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1.  Design preferences and cognitive styles: experimentation by automated website synthesis 
This article aims to demonstrate computational synthesis of Web-based experiments in undertaking experimentation on relationships among the participants' design preference, rationale, and cognitive test performance. The exemplified experiments were computationally synthesised, including the websites as materials, experiment protocols as methods, and cognitive tests as protocol modules. This work also exemplifies the use of a website synthesiser as an essential instrument enabling the participants to explore different possible designs, which were generated on the fly, before selection of preferred designs.
The participants were given interactive tree and table generators so that they could explore some different ways of presenting causality information in tables and trees as the visualisation formats. The participants gave their preference ratings for the available designs, as well as their rationale (criteria) for their design decisions. The participants were also asked to take four cognitive tests, which focus on the aspects of visualisation and analogy-making. The relationships among preference ratings, rationale, and the results of cognitive tests were analysed by conservative non-parametric statistics including Wilcoxon test, Krustal-Wallis test, and Kendall correlation.
In the test, 41 of the total 64 participants preferred graphical (tree-form) to tabular presentation. Despite the popular preference for graphical presentation, the given tabular presentation was generally rated to be easier than graphical presentation to interpret, especially by those who were scored lower in the visualization and analogy-making tests.
This piece of evidence helps generate a hypothesis that design preferences are related to specific cognitive abilities. Without the use of computational synthesis, the experiment setup and scientific results would be impractical to obtain.
PMCID: PMC3386886  PMID: 22748000
2.  Automated experimentation in ecological networks 
In ecological networks, natural communities are studied from a complex systems perspective by representing interactions among species within them in the form of a graph, which is in turn analysed using mathematical tools. Topological features encountered in complex networks have been proved to provide the systems they represent with interesting attributes such as robustness and stability, which in ecological systems translates into the ability of communities to resist perturbations of different kinds. A focus of research in community ecology is on understanding the mechanisms by which these complex networks of interactions among species in a community arise. We employ an agent-based approach to model ecological processes operating at the species' interaction level for the study of the emergence of organisation in ecological networks.
We have designed protocols of interaction among agents in a multi-agent system based on ecological processes occurring at the interaction level between species in plant-animal mutualistic communities. Interaction models for agents coordination thus engineered facilitate the emergence of network features such as those found in ecological networks of interacting species, in our artificial societies of agents.
Agent based models developed in this way facilitate the automation of the design an execution of simulation experiments that allow for the exploration of diverse behavioural mechanisms believed to be responsible for community organisation in ecological communities. This automated way of conducting experiments empowers the study of ecological networks by exploiting the expressive power of interaction models specification in agent systems.
PMCID: PMC3117761  PMID: 21554669

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