Since the often quoted article Tsutsui and Mino 1990, there has been some confusion over what this article proves. Rowat, 2007, uses the same method plus Dockner et al 2000 on another example. We explain that what is proved by Tsutsui and Mino is not what they claim, and what is proved by Rowat is misnamed as a perfect equilibrium. However, Rowat’s example can easily be saved as noted by several authors. Using a more general model, we discuss which parameter values yield his results and we give an explicit and simpler derivation concerning his original piecewise linear strategies.
With wide application of Artificial Intelligence (AI), it has become particularly important to make decisions of AI systems explainable and transparent. In this paper, we proposed a new Explainable Artificial Intelligence (XAI) method called ShapG (Explanations based on Shapley value for Graphs) for measuring feature importance. ShapG is a model-agnostic global explanation method. At the first stage, it defines an undirected graph based on the dataset, where nodes represent features and edges are added based on calculation of correlation coefficients between features. At the second stage, it calculates an approximated Shapley value by sampling the data taking into account this graph structure. The sampling approach of ShapG allows to calculate the importance of features efficiently, i.e. to reduce computational complexity. Comparison of ShapG with other existing XAI methods shows that it provides more accurate explanations for two examined datasets. We also compared other XAI methods developed based on cooperative game theory with ShapG in running time, and the results show that ShapG exhibits obvious advantages in its running time, which further proves efficiency of ShapG. In addition, extensive experiments demonstrate a wide range of applicability of the ShapG method for explaining complex models. We find ShapG an important tool in improving explainability and transparency of AI systems and believe it can be widely used in various fields. (Joint work with Chi Zhao and Jing Liu.)
In this paper, I use a tractable predator-prey model with endogenous harvesting to assess the impact of a prey refuge on fishery performance. Using a two-stage game framework, where the prey-refuge consistently protects a portion of the environment from predators, this paper investigates: $(i)$ how fishers modify their behavior in the presence of a prey-refuge and $(ii)$ the conditions under which the prey-refuge enhances the social welfare. The results show that reducing the intensity of species interactions via the prey-refuge diminishes fishing pressure on both prey and predator populations. Interestingly, although full prey protection maximizes the payoff from prey harvesting, it does not necessarily minimize the predator’s fishing payoff. Necessary and sufficient conditions for the existence of positive cooperative surplus are provided. Numerical examples reveal that the overall efficiency of the fishery, influenced by the prey-refuge, is highly contingent on fishers’ willingness to wait for its benefits (i.e., the discount factor). Specifically, when fishers are sufficiently patient, the prey-refuge improves social welfare. Finally, prey refuge implementation can also occur when transfers are not allowed, or when fishers coordinate their fishing strategy. This paper contributes to the fishery management literature by proposing an alternative approach that can promote efficiency through indirect incentives.
