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determine the immediate context as well as the general attitude of the farmer, but in
this case the quotes do not indicate this, but a year-long view taking into account the
whole cycle. Indeed many of the quotes involve consideration of the year taken as a
whole.
Looking at the above, very sketchy, analysis I hope it is starting to be clear how a
simulation of many farmers might be programmed. The narrative elements clearly
talk about different stages of the yearly cycle and their timing, so at least a monthly
granularity needs to be considered. The farmer obviously considers the response of
other farmers to opportunities, so that a multi-agent model makes sense with each
looking to the innovations and decisions of others as part of their decision making.
Different kinds of situation relating to kinds of risk dominate the thinking and might
form the backbone of the model, involving some rich but “fuzzy” mechanism
determining whether and when farmers switch between survival, comfort and
entrepreurial contexts, for example it may be there is evidence that switching to
survival is fairly rapid, but it takes many good years to switch to comfort and more if
you are a tenant farmer. The quotes do not present any evidence the farmer has been
in an entrepreneurial context, though it is possible that either newcomers to farming
or very comfortable farmers might. Clearly the choices made by a farmer are
perceived as being tightly constrained by what is possible, so this is an important part
of behaviour determination, and probably should be part of a simulation.
The simulation in [3] took a particular framework (Bennett's theory of adaptive
dynamics [2]) and a knowledge engineering approach, looking at the factors that seem
to be significant in each decision (primarily how much of each crop to grow). There
were two kinds of agent: adaptive and sceptic (sceptic of climate change and hence
effectively non-adaptive. Given these kinds of agent there is essentially a complex
risk-benefit analysis depending on climate, capital etc. of the farm. In contrast the
CSNE analysis suggests an extension of this where farmers might change their
Thus, although the above illustration clearly has depended on the excellent
standard of elicitation and selection in [3] making my task easier, it suggests an
enhanced version of the model developed there.
7
From CSAR Analysis to Program Code
The overall aim is to make the relationship between the program code that defines
agent behaviour within a simulation and the original narrative data as close as
possible. To this end we proposed above a structure that we hypothesise will
facilitate this by brining the coding analysis closer to how humans and human
language work. Another step to facilitate the same goal is to bring the computer code
closer to the analysis. That is to make the structure of the coding as coherent with the
analysis as possible. Almost no agent-based coding schemes provide ready structures
for context recognition/use and very few have anything that might help with
reasoning about scope. Thus part of the development of a CSNE analysis should be
to provide these structures, narrowing the 'jump' from analysis to code, just as we are
trying to narrow the 'jump' from data to analysis.
