In this review we deal with open quantum systems within the Bohmian mechanics framework which has the advantage to provide a clear picture of quantum phenomena in terms of trajectories, originally in configuration space. The gradual decoherence process is studied from linear and nonlinear Schrödinger equations through Bohmian trajectories as well as by using the so-called quantum-classical transition differential equation through scaled trajectories. This transition is governed by a continuous parameter, the transition parameter, covering these two extreme open dynamical regimes. (...) Thus, two sources of decoherence of different nature are going to be considered. Several examples will be presented and discussed in order to illustrate the corresponding theory behind each case, namely: the so-called Brownian–Bohmian motion leading to quantum diffusion coefficients, dissipative diffraction in time, dissipative tunnelling for a parabolic barrier under the presence of an electric field and stochastic early arrivals for the same type of barrier. In order to simplify the notations and physical discussion, the theoretical developments will be carried out in one dimension throughout all this wok. One of the main goals is to analyze the gradual decoherence process existing in these open dynamical regimes in terms of trajectories, leading to a more intuitive way of understanding the underlying physics in order to gain new insights. (shrink)

Since the launch of applications such as DALL-E, Midjourney, and Stable Diffusion, generative artificial intelligence has been controversial as a tool for creating artwork. While some have presented longtermist worries about these technologies as harbingers of fully automated futures to come, more pressing is the impact of generative AI on creative labour in the present. Already, business leaders have begun replacing human artistic labour with AI-generated images. In response, the artistic community has launched a protest movement, which argues that AI (...) image generation is a kind of theft. This paper analyzes, substantiates, and critiques these arguments, concluding that AI image generators involve an unethical kind of labour theft. If correct, many other AI applications also rely upon theft. (shrink)

Creativity is generally thought to be the production of things that are novel and valuable. Humans are unique in the extent of their creativity, which plays a central role in innovation and problem solving, as well as in the arts. But what are the cognitive sources of novelty? More particularly, what are the cognitive sources of stochasticity in creative production? I will argue that they belong to two broad categories. One is associative, enabling the selection of goal-relevant ideas that have (...) become activated by happenstance in an unrelated context. The other relies on selection processes that leverage stochastic fluctuations in neural activity. At the same time, I will address a central puzzle, which is to understand how the outputs of stochastic processes can nevertheless generally fall within task constraints. While the components appealed to in the accounts that I offer are well established, the ways in which I combine them together are new. (shrink)

The articles examines how failure, especially in so-called 'stochastic' arts or sciences like medicine and navigation stimulated reflections about the nature of the knowledge required of a genuine art (techne) or science.

HSimulator is a multithread simulator for mass-action biochemical reaction systems placed in a well-mixed environment. HSimulator provides optimized implementation of a set of widespread state-of-the-art stochastic, deterministic, and hybrid simulation strategies including the first publicly available implementation of the Hybrid Rejection-based Stochastic Simulation Algorithm. HRSSA, the fastest hybrid algorithm to date, allows for an efficient simulation of the models while ensuring the exact simulation of a subset of the reaction network modeling slow reactions. Benchmarks show that HSimulator is (...) often considerably faster than the other considered simulators. The software, running on Java v6.0 or higher, offers a simulation GUI for modeling and visually exploring biological processes and a Javadoc-documented Java library to support the development of custom applications. HSimulator is released under the COSBI Shared Source license agreement. (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)

Business ethics courses have been launched with professors from business pulling on one oar, and professors of philosophy pulling on the other, but they lack a sense of direction. Let's begin with the basics: What is an ehtical decision? More fundamentally, why the interest in professional ethics in the first place?There are over 300 centers for the study of appIied ethics in this country-why? The events which face our society today are outside the business-oriented collection of shared beIiefs that set (...) our pubIic policy agenda. Cur beIiefs are too narrow, thus we see, understand, and control small slices of life.Business ethics should be the study of the structure and impact on us of what we call "business science," e.g., accounting, marketing, economics, law, etc., and the corresponding study of the process of what Carl Jung called individuation: learning to become one's own uniqueself in the face of these bodies of professional knowledge which have structured our lives and charted the direction for our sensibilities for too long. (shrink)

Business ethics courses have been launched with professors from business pulling on one oar, and professors of philosophy pulling on the other, but they lack a sense of direction. Let's begin with the basics: What is an ehtical decision? More fundamentally, why the interest in professional ethics in the first place?There are over 300 centers for the study of appIied ethics in this country-why? The events which face our society today are outside the business-oriented collection of shared beIiefs that set (...) our pubIic policy agenda. Cur beIiefs are too narrow, thus we see, understand, and control small slices of life.Business ethics should be the study of the structure and impact on us of what we call "business science," e.g., accounting, marketing, economics, law, etc., and the corresponding study of the process of what Carl Jung called individuation: learning to become one's own uniqueself in the face of these bodies of professional knowledge which have structured our lives and charted the direction for our sensibilities for too long. (shrink)

