Critiquing

, and in some of the interfaces, users can register to critique results, annotate models, and generate interactive guided walkthroughs that highlight notable details. To improve the models with input from the scientific community, node-based guided tours of interactive models are offered to collect confidence votes for various parameters from invited content experts and will soon expand to an open trust-weighted voting format. A simple online table editor allows researchers to provide new ingredients or to modify recipe parameters (citations are required to validate each modification). Project leaders hand curate these tables to validate and weight the parameters and to update project recipes.

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When agent modeling?

 

When there is a natural representation as agents

– When there are decision and behaviors that can be defined discretely

 

(with boundaries)

 

– When it is important that agents adapt and change their behavior

 

– When it is important that agents learn and engage in dynamic strategic behavior

 

– When it is important that agents have a dynamic relationships with other agents, and agent relationships form and dissolve

 

– When it is important that agents form organizations and adaptation and learning are important at the organization level

 

– When it is important that agents have a spatial component to their behaviors and interactions

 

When the past is no predictor of the future

 

When scale-up to arbitrary levels is important

 

When process structural change needs to be a result of the model, rather than an input to the model

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