In one of my previous posts in this blog I have talked about research into human tree selection behaviour. Since the previous post was published we have learned new things, which highlighted how complex and fascinating this research is.
Experience is for example a term that often comes up in the context of human resources. It is generally perceived as something positive that complements knowledge received through education. In some countries there is even the widespread believe that experience can compensate for a lack of education, but I tend to disagree here: The value of experience made in a certain field much depends on the prior knowledge received from school or university education. The more you know about a certain field the more you tend to gain from experience.
However, my recent work with Lucie Vítková has shown, that experience sometimes can also prevent people from taking in new skills as part of training: People with many years of practical work and associated experience are somewhat unwilling to leave their comfort zone and to try new ways. Our research also showed that people with little experience on the other hand tend to be open to new suggestions and are ready to implement them.
Research on human tree selection behaviour has a lot to do with assessing agreement. The question of agreement is crucial to the forest industry, perhaps today even more so, as environmentally friendly ways of forest management are meant to maintain biodiversity and to mitigate adverse effects of climate change among other things. It is therefore important to know how much agreement and chance there is when professional staff mark trees for various purposes.
Outside forest and agricultural sciences there is a large body of literature dealing with agreement. In medical science and in psychology there are many applications where humans vote for something or rate items that they are confronted with. Even the binary case with “1” meaning “approval” or “selection” and “0” denoting “rejection” is quite common. A popular statistic used to quantify agreement is for example Fleiss’ kappa that was designed to express agreement in a single number based on matrix data. This statistic has become a kind of standard, although there are also other alternatives.
However, expressing human behaviour in a single number is difficult. The more we look into this matter, the more we understand how complex this type of behaviour actually is. Apart from a large data matrix with trees in rows and raters in columns there is an interaction between raters and trees that is hard to disentangle. We can distinguish between an active behaviour of the raters and a passive rater behaviour, which is influenced by the trees they rate. The former is easier to understand and describes the marking behaviour of each rater, comparatively independent of the trees, i.e. whether they tend to mark many or few trees. The passive tree selection behaviour is consciously and subconsciously influenced by the trees, i.e. some of them are more attractive to one rater than to another.
Both processes influence the level of agreement. A good characteristic should take both processes into account to arrive at a balanced assessment of agreement. However, currently it is mostly the passive behaviour, which the existing characteristics including Fleiss’ kappa quantify. My group and I are currently looking more into these processes in order to better understand them and to find ways to quantify them. This is an intriguing quest, since this work also tells us more about ourselves and about how our mind functions.