We explore themes in Nobel Prize–winning economist James M. Buchanan’s work and apply his Ethics and Economic Progress to problems facing individuals and firms. We focus on Buchanan’s analysis of the individual work ethic, his exhortations to “pay the preacher” of the “institutions of moral-ethical communication,” and his notion of law as “public capital.” We highlight several ways people with other-regarding preferences can contribute to social flourishing and some of the ways those who have “affected to trade for the public (...) good” might want to redirect their efforts. We show how Buchanan’s work has considerable implications for business ethics. Just as his economic analysis of politics changed how we understand government, we think his economic analysis of ethics can (and should) change how we understand business. (shrink)

Abstract…observations of skylfull pylotts is the onlye waye to bring it in rule; for it passeth the reach of naturall philosophy. – Michael Gabriel, 1576 (Collinson, 1867, p. 30)Abstract The tension between empirical data and formal theory pervades the entire history of geomagnetism, from the Middle Ages up to the present day. This paper explores its early-modern history (1500–1800), using a hybrid approach: it applies a methodological framework used in modern geophysics to interpret early-modern developments, exploring to what extent formal (...) conjectures shaped observation and vice versa. A range of pertinent case studies supports classification of this entire period as proto-scientific, characterised by the initial formation of theories being largely disconnected from observational constraints, and their subsequent evolution being advanced primarily by their empirical falsification, and not necessarily associated with the introduction of an alternative. The few exceptional instances of purely data-driven discovery were essentially due to an improved signal-to-noise ratio. (shrink)

NOT SAUSSURE: A CRITIQUE OF POST?SAUSSUREAN LITERARY THEORY by Raymond Tallis London: Macmillan, 1988. 273 pp., £33 (£10.95 paper).

The increasing availability of passively observed data has yielded a growing interest in “data fusion” methods, which involve merging data from observational and experimental sources to draw causal conclusions. Such methods often require a precarious tradeoff between the unknown bias in the observational dataset and the often-large variance in the experimental dataset. We propose an alternative approach, which avoids this tradeoff: rather than using observational data for inference, we use it to design a more efficient experiment. We consider the case (...) of a stratified experiment with a binary outcome and suppose pilot estimates for the stratum potential outcome variances can be obtained from the observational study. We extend existing results to generate confidence sets for these variances, while accounting for the possibility of unmeasured confounding. Then, we pose the experimental design problem as a regret minimization problem subject to the constraints imposed by our confidence sets. We show that this problem can be converted into a concave maximization and solved using conventional methods. Finally, we demonstrate the practical utility of our methods using data from the Women’s Health Initiative. (shrink)

Helps individual readers to overcome procrastination and build self-esteem Reveals how to create a culture of accountability, and how to hold someone accountable Gives leaders a step-by-step process for helping team members become more self-responsible Explains commitment reluctance and how to encourage self-responsibility among team members Uncovers why we blame others and shows how to defeat a blame culture Provides an easy read with no consultant-speak In recent years, HORN Training and Consulting was awarded the distinguished Gold Medal by the (...) Canadian Society for Training and Development for its leadership development program designed for one of the world’s largest international banks; and HORN is currently executing leadership development projects on a global scale for companies in the consumer packaged goods and pharmaceutical industries. (shrink)

A collection of photographs depicts life on Chicago's west side between 1949 and mid-1960s.

1. ʻArve naḥal, ḥashivuto shel kol Yehudi --2. Isur hitḥabrut ʻim reshaʻim--3. Ketsad la-ʻazor le-vaʻale teshuvah.

Adults spend most of their conscious life speaking, comprehending, writing, and reading discourse. It is entirely appropriate for cognitive science to investigate discourse especially as transmitted texts or printed media, such as books, newspapers, magazines, and computers. However, there is another reason why text understanding has been one of the prototypical areas of study in cognitive science: Interdisciplinary work is absolutely essential. As cognitive scientists have unraveled the puzzles of text comprehension, they have embraced the insights and methodologies from several (...) disciplines. (shrink)

A humanidade tem sido tomada por angústias tão sutis que, aos poucos, vai se dissolvendo – física e psicologicamente – como se estivesse sendo consumida de dentro para fora. Neste sentido a saída – ainda que imediata e provisória – é propor um jogo de Xadrez com a morte. Não à toa Ingmar Bergman escolhe o Xadrez como a forma da sua personagem desafiar “alguém” tão poderoso. O jogo é um chamamento à razão, à luz capaz de organizar as ideias (...) e dar um rumo à vida. (shrink)