia  Authority and the Individual (BR)  authority vs. observation –,  autonomy of physical world , – Aztecs and cannibalism  and infanticide  Babylonians and eclipses  Bacon, Francis and witchcraft  bacteriological war  Baltic–White Sea canal  barbarians, and late Roman Empire ,  Barbarossa (Frederick I)  , independence of  Bede, the Venerable on comets  belief(s) on basis of authority, as unscientific – science as determinant  in superstition  uncivilized peoples  benefits of scientific technique – _Russell_ journal (home office): E:CPBRRUSSJOURTYPE2402\INDEXISS.242 : 2005-05-19 13:34 Index to Impact of Science on Society  Bentham , ,  Bergson, Henri  Bible, false statements in  see also individual books of biological conditions of stability (of society) –, – biology technique(s) from – birth control and population , , – birth rate, low ,  Black Death , – Blimps, Col., Romans as  breeding, scientific – Bright, John  Britain, happiness in  British Empire... 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In lieu of an abstract, here is a brief excerpt of the content:Public Health Ethics: Health by the NumbersMartina Darragh (bio) and Pat Milmoe McCarrick (bio)Hippocrates had nothing to say about public health. Rather, the idea that a government should protect its citizens from disease by maintaining sanitary conditions has its origin in Renaissance humanities texts, and the notion that physicians have public health responsibilities emerged in the works of such Enlightenment authors as Johann Peter Frank, Benjamin Rush, and John (...) Gregory (II, Coughlin and Beauchamp 1996). When the spread of infectious diseases such as typhoid and cholera accompanied the growth of the Industrial Revolution, England was the first to respond by passing the Public Health Act of 1848. The motivation for this reform was not based in humanitarian concerns but in the realization that unsanitary conditions were costly to industry. “... [The] founders of the public health movement were guided by nineteenth century utilitarian moral theories... the greatest good for the greatest number... accompanied by a growing appreciation for the innate value and dignity of mankind... however, the rights and autonomy of individuals have not always been respected in pursuing these important objectives” (I, Coughlin 1995, p. 1).Public health projects have dramatically improved global health status. One measure of their success is that “... most poor people now live longer, on average, than the wealthiest people a century ago” (I, Beaglehole and Bonita 1997, p. 13) These projects are not limited to such activities as disinfecting sewers. “Public health is what we, as a society, do to collectively assure the conditions in which people can be healthy. This requires that continuing and emerging threats to the health of the public be successfully countered. These threats include immediate crises, such as the AIDS epidemic; enduring problems, such as injuries and chronic illness; and impending crises foreshadowed by such developments as the toxic by-products of a modern economy” (I, Institute of Medicine (IOM) 1988, p. 1) The practice of public health has embraced this IOM challenge and includes the subspecialties of epidemiology, health promotion and education, public health administration, international health, maternal and child health, biostatistics, environmental health, and nutrition. This annotated bibliography focuses on epidemiology, [End Page 339] health promotion and education, and public health administration, and includes a wide variety of opinions on each topic.EpidemiologyAt first glance, it is hard to see what ethical issues could arise in population studies of the causes and control of illness. Disclosure of risk information in the clinical encounter is complicated by issues of uncertainty with regard to the data itself. Causation and causal inference may be affected by “wish bias,” leading to studies that use the same data but reach widely varying conclusions (II, Weed 1997). Given the complexity of the statistical calculations used to validate public health recommendations, and mass media’s portrayal of them, the public may be unintentionally deceived by population-based recommendations (V, Seedhouse 1997; II, Lupton 1995). With the addition of genetic screening, another layer of statistical complexity is added to this mix (VI, Stone and Steward 1996).Health Promotion and EducationIncreased life expectancy brought about by advances in public health has raised a new set of problems. With the realization that illness can be promoted in part by individual life-style choices, population-based recommendations about risk factors become important in the clinical encounter. This shift is not without difficulties; one author notes that he has yet to receive any risk information from his physicians, even when he asks “the experts” (V, Dugdale 1998). This may be due in part to the fact that “... public health lacks a vocabulary with which to speak about and identify commonalities among health problems experienced by very different populations” (IV, Mann 1997, p. 8). Furthermore, the mix of disciplines informing health promotion activities (sociology, psychology, recreation medicine, and the like) creates an illusion of shared ethics where none exists, which in turn leads to programs based on value systems that may not be meaningful for the populations they serve (V, Seedhouse 1997).Given these factors, a number of authors are skeptical of introducing public health measures into the clinical encounter. “We might do well to encourage people to live lives of modified hedonism so that they might... (shrink)

