David Danks
Dados Biográficos
| ID | 1014873 |
|---|---|
| NOME | David Danks |
| PRENOMES | David |
| SOBRENOME | Danks |
| ASSINATURA | DANKS D |
| AFILIAÇÕES | Carnegie Mellon University |
| ORCID | 0000-0003-4541-5966 |
| VERIFICADO | Sim |
| TOTAL DE OBRAS | 22 |
| TOTAL DE CITAÇÕES | 82 |
| TOTAL COMO AUTOR | 21 |
| TOTAL COMO EDITOR | 1 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2004 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2026 |
| ÍNDICE H | 4 |
Choosing with Unknown Causal Information
Decision-making under uncertainty and causal thinking are fundamental aspects of intelligent reasoning. Decision-making has been well studied when the available 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 associative information: maximize expected utility. There is an ongoing debate around the origin of probabi…
Do It Yourself Content and the Wisdom of the Crowds
Preface – Addendum
An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content
Environmental epistemology
Artificial intelligence and humanitarian obligations
Artificial Intelligence (AI) offers numerous opportunities to improve military Intelligence, Surveillance, and Reconnaissance operations. And, modern militaries recognize the strategic value of reducing civilian harm. Grounded in these two assertions we focus on the transformative potential that AI ISR systems have for improving the respect for and protection of humanitarian relief operations. Specifically, we propose that establishing an interfa…
Algorithmic Fairness and the Situated Dynamics of Justice
Machine learning algorithms are increasingly used to shape high-stake allocations, sparking research efforts to orient algorithm design towards ideals of justice and fairness. In this research on algorithmic fairness, normative theorizing has primarily focused on identification of “ideally fair” target states. In this paper, we argue that this preoccupation with target states in abstraction from the situated dynamics of deployment is misguided. W…
Mixtures and Psychological Inference with Resting State fMRI
In this article, we examine the use of resting state fMRI data for psychological inferences. We argue that resting state studies hold the paired promises of discovering novel functional brain networks, and of avoiding some of the limitations of task-based fMRI. However, we argue that the very features of experimental design that enable resting state fMRI to support exploratory science also generate a novel confound. We argue that seemingly key fe…
Causal Pluralism in Philosophy
An increasing number of arguments for causal pluralism invoke empirical psychological data. Different aspects of causal cognition—specifically, causal perception and causal inference—are thought to involve distinct cognitive processes and representations, and they thereby distinctively support transference and dependency theories of causation, respectively. We argue that this dualistic picture of causal concepts arises from methodological differe…
Algorithmic bias
Data-driven algorithms are widely used to make or assist decisions in sensitive domains, including healthcare, social services, education, hiring, and criminal justice. In various cases, such algorithms have preserved or even exacerbated biases against vulnerable communities, sparking a vibrant field of research focused on so-called algorithmic biases. This research includes work on identification, diagnosis, and response to biases in algorithm-b…
Amalgamating evidence of dynamics
Building Theories
Causal discovery algorithms
Many investigations into the world, including philosophical ones, aim to discover causal knowledge, and many experimental methods have been developed to assist in causal discovery. More recently, algorithms have emerged that can also learn causal structure from purely or mostly observational data, as well as experimental data. These methods have started to be applied in various philosophical contexts, such as debates about our concepts of free wi…
Goal-dependence in (scientific) ontology
A Modern Pascal's Wager for Mass Electronic Surveillance
Debates about the moral permissibility of mass electronic surveillance often turn on whether consequentialist considerations legitimately trump relevant deontological rights and principles. In order to establish such overriding consequences, many proponents of mass surveillance employ a modern analogue of Pascal's wager: they contend that the (negative) consequences of no surveillance are so severe that any probability of such outcomes legitimate…
Model change and reliability in scientific inference
Unifying the Mind
Our ordinary, everyday thinking requires an astonishing range of cognitive activities, yet our cognition seems to take place seamlessly. We move between cognitive processes with ease, and different types of cognition seem to share information readily. In this book, David Danks proposes a novel cognitive architecture that can partially explain two aspects of human cognition: its relatively integrated nature and our effortless ability to focus on t…
Causation
Empirical research has recently emerged as a key method for understanding the nature of causation, and our concept of causation. One thread of research aims to test intuitions about the nature of causation in a variety of classic cases. These experiments have principally been used to try to resolve certain debates within analytic philosophy, most notably that between proponents of transference and dependence views of causation. The other major th…
The Independence Thesis
Several philosophers of science have argued that epistemically rational individuals might form epistemically irrational groups and that, conversely, rational groups might be composed of irrational individuals. We call the conjunction of these two claims the Independence Thesis, as they entail that methodological prescriptions for scientific communities and those for individual scientists are logically independent. We defend the inconsistency thes…
