2016 has started in a rather intensive fashion. Our microeconomic course is now
in its third phase, and the topic of this module happens to be game theory.
This is a very interesting topic within economic theory. It’s also one of the
most popular topics in modern economics. It’s closely related to information
economics, which is the topic of the last micro course later this spring.
Unfortunately, game theory is also very difficult field to master. There are so
many solution concepts. In this theory we are interested in strategic
interactions of economic agents. I would certainly like to apply this theory,
and its applications in information economics, later in the resource work,
perhaps even in this project and in some of the PhD-articles. Thus, the
motivation is there for sure, but I’m a little sceptical as far as my potential
goes. I will do my best and hope that I gain some understanding about this
fascinating subject. By the way, the second micro course went surprisingly
well, I’m happy to say.
happy to say that the actual work has taken steps ahead. I was finally able to
find the equations that work in the optimization. The model was ready and also
the analytical solution for the dynamic optimization problem was provided.
Thus, I almost had the material ready for the first article.
decided to ask information about the data directly from the Valkama and Turtola
themselves (the ones who wrote the articles that we have been using in the
work). Valkama and Turtola will join us as a writers in the first article. We
had a skype-meeting and it was very useful indeed. It turned out that we had
misunderstood some aspects in their articles and now we have to reconsider
couple of things before we can move forward. So this is two steps ahead and one
step behind kind of a situation. The fundamental problem is that there is so
great variation within the data that one cannot actually find any kind of
causality in there. In other words, it’s really hard to say what factors affect
the yield response or yield in particular soil.
It was also
interesting to notice that biologists, or ecologists, and economists approach
modelling a little bit differently; it seems that biologists use models to
explain phenomena’s that take place in the environment whereas we economists
use models in optimization and such where the crucial question usually is: what
would happen if.. Hence, we have to develop a model that can be used in dynamic
optimization. With the limited data, some crucial assumptions have to be made.
Biologists might be a little be reluctant to approve these assumptions, because
the existing data doesn’t provide clear evidence to justify these assumptions.
Now we have to find some kind of balance between the two worlds so that we are
able to deliver this joint article. I guess these is a common situation in multidisciplinary
resource work. It can be very challenging, but I might assume that eventually multidisciplinary
study may provide more robust results and models than monodisciplinary study.
department, our office turned into an international one as we got couple of
foreign PhD-students into our gang. A male came from Turkey and a female came
from Taiwan. Actually the male came from Spanish University and female from
university in Holland. It’s very nice to have here people from abroad because
now we have to speak English all the time. Thus, our language skills might be
developing. Of course it’s also always interesting to wider once worldview by
having conversations with people from around the world. Hence, I would say that
our working environment is now more interesting.
for now. We keep studying and working and improving. Although it might feel
hard sometimes, in the end, it’s all about fun and games. At least one should
try see the obstacles and problems as such, I guess.