Search

Technology development, especially in petroleum industries, is one of the most important ways of economic development in oil-rich countries. However, despite such challenges as structural economic problems, the issue of moral hazards, and the conflict of interests, government policies are crucial. This study analyzed the long-term behavior of the government and the private sector as two main actors using the evolutionary game theory and solved their strategies by replicator dynamic equations. Finally, based on a real-world case in Iran, a numerical study was performed to better understand the characteristics of the game model under realistic conditions. The outputs indicate that the main approach of the government, with minimal incentive mechanisms, should be legal monitoring. Sensitivity analyses on some key parameters show that the important factor in project implementation is attention to the private sector's revenues and expenditures, and the government's revenues and expenditures have little effect on the outputs and behavior change. [ABSTRACT FROM AUTHOR]

We live in a very complex world where we face complex phenomena such as social norms and new technologies. To deal with such phenomena, social scientists often use reductionism approach where they reduce them to some lower-lever variables and model the relationships among them through a scheme of equations. This approach that is called equation based modeling (EBM) has some basic weaknesses in modeling real complex systems so that assumptions such as unbounded rationality and perfect information are strongly emphasized while adaptability and evolutionary nature of all engaged agents along with network effects go unaddressed. In tackling deficiencies of reductionism, the complex adaptive system (CAS) framework has been proven very influential in the past two decades. In contrast to reductionism, under CAS framework, complex phenomena are studied in an organic manner where their agents are supposed to be both boundedly rational and adaptive. As the most powerful methodology for CAS modeling, agent-based modeling (ABM) has gained a growing popularity among academics and practit ioners. ABMs show how agents' simple behavioral rules and their local interactions at micro - scale can generate surprisingly complex patterns at macro-scale. Despite a growing number of ABM publications, those researchers unfamiliar with it have to study a number of works to understand (1) why and what of ABM, (2) its differences with EBM (3) its main functionalities in scientific studies and (4) some of its applications in management science. So, this paper's major contribution is to help researchers particularly those unfamiliar with ABM to get insights regarding its philosophy and use and gain a big picture of it. [ABSTRACT FROM AUTHOR]