The current generation of Catholic seminarians is among the first ones to be trained to priesthood in a fully digital age, with unlimited access to sources for news, research, and controversies about science and religion, including the one opposing creationism and Darwinian evolution, despite favorable statements on evolution by twentieth and twenty-first century Popes. This article presents an online survey conducted in 2019 among 229 Brazilian seminarians; 48 percent of them espoused evolutionary views (below the average of Brazilians, and (...) Brazilian Catholics, polled in 2010 and 2014), while 55.9 percent considered evolution partially or fully compatible with Catholicism. Favorable views on evolution were more frequent among seminarians with a higher knowledge of the theory, and among those aware of papal statements on the subject, although it is not certain whether such awareness is the reason for a higher acceptance of evolution. Establishing relations of causation, which requires further research, can give evolution advocates a valuable tool to improve its acceptance among Catholics. (shrink)

Evidential decision theory (EDT) says that the choiceworthiness of an option depends on its evidential connections to possible outcomes. Causal decision theory (CDT) holds that it depends on your beliefs about its causal connections. While Newcomb cases support CDT, Arif Ahmed has described examples that support EDT. A new account is needed to get all cases right. I argue that an option A's choiceworthiness is determined by the probability that a good outcome ensues at possible A‐worlds that match actuality in (...) the facts causally unaffected by your decision (the “unaffected facts”). Moreover, you should evaluate A on the assumption that A is compossible with the unaffected facts. This view entails that you should use EDT when evaluating A on the assumption that the unaffected facts determine your action, but use CDT when assessing A on the opposite assumption. A's choiceworthiness equals a weighted average of these conditional assessments. The weights are determined by your beliefs about whether the unaffected fact determine your action. This account gets both Newcomb and Ahmed cases right. According to an influential view, whether you take the unaffected facts to determine your action can make a difference to whether you can regard yourself as free and the action as being under your control. While my account is neutral on this issue, it entails that whether you take the unaffected facts to determine your action is important in a different way: it matters to whether you should follow EDT or CDT. (shrink)

