Chemical production activities in industrial districts pose great threats to the surroundingatmospheric environment and human health. Therefore, developing appropriate and intelligentpollution controlling strategies for the management team to monitor chemical production processesis significantly essential in a chemical industrial district. The literature shows that playing a chemicalplant environmental protection (CPEP) game can force the chemical plants to be more compliantwith environmental protection authorities and reduce the potential risks of hazardous gas dispersionaccidents. However, results of the current literature strictly rely on several perfect assumptions whichrarely hold in real-world domains, especially when dealing with human adversaries. To addressbounded rationality and limited observability in human cognition, the CPEP game is extended togenerate robust schedules of inspection resources for inspection agencies. The present paper isinnovative on the following contributions: (i) The CPEP model is extended by taking observationfrequency and observation cost of adversaries into account, and thus better reflects the industrialreality; (ii) Uncertainties such as attackers with bounded rationality, attackers with limited observationand incomplete information (i.e., the attacker’s parameters) are integrated into the extended CPEPmodel; (iii) Learning curve theory is employed to determine the attacker’s observability in the gamesolver. Results in the case study imply that this work improves the decision-making process forenvironmental protection authorities in practical fields by bringing more rewards to the inspectionagencies and by acquiring more compliance from chemical plants.
|Journal||International Journal of Environmental Research and Public Health|
|Publication status||Published - 2018|
- Bounded rationality
- Chemical plant environmental protection game
- Human cognition
- Learning curves
- Limited observation