How often do teams start to work with biological model systems without knowing the fundamental biology of species concerned or without directly consulting experienced people? To understand the functioning and evolution of a local model system with unique spatio-temporal characteristics, observation and experience will ultimately be more important than the knowledge from libraries dealing with model systems that express other characteristics. It is therefore unlikely that laboratory directors overloaded with administration tasks, or any citizen without adequate background knowledge, can identify a feasible science topic among a multitude of options defined by experts. Citizens might base reasoning on one observation (an anecdote) and think this may apply to any situation. But is this true?
If people do not agree, please explain why. I would for instance argue that the scale of analysis might be important, e.g. 'curiosity' at the individual versus science community level....
One place to look for levels of curiousity (possibly optimal levels of curiousity) is in the study and creation of spatial patterns. See, for example, spatial pattern in the attached image, which is the outcome of learning to draw patterns using python and pygame:
http://codeformathartfun.blogspot.ca/2009/02/using-python-and-pygame-to-draw.html
Hello James,
this is a good way to occupy people... . If you would ask 20 people for their impressions, you will get 20 different answers....
Marcel wrote "Ultimately, it is the scientific community as a whole that will provide solutions to problems ...."
I agree with this, though of course the "scientific community as a whole" never comes together to focus on any particular problem (question). I think ResearchGate has the potential to help with this, but is still in its infancy and not yet close to its potential.
Regarding the issue of "optimality in curiosity', it seems you are asking about optimal social systems among scientists for addressing questions of scientific curiosity. It's a good question and is about the social science of scientists, which you also seem to be driving at in your question about rewards.
I asked Google Scholar and found that there are articles about the "social science of science policy", but it seems these tend to lack theoretical foundations. See for example: http://academicexecutives.elsevier.com/articles/unfolding-science-science-and-innovation-policy
Marcel,
Optimal curiosity appears to be subject-dependent. That is, some subjects peak our curiously more than other subjects. There is a straightforward way to work towards our being highly curious about a subject. Briefly, here are the steps of the method:
1. Pick a subject that you have wondered about. By way of illustration, each step of this method is illustrated in terms of microfossils sometimes found embedded in quartz.
The sample subject is micro palaeontology.
2. Pick sources of information about the selected subject that you wondered about. For example, find pieces of Brasilian amethyst and borrow some books on micro palaeontology from your local university library. In doing this, look for amethyst that has what look like particles of pepper embedded in the crystalline surfaces.
3. Study the sources of information and write down questions not answered by what you have found so far. For example, use a digital stereo microscope such as a Zeiss V-8 to inspect sample pieces of Brasilian amethyst.
4. Identify patterns in what can be observed in source of information.
5. Analyse and compare the patterns you find. Determine the similarity distance between the patterns. Ask the question: Which patterns are more salient?
6. From the unanswered questions in steps 3 and 5, repeat step 1 with a subset of the subject you find interesting.
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