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A key difficulty here is that, even though interactions are clearly all over the place, they’re hard to estimate. Remember, you need 16 times the sample size to estimate an interaction than to estimate a main effect. So, along with accepting the importance of interactions, we also have to accept inevitable uncertainty in their estimation. We have to move away from the idea that a statistical analysis will give us effective certainty for the things we care about. "Representative agents" are homogenous. They serve as a "methodological convenience." This implies that the scope and scale are limited. Model implications cannot be extended beyond the boundaries of the assumptions and data. This doesn't imply that representative agent models are necessarily useless. But the temptation to
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Mike Norman considers the following as important: Causality, modeling
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A key difficulty here is that, even though interactions are clearly all over the place, they’re hard to estimate. Remember, you need 16 times the sample size to estimate an interaction than to estimate a main effect. So, along with accepting the importance of interactions, we also have to accept inevitable uncertainty in their estimation. We have to move away from the idea that a statistical analysis will give us effective certainty for the things we care about. "Representative agents" are homogenous. They serve as a "methodological convenience." This implies that the scope and scale are limited. Model implications cannot be extended beyond the boundaries of the assumptions and data. This doesn't imply that representative agent models are necessarily useless. But the temptation to
Topics:
Mike Norman considers the following as important: Causality, modeling
This could be interesting, too:
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A key difficulty here is that, even though interactions are clearly all over the place, they’re hard to estimate. Remember, you need 16 times the sample size to estimate an interaction than to estimate a main effect. So, along with accepting the importance of interactions, we also have to accept inevitable uncertainty in their estimation. We have to move away from the idea that a statistical analysis will give us effective certainty for the things we care about."Representative agents" are homogenous. They serve as a "methodological convenience." This implies that the scope and scale are limited. Model implications cannot be extended beyond the boundaries of the assumptions and data.
This doesn't imply that representative agent models are necessarily useless. But the temptation to overextend them must be avoided to prevent conclusions from falling into fallacy — due to hasty generalization, for instance.
What this means is that it is very difficult to get a binary causal connection of data to result through a linear function where human agents are involved owing to heterogenous causal factors. Causality involves a constellation of causal factors, perhaps including catalysts, whose distribution varies with respect to time and conditions.
Identifying the constellation of factors and estimating their relative weights in a causal process in which the assumptions also require identification is challenging. Therefore, simple static models are usually overly simplistic regarding events of any degree of complexity, as are most design problems involving social agents affected by systemic relationships.
Getting the "science" right in social science is more difficult in the life sciences than the natural sciences, and much more difficult in the social sciences, in most questions that matter anyway.
Statistical Modeling, Causal Inference, and Social Science
“Causal Processes in Psychology Are Heterogeneous”
Andrew Gelman | Professor of Statistics and Political Science and Director of the Applied Statistics Center, Columbia University
“Causal Processes in Psychology Are Heterogeneous”
Andrew Gelman | Professor of Statistics and Political Science and Director of the Applied Statistics Center, Columbia University