Effects of shared governance and cost redistribution on air pollution control: a study of game theory-based cooperation

This study seeks cost-effective strategies for PM2.5 reduction to generate insights into minimizing pollution abatement costs subject to different scenarios. This study theorizes that the cooperation of PM2.5 abatement has potential gains for participants and develop an empirical way to compare the costs and efficiency of PM2.5 abatement involving the variation of environmental conditions. This study revises the cooperative game model in the context of threshold effects using data obtained from the Beijing-Tianjin-Hebei metropolitan cluster in China. In general, the results support the key assertion that cooperation in the metropolitan cluster plays a vital role in optimizing the efficiency and costs of PM2.5 abatement. In addition to extending the application of the revised model, this study provides a way to estimate the costs and the mitigation benefits of meeting the pollution targets for each coparticipant and take the scenario of multiparty cooperation into account as well as the scenarios involving other types of pollutants. The empirical findings have important policy implications for regional shared governance, decentralization, and resource reallocation. Economic incentive-based shared governance and cost reallocation work better than traditional regulations.

Regional green innovation efficiency and dynamic evolution of Chinese industrial enterprises: a three-stage super-efficiency DEA method based on cooperative game

The level of development of industrial enterprises is related to a country's or region's overall competitiveness. It is critical to assess the green innovation efficiency of regional industrial enterprises scientifically and effectively in order to improve a country's overall green innovation capability. The green innovation system is divided into three sub-stages in this paper: technology development, economic transformation, and environmental protection. Based on the theory of innovation value chain, a three-stage super-efficiency DEA model of the cooperative game including shared inputs and undesirable outputs is constructed to calculate the overall efficiency, three sub-stages efficiency, and dynamic evolution of green innovation of industrial enterprises in China's provincial administrative regions and eight economic zones from 2015 to 2019 (divided by the time of ultimate output). The results indicate that (1) in terms of overall efficiency, the efficiency of green innovation is not high, and there are clear regional differences, as evidenced by the following states: the middle reaches of the Yangtze River economic zone > the eastern coastal economic zone > the southern coastal economic zone > the northern coastal economic zone > the northeastern economic zone > the northwestern economic zone > the middle reaches of the Yellow River economic zone, and the overall efficiency of the southwestern economic zone fluctuates around the average level of China; (2) from the standpoint of various stages, economic transformation stage efficiency > overall efficiency > technology development stage efficiency > environmental protection stage efficiency. The improvement of overall efficiency is largely dependent on the high efficiency of the economic transformation stage, but low efficiency in the environmental protection stage results in overall low efficiency; (3) from the perspective of the dynamic evolution trend, the overall efficiency and three sub-stages have been improved to varying degrees. However, due to the low efficiency of the environmental protection stage, there is still a long way to go to achieve the goal of innovation-driven development; (4) based on the classification analysis, it was determined that the green innovation efficiency of industrial enterprises in only a few regions belongs to the "three high innovation type," which must take targeted measures to improve the inefficient innovation process links.

Robust optimization with equity and efficiency framework for basin-wide water resources planning

This study proposes an equitable and effective water resource planning framework that handles competing regions and conflicting water departments within water-stressed watersheds under uncertainty. To cope with uncertainty, a robust optimization method based on an ellipsoidal uncertainty set is presented to keep the best solution viable and less conservative while attempting to find a balance between the reliability and optimum goals. The comprehensive framework consists of two predominant steps: equitable initial water allocation robust optimization programming, which employs the absolute difference between the supply and demand of water resources as the objective function and the Gini coefficient as the restricted condition, given that both surface water and groundwater are random, and subsequent effective water re-allocation robust modeling, which applies cooperative game theory to achieve maximum welfare in a river basin under uncertainty of the benefit coefficient. A realistic example of the Tuojiang River Basin was conducted, and the simulation experiments showed a significant increase (79.20%) in reliability with only an 11.44% increase in objective values and 2.76% and 15.17% decreases in the amount of surface water and groundwater, respectively. The findings of the analysis reveal that robust policies achieve adaptive optimal decision-making under uncertainty at a low cost.

