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Epistemic Landscapes Reloaded
by
Pinto, Manuela Fernández
, Pinto, Daniel Fernández
in
agent-based models
/ Change agents
/ cognition
/ Cognitive models
/ Communities
/ Contributions
/ division of cognitive labor
/ Economic models
/ ELM
/ epistemic landscape
/ epistemic landscape model
/ Epistemology
/ Landscape
/ Modeling
/ Parametric models
/ Philosophy of science
/ Programming languages
/ Rational choice theory
/ Simulation
/ Simulations
/ Social epistemology
2018
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Epistemic Landscapes Reloaded
by
Pinto, Manuela Fernández
, Pinto, Daniel Fernández
in
agent-based models
/ Change agents
/ cognition
/ Cognitive models
/ Communities
/ Contributions
/ division of cognitive labor
/ Economic models
/ ELM
/ epistemic landscape
/ epistemic landscape model
/ Epistemology
/ Landscape
/ Modeling
/ Parametric models
/ Philosophy of science
/ Programming languages
/ Rational choice theory
/ Simulation
/ Simulations
/ Social epistemology
2018
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Do you wish to request the book?
Epistemic Landscapes Reloaded
by
Pinto, Manuela Fernández
, Pinto, Daniel Fernández
in
agent-based models
/ Change agents
/ cognition
/ Cognitive models
/ Communities
/ Contributions
/ division of cognitive labor
/ Economic models
/ ELM
/ epistemic landscape
/ epistemic landscape model
/ Epistemology
/ Landscape
/ Modeling
/ Parametric models
/ Philosophy of science
/ Programming languages
/ Rational choice theory
/ Simulation
/ Simulations
/ Social epistemology
2018
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Journal Article
Epistemic Landscapes Reloaded
2018
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Overview
Weisberg and Muldoon’s epistemic landscape model (ELM) has been one of the most significant contributions to the use of agent-based models in philosophy. The model provides an innovative approach to establishing the optimal distribution of cognitive labor in scientific communities, using an epistemic landscape. In the paper, we provide a critical examination of ELM. First, we show that the computing mechanism for ELM is correct insofar as we are able to replicate the results using another programming language. Second, we show that small changes in the rules that determine the behavior of individual agents can lead to important changes in simulation results. Accordingly, we claim that ELM results are robust with respect to the computing mechanism, but not necessarily across parameter space. We conclude by reflecting on the possible lessons to be gained from ELM as a class of simulations or cluster of models.
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