Lojalitet i licensavtal

Loyalty in Licensing Agreements The purpose of the dissertation is to analyse the impact of the principle of loyalty on licensing agreements from the viewpoint of Finnish law using the traditional legal method (Rechtsdogmatik) combined with empirical data in the form of licensing agreements. The need for good licensing agreements is crucial. One should avoid mechanical and stereotyped agreements in favour of more conscious and goal-oriented ones. When the parties' will and goals have been made clear, the drafting technique should be chosen accordingly. The importance of the principle of loyalty in the interpretation of licensing agreements varies according to their degree of relationality. This is a concept originating in the relational contract theory, more precisely Ian Macneil's spectrum of contracts, where contracts can be placed on an axis according to their degree of relationality. In the dissertation different factors are used to conclude whether the licensing agreement at hand is to be placed on the axis closer to the transactional pole or closer to the relational pole. A conclusion of the dissertation is that few licensing agreements can be placed so close to the transactional pole, that the principle of loyalty lacks importance altogether. The impact of the principle of loyalty the main focus of which is on fostering the contracting parties to behave in accordance with best practices, not for example on altering contract terms is analysed in different situations where the parties' interests typically collide. These situations are discussed from the point of view of three patent and knowhow licensing agreements that differ as to their degree of relationality. A balance needs to be struck between freedom of contract and relational needs. Especially when interpreting more modern licensing agreements, one should not focus on the written document alone, as is often recommended in the literature on Nordic intellectual property law. Neither is the principle of caveat emptor a proper starting point. Moreover, where the parties are of equal bargaining power, one should not assume that the grants in licensing agreements are to be interpreted narrowly. Focus in the interpretation should instead be on the entirety of the circumstances.

Non-industrial private forest landowners’ choices of timber management strategies.

The factors affecting the non-industrial, private forest landowners' (hereafter referred to using the acronym NIPF) strategic decisions in management planning are studied. A genetic algorithm is used to induce a set of rules predicting potential cut of the landowners' choices of preferred timber management strategies. The rules are based on variables describing the characteristics of the landowners and their forest holdings. The predictive ability of a genetic algorithm is compared to linear regression analysis using identical data sets. The data are cross-validated seven times applying both genetic algorithm and regression analyses in order to examine the data-sensitivity and robustness of the generated models. The optimal rule set derived from genetic algorithm analyses included the following variables: mean initial volume, landowner's positive price expectations for the next eight years, landowner being classified as farmer, and preference for the recreational use of forest property. When tested with previously unseen test data, the optimal rule set resulted in a relative root mean square error of 0.40. In the regression analyses, the optimal regression equation consisted of the following variables: mean initial volume, proportion of forestry income, intention to cut extensively in future, and positive price expectations for the next two years. The R2 of the optimal regression equation was 0.34 and the relative root mean square error obtained from the test data was 0.38. In both models, mean initial volume and positive stumpage price expectations were entered as significant predictors of potential cut of preferred timber management strategy. When tested with the complete data set of 201 observations, both the optimal rule set and the optimal regression model achieved the same level of accuracy.