8,918 research outputs found
What Might a Theory of Causation Do for Sport?
The purpose of this research is to articulate how a theory of causation might be serviceable to a theory of sport. This article makes conceptual links between Bernard Suits’ theory of game-playing, causation, and theories of causation. It justifies theories of causation while drawing on connections between sport and counterfactuals. It articulates the value of theories of causation while emphasizing possible limitations. A singularist theory of causation is found to be more broadly serviceable with particular regard to its analysis of sports
We Are Not Your Real Parents: Telling Causal from Confounded using MDL
Given data over variables we consider the problem of finding out whether jointly causes or whether they are all confounded by an unobserved latent variable . To do so, we take an information-theoretic approach based on Kolmogorov complexity. In a nutshell, we follow the postulate that first encoding the true cause, and then the effects given that cause, results in a shorter description than any other encoding of the observed variables. The ideal score is not computable, and hence we have to approximate it. We propose to do so using the Minimum Description Length (MDL) principle. We compare the MDL scores under the models where causes and where there exists a latent variables confounding both and and show our scores are consistent. To find potential confounders we propose using latent factor modeling, in particular, probabilistic PCA (PPCA). Empirical evaluation on both synthetic and real-world data shows that our method, CoCa, performs very well -- even when the true generating process of the data is far from the assumptions made by the models we use. Moreover, it is robust as its accuracy goes hand in hand with its confidence
von Neumann-Morgenstern and Savage Theorems for Causal Decision Making
Causal thinking and decision making under uncertainty are fundamental aspects
of intelligent reasoning. Decision making under uncertainty has been well
studied when information is considered at the associative (probabilistic)
level. The classical Theorems of von Neumann-Morgenstern and Savage provide a
formal criterion for rational choice using purely associative information.
Causal inference often yields uncertainty about the exact causal structure, so
we consider what kinds of decisions are possible in those conditions. In this
work, we consider decision problems in which available actions and consequences
are causally connected. After recalling a previous causal decision making
result, which relies on a known causal model, we consider the case in which the
causal mechanism that controls some environment is unknown to a rational
decision maker. In this setting we state and prove a causal version of Savage's
Theorem, which we then use to develop a notion of causal games with its
respective causal Nash equilibrium. These results highlight the importance of
causal models in decision making and the variety of potential applications.Comment: Submitted to Journal of Causal Inferenc
Causality re-established
Causality never gained the status of a "law" or "principle" in physics. Some
recent literature even popularized the false idea that causality is a notion
that should be banned from theory. Such misconception relies on an alleged
universality of reversibility of laws of physics, based either on determinism
of classical theory, or on the multiverse interpretation of quantum theory, in
both cases motivated by mere interpretational requirements for realism of the
theory. Here, I will show that a properly defined unambiguous notion of
causality is a theorem of quantum theory, which is also a falsifiable
proposition of the theory. Such causality notion appeared in the literature
within the framework of operational probabilistic theories. It is a genuinely
theoretical notion, corresponding to establish a definite partial order among
events, in the same way as we do by using the future causal cone on Minkowski
space. The causality notion is logically completely independent of the
misidentified concept of "determinism", and, being a consequence of quantum
theory, is ubiquitous in physics. In addition, as classical theory can be
regarded as a restriction of quantum theory, causality holds also in the
classical case, although the determinism of the theory trivializes it. I then
conclude arguing that causality naturally establishes an arrow of time. This
implies that the scenario of the "Block Universe" and the connected "Past
Hypothesis" are incompatible with causality, and thus with quantum theory: they
both are doomed to remain mere interpretations and, as such, not falsifiable,
similar to the hypothesis of "super-determinism". This article is part of a
discussion meeting issue "Foundations of quantum mechanics and their impact on
contemporary society".Comment: Presented at the Royal Society of London, on 11/12/ 2017, at the
conference "Foundations of quantum mechanics and their impact on contemporary
society". To appear on Philosophical Transactions of the Royal Society
Using graphical models and multi-attribute utility theory for probabilistic uncertainty handling in large systems, with application to nuclear emergency management
Although many decision-making problems involve uncertainty, uncertainty handling within large decision support systems (DSSs) is challenging. One domain where uncertainty handling is critical is emergency response management, in particular nuclear emergency response, where decision making takes place in an uncertain, dynamically changing environment. Assimilation and analysis of data can help to reduce these uncertainties, but it is critical to do this in an efficient and defensible way. After briefly introducing the structure of a typical DSS for nuclear emergencies, the paper sets up a theoretical structure that enables a formal Bayesian decision analysis to be performed for environments like this within a DSS architecture. In such probabilistic DSSs many input conditional probability distributions are provided by different sets of experts overseeing different aspects of the emergency. These probabilities are then used by the decision maker (DM) to find her optimal decision. We demonstrate in this paper that unless due care is taken in such a composite framework, coherence and rationality may be compromised in a sense made explicit below. The technology we describe here builds a framework around which Bayesian data updating can be performed in a modular way, ensuring both coherence and efficiency, and provides sufficient unambiguous information to enable the DM to discover her expected utility maximizing policy
Active causation and the origin of meaning
Purpose and meaning are necessary concepts for understanding mind and
culture, but appear to be absent from the physical world and are not part of
the explanatory framework of the natural sciences. Understanding how meaning
(in the broad sense of the term) could arise from a physical world has proven
to be a tough problem. The basic scheme of Darwinian evolution produces
adaptations that only represent apparent ("as if") goals and meaning. Here I
use evolutionary models to show that a slight, evolvable extension of the basic
scheme is sufficient to produce genuine goals. The extension, targeted
modulation of mutation rate, is known to be generally present in biological
cells, and gives rise to two phenomena that are absent from the non-living
world: intrinsic meaning and the ability to initiate goal-directed chains of
causation (active causation). The extended scheme accomplishes this by
utilizing randomness modulated by a feedback loop that is itself regulated by
evolutionary pressure. The mechanism can be extended to behavioural variability
as well, and thus shows how freedom of behaviour is possible. A further
extension to communication suggests that the active exchange of intrinsic
meaning between organisms may be the origin of consciousness, which in
combination with active causation can provide a physical basis for the
phenomenon of free will.Comment: revised and extende
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