The overall health of marine resources is deteriorating since last few decades, raising serious concerns. At the same time, multiple policies aid liberalizing international trade regulations and enhancing fisheries subsidies, affecting ecological dynamics in fisheries sector. Such policies boost economic growth by generating welfare gains. On the other hand, if such policies are myopic, it might lead to excessive harvesting which does not give the resources a chance to recover. As a result, in the long run, declining stock leads to high harvest cost and loss of potential revenue. Therefore, the inherent ambiguity of long term welfare effects of such policies needs further investigation. Furthermore, the concern of declining environmental health has affected consumer's choices in buying seafood products. It has been observed that sustainably sourced seafood products earn significant market premium. This relatively new demand trend gives rise to `consumer stock effect' where value of fish increases with the increase in it's stock. This effect might lead to significant implications for optimal fisheries management. This doctoral thesis analyses welfare and management implications of economic determinants including international trade, subsidies and consumer preferences in fisheries. More specifically, the thesis attempts to answer whether the above mentioned economic aspects lead to a positive or negative outcome on both ecological resources and economic growth in the long run. Using a mix of qualitative and quantitative approach to investigate the problems, this thesis shows that both trade liberalization and fisheries subsidies impact resource stock negatively. In terms of long-run welfare, trade may affect high-income and low-income countries differently, whereas subsidies affect welfare depending on the health of the stock size in long run. Furthermore, consumer preferences for sustainability can significantly influence long-run harvest pattern under optimal management, where catches will be much lower than without considering the consumer preferences for sustainability.

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Off-policy reinforcement learning is useful in many contexts. Maei, Sutton, Szepesvari, and others, have recently introduced a new class of algorithms, the most advanced of which is GQ(lambda), for off-policy reinforcement learning. These algorithms are the first stable methods for general off-policy learning whose computational complexity scales linearly with the number of parameters, thereby making them potentially applicable to large applications involving function approximation. Despite these promising theoretical properties, these algorithms have received no significant empirical test of their effectiveness in off-policy settings prior to the current work. Here, GQ(lambda) is applied to a variety of prediction and control domains, including on a mobile robot, where it is able to learn multiple optimal policies in parallel from random actions. Overall, we find GQ(lambda) to be a promising algorithm for use with large real-world continuous learning tasks. We believe it could be the base algorithm of an autonomous sensorimotor robot.