THE ART OF CAUSAL CONJECTURE Glenn Shafer Table of Contents Chapter 1. Introduction........................................................................................ ...........1 1.1. Probability Trees..........................................................................................3 1.2. Many Observers, Many Stances, Many Natures..........................................8 1.3. Causal Relations as Relations in Nature’s Tree...........................................9 1.4. Evidence............................................................................................ ...........13 1.5. Measuring the Average Effect of a Cause....................................................17 1.6. Causal Diagrams..........................................................................................20 1.7. Humean Events............................................................................................23 1.8. Three Levels of Causal Language................................................................27 1.9. An Outline of the Book................................................................................27 Chapter 2. Event Trees............................................................................................... .....31 2.1. Situations and Events...................................................................................32 2.2. The Ordering of Situations and Moivrean Events.......................................35 2.3. Cuts................................................................................................ ..............39 2.4. Humean Events............................................................................................43 (...) 2.5. Moivrean Variables......................................................................................49 2.6. Humean Variables........................................................................................53 2.7. Event Trees for Stochastic Processes...........................................................54 2.8. Timing in Event Trees.................................................................................56 2.9. Intersecting Event Trees...............................................................................60 2.10. Notes on the Literature...............................................................................61 Chapter 3. Probability Trees...........................................................................................63 3.1. Some Types of Probability Trees.................................................................64 ii 3.2. Axioms for the Probabilities of Moivrean Events.......................................68 3.3. Zero Probabilities....................................................................................... ..70 3.4. A Sample-Space Analysis of the Event-Tree Axioms.................................72 3.5. Probabilities and Expected Values for Variables.........................................74 3.6. Martingales......................................................................................... .........79 3.7. The Expectation of a Variable in a Cut........................................................83 3.8. Conditional Expected Value and Conditional Expectation.........................87 Chapter 4. The Meaning of Probability...........................................................................91 4.1. The Interpretation of Expected Value..........................................................92 4.2. The Interpretation of Expectation................................................................95 4.3. The Long Run..............................................................................................98 4.4. Changes in Belief.........................................................................................101 4.5. The Empirical Validation of Probability......................................................106 4.6. The Diversity of Uses of Probability...........................................................108 4.7. Notes on the Literature.................................................................................110 Chapter 5. Independent Events.......................................................................................113 5.1. Independence........................................................................................ .......114 5.2. Weak Independence.....................................................................................118 5.3. The Principle of the Common Cause...........................................................121 5.4. Conditional Independence............................................................................128 5.5. Notes on the Literature.................................................................................133 Chapter 6. Events Tracking Events.................................................................................135 6.1. Tracking............................................................................................ ...........137 6.2. Tracking and Conditional Independence.....................................................142 6.3. Stochastic Subsequence...............................................................................143 6.4. Singular Diagrams for Stochastic Subsequence...........................................147 6.5. Conjunctive and Interactive Forks...............................................................149 Chapter 7. Events as Signs of Events..............................................................................153 iii 7.1. Sign................................................................................................ ..............154 7.2. Weak Sign................................................................................................ ....159 7.3. The Ethics of Causal Talk............................................................................160 7.4. Screening Off...............................................................................................16 2 Chapter 8. Independent Variables...................................................................................167 8.1. Unconditional Independence........................................................................170 8.2. Conditional Independence............................................................................175 8.3. Independence for Partitions.........................................................................177 8.4. Independence for Families of Variables......................................................182 8.5. Individual Properties of the Independence Relations...................................186 Chapter 9. Variables Tracking Variables........................................................................189 9.1. Tracking and Conditional Independence: A Summary...............................190 9.2. Strong Tracking............................................................................................ 192 9.3. Strong Tracking and Conditional Independence..........................................198 9.4. Stochastic Subsequence...............................................................................201 9.5. Functional Dependence................................................................................203 9.6. Tracking in Mean.........................................................................................204 9.7. Linear Tracking............................................................................................ 207 9.8. Tracking by Partitions..................................................................................210 9.9. Tracking by Families of Variables...............................................................212 Chapter 10. Variables as Signs of Variables...................................................................215 10.1. Sign................................................................................................ ............219 10.2. Linear Sign................................................................................................ .222 10.3. Scored Sign................................................................................................ 225 10.4. Families of Variables.................................................................................227 Chapter 11. AnTheory of Event Trees.............................................................229 11.1. Event Trees as Sets of Sets........................................................................230 11.2. Event Trees as Partially Ordered Sets........................................................232 iv 11.3. Regular Event Trees...................................................................................240 11.4. The Resolution of Moivrean Variables......................................................244 11.5. Humean Events and Variables...................................................................246 Chapter 12. Martingale Trees..........................................................................................247 12.1. Examples of Decision Trees......................................................................249 12.2. The Meaning of Probability in a Decision Tree.........................................253 12.3. Martingales......................................................................................... .......257 12.4. The Structure of Martingale Trees.............................................................261 12.5. Probability and Causality...........................................................................265 12.6. Lower and Upper Probability.....................................................................269 12.7. The Law of Large Numbers.......................................................................272 12.8. Notes on the Literature...............................................................................274 Chapter 13. Refining............................................................................................ ...........275 13.1. Examples of Refinement............................................................................277 13.2. A Constructive Definition of Finite Refinement.......................................281 13.3. Axioms for Refinement..............................................................................282 13.4. Lifting Moivrean Events and Variables.....................................................288 13.5. Refining Martingale Trees.........................................................................288 13.6. Grounding........................................................................................... .......294 Chapter 14. Principles of Causal Conjecture..................................................................299 14.1. The Diversity of Causal Explanation.........................................................302 14.2. The Mean Effect of the Happening of a Moivrean Event..........................305 14.3. The Effect of a Humean Variable..............................................................311 14.4. Attribution and Generality.........................................................................316 14.5. The Statistical Measurement of the Effect of a Cause...............................319 14.6. Measurement by Experiment.....................................................................320 14.7. Using Our Knowledge of How Things Work............................................322 v 14.8. Sampling Error...........................................................................................329 14.9. The Sampling Frame..................................................................................329 14.10. Notes on the Literature.............................................................................330 Chapter 15. Causal Models.............................................................................................3 31 15.1. The Causal Interpretation of Statistical Prediction....................................333 15.2. Generalizing to a Family of Exogenous Variables....................................337 15.3 Some Joint Causal Diagrams......................................................................339 15.4. Causal Path Diagrams................................................................................342 15.5. Causal Relevance Diagrams.......................................................................346 15.6. The Meaning of Latent Variables..............................................................352 15.7. Notes on the Literature...............................................................................357 Chapter 16. Representing Probability Trees...................................................................359 16.1. Three Graphical Representations...............................................................361 16.2. Skeletal Simplifications.............................................................................368 16.3. Martingale Trees in Type Theory...............................................................371 Appendix A. Huygens’s Probability Trees.....................................................................379 Huygens’s Manuscript in Translation..................................................................380 Appendix B. Some Elements of Graph Theory..............................................................385 B1. Undirected Graphs........................................................................................385 B2. Directed Graphs............................................................................................38 6 Appendix C. Some Elements of Order Theory...............................................................393 C1. Partial and Quasi Orderings.........................................................................393 C2. Singular and Joint Diagrams for Binary Relations.......................................394 C3. Lattices............................................................................................ .............395 C4. The Lattice of Partitions of a Set..................................................................396 Appendix D. The Sample-Space Framework for Probability.........................................399 D1. Probability Measures....................................................................................399 D2. Variables........................................................................................... ...........400 vi D3. Families of Variables...................................................................................401 D4. Expected Value............................................................................................402 D5. The Law of Large Numbers.........................................................................405 D6. Conditional Probability................................................................................406 D7. Conditional Expected Value........................................................................407 Appendix E. Prediction in Probability Spaces................................................................409 E1. Conditional Distribution...............................................................................411 E2. Regression on a Single Variable...................................................................412 E3. Regression on a Partition or a Family of Variables......................................415 E4. Linear Regression on a Single Variable.......................................................418 E5. Linear Regression on a Family of Variables................................................422 Appendix F. Sample-Space Concepts of Independence.................................................425 F1. Overview............................................................................................ ...........426 F2. Independence Proper.....................................................................................432 F3. Unpredictability in Mean..............................................................................434 F4. Simple Uncorrelatedness..............................................................................437 F5. Mixed Uncorrelatedness...............................................................................438 F6. Partial Uncorrelatedness...............................................................................440 F7. Independence for Partitions..........................................................................442 F8. Independence for Families of Variables.......................................................445 F9. The Basic Role of Uncorrelatedness.............................................................448 F10. Dawid’s Axioms.........................................................................................449 Appendix G. Prediction Diagrams..................................................................................453 G1. Path Diagrams............................................................................................ ..454 G2. Generalized Path Diagrams..........................................................................462 G3. Relevance Diagrams.....................................................................................466 G4. Bubbled Relevance Diagrams......................................................................475 Appendix H. Abstract Stochastic Processes...................................................................479 vii H1. Probability Conditionals and Probability Distributions...............................477 H2. Abstract Stochastic Processes......................................................................479 H3. Embedding Variables and Processes in a Sample Space.............................480 References.......................................................................................... ..............................491. (shrink)