Actual causation
Biological Codes and Topological Causation
Various causal details of the genetic process of translation have been singled out to account for its privileged status as a ‘code’. We explicate the biological uses of coding talk by characterizing a class of special causal processes in which topological properties are the causally relevant ones. This class contains both the process of translation and communication theoretic coding processes as special cases. We propose a formalism in terms of g…
Scientific Coherence and the Fusion of Experimental Results
A pervasive feature of the sciences, particularly the applied sciences, is an experimental focus on a few (often only one) possible causal connections. At the same time, scientists often advance and apply relatively broad models that incorporate many different causal mechanisms. We are naturally led to ask whether there are normative rules for integrating multiple local experimental conclusions into models covering many additional variables. In t…
A Theory of Causal Learning in Children
The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate "causal map" of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or Bayes nets. Children's causa…
The Independence Thesis
Several philosophers of science have argued that epistemically rational individuals might form epistemically irrational groups and that, conversely, rational groups might be composed of irrational individuals. We call the conjunction of these two claims the Independence Thesis, as they entail that methodological prescriptions for scientific communities and those for individual scientists are logically independent. We defend the inconsistency thes…
Algorithmic bias
Data-driven algorithms are widely used to make or assist decisions in sensitive domains, including healthcare, social services, education, hiring, and criminal justice. In various cases, such algorithms have preserved or even exacerbated biases against vulnerable communities, sparking a vibrant field of research focused on so-called algorithmic biases. This research includes work on identification, diagnosis, and response to biases in algorithm-b…
Actual causation
Scientific Coherence and the Fusion of Experimental Results
A pervasive feature of the sciences, particularly the applied sciences, is an experimental focus on a few (often only one) possible causal connections. At the same time, scientists often advance and apply relatively broad models that incorporate many different causal mechanisms. We are naturally led to ask whether there are normative rules for integrating multiple local experimental conclusions into models covering many additional variables. In t…
Goal-dependence in (scientific) ontology
Mixtures and Psychological Inference with Resting State fMRI
In this article, we examine the use of resting state fMRI data for psychological inferences. We argue that resting state studies hold the paired promises of discovering novel functional brain networks, and of avoiding some of the limitations of task-based fMRI. However, we argue that the very features of experimental design that enable resting state fMRI to support exploratory science also generate a novel confound. We argue that seemingly key fe…
Amalgamating evidence of dynamics
Model change and reliability in scientific inference
Biological Codes and Topological Causation
Various causal details of the genetic process of translation have been singled out to account for its privileged status as a ‘code’. We explicate the biological uses of coding talk by characterizing a class of special causal processes in which topological properties are the causally relevant ones. This class contains both the process of translation and communication theoretic coding processes as special cases. We propose a formalism in terms of g…
Environmental epistemology
Causal discovery algorithms
Many investigations into the world, including philosophical ones, aim to discover causal knowledge, and many experimental methods have been developed to assist in causal discovery. More recently, algorithms have emerged that can also learn causal structure from purely or mostly observational data, as well as experimental data. These methods have started to be applied in various philosophical contexts, such as debates about our concepts of free wi…
Causation
Empirical research has recently emerged as a key method for understanding the nature of causation, and our concept of causation. One thread of research aims to test intuitions about the nature of causation in a variety of classic cases. These experiments have principally been used to try to resolve certain debates within analytic philosophy, most notably that between proponents of transference and dependence views of causation. The other major th…
A Theory of Causal Learning in Children
The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate "causal map" of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or Bayes nets. Children's causa…
Scientific Coherence and the Fusion of Experimental Results
A pervasive feature of the sciences, particularly the applied sciences, is an experimental focus on a few (often only one) possible causal connections. At the same time, scientists often advance and apply relatively broad models that incorporate many different causal mechanisms. We are naturally led to ask whether there are normative rules for integrating multiple local experimental conclusions into models covering many additional variables. In t…
Biological Codes and Topological Causation
Various causal details of the genetic process of translation have been singled out to account for its privileged status as a ‘code’. We explicate the biological uses of coding talk by characterizing a class of special causal processes in which topological properties are the causally relevant ones. This class contains both the process of translation and communication theoretic coding processes as special cases. We propose a formalism in terms of g…
