Add to ORKGA common strategy for simplifying complex systems involves partitioning them into subsystems whose behaviors are roughly independent of one another at shorter time-scales. Dynamic causal models (Iwasaki and Simon, 1994) explain how doing so reveals a system's non-equilibrium causal relationships. Here I use these models to elucidate the idealizations and abstractions involved in representing a system at a time-scale. The models reveal that key features of causal representations - such as which variables are exogenous - may vary with the time-scale at which a system is considered. This has implications for debates regarding which systems can be understood causally
Causal forces are a way of summarizing forecasters ' expectations about what will happen to a t...
I discuss two categories of causal relationships: primitive causal interactions of the sort characte...
Presentism is the view that our most unrestricted quantifiers range over only those objects temporal...
A common strategy for simplifying complex systems involves partitioning them into subsystems whose ...
The dynamics model, which is based on Talmy’s (1988) theory of force dynamics, characterizes causati...
Qualitative simulation faces an intrinsic problem of scale: the number of limit hypotheses grows exp...
One of the fundamental purposes of causal models is using them to predict the effects of manipulatin...
We seem to experience a world abounding with events that exhibit dynamic temporal structure; birds f...
A self-organized complex natural system, such as a biological, a neural or a social system, is chara...
In this paper I consider general obstacles to the recovery of a causal system from its probability d...
The problem of how humans and other intelligent systems construct causal representations from non-ca...
Causal representations are distinguished from non-causal ones by their ability to predict the resul...
<p>Even if one can experiment on relevant factors, learning the causal structure of a dynamical syst...
Discovering causal dependence is central to understanding the behavior of complex systems and to sel...
Summary. Time dynamics are often ignored in causal modelling. Clearly, causality must oper-ate in ti...
Causal forces are a way of summarizing forecasters ' expectations about what will happen to a t...
I discuss two categories of causal relationships: primitive causal interactions of the sort characte...
Presentism is the view that our most unrestricted quantifiers range over only those objects temporal...
A common strategy for simplifying complex systems involves partitioning them into subsystems whose ...
The dynamics model, which is based on Talmy’s (1988) theory of force dynamics, characterizes causati...
Qualitative simulation faces an intrinsic problem of scale: the number of limit hypotheses grows exp...
One of the fundamental purposes of causal models is using them to predict the effects of manipulatin...
We seem to experience a world abounding with events that exhibit dynamic temporal structure; birds f...
A self-organized complex natural system, such as a biological, a neural or a social system, is chara...
In this paper I consider general obstacles to the recovery of a causal system from its probability d...
The problem of how humans and other intelligent systems construct causal representations from non-ca...
Causal representations are distinguished from non-causal ones by their ability to predict the resul...
<p>Even if one can experiment on relevant factors, learning the causal structure of a dynamical syst...
Discovering causal dependence is central to understanding the behavior of complex systems and to sel...
Summary. Time dynamics are often ignored in causal modelling. Clearly, causality must oper-ate in ti...
Causal forces are a way of summarizing forecasters ' expectations about what will happen to a t...
I discuss two categories of causal relationships: primitive causal interactions of the sort characte...
Presentism is the view that our most unrestricted quantifiers range over only those objects temporal...