Books > Science & Mathematics > Mathematics > Probability & statistics
|
Buy Now
Agent-based Models and Causal Inference (Hardcover)
Loot Price: R2,096
Discovery Miles 20 960
|
|
Agent-based Models and Causal Inference (Hardcover)
Expected to ship within 12 - 17 working days
|
Donate to Gift Of The Givers
Total price: R2,116
Discovery Miles: 21 160
|
Agent-based Models and Causal Inference Scholars of causal
inference have given little credence to the possibility that ABMs
could be an important tool in warranting causal claims. Manzo's
book makes a convincing case that this is a mistake. The book
starts by describing the impressive progress that ABMs have made as
a credible methodology in the last several decades. It then goes on
to compare the inferential threats to ABMs versus the traditional
methods of RCTs, regression, and instrumental variables showing
that they have a common vulnerability of being based on untestable
assumptions. The book concludes by looking at four examples where
an analysis based on ABMs complements and augments the evidence for
specific causal claims provided by other methods. Manzo has done a
most convincing job of showing that ABMs can be an important
resource in any researcher's tool kit. --Christopher Winship,
Diker-Tishman Professor of Sociology, Harvard University, USA
Agent-based Models and Causal Inference is a first-rate
contribution to the debate on, and practice of, causal claims. With
exemplary rigor, systematic precision and pedagogic clarity, this
book contrasts the assumptions about causality that undergird
agent-based models, experimental methods, and statistically based
observational methods, discusses the challenges these methods face
as far as inferences go, and, in light of this discussion,
elaborates the case for combining these methods' respective
strengths: a remarkable achievement. --Ivan Ermakoff, Professor of
Sociology, University of Wisconsin-Madison, USA Agent-based models
are a uniquely powerful tool for understanding how patterns in
society may arise in often surprising and counter-intuitive ways.
This book offers a strong and deeply reflected argument for how
ABM's can do much more: add to actual empirical explanation. The
work is of great value to all social scientists interested in
learning how computational modelling can help unraveling the
complexity of the real social world. --Andreas Flache, Professor of
Sociology at the University of Groningen, Netherlands Agent-based
Models and Causal Inference is an important and much-needed
contribution to sociology and computational social science. The
book provides a rigorous new contribution to current understandings
of the foundation of causal inference and justification in the
social sciences. It provides a powerful and cogent alternative to
standard statistical causal-modeling approaches to causation.
Especially valuable is Manzo's careful analysis of the conditions
under which an agent-based simulation is relevant to causal
inference. The book represents an exceptional contribution to
sociology, the philosophy of social science, and the epistemology
of simulations and models. --Daniel Little, Professor of
philosophy, University of Michigan, USA Agent-based Models and
Causal Inference delivers an insightful investigation into the
conditions under which different quantitative methods can
legitimately hold to be able to establish causal claims. The book
compares agent-based computational methods with randomized
experiments, instrumental variables, and various types of causal
graphs. Organized in two parts, Agent-based Models and Causal
Inference connects the literature from various fields, including
causality, social mechanisms, statistical and experimental methods
for causal inference, and agent-based computation models to help
show that causality means different things within different methods
for causal analysis, and that persuasive causal claims can only be
built at the intersection of these various methods. Readers will
also benefit from the inclusion of: A thorough comparison between
agent-based computation models to randomized experiments,
instrumental variables, and several types of causal graphs A
compelling argument that observational and experimental methods are
not qualitatively superior to simulation-based methods in their
ability to establish causal claims Practical discussions of how
statistical, experimental and computational methods can be combined
to produce reliable causal inferences Perfect for academic social
scientists and scholars in the fields of computational social
science, philosophy, statistics, experimental design, and ecology,
Agent-based Models and Causal Inference will also earn a place in
the libraries of PhD students seeking a one-stop reference on the
issue of causal inference in agent-based computational models.
General
Is the information for this product incomplete, wrong or inappropriate?
Let us know about it.
Does this product have an incorrect or missing image?
Send us a new image.
Is this product missing categories?
Add more categories.
Review This Product
No reviews yet - be the first to create one!
|
You might also like..
|