Actual causation
The Independence Thesis
Several philosophers of science have argued that epistemically rational individuals might form epistemically irrational groups and that, conversely, rational groups might be composed of irrational individuals. We call the conjunction of these two claims the Independence Thesis, as they entail that methodological prescriptions for scientific communities and those for individual scientists are logically independent. We defend the inconsistency thes…
Causation
Empirical research has recently emerged as a key method for understanding the nature of causation, and our concept of causation. One thread of research aims to test intuitions about the nature of causation in a variety of classic cases. These experiments have principally been used to try to resolve certain debates within analytic philosophy, most notably that between proponents of transference and dependence views of causation. The other major th…
A Modern Pascal's Wager for Mass Electronic Surveillance
Debates about the moral permissibility of mass electronic surveillance often turn on whether consequentialist considerations legitimately trump relevant deontological rights and principles. In order to establish such overriding consequences, many proponents of mass surveillance employ a modern analogue of Pascal's wager: they contend that the (negative) consequences of no surveillance are so severe that any probability of such outcomes legitimate…
Model change and reliability in scientific inference
Unifying the Mind
Our ordinary, everyday thinking requires an astonishing range of cognitive activities, yet our cognition seems to take place seamlessly. We move between cognitive processes with ease, and different types of cognition seem to share information readily. In this book, David Danks proposes a novel cognitive architecture that can partially explain two aspects of human cognition: its relatively integrated nature and our effortless ability to focus on t…
Goal-dependence in (scientific) ontology
Building Theories
Causal discovery algorithms
Many investigations into the world, including philosophical ones, aim to discover causal knowledge, and many experimental methods have been developed to assist in causal discovery. More recently, algorithms have emerged that can also learn causal structure from purely or mostly observational data, as well as experimental data. These methods have started to be applied in various philosophical contexts, such as debates about our concepts of free wi…
Amalgamating evidence of dynamics
Causal Pluralism in Philosophy
An increasing number of arguments for causal pluralism invoke empirical psychological data. Different aspects of causal cognition—specifically, causal perception and causal inference—are thought to involve distinct cognitive processes and representations, and they thereby distinctively support transference and dependency theories of causation, respectively. We argue that this dualistic picture of causal concepts arises from methodological differe…
Algorithmic bias
Data-driven algorithms are widely used to make or assist decisions in sensitive domains, including healthcare, social services, education, hiring, and criminal justice. In various cases, such algorithms have preserved or even exacerbated biases against vulnerable communities, sparking a vibrant field of research focused on so-called algorithmic biases. This research includes work on identification, diagnosis, and response to biases in algorithm-b…
Algorithmic Fairness and the Situated Dynamics of Justice
Machine learning algorithms are increasingly used to shape high-stake allocations, sparking research efforts to orient algorithm design towards ideals of justice and fairness. In this research on algorithmic fairness, normative theorizing has primarily focused on identification of “ideally fair” target states. In this paper, we argue that this preoccupation with target states in abstraction from the situated dynamics of deployment is misguided. W…
Mixtures and Psychological Inference with Resting State fMRI
In this article, we examine the use of resting state fMRI data for psychological inferences. We argue that resting state studies hold the paired promises of discovering novel functional brain networks, and of avoiding some of the limitations of task-based fMRI. However, we argue that the very features of experimental design that enable resting state fMRI to support exploratory science also generate a novel confound. We argue that seemingly key fe…
Artificial intelligence and humanitarian obligations
Artificial Intelligence (AI) offers numerous opportunities to improve military Intelligence, Surveillance, and Reconnaissance operations. And, modern militaries recognize the strategic value of reducing civilian harm. Grounded in these two assertions we focus on the transformative potential that AI ISR systems have for improving the respect for and protection of humanitarian relief operations. Specifically, we propose that establishing an interfa…
Environmental epistemology
Preface – Addendum
An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content
Choosing with Unknown Causal Information
Decision-making under uncertainty and causal thinking are fundamental aspects of intelligent reasoning. Decision-making has been well studied when the available 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 associative information: maximize expected utility. There is an ongoing debate around the origin of probabi…
Do It Yourself Content and the Wisdom of the Crowds
Computer Science (16 obras) · Epistemology (16 obras) · Philosophy (14 obras) · Mathematics (11 obras) · Psychology (10 obras) · Philosophy of science (7 obras) · Sociology (7 obras) · Metaphysics (6 obras) · Normative (6 obras) · Philosophy and History of Science (6 obras)