Statistics in public life .................................................................................................... .....5 Things and numbers............................................................................................. ...................8 Representative samples............................................................................................. ..........8 Averages: meaning and relevance .....................................................................................9 Correlations........................................................................................ ................................10 Applied statistics .................................................................................................... ................13 Relative risks .................................................................................................... ..................14 Relative risk versus absolute risk.....................................................................................16 Problems of classification and confounding factors....................................................17 Epidemiological research............................................................................................ ..........19 Publication bias................................................................................................ ..................20 Statistical significance versus scientific relevance................................................................24 Relative risk again............................................................................................... ...............24 P-values............................................................................................ ...................................25 Confidence intervals .................................................................................................... .....26 Correlation is not causation .............................................................................................26 An infamous episode .................................................................................................... ....27 Terror, utopianism and power .............................................................................................29 Faith and science .................................................................................................... ...........29 Fear and power: the precautionary principle.................................................................30 Utopian salvation........................................................................................... ....................32....

It is often natural to compare two events by describing one as ‘more of a cause’ of some effect than the other. But what do such comparisons amount to, exactly? This paper aims to provide a guided tour of the recent literature on ‘degrees of causation’. Section 2 looks at what I call ‘dependence measures’, which arise from thinking of causes as difference‐makers. Section 3 looks at what I call ‘production measures’, which arise from thinking of causes as jointly (...) sufficient for their effects. Finally, section 4 examines the important question of whether there is any sense in which an agent is more responsible for an outcome in virtue of her action being more of a cause of it. I describe a puzzle that emerges from this question, and explore various strategies for resolving it. (shrink)

Hobbes is known for bridging natural and political philosophy, but less attention has been given to how this distinguishes the Hobbesian conception of the self from individualist strands of liberalism. First, Hobbes’s determinism suggests a conception of the self in which externalities determine the will and what the self is at every moment. Second, there is no stable conception of the self because externalities keep it in a constant state of flux. The metaphysical underpinnings of his project downplay the notion (...) of a purely individualistic conception of the self, pointing to a positivist theory of criminology relying upon external forces. This theory is especially prescient with respect to twentieth-century variants of positivism that focus upon how social organization affects personality. In a sense, then, modern criminological theory is indebted to Hobbes’s focus upon the connections between externalities and the self; a focus that illuminates new ways of viewing responsibility and accountability. (shrink)

A growing number of philosophers are bringing interventionism into the field of supervenient causation. Many argue that interventionist supervenient causation is exempted from the fixability condition. However, this approach looks ad hoc, inconsistent with the general interventionist requirement on fixation. Moreover, it leads to false judgments about the causal efficacy of supervenient/subvenient properties. This article aims to develop a novel interventionist account of supervenient causation that respects the fixability requirement. The treatment of intervention and fixation that I (...) propose can accommodate some theoretical constraints on causation and deliver correct causal verdicts in classic examples. It is also worth noting that this interventionist account offers a promising defense of mental causation without postulating mental-physical overdetermination. (shrink)

Pearl opened the door to formally defining actual causation using causal models. His approach rests on two strategies: first, capturing the widespread intuition that X = x causes Y = y iff X = x is a Necessary Element of a Sufficient Set for Y = y, and second, showing that his definition gives intuitive answers on a wide set of problem cases. This inspired dozens of variations of his definition of actual causation, the most prominent of which (...) are due to Halpern & Pearl. Yet all of them ignore Pearl’s first strategy, and the second strategy taken by itself is unable to deliver a consensus. This paper offers a way out by going back to the first strategy: it offers six formal definitions of causal sufficiency and two interpretations of necessity. Combining the two gives twelve new definitions of actual causation. Several interesting results about these definitions and their relation to the various Halpern & Pearl definitions are presented. Afterwards the second strategy is evaluated as well. In order to maximize neutrality, the paper relies mostly on the examples and intuitions of Halpern & Pearl. One definition comes out as being superior to all others, and is therefore suggested as a new definition of actual causation. (shrink)