Cooperative game theory approach to develop an incentive mechanism for biopesticide adoption through farmer producer organizations

Biopesticides have been recognized as viable alternatives to chemical pesticides in controlling agricultural pests for plants and reducing harmful chemical residues. However, small and marginal farmers are facing challenges while adopting biopesticides, namely, high cost and complicated application techniques, resulting in a low level of farmer acceptance. Accordingly, Farmer Producer Organizations (FPOs), voluntarily formed by farmers, develop mutual technical assistance among their members to solve the technical problems of biopesticide adoption. This study assumes that as a new form of farmer cooperative, FPOs have the potential to promote biopesticide adoption through the implementation of collective pesticide adoption (CPA). Along this line, this paper uses a cooperative game-based hybrid method to develop an incentive mechanism of biopesticide adoption for FPOs to implement CPAs. First, we construct a CPA decision model for mixed pesticides (i.e., biopesticides and chemical pesticides) based on multichoice goal programming (MCGP) to compromise the conflicting objectives regarding cost efficiency and chemical residue reduction, thereby obtaining the optimal total cost of pesticide adoption. Second, recognizing the optimal total adoption cost as a baseline, we devise a cooperative game-based cost allocation scheme to maintain farmers' voluntary participation in FPOs. This study demonstrates that the CPA implemented based on our proposed models can at least match if not surpass the economic and environmental performance of farmers' independent pesticide adoption (IPA). We further demonstrate that the proposed cooperative game solution is more suitable for the FPO's cost allocation issue than the eminent solutions, such as the Shapley value.

Optimal power allocation for multiple DoS attackers in wireless networked control systems

In this paper, an optimal power allocation strategy for multiple denial-of-service attackers is investigated in a wireless networked control system with stochastic disturbances. In the presence of denial-of-service attackers, effective signals sent by a transmitter are interfered with causing degradation of signal-to-interference-plus-noise ratio and even packet loss. The effect of multiple attacks on the wireless networked control system is maximized by cooperative game. An H_∞ minimax controller is also designed to guarantee an optimal performance for the wireless networked control system in the worst case. A numerical example is conducted to verify the validity of the proposed methodology.

Network partitioning algorithms as cooperative games

The paper is devoted to game-theoretic methods for community detection in networks. The traditional methods for detecting community structure are based on selecting dense subgraphs inside the network. Here we propose to use the methods of cooperative game theory that highlight not only the link density but also the mechanisms of cluster formation. Specifically, we suggest two approaches from cooperative game theory: the first approach is based on the Myerson value, whereas the second approach is based on hedonic games. Both approaches allow to detect clusters with various resolutions. However, the tuning of the resolution parameter in the hedonic games approach is particularly intuitive. Furthermore, the modularity-based approach and its generalizations as well as ratio cut and normalized cut methods can be viewed as particular cases of the hedonic games. Finally, for approaches based on potential hedonic games we suggest a very efficient computational scheme using Gibbs sampling.

Banzhaf random forests: Cooperative game theory based random forests with consistency

Random forests algorithms have been widely used in many classification and regression applications. However, the theory of random forests lags far behind their applications. In this paper, we propose a novel random forests classification algorithm based on cooperative game theory. The Banzhaf power index is employed to evaluate the power of each feature by traversing possible feature coalitions. Hence, we call the proposed algorithm Banzhaf random forests (BRFs). Unlike the previously used information gain ratio, which only measures the power of each feature for classification and pays less attention to the intrinsic structure of the feature variables, the Banzhaf power index can measure the importance of each feature by computing the dependency among the group of features. More importantly, we have proved the consistency of BRFs, which narrows the gap between the theory and applications of random forests. Extensive experiments on several UCI benchmark data sets and three real world applications show that BRFs perform significantly better than existing consistent random forests on classification accuracy, and better than or at least comparable with Breiman's random forests, support vector machines (SVMs) and k-nearest neighbors (KNNs) classifiers.

On international fisheries agreements, entry deterrence, and ecological uncertainty

A prerequisite for an international fisheries agreement (IFA) to be stable is that parties expect the benefits from joining the agreement to exceed the benefits from free riding on the agreement, and parties only comply with the agreement as long as this is true. The agreement, therefore, implicitly builds on an expectation of the ecological condition of the natural resource. Game theoretical models often assume that all parties have the same (often perfect) information about the resource and that the exploitation is an equilibrium use of the stock. As stated by experts in natural science, the fish ecology still has many open questions, for example how to predict population dynamics, migration patterns, food availability, etc. In some cases, parties disagree about the state, abundance, and migration of a stock, which can reduce the possibilities of reaching an agreement for exploitation of the stock. This paper develops a model and applies it to the North-East Atlantic mackerel fishery, in order to analyze an IFA under different ecological scenarios, and also combines the model with the economic theory of entry deterrence. The model is used empirically to determine whether the parties with original access to the resource have an advantage when forming an agreement with a new party in having the ability to fish the stock down to a smaller size and thereby prevent another party from entering into the fishery. With a basis in entry deterrence, combined with lack of information, the paper illustrates the obstacles that have made an agreement for the North-East Atlantic mackerel so difficult to achieve.