It has become a popular view among non-reductive physicalists that it is possible to devise empirical tests generating evidence for the causal efficacy of the mental, whereby the exclusion worries that have haunted the position of non-reductive physicalism for decades can be dissolved once and for all. This paper aims to show that these evidentialist hopes are vain. I argue that, if the mental is taken to supervene non-reductively on the physical, there cannot exist empirical evidence for its causal efficacy. (...) While causal structures without non-reductive supervenience relations can be conclusively identified in ideal discovery circumstances, it is impossible, in principle, to generate evidence that would favour models with mental causation over models without. Ascribing causal efficacy to the mental, for the non-reductive physicalist, is a modelling choice that must be made on the basis of metaphysical background theories or pragmatic maxims guiding the selection among empirically indistinguishable models. (shrink)

Do the senses represent causation? Many commentators read Nicolas Malebranche as anticipating David Hume’s negative answer to this question. I disagree with this assessment. When a yellow billiard ball strikes a red billiard ball, Malebranche holds that we see the yellow ball as causing the red ball to move. Given Malebranche’s occasionalism, he insists that the visual experience of causal interaction is illusory. Nevertheless, Malebranche holds that the senses represent finite things as causally efficacious. This experience of creaturely causality (...) explains why Aristotelian philosophers and others struggle to recognize occasionalism’s truth. (shrink)

Presents an interpretation of David Hume's account of what a 'cause' is. This book emphasises on the connections between Hume's theories of cause, space and time, morals, and aesthetics.

This paper identifies and critiques a theory of mental causation defended by some proponents of nonredutive physicalism that I call “intralevelism.” Intralevelist theories differ in their details. On all versions, the causal outcome of the manifestation of physical properties is physical and the causal outcome of the manifestation of mental properties is mental. Thus, mental causation on this view is intralevel mental to mental causation. This characterization of mental causation as intralevel is taken to insulate nonreductive (...) physicalism from some objections to nonreductive physicalism, including versions of the exclusion argument. This paper examines some features of three recent versions of intralevelism defended by John Gibbons, Markus Schlosser, and Amie Thomasson. This paper shows that the distinctive problems faced by these three representative versions of intralevelism suggest that the intralevelist strategy does not provide a viable solution to the exclusion problem. (shrink)

Abstract In this paper, we show that Greek distinguishes empirically ability as a precondition for action, and ability as initiating and sustaining force for action. In this latter case, the ability verb behaves like an action verb, and the sentence has the logical form of a causative structure φ CAUSE [BECOME ψ] (Dowty 1979). The distinction between ability as potential for action and ability as action itself has a venerable tradition that goes back to Aristotle, and is recently implied in (...) a number of analyses (Mari and Martin 2007, 2009, Thomason 2005). We show first that the phenomenon is not just aspectual ( pace Bhatt 1999, Hacquard 2006, 2009, Pinon 2003): actualized ability emerges with the ability verb also with imperfective aspect and present tense. They key, we argue is causation, which triggers a shift from pure ability, to ability as force (in the sense of Copley and Harley 2010, i.e. as action initiating energy). In Greek, the action reading of the ability modal comes about in an apparent co-ordinate causative structure, where the two clauses are connected with conjunction ke ‘and’— a pattern that we find also in other languages, including English, at least with some action verbs such as try, allow . Our analysis implies a meaning of ability richer than mere possibility ( pace Hacquard); and, by capitalizing on the causative meaning and the presence of force in causative structures, our analysis enables a principled explanation of the shift to action-ability without positing ambiguity for the ability verb ( pace Bhatt 1999). (shrink)

_ Source: _Volume 55, Issue 4, pp 239 - 272 According to Peter Auriol, OFM, efficient causation is a composite being consisting of items belonging to three distinct categories: a change, an action, and a passion. The change functions as the subject bearing action and passion. After presenting Aristotle’s account of action and passion, which constitutes the background to Auriol’s theory of causation, this paper considers Auriol’s interpretation of Aristotle’s account in contrast to an alternative interpretation defended by (...) Hervaeus Natalis and William of Ockham. Finally, it shows how Auriol, on the basis of his interpretation of Aristotle, develops his own account of efficient causation as a composite being. (shrink)

In this article I review the core elements of Carolina Sartorio’s actual causal sequence account of free will and moral responsibility, and propose two revisions. First, I suggest replacing the contested notion of absence causation by the relatively uncontroversial notion of causal explanation by absences. Second, I propose retaining explanation by unreduced dispositions, of which Sartorio appears to be wary. I then set out a response to her critical treatment of manipulation arguments against compatibilism. Lastly, I point out that (...) Sartorio’s reasons-sensitivity condition on moral responsibility is amenable to a conception of moral responsibility that, unlike the one she endorses, dispenses with basic desert. (shrink)

This paper argues that the knowledge asymmetry (the fact that we know more about the past than the future) can be explained as a consequence of the causal Markov condition. The causal Markov condition implies that causes of a common effect are generally statistically independent, whereas effects of a common cause are generally correlated. I show that together with certain facts about the physics of our world, the statistical independence of causes severely limits our ability to predict the future, whereas (...) correlations between joint effects make it so that no such limitation holds in the reverse temporal direction. In so far as the fact that our world conforms to the causal Markov condition can itself be explained in terms of the initial conditions of the universe, my view is compatible with Albert’s well-known account of the origins of temporal asymmetries, but also provides a more illuminating way to derive the knowledge asymmetry from those initial conditions. (shrink)

The UK Supreme Court in Montgomery v Lanarkshire Health Board adopts an approach to information disclosure in connection with clinical treatment that moves away from medical paternalism towards a more patient-centred approach. In doing so, it reinforces the protection afforded to informed consent and autonomous patient decision making under the law of negligence. However, some commentators have expressed a concern that the widening of the healthcare providers’ duty of disclosure may provide impetus, in future cases, for courts to adopt a (...) more rigorous approach to the application of causation principles. The aim would be to limit liability but, in turn, it would also limit autonomy protection. Such a restrictive approach has recently been adopted in Australia as a result of the High Court decision in Wallace v Kam. This paper considers whether such an approach is likely under English negligence law and discusses case law from both jurisdictions in order to provide a point of comparison from which to scope the post- Montgomery future. (shrink)

In this article, we document how, in the public arena, British readers of the first edition of Isaac Newton's Philosophiae naturalis principia mathematica (1687) tried to make sense of the relation between gravity, matter, and divine and natural causation—an issue on which Newton had remained entirely silent in the first edition of the Principia. We show that readers attached new meanings to the Principia so that parts of it migrated to a different intellectual debate. It will be shown that (...) one particular result Newton obtained in the Principia, namely the rejection of a vacuum in Corollary 3 to Proposition 6 in Book 3, was the most important locus in debates on the relation between gravity, matter, and causation. (shrink)

This study enriches the literature on entrepreneurial decisions by investigating the antecedents of the synergetic use of causal and effectual logic. Based on entrepreneurial metacognition and emotional complexity theories, we argued that the emotional complexity of an entrepreneur, referred to as the granular experience of, or variety in, experienced emotions during the entrepreneurial task, would contribute to the synergetic use of decision logic. With survey data gathered from 218 Chinese entrepreneurs, we found that entrepreneurs with higher emotional complexity are more (...) likely to adopt two types of entrepreneurial logic in tandem, and cognitive flexibility mediates this positive relationship. Thereby, this study helps to unravel some of the complexities behind the choice of decision logic of entrepreneurs